WorldWideScience

Sample records for supporting large-scale computational

  1. Development of computational infrastructure to support hyper-resolution large-ensemble hydrology simulations from local-to-continental scales

    Data.gov (United States)

    National Aeronautics and Space Administration — Development of computational infrastructure to support hyper-resolution large-ensemble hydrology simulations from local-to-continental scales A move is currently...

  2. Multi-Agent System Supporting Automated Large-Scale Photometric Computations

    Directory of Open Access Journals (Sweden)

    Adam Sȩdziwy

    2016-02-01

    Full Text Available The technologies related to green energy, smart cities and similar areas being dynamically developed in recent years, face frequently problems of a computational nature rather than a technological one. The example is the ability of accurately predicting the weather conditions for PV farms or wind turbines. Another group of issues is related to the complexity of the computations required to obtain an optimal setup of a solution being designed. In this article, we present the case representing the latter group of problems, namely designing large-scale power-saving lighting installations. The term “large-scale” refers to an entire city area, containing tens of thousands of luminaires. Although a simple power reduction for a single street, giving limited savings, is relatively easy, it becomes infeasible for tasks covering thousands of luminaires described by precise coordinates (instead of simplified layouts. To overcome this critical issue, we propose introducing a formal representation of a computing problem and applying a multi-agent system to perform design-related computations in parallel. The important measure introduced in the article indicating optimization progress is entropy. It also allows for terminating optimization when the solution is satisfying. The article contains the results of real-life calculations being made with the help of the presented approach.

  3. Large Scale Computations in Air Pollution Modelling

    DEFF Research Database (Denmark)

    Zlatev, Z.; Brandt, J.; Builtjes, P. J. H.

    Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998......Proceedings of the NATO Advanced Research Workshop on Large Scale Computations in Air Pollution Modelling, Sofia, Bulgaria, 6-10 July 1998...

  4. Large scale cluster computing workshop

    International Nuclear Information System (INIS)

    Dane Skow; Alan Silverman

    2002-01-01

    Recent revolutions in computer hardware and software technologies have paved the way for the large-scale deployment of clusters of commodity computers to address problems heretofore the domain of tightly coupled SMP processors. Near term projects within High Energy Physics and other computing communities will deploy clusters of scale 1000s of processors and be used by 100s to 1000s of independent users. This will expand the reach in both dimensions by an order of magnitude from the current successful production facilities. The goals of this workshop were: (1) to determine what tools exist which can scale up to the cluster sizes foreseen for the next generation of HENP experiments (several thousand nodes) and by implication to identify areas where some investment of money or effort is likely to be needed. (2) To compare and record experimences gained with such tools. (3) To produce a practical guide to all stages of planning, installing, building and operating a large computing cluster in HENP. (4) To identify and connect groups with similar interest within HENP and the larger clustering community

  5. Computing in Large-Scale Dynamic Systems

    NARCIS (Netherlands)

    Pruteanu, A.S.

    2013-01-01

    Software applications developed for large-scale systems have always been difficult to de- velop due to problems caused by the large number of computing devices involved. Above a certain network size (roughly one hundred), necessary services such as code updating, topol- ogy discovery and data

  6. Large-scale computing with Quantum Espresso

    International Nuclear Information System (INIS)

    Giannozzi, P.; Cavazzoni, C.

    2009-01-01

    This paper gives a short introduction to Quantum Espresso: a distribution of software for atomistic simulations in condensed-matter physics, chemical physics, materials science, and to its usage in large-scale parallel computing.

  7. NASA's Information Power Grid: Large Scale Distributed Computing and Data Management

    Science.gov (United States)

    Johnston, William E.; Vaziri, Arsi; Hinke, Tom; Tanner, Leigh Ann; Feiereisen, William J.; Thigpen, William; Tang, Harry (Technical Monitor)

    2001-01-01

    Large-scale science and engineering are done through the interaction of people, heterogeneous computing resources, information systems, and instruments, all of which are geographically and organizationally dispersed. The overall motivation for Grids is to facilitate the routine interactions of these resources in order to support large-scale science and engineering. Multi-disciplinary simulations provide a good example of a class of applications that are very likely to require aggregation of widely distributed computing, data, and intellectual resources. Such simulations - e.g. whole system aircraft simulation and whole system living cell simulation - require integrating applications and data that are developed by different teams of researchers frequently in different locations. The research team's are the only ones that have the expertise to maintain and improve the simulation code and/or the body of experimental data that drives the simulations. This results in an inherently distributed computing and data management environment.

  8. High-Resiliency and Auto-Scaling of Large-Scale Cloud Computing for OCO-2 L2 Full Physics Processing

    Science.gov (United States)

    Hua, H.; Manipon, G.; Starch, M.; Dang, L. B.; Southam, P.; Wilson, B. D.; Avis, C.; Chang, A.; Cheng, C.; Smyth, M.; McDuffie, J. L.; Ramirez, P.

    2015-12-01

    Next generation science data systems are needed to address the incoming flood of data from new missions such as SWOT and NISAR where data volumes and data throughput rates are order of magnitude larger than present day missions. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. We present our experiences on deploying a hybrid-cloud computing science data system (HySDS) for the OCO-2 Science Computing Facility to support large-scale processing of their Level-2 full physics data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer ~10X costs savings but with an unpredictable computing environment based on market forces. We will present how we enabled high-tolerance computing in order to achieve large-scale computing as well as operational cost savings.

  9. Personalized Opportunistic Computing for CMS at Large Scale

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    **Douglas Thain** is an Associate Professor of Computer Science and Engineering at the University of Notre Dame, where he designs large scale distributed computing systems to power the needs of advanced science and...

  10. A large-scale computer facility for computational aerodynamics

    International Nuclear Information System (INIS)

    Bailey, F.R.; Balhaus, W.F.

    1985-01-01

    The combination of computer system technology and numerical modeling have advanced to the point that computational aerodynamics has emerged as an essential element in aerospace vehicle design methodology. To provide for further advances in modeling of aerodynamic flow fields, NASA has initiated at the Ames Research Center the Numerical Aerodynamic Simulation (NAS) Program. The objective of the Program is to develop a leading-edge, large-scale computer facility, and make it available to NASA, DoD, other Government agencies, industry and universities as a necessary element in ensuring continuing leadership in computational aerodynamics and related disciplines. The Program will establish an initial operational capability in 1986 and systematically enhance that capability by incorporating evolving improvements in state-of-the-art computer system technologies as required to maintain a leadership role. This paper briefly reviews the present and future requirements for computational aerodynamics and discusses the Numerical Aerodynamic Simulation Program objectives, computational goals, and implementation plans

  11. A Web-based Distributed Voluntary Computing Platform for Large Scale Hydrological Computations

    Science.gov (United States)

    Demir, I.; Agliamzanov, R.

    2014-12-01

    Distributed volunteer computing can enable researchers and scientist to form large parallel computing environments to utilize the computing power of the millions of computers on the Internet, and use them towards running large scale environmental simulations and models to serve the common good of local communities and the world. Recent developments in web technologies and standards allow client-side scripting languages to run at speeds close to native application, and utilize the power of Graphics Processing Units (GPU). Using a client-side scripting language like JavaScript, we have developed an open distributed computing framework that makes it easy for researchers to write their own hydrologic models, and run them on volunteer computers. Users will easily enable their websites for visitors to volunteer sharing their computer resources to contribute running advanced hydrological models and simulations. Using a web-based system allows users to start volunteering their computational resources within seconds without installing any software. The framework distributes the model simulation to thousands of nodes in small spatial and computational sizes. A relational database system is utilized for managing data connections and queue management for the distributed computing nodes. In this paper, we present a web-based distributed volunteer computing platform to enable large scale hydrological simulations and model runs in an open and integrated environment.

  12. Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

    OpenAIRE

    Qiang Liu; Yi Qin; Guodong Li

    2018-01-01

    Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal...

  13. Large Scale Computing and Storage Requirements for Nuclear Physics Research

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, Richard A.; Wasserman, Harvey J.

    2012-03-02

    IThe National Energy Research Scientific Computing Center (NERSC) is the primary computing center for the DOE Office of Science, serving approximately 4,000 users and hosting some 550 projects that involve nearly 700 codes for a wide variety of scientific disciplines. In addition to large-scale computing resources NERSC provides critical staff support and expertise to help scientists make the most efficient use of these resources to advance the scientific mission of the Office of Science. In May 2011, NERSC, DOE’s Office of Advanced Scientific Computing Research (ASCR) and DOE’s Office of Nuclear Physics (NP) held a workshop to characterize HPC requirements for NP research over the next three to five years. The effort is part of NERSC’s continuing involvement in anticipating future user needs and deploying necessary resources to meet these demands. The workshop revealed several key requirements, in addition to achieving its goal of characterizing NP computing. The key requirements include: 1. Larger allocations of computational resources at NERSC; 2. Visualization and analytics support; and 3. Support at NERSC for the unique needs of experimental nuclear physicists. This report expands upon these key points and adds others. The results are based upon representative samples, called “case studies,” of the needs of science teams within NP. The case studies were prepared by NP workshop participants and contain a summary of science goals, methods of solution, current and future computing requirements, and special software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, “multi-core” environment that is expected to dominate HPC architectures over the next few years. The report also includes a section with NERSC responses to the workshop findings. NERSC has many initiatives already underway that address key workshop findings and all of the action items are aligned with NERSC strategic plans.

  14. Large Scale Computing and Storage Requirements for Basic Energy Sciences Research

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, Richard; Wasserman, Harvey

    2011-03-31

    The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility supporting research within the Department of Energy's Office of Science. NERSC provides high-performance computing (HPC) resources to approximately 4,000 researchers working on about 400 projects. In addition to hosting large-scale computing facilities, NERSC provides the support and expertise scientists need to effectively and efficiently use HPC systems. In February 2010, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR) and DOE's Office of Basic Energy Sciences (BES) held a workshop to characterize HPC requirements for BES research through 2013. The workshop was part of NERSC's legacy of anticipating users future needs and deploying the necessary resources to meet these demands. Workshop participants reached a consensus on several key findings, in addition to achieving the workshop's goal of collecting and characterizing computing requirements. The key requirements for scientists conducting research in BES are: (1) Larger allocations of computational resources; (2) Continued support for standard application software packages; (3) Adequate job turnaround time and throughput; and (4) Guidance and support for using future computer architectures. This report expands upon these key points and presents others. Several 'case studies' are included as significant representative samples of the needs of science teams within BES. Research teams scientific goals, computational methods of solution, current and 2013 computing requirements, and special software and support needs are summarized in these case studies. Also included are researchers strategies for computing in the highly parallel, 'multi-core' environment that is expected to dominate HPC architectures over the next few years. NERSC has strategic plans and initiatives already underway that address key workshop findings. This report includes a

  15. Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

    Directory of Open Access Journals (Sweden)

    Qiang Liu

    2018-05-01

    Full Text Available Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal computer, a Graphics Processing Unit (GPU-based, high-performance computing method using the OpenACC application was adopted to parallelize the shallow water model. An unstructured data management method was presented to control the data transportation between the GPU and CPU (Central Processing Unit with minimum overhead, and then both computation and data were offloaded from the CPU to the GPU, which exploited the computational capability of the GPU as much as possible. The parallel model was validated using various benchmarks and real-world case studies. The results demonstrate that speed-ups of up to one order of magnitude can be achieved in comparison with the serial model. The proposed parallel model provides a fast and reliable tool with which to quickly assess flood hazards in large-scale areas and, thus, has a bright application prospect for dynamic inundation risk identification and disaster assessment.

  16. Large Scale Computing and Storage Requirements for High Energy Physics

    International Nuclear Information System (INIS)

    Gerber, Richard A.; Wasserman, Harvey

    2010-01-01

    The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility for the Department of Energy's Office of Science, providing high-performance computing (HPC) resources to more than 3,000 researchers working on about 400 projects. NERSC provides large-scale computing resources and, crucially, the support and expertise needed for scientists to make effective use of them. In November 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of High Energy Physics (HEP) held a workshop to characterize the HPC resources needed at NERSC to support HEP research through the next three to five years. The effort is part of NERSC's legacy of anticipating users needs and deploying resources to meet those demands. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. The chief findings: (1) Science teams need access to a significant increase in computational resources to meet their research goals; (2) Research teams need to be able to read, write, transfer, store online, archive, analyze, and share huge volumes of data; (3) Science teams need guidance and support to implement their codes on future architectures; and (4) Projects need predictable, rapid turnaround of their computational jobs to meet mission-critical time constraints. This report expands upon these key points and includes others. It also presents a number of case studies as representative of the research conducted within HEP. Workshop participants were asked to codify their requirements in this case study format, summarizing their science goals, methods of solution, current and three-to-five year computing requirements, and software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, multi-core environment that is expected to dominate HPC architectures over the next few years. The report includes

  17. Exploiting Data Sparsity for Large-Scale Matrix Computations

    KAUST Repository

    Akbudak, Kadir

    2018-02-24

    Exploiting data sparsity in dense matrices is an algorithmic bridge between architectures that are increasingly memory-austere on a per-core basis and extreme-scale applications. The Hierarchical matrix Computations on Manycore Architectures (HiCMA) library tackles this challenging problem by achieving significant reductions in time to solution and memory footprint, while preserving a specified accuracy requirement of the application. HiCMA provides a high-performance implementation on distributed-memory systems of one of the most widely used matrix factorization in large-scale scientific applications, i.e., the Cholesky factorization. It employs the tile low-rank data format to compress the dense data-sparse off-diagonal tiles of the matrix. It then decomposes the matrix computations into interdependent tasks and relies on the dynamic runtime system StarPU for asynchronous out-of-order scheduling, while allowing high user-productivity. Performance comparisons and memory footprint on matrix dimensions up to eleven million show a performance gain and memory saving of more than an order of magnitude for both metrics on thousands of cores, against state-of-the-art open-source and vendor optimized numerical libraries. This represents an important milestone in enabling large-scale matrix computations toward solving big data problems in geospatial statistics for climate/weather forecasting applications.

  18. Exploiting Data Sparsity for Large-Scale Matrix Computations

    KAUST Repository

    Akbudak, Kadir; Ltaief, Hatem; Mikhalev, Aleksandr; Charara, Ali; Keyes, David E.

    2018-01-01

    Exploiting data sparsity in dense matrices is an algorithmic bridge between architectures that are increasingly memory-austere on a per-core basis and extreme-scale applications. The Hierarchical matrix Computations on Manycore Architectures (HiCMA) library tackles this challenging problem by achieving significant reductions in time to solution and memory footprint, while preserving a specified accuracy requirement of the application. HiCMA provides a high-performance implementation on distributed-memory systems of one of the most widely used matrix factorization in large-scale scientific applications, i.e., the Cholesky factorization. It employs the tile low-rank data format to compress the dense data-sparse off-diagonal tiles of the matrix. It then decomposes the matrix computations into interdependent tasks and relies on the dynamic runtime system StarPU for asynchronous out-of-order scheduling, while allowing high user-productivity. Performance comparisons and memory footprint on matrix dimensions up to eleven million show a performance gain and memory saving of more than an order of magnitude for both metrics on thousands of cores, against state-of-the-art open-source and vendor optimized numerical libraries. This represents an important milestone in enabling large-scale matrix computations toward solving big data problems in geospatial statistics for climate/weather forecasting applications.

  19. Large Scale Computing for the Modelling of Whole Brain Connectivity

    DEFF Research Database (Denmark)

    Albers, Kristoffer Jon

    organization of the brain in continuously increasing resolution. From these images, networks of structural and functional connectivity can be constructed. Bayesian stochastic block modelling provides a prominent data-driven approach for uncovering the latent organization, by clustering the networks into groups...... of neurons. Relying on Markov Chain Monte Carlo (MCMC) simulations as the workhorse in Bayesian inference however poses significant computational challenges, especially when modelling networks at the scale and complexity supported by high-resolution whole-brain MRI. In this thesis, we present how to overcome...... these computational limitations and apply Bayesian stochastic block models for un-supervised data-driven clustering of whole-brain connectivity in full image resolution. We implement high-performance software that allows us to efficiently apply stochastic blockmodelling with MCMC sampling on large complex networks...

  20. Parallel Computational Fluid Dynamics 2007 : Implementations and Experiences on Large Scale and Grid Computing

    CERN Document Server

    2009-01-01

    At the 19th Annual Conference on Parallel Computational Fluid Dynamics held in Antalya, Turkey, in May 2007, the most recent developments and implementations of large-scale and grid computing were presented. This book, comprised of the invited and selected papers of this conference, details those advances, which are of particular interest to CFD and CFD-related communities. It also offers the results related to applications of various scientific and engineering problems involving flows and flow-related topics. Intended for CFD researchers and graduate students, this book is a state-of-the-art presentation of the relevant methodology and implementation techniques of large-scale computing.

  1. Large-scale computing techniques for complex system simulations

    CERN Document Server

    Dubitzky, Werner; Schott, Bernard

    2012-01-01

    Complex systems modeling and simulation approaches are being adopted in a growing number of sectors, including finance, economics, biology, astronomy, and many more. Technologies ranging from distributed computing to specialized hardware are explored and developed to address the computational requirements arising in complex systems simulations. The aim of this book is to present a representative overview of contemporary large-scale computing technologies in the context of complex systems simulations applications. The intention is to identify new research directions in this field and

  2. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  3. Large Scale Computing and Storage Requirements for High Energy Physics

    Energy Technology Data Exchange (ETDEWEB)

    Gerber, Richard A.; Wasserman, Harvey

    2010-11-24

    The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility for the Department of Energy's Office of Science, providing high-performance computing (HPC) resources to more than 3,000 researchers working on about 400 projects. NERSC provides large-scale computing resources and, crucially, the support and expertise needed for scientists to make effective use of them. In November 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of High Energy Physics (HEP) held a workshop to characterize the HPC resources needed at NERSC to support HEP research through the next three to five years. The effort is part of NERSC's legacy of anticipating users needs and deploying resources to meet those demands. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. The chief findings: (1) Science teams need access to a significant increase in computational resources to meet their research goals; (2) Research teams need to be able to read, write, transfer, store online, archive, analyze, and share huge volumes of data; (3) Science teams need guidance and support to implement their codes on future architectures; and (4) Projects need predictable, rapid turnaround of their computational jobs to meet mission-critical time constraints. This report expands upon these key points and includes others. It also presents a number of case studies as representative of the research conducted within HEP. Workshop participants were asked to codify their requirements in this case study format, summarizing their science goals, methods of solution, current and three-to-five year computing requirements, and software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, multi-core environment that is expected to dominate HPC architectures over the next few years

  4. Computing the universe: how large-scale simulations illuminate galaxies and dark energy

    Science.gov (United States)

    O'Shea, Brian

    2015-04-01

    High-performance and large-scale computing is absolutely to understanding astronomical objects such as stars, galaxies, and the cosmic web. This is because these are structures that operate on physical, temporal, and energy scales that cannot be reasonably approximated in the laboratory, and whose complexity and nonlinearity often defies analytic modeling. In this talk, I show how the growth of computing platforms over time has facilitated our understanding of astrophysical and cosmological phenomena, focusing primarily on galaxies and large-scale structure in the Universe.

  5. Proceedings of the meeting on large scale computer simulation research

    International Nuclear Information System (INIS)

    2004-04-01

    The meeting to summarize the collaboration activities for FY2003 on the Large Scale Computer Simulation Research was held January 15-16, 2004 at Theory and Computer Simulation Research Center, National Institute for Fusion Science. Recent simulation results, methodologies and other related topics were presented. (author)

  6. Large scale computing in theoretical physics: Example QCD

    International Nuclear Information System (INIS)

    Schilling, K.

    1986-01-01

    The limitations of the classical mathematical analysis of Newton and Leibniz appear to be more and more overcome by the power of modern computers. Large scale computing techniques - which resemble closely the methods used in simulations within statistical mechanics - allow to treat nonlinear systems with many degrees of freedom such as field theories in nonperturbative situations, where analytical methods do fail. The computation of the hadron spectrum within the framework of lattice QCD sets a demanding goal for the application of supercomputers in basic science. It requires both big computer capacities and clever algorithms to fight all the numerical evils that one encounters in the Euclidean world. The talk will attempt to describe both the computer aspects and the present state of the art of spectrum calculations within lattice QCD. (orig.)

  7. Direct Computation of Sound Radiation by Jet Flow Using Large-scale Equations

    Science.gov (United States)

    Mankbadi, R. R.; Shih, S. H.; Hixon, D. R.; Povinelli, L. A.

    1995-01-01

    Jet noise is directly predicted using large-scale equations. The computational domain is extended in order to directly capture the radiated field. As in conventional large-eddy-simulations, the effect of the unresolved scales on the resolved ones is accounted for. Special attention is given to boundary treatment to avoid spurious modes that can render the computed fluctuations totally unacceptable. Results are presented for a supersonic jet at Mach number 2.1.

  8. Large-scale theoretical calculations in molecular science - design of a large computer system for molecular science and necessary conditions for future computers

    Energy Technology Data Exchange (ETDEWEB)

    Kashiwagi, H [Institute for Molecular Science, Okazaki, Aichi (Japan)

    1982-06-01

    A large computer system was designed and established for molecular science under the leadership of molecular scientists. Features of the computer system are an automated operation system and an open self-service system. Large-scale theoretical calculations have been performed to solve many problems in molecular science, using the computer system. Necessary conditions for future computers are discussed on the basis of this experience.

  9. Large-scale theoretical calculations in molecular science - design of a large computer system for molecular science and necessary conditions for future computers

    International Nuclear Information System (INIS)

    Kashiwagi, H.

    1982-01-01

    A large computer system was designed and established for molecular science under the leadership of molecular scientists. Features of the computer system are an automated operation system and an open self-service system. Large-scale theoretical calculations have been performed to solve many problems in molecular science, using the computer system. Necessary conditions for future computers are discussed on the basis of this experience. (orig.)

  10. Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

    Science.gov (United States)

    Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

    2016-01-01

    With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

  11. Large scale particle simulations in a virtual memory computer

    International Nuclear Information System (INIS)

    Gray, P.C.; Million, R.; Wagner, J.S.; Tajima, T.

    1983-01-01

    Virtual memory computers are capable of executing large-scale particle simulations even when the memory requirements exceeds the computer core size. The required address space is automatically mapped onto slow disc memory the the operating system. When the simulation size is very large, frequent random accesses to slow memory occur during the charge accumulation and particle pushing processes. Assesses to slow memory significantly reduce the excecution rate of the simulation. We demonstrate in this paper that with the proper choice of sorting algorithm, a nominal amount of sorting to keep physically adjacent particles near particles with neighboring array indices can reduce random access to slow memory, increase the efficiency of the I/O system, and hence, reduce the required computing time. (orig.)

  12. Large-scale particle simulations in a virtual-memory computer

    International Nuclear Information System (INIS)

    Gray, P.C.; Wagner, J.S.; Tajima, T.; Million, R.

    1982-08-01

    Virtual memory computers are capable of executing large-scale particle simulations even when the memory requirements exceed the computer core size. The required address space is automatically mapped onto slow disc memory by the operating system. When the simulation size is very large, frequent random accesses to slow memory occur during the charge accumulation and particle pushing processes. Accesses to slow memory significantly reduce the execution rate of the simulation. We demonstrate in this paper that with the proper choice of sorting algorithm, a nominal amount of sorting to keep physically adjacent particles near particles with neighboring array indices can reduce random access to slow memory, increase the efficiency of the I/O system, and hence, reduce the required computing time

  13. An efficient and novel computation method for simulating diffraction patterns from large-scale coded apertures on large-scale focal plane arrays

    Science.gov (United States)

    Shrekenhamer, Abraham; Gottesman, Stephen R.

    2012-10-01

    A novel and memory efficient method for computing diffraction patterns produced on large-scale focal planes by largescale Coded Apertures at wavelengths where diffraction effects are significant has been developed and tested. The scheme, readily implementable on portable computers, overcomes the memory limitations of present state-of-the-art simulation codes such as Zemax. The method consists of first calculating a set of reference complex field (amplitude and phase) patterns on the focal plane produced by a single (reference) central hole, extending to twice the focal plane array size, with one such pattern for each Line-of-Sight (LOS) direction and wavelength in the scene, and with the pattern amplitude corresponding to the square-root of the spectral irradiance from each such LOS direction in the scene at selected wavelengths. Next the set of reference patterns is transformed to generate pattern sets for other holes. The transformation consists of a translational pattern shift corresponding to each hole's position offset and an electrical phase shift corresponding to each hole's position offset and incoming radiance's direction and wavelength. The set of complex patterns for each direction and wavelength is then summed coherently and squared for each detector to yield a set of power patterns unique for each direction and wavelength. Finally the set of power patterns is summed to produce the full waveband diffraction pattern from the scene. With this tool researchers can now efficiently simulate diffraction patterns produced from scenes by large-scale Coded Apertures onto large-scale focal plane arrays to support the development and optimization of coded aperture masks and image reconstruction algorithms.

  14. Large-scale computation in solid state physics - Recent developments and prospects

    International Nuclear Information System (INIS)

    DeVreese, J.T.

    1985-01-01

    During the past few years an increasing interest in large-scale computation is developing. Several initiatives were taken to evaluate and exploit the potential of ''supercomputers'' like the CRAY-1 (or XMP) or the CYBER-205. In the U.S.A., there first appeared the Lax report in 1982 and subsequently (1984) the National Science Foundation in the U.S.A. announced a program to promote large-scale computation at the universities. Also, in Europe several CRAY- and CYBER-205 systems have been installed. Although the presently available mainframes are the result of a continuous growth in speed and memory, they might have induced a discontinuous transition in the evolution of the scientific method; between theory and experiment a third methodology, ''computational science'', has become or is becoming operational

  15. The TeraShake Computational Platform for Large-Scale Earthquake Simulations

    Science.gov (United States)

    Cui, Yifeng; Olsen, Kim; Chourasia, Amit; Moore, Reagan; Maechling, Philip; Jordan, Thomas

    Geoscientific and computer science researchers with the Southern California Earthquake Center (SCEC) are conducting a large-scale, physics-based, computationally demanding earthquake system science research program with the goal of developing predictive models of earthquake processes. The computational demands of this program continue to increase rapidly as these researchers seek to perform physics-based numerical simulations of earthquake processes for larger meet the needs of this research program, a multiple-institution team coordinated by SCEC has integrated several scientific codes into a numerical modeling-based research tool we call the TeraShake computational platform (TSCP). A central component in the TSCP is a highly scalable earthquake wave propagation simulation program called the TeraShake anelastic wave propagation (TS-AWP) code. In this chapter, we describe how we extended an existing, stand-alone, wellvalidated, finite-difference, anelastic wave propagation modeling code into the highly scalable and widely used TS-AWP and then integrated this code into the TeraShake computational platform that provides end-to-end (initialization to analysis) research capabilities. We also describe the techniques used to enhance the TS-AWP parallel performance on TeraGrid supercomputers, as well as the TeraShake simulations phases including input preparation, run time, data archive management, and visualization. As a result of our efforts to improve its parallel efficiency, the TS-AWP has now shown highly efficient strong scaling on over 40K processors on IBM’s BlueGene/L Watson computer. In addition, the TSCP has developed into a computational system that is useful to many members of the SCEC community for performing large-scale earthquake simulations.

  16. Optimization and large scale computation of an entropy-based moment closure

    Science.gov (United States)

    Kristopher Garrett, C.; Hauck, Cory; Hill, Judith

    2015-12-01

    We present computational advances and results in the implementation of an entropy-based moment closure, MN, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as PN, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. These results show, in particular, load balancing issues in scaling the MN algorithm that do not appear for the PN algorithm. We also observe that in weak scaling tests, the ratio in time to solution of MN to PN decreases.

  17. Towards Portable Large-Scale Image Processing with High-Performance Computing.

    Science.gov (United States)

    Huo, Yuankai; Blaber, Justin; Damon, Stephen M; Boyd, Brian D; Bao, Shunxing; Parvathaneni, Prasanna; Noguera, Camilo Bermudez; Chaganti, Shikha; Nath, Vishwesh; Greer, Jasmine M; Lyu, Ilwoo; French, William R; Newton, Allen T; Rogers, Baxter P; Landman, Bennett A

    2018-05-03

    High-throughput, large-scale medical image computing demands tight integration of high-performance computing (HPC) infrastructure for data storage, job distribution, and image processing. The Vanderbilt University Institute for Imaging Science (VUIIS) Center for Computational Imaging (CCI) has constructed a large-scale image storage and processing infrastructure that is composed of (1) a large-scale image database using the eXtensible Neuroimaging Archive Toolkit (XNAT), (2) a content-aware job scheduling platform using the Distributed Automation for XNAT pipeline automation tool (DAX), and (3) a wide variety of encapsulated image processing pipelines called "spiders." The VUIIS CCI medical image data storage and processing infrastructure have housed and processed nearly half-million medical image volumes with Vanderbilt Advanced Computing Center for Research and Education (ACCRE), which is the HPC facility at the Vanderbilt University. The initial deployment was natively deployed (i.e., direct installations on a bare-metal server) within the ACCRE hardware and software environments, which lead to issues of portability and sustainability. First, it could be laborious to deploy the entire VUIIS CCI medical image data storage and processing infrastructure to another HPC center with varying hardware infrastructure, library availability, and software permission policies. Second, the spiders were not developed in an isolated manner, which has led to software dependency issues during system upgrades or remote software installation. To address such issues, herein, we describe recent innovations using containerization techniques with XNAT/DAX which are used to isolate the VUIIS CCI medical image data storage and processing infrastructure from the underlying hardware and software environments. The newly presented XNAT/DAX solution has the following new features: (1) multi-level portability from system level to the application level, (2) flexible and dynamic software

  18. Application of parallel computing techniques to a large-scale reservoir simulation

    International Nuclear Information System (INIS)

    Zhang, Keni; Wu, Yu-Shu; Ding, Chris; Pruess, Karsten

    2001-01-01

    Even with the continual advances made in both computational algorithms and computer hardware used in reservoir modeling studies, large-scale simulation of fluid and heat flow in heterogeneous reservoirs remains a challenge. The problem commonly arises from intensive computational requirement for detailed modeling investigations of real-world reservoirs. This paper presents the application of a massive parallel-computing version of the TOUGH2 code developed for performing large-scale field simulations. As an application example, the parallelized TOUGH2 code is applied to develop a three-dimensional unsaturated-zone numerical model simulating flow of moisture, gas, and heat in the unsaturated zone of Yucca Mountain, Nevada, a potential repository for high-level radioactive waste. The modeling approach employs refined spatial discretization to represent the heterogeneous fractured tuffs of the system, using more than a million 3-D gridblocks. The problem of two-phase flow and heat transfer within the model domain leads to a total of 3,226,566 linear equations to be solved per Newton iteration. The simulation is conducted on a Cray T3E-900, a distributed-memory massively parallel computer. Simulation results indicate that the parallel computing technique, as implemented in the TOUGH2 code, is very efficient. The reliability and accuracy of the model results have been demonstrated by comparing them to those of small-scale (coarse-grid) models. These comparisons show that simulation results obtained with the refined grid provide more detailed predictions of the future flow conditions at the site, aiding in the assessment of proposed repository performance

  19. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems.

    Science.gov (United States)

    Shen, Lili; Guo, Jiming; Wang, Lei

    2018-06-06

    The network real-time kinematic (RTK) technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI), and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs), robotic equipment, etc.) require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC) approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC) according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS) data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  20. Deterministic sensitivity and uncertainty analysis for large-scale computer models

    International Nuclear Information System (INIS)

    Worley, B.A.; Pin, F.G.; Oblow, E.M.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.

    1988-01-01

    This paper presents a comprehensive approach to sensitivity and uncertainty analysis of large-scale computer models that is analytic (deterministic) in principle and that is firmly based on the model equations. The theory and application of two systems based upon computer calculus, GRESS and ADGEN, are discussed relative to their role in calculating model derivatives and sensitivities without a prohibitive initial manpower investment. Storage and computational requirements for these two systems are compared for a gradient-enhanced version of the PRESTO-II computer model. A Deterministic Uncertainty Analysis (DUA) method that retains the characteristics of analytically computing result uncertainties based upon parameter probability distributions is then introduced and results from recent studies are shown. 29 refs., 4 figs., 1 tab

  1. Large-scale Intelligent Transporation Systems simulation

    Energy Technology Data Exchange (ETDEWEB)

    Ewing, T.; Canfield, T.; Hannebutte, U.; Levine, D.; Tentner, A.

    1995-06-01

    A prototype computer system has been developed which defines a high-level architecture for a large-scale, comprehensive, scalable simulation of an Intelligent Transportation System (ITS) capable of running on massively parallel computers and distributed (networked) computer systems. The prototype includes the modelling of instrumented ``smart`` vehicles with in-vehicle navigation units capable of optimal route planning and Traffic Management Centers (TMC). The TMC has probe vehicle tracking capabilities (display position and attributes of instrumented vehicles), and can provide 2-way interaction with traffic to provide advisories and link times. Both the in-vehicle navigation module and the TMC feature detailed graphical user interfaces to support human-factors studies. The prototype has been developed on a distributed system of networked UNIX computers but is designed to run on ANL`s IBM SP-X parallel computer system for large scale problems. A novel feature of our design is that vehicles will be represented by autonomus computer processes, each with a behavior model which performs independent route selection and reacts to external traffic events much like real vehicles. With this approach, one will be able to take advantage of emerging massively parallel processor (MPP) systems.

  2. A Self-Organizing Spatial Clustering Approach to Support Large-Scale Network RTK Systems

    Directory of Open Access Journals (Sweden)

    Lili Shen

    2018-06-01

    Full Text Available The network real-time kinematic (RTK technique can provide centimeter-level real time positioning solutions and play a key role in geo-spatial infrastructure. With ever-increasing popularity, network RTK systems will face issues in the support of large numbers of concurrent users. In the past, high-precision positioning services were oriented towards professionals and only supported a few concurrent users. Currently, precise positioning provides a spatial foundation for artificial intelligence (AI, and countless smart devices (autonomous cars, unmanned aerial-vehicles (UAVs, robotic equipment, etc. require precise positioning services. Therefore, the development of approaches to support large-scale network RTK systems is urgent. In this study, we proposed a self-organizing spatial clustering (SOSC approach which automatically clusters online users to reduce the computational load on the network RTK system server side. The experimental results indicate that both the SOSC algorithm and the grid algorithm can reduce the computational load efficiently, while the SOSC algorithm gives a more elastic and adaptive clustering solution with different datasets. The SOSC algorithm determines the cluster number and the mean distance to cluster center (MDTCC according to the data set, while the grid approaches are all predefined. The side-effects of clustering algorithms on the user side are analyzed with real global navigation satellite system (GNSS data sets. The experimental results indicate that 10 km can be safely used as the cluster radius threshold for the SOSC algorithm without significantly reducing the positioning precision and reliability on the user side.

  3. Large-scale ligand-based predictive modelling using support vector machines.

    Science.gov (United States)

    Alvarsson, Jonathan; Lampa, Samuel; Schaal, Wesley; Andersson, Claes; Wikberg, Jarl E S; Spjuth, Ola

    2016-01-01

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

  4. Deterministic sensitivity and uncertainty analysis for large-scale computer models

    International Nuclear Information System (INIS)

    Worley, B.A.; Pin, F.G.; Oblow, E.M.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.

    1988-01-01

    The fields of sensitivity and uncertainty analysis have traditionally been dominated by statistical techniques when large-scale modeling codes are being analyzed. These methods are able to estimate sensitivities, generate response surfaces, and estimate response probability distributions given the input parameter probability distributions. Because the statistical methods are computationally costly, they are usually applied only to problems with relatively small parameter sets. Deterministic methods, on the other hand, are very efficient and can handle large data sets, but generally require simpler models because of the considerable programming effort required for their implementation. The first part of this paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. This second part of the paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. This paper is applicable to low-level radioactive waste disposal system performance assessment

  5. Large-scale parallel genome assembler over cloud computing environment.

    Science.gov (United States)

    Das, Arghya Kusum; Koppa, Praveen Kumar; Goswami, Sayan; Platania, Richard; Park, Seung-Jong

    2017-06-01

    The size of high throughput DNA sequencing data has already reached the terabyte scale. To manage this huge volume of data, many downstream sequencing applications started using locality-based computing over different cloud infrastructures to take advantage of elastic (pay as you go) resources at a lower cost. However, the locality-based programming model (e.g. MapReduce) is relatively new. Consequently, developing scalable data-intensive bioinformatics applications using this model and understanding the hardware environment that these applications require for good performance, both require further research. In this paper, we present a de Bruijn graph oriented Parallel Giraph-based Genome Assembler (GiGA), as well as the hardware platform required for its optimal performance. GiGA uses the power of Hadoop (MapReduce) and Giraph (large-scale graph analysis) to achieve high scalability over hundreds of compute nodes by collocating the computation and data. GiGA achieves significantly higher scalability with competitive assembly quality compared to contemporary parallel assemblers (e.g. ABySS and Contrail) over traditional HPC cluster. Moreover, we show that the performance of GiGA is significantly improved by using an SSD-based private cloud infrastructure over traditional HPC cluster. We observe that the performance of GiGA on 256 cores of this SSD-based cloud infrastructure closely matches that of 512 cores of traditional HPC cluster.

  6. 3D fast adaptive correlation imaging for large-scale gravity data based on GPU computation

    Science.gov (United States)

    Chen, Z.; Meng, X.; Guo, L.; Liu, G.

    2011-12-01

    In recent years, large scale gravity data sets have been collected and employed to enhance gravity problem-solving abilities of tectonics studies in China. Aiming at the large scale data and the requirement of rapid interpretation, previous authors have carried out a lot of work, including the fast gradient module inversion and Euler deconvolution depth inversion ,3-D physical property inversion using stochastic subspaces and equivalent storage, fast inversion using wavelet transforms and a logarithmic barrier method. So it can be say that 3-D gravity inversion has been greatly improved in the last decade. Many authors added many different kinds of priori information and constraints to deal with nonuniqueness using models composed of a large number of contiguous cells of unknown property and obtained good results. However, due to long computation time, instability and other shortcomings, 3-D physical property inversion has not been widely applied to large-scale data yet. In order to achieve 3-D interpretation with high efficiency and precision for geological and ore bodies and obtain their subsurface distribution, there is an urgent need to find a fast and efficient inversion method for large scale gravity data. As an entirely new geophysical inversion method, 3D correlation has a rapid development thanks to the advantage of requiring no a priori information and demanding small amount of computer memory. This method was proposed to image the distribution of equivalent excess masses of anomalous geological bodies with high resolution both longitudinally and transversely. In order to tranform the equivalence excess masses into real density contrasts, we adopt the adaptive correlation imaging for gravity data. After each 3D correlation imaging, we change the equivalence into density contrasts according to the linear relationship, and then carry out forward gravity calculation for each rectangle cells. Next, we compare the forward gravity data with real data, and

  7. Cerebral methodology based computing to estimate real phenomena from large-scale nuclear simulation

    International Nuclear Information System (INIS)

    Suzuki, Yoshio

    2011-01-01

    Our final goal is to estimate real phenomena from large-scale nuclear simulations by using computing processes. Large-scale simulations mean that they include scale variety and physical complexity so that corresponding experiments and/or theories do not exist. In nuclear field, it is indispensable to estimate real phenomena from simulations in order to improve the safety and security of nuclear power plants. Here, the analysis of uncertainty included in simulations is needed to reveal sensitivity of uncertainty due to randomness, to reduce the uncertainty due to lack of knowledge and to lead a degree of certainty by verification and validation (V and V) and uncertainty quantification (UQ) processes. To realize this, we propose 'Cerebral Methodology based Computing (CMC)' as computing processes with deductive and inductive approaches by referring human reasoning processes. Our idea is to execute deductive and inductive simulations contrasted with deductive and inductive approaches. We have established its prototype system and applied it to a thermal displacement analysis of a nuclear power plant. The result shows that our idea is effective to reduce the uncertainty and to get the degree of certainty. (author)

  8. A review of parallel computing for large-scale remote sensing image mosaicking

    OpenAIRE

    Chen, Lajiao; Ma, Yan; Liu, Peng; Wei, Jingbo; Jie, Wei; He, Jijun

    2015-01-01

    Interest in image mosaicking has been spurred by a wide variety of research and management needs. However, for large-scale applications, remote sensing image mosaicking usually requires significant computational capabilities. Several studies have attempted to apply parallel computing to improve image mosaicking algorithms and to speed up calculation process. The state of the art of this field has not yet been summarized, which is, however, essential for a better understanding and for further ...

  9. Large-scale computer networks and the future of legal knowledge-based systems

    NARCIS (Netherlands)

    Leenes, R.E.; Svensson, Jorgen S.; Hage, J.C.; Bench-Capon, T.J.M.; Cohen, M.J.; van den Herik, H.J.

    1995-01-01

    In this paper we investigate the relation between legal knowledge-based systems and large-scale computer networks such as the Internet. On the one hand, researchers of legal knowledge-based systems have claimed huge possibilities, but despite the efforts over the last twenty years, the number of

  10. Visual analysis of inter-process communication for large-scale parallel computing.

    Science.gov (United States)

    Muelder, Chris; Gygi, Francois; Ma, Kwan-Liu

    2009-01-01

    In serial computation, program profiling is often helpful for optimization of key sections of code. When moving to parallel computation, not only does the code execution need to be considered but also communication between the different processes which can induce delays that are detrimental to performance. As the number of processes increases, so does the impact of the communication delays on performance. For large-scale parallel applications, it is critical to understand how the communication impacts performance in order to make the code more efficient. There are several tools available for visualizing program execution and communications on parallel systems. These tools generally provide either views which statistically summarize the entire program execution or process-centric views. However, process-centric visualizations do not scale well as the number of processes gets very large. In particular, the most common representation of parallel processes is a Gantt char t with a row for each process. As the number of processes increases, these charts can become difficult to work with and can even exceed screen resolution. We propose a new visualization approach that affords more scalability and then demonstrate it on systems running with up to 16,384 processes.

  11. Towards Large-area Field-scale Operational Evapotranspiration for Water Use Mapping

    Science.gov (United States)

    Senay, G. B.; Friedrichs, M.; Morton, C.; Huntington, J. L.; Verdin, J.

    2017-12-01

    Field-scale evapotranspiration (ET) estimates are needed for improving surface and groundwater use and water budget studies. Ideally, field-scale ET estimates would be at regional to national levels and cover long time periods. As a result of large data storage and computational requirements associated with processing field-scale satellite imagery such as Landsat, numerous challenges remain to develop operational ET estimates over large areas for detailed water use and availability studies. However, the combination of new science, data availability, and cloud computing technology is enabling unprecedented capabilities for ET mapping. To demonstrate this capability, we used Google's Earth Engine cloud computing platform to create nationwide annual ET estimates with 30-meter resolution Landsat ( 16,000 images) and gridded weather data using the Operational Simplified Surface Energy Balance (SSEBop) model in support of the National Water Census, a USGS research program designed to build decision support capacity for water management agencies and other natural resource managers. By leveraging Google's Earth Engine Application Programming Interface (API) and developing software in a collaborative, open-platform environment, we rapidly advance from research towards applications for large-area field-scale ET mapping. Cloud computing of the Landsat image archive combined with other satellite, climate, and weather data, is creating never imagined opportunities for assessing ET model behavior and uncertainty, and ultimately providing the ability for more robust operational monitoring and assessment of water use at field-scales.

  12. Large-scale simulations of error-prone quantum computation devices

    International Nuclear Information System (INIS)

    Trieu, Doan Binh

    2009-01-01

    The theoretical concepts of quantum computation in the idealized and undisturbed case are well understood. However, in practice, all quantum computation devices do suffer from decoherence effects as well as from operational imprecisions. This work assesses the power of error-prone quantum computation devices using large-scale numerical simulations on parallel supercomputers. We present the Juelich Massively Parallel Ideal Quantum Computer Simulator (JUMPIQCS), that simulates a generic quantum computer on gate level. It comprises an error model for decoherence and operational errors. The robustness of various algorithms in the presence of noise has been analyzed. The simulation results show that for large system sizes and long computations it is imperative to actively correct errors by means of quantum error correction. We implemented the 5-, 7-, and 9-qubit quantum error correction codes. Our simulations confirm that using error-prone correction circuits with non-fault-tolerant quantum error correction will always fail, because more errors are introduced than being corrected. Fault-tolerant methods can overcome this problem, provided that the single qubit error rate is below a certain threshold. We incorporated fault-tolerant quantum error correction techniques into JUMPIQCS using Steane's 7-qubit code and determined this threshold numerically. Using the depolarizing channel as the source of decoherence, we find a threshold error rate of (5.2±0.2) x 10 -6 . For Gaussian distributed operational over-rotations the threshold lies at a standard deviation of 0.0431±0.0002. We can conclude that quantum error correction is especially well suited for the correction of operational imprecisions and systematic over-rotations. For realistic simulations of specific quantum computation devices we need to extend the generic model to dynamic simulations, i.e. time-dependent Hamiltonian simulations of realistic hardware models. We focus on today's most advanced technology, i

  13. The multilevel fast multipole algorithm (MLFMA) for solving large-scale computational electromagnetics problems

    CERN Document Server

    Ergul, Ozgur

    2014-01-01

    The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetic Problems provides a detailed and instructional overview of implementing MLFMA. The book: Presents a comprehensive treatment of the MLFMA algorithm, including basic linear algebra concepts, recent developments on the parallel computation, and a number of application examplesCovers solutions of electromagnetic problems involving dielectric objects and perfectly-conducting objectsDiscusses applications including scattering from airborne targets, scattering from red

  14. Large Scale Beam-beam Simulations for the CERN LHC using Distributed Computing

    CERN Document Server

    Herr, Werner; McIntosh, E; Schmidt, F

    2006-01-01

    We report on a large scale simulation of beam-beam effects for the CERN Large Hadron Collider (LHC). The stability of particles which experience head-on and long-range beam-beam effects was investigated for different optical configurations and machine imperfections. To cover the interesting parameter space required computing resources not available at CERN. The necessary resources were available in the LHC@home project, based on the BOINC platform. At present, this project makes more than 60000 hosts available for distributed computing. We shall discuss our experience using this system during a simulation campaign of more than six months and describe the tools and procedures necessary to ensure consistent results. The results from this extended study are presented and future plans are discussed.

  15. Large scale network-centric distributed systems

    CERN Document Server

    Sarbazi-Azad, Hamid

    2014-01-01

    A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems. Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issu

  16. Large scale inverse problems computational methods and applications in the earth sciences

    CERN Document Server

    Scheichl, Robert; Freitag, Melina A; Kindermann, Stefan

    2013-01-01

    This book is thesecond volume of three volume series recording the ""Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment"" taking place in Linz, Austria, October 3-7, 2011. The volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications.

  17. Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

    Science.gov (United States)

    Dong, Xianlei; Bollen, Johan

    2015-01-01

    Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

  18. Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics.

    Directory of Open Access Journals (Sweden)

    Xianlei Dong

    Full Text Available Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

  19. Large-scale simulations of error-prone quantum computation devices

    Energy Technology Data Exchange (ETDEWEB)

    Trieu, Doan Binh

    2009-07-01

    The theoretical concepts of quantum computation in the idealized and undisturbed case are well understood. However, in practice, all quantum computation devices do suffer from decoherence effects as well as from operational imprecisions. This work assesses the power of error-prone quantum computation devices using large-scale numerical simulations on parallel supercomputers. We present the Juelich Massively Parallel Ideal Quantum Computer Simulator (JUMPIQCS), that simulates a generic quantum computer on gate level. It comprises an error model for decoherence and operational errors. The robustness of various algorithms in the presence of noise has been analyzed. The simulation results show that for large system sizes and long computations it is imperative to actively correct errors by means of quantum error correction. We implemented the 5-, 7-, and 9-qubit quantum error correction codes. Our simulations confirm that using error-prone correction circuits with non-fault-tolerant quantum error correction will always fail, because more errors are introduced than being corrected. Fault-tolerant methods can overcome this problem, provided that the single qubit error rate is below a certain threshold. We incorporated fault-tolerant quantum error correction techniques into JUMPIQCS using Steane's 7-qubit code and determined this threshold numerically. Using the depolarizing channel as the source of decoherence, we find a threshold error rate of (5.2{+-}0.2) x 10{sup -6}. For Gaussian distributed operational over-rotations the threshold lies at a standard deviation of 0.0431{+-}0.0002. We can conclude that quantum error correction is especially well suited for the correction of operational imprecisions and systematic over-rotations. For realistic simulations of specific quantum computation devices we need to extend the generic model to dynamic simulations, i.e. time-dependent Hamiltonian simulations of realistic hardware models. We focus on today's most advanced

  20. Large-Scale, Parallel, Multi-Sensor Atmospheric Data Fusion Using Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2013-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the 'A-Train' platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration analyses of important climate variables presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (MERRA), stratify the comparisons using a classification of the 'cloud scenes' from CloudSat, and repeat the entire analysis over 10 years of data. To efficiently assemble such datasets, we are utilizing Elastic Computing in the Cloud and parallel map/reduce-based algorithms. However, these problems are Data Intensive computing so the data transfer times and storage costs (for caching) are key issues. SciReduce is a Hadoop-like parallel analysis system, programmed in parallel python, that is designed from the ground up for Earth science. SciReduce executes inside VMWare images and scales to any number of nodes in the Cloud. Unlike Hadoop, SciReduce operates on bundles of named numeric arrays, which can be passed in memory or serialized to disk in netCDF4 or HDF5. Figure 1 shows the architecture of the full computational system, with SciReduce at the core. Multi-year datasets are automatically 'sharded' by time and space across a cluster of nodes so that years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the cached input and intermediate datasets. We are using SciReduce to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a NASA MEASURES grant. We will

  1. Large-scale visualization system for grid environment

    International Nuclear Information System (INIS)

    Suzuki, Yoshio

    2007-01-01

    Center for Computational Science and E-systems of Japan Atomic Energy Agency (CCSE/JAEA) has been conducting R and Ds of distributed computing (grid computing) environments: Seamless Thinking Aid (STA), Information Technology Based Laboratory (ITBL) and Atomic Energy Grid InfraStructure (AEGIS). In these R and Ds, we have developed the visualization technology suitable for the distributed computing environment. As one of the visualization tools, we have developed the Parallel Support Toolkit (PST) which can execute the visualization process parallely on a computer. Now, we improve PST to be executable simultaneously on multiple heterogeneous computers using Seamless Thinking Aid Message Passing Interface (STAMPI). STAMPI, we have developed in these R and Ds, is the MPI library executable on a heterogeneous computing environment. The improvement realizes the visualization of extremely large-scale data and enables more efficient visualization processes in a distributed computing environment. (author)

  2. On the Large-Scaling Issues of Cloud-based Applications for Earth Science Dat

    Science.gov (United States)

    Hua, H.

    2016-12-01

    Next generation science data systems are needed to address the incoming flood of data from new missions such as NASA's SWOT and NISAR where its SAR data volumes and data throughput rates are order of magnitude larger than present day missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Experiences have shown that to embrace efficient cloud computing approaches for large-scale science data systems requires more than just moving existing code to cloud environments. At large cloud scales, we need to deal with scaling and cost issues. We present our experiences on deploying multiple instances of our hybrid-cloud computing science data system (HySDS) to support large-scale processing of Earth Science data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer 75%-90% costs savings but with an unpredictable computing environment based on market forces.

  3. Using technology to support investigations in the electronic age: tracking hackers to large scale international computer fraud

    Science.gov (United States)

    McFall, Steve

    1994-03-01

    With the increase in business automation and the widespread availability and low cost of computer systems, law enforcement agencies have seen a corresponding increase in criminal acts involving computers. The examination of computer evidence is a new field of forensic science with numerous opportunities for research and development. Research is needed to develop new software utilities to examine computer storage media, expert systems capable of finding criminal activity in large amounts of data, and to find methods of recovering data from chemically and physically damaged computer storage media. In addition, defeating encryption and password protection of computer files is also a topic requiring more research and development.

  4. Large-Scale Data Collection Metadata Management at the National Computation Infrastructure

    Science.gov (United States)

    Wang, J.; Evans, B. J. K.; Bastrakova, I.; Ryder, G.; Martin, J.; Duursma, D.; Gohar, K.; Mackey, T.; Paget, M.; Siddeswara, G.

    2014-12-01

    Data Collection management has become an essential activity at the National Computation Infrastructure (NCI) in Australia. NCI's partners (CSIRO, Bureau of Meteorology, Australian National University, and Geoscience Australia), supported by the Australian Government and Research Data Storage Infrastructure (RDSI), have established a national data resource that is co-located with high-performance computing. This paper addresses the metadata management of these data assets over their lifetime. NCI manages 36 data collections (10+ PB) categorised as earth system sciences, climate and weather model data assets and products, earth and marine observations and products, geosciences, terrestrial ecosystem, water management and hydrology, astronomy, social science and biosciences. The data is largely sourced from NCI partners, the custodians of many of the national scientific records, and major research community organisations. The data is made available in a HPC and data-intensive environment - a ~56000 core supercomputer, virtual labs on a 3000 core cloud system, and data services. By assembling these large national assets, new opportunities have arisen to harmonise the data collections, making a powerful cross-disciplinary resource.To support the overall management, a Data Management Plan (DMP) has been developed to record the workflows, procedures, the key contacts and responsibilities. The DMP has fields that can be exported to the ISO19115 schema and to the collection level catalogue of GeoNetwork. The subset or file level metadata catalogues are linked with the collection level through parent-child relationship definition using UUID. A number of tools have been developed that support interactive metadata management, bulk loading of data, and support for computational workflows or data pipelines. NCI creates persistent identifiers for each of the assets. The data collection is tracked over its lifetime, and the recognition of the data providers, data owners, data

  5. Standing Together for Reproducibility in Large-Scale Computing: Report on reproducibility@XSEDE

    OpenAIRE

    James, Doug; Wilkins-Diehr, Nancy; Stodden, Victoria; Colbry, Dirk; Rosales, Carlos; Fahey, Mark; Shi, Justin; Silva, Rafael F.; Lee, Kyo; Roskies, Ralph; Loewe, Laurence; Lindsey, Susan; Kooper, Rob; Barba, Lorena; Bailey, David

    2014-01-01

    This is the final report on reproducibility@xsede, a one-day workshop held in conjunction with XSEDE14, the annual conference of the Extreme Science and Engineering Discovery Environment (XSEDE). The workshop's discussion-oriented agenda focused on reproducibility in large-scale computational research. Two important themes capture the spirit of the workshop submissions and discussions: (1) organizational stakeholders, especially supercomputer centers, are in a unique position to promote, enab...

  6. A full scale approximation of covariance functions for large spatial data sets

    KAUST Repository

    Sang, Huiyan

    2011-10-10

    Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

  7. A full scale approximation of covariance functions for large spatial data sets

    KAUST Repository

    Sang, Huiyan; Huang, Jianhua Z.

    2011-01-01

    Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

  8. Trends in large-scale testing of reactor structures

    International Nuclear Information System (INIS)

    Blejwas, T.E.

    2003-01-01

    Large-scale tests of reactor structures have been conducted at Sandia National Laboratories since the late 1970s. This paper describes a number of different large-scale impact tests, pressurization tests of models of containment structures, and thermal-pressure tests of models of reactor pressure vessels. The advantages of large-scale testing are evident, but cost, in particular limits its use. As computer models have grown in size, such as number of degrees of freedom, the advent of computer graphics has made possible very realistic representation of results - results that may not accurately represent reality. A necessary condition to avoiding this pitfall is the validation of the analytical methods and underlying physical representations. Ironically, the immensely larger computer models sometimes increase the need for large-scale testing, because the modeling is applied to increasing more complex structural systems and/or more complex physical phenomena. Unfortunately, the cost of large-scale tests is a disadvantage that will likely severely limit similar testing in the future. International collaborations may provide the best mechanism for funding future programs with large-scale tests. (author)

  9. Really Large Scale Computer Graphic Projection Using Lasers and Laser Substitutes

    Science.gov (United States)

    Rother, Paul

    1989-07-01

    This paper reflects on past laser projects to display vector scanned computer graphic images onto very large and irregular surfaces. Since the availability of microprocessors and high powered visible lasers, very large scale computer graphics projection have become a reality. Due to the independence from a focusing lens, lasers easily project onto distant and irregular surfaces and have been used for amusement parks, theatrical performances, concert performances, industrial trade shows and dance clubs. Lasers have been used to project onto mountains, buildings, 360° globes, clouds of smoke and water. These methods have proven successful in installations at: Epcot Theme Park in Florida; Stone Mountain Park in Georgia; 1984 Olympics in Los Angeles; hundreds of Corporate trade shows and thousands of musical performances. Using new ColorRayTM technology, the use of costly and fragile lasers is no longer necessary. Utilizing fiber optic technology, the functionality of lasers can be duplicated for new and exciting projection possibilities. The use of ColorRayTM technology has enjoyed worldwide recognition in conjunction with Pink Floyd and George Michaels' world wide tours.

  10. Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

    Science.gov (United States)

    Johnston, William E.; Gannon, Dennis; Nitzberg, Bill

    2000-01-01

    We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3

  11. Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

    Science.gov (United States)

    Zhao, Shanrong; Prenger, Kurt; Smith, Lance; Messina, Thomas; Fan, Hongtao; Jaeger, Edward; Stephens, Susan

    2013-06-27

    Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available

  12. Seismic tests of a pile-supported structure in liquefiable sand using large-scale blast excitation

    International Nuclear Information System (INIS)

    Kamijo, Naotaka; Saito, Hideaki; Kusama, Kazuhiro; Kontani, Osamu; Nigbor, Robert

    2004-01-01

    Extensive, large-amplitude vibration tests of a pile-supported structure in a liquefiable sand deposit have been performed at a large-scale mining site. Ground motions from large-scale blasting operations were used as excitation forces for vibration tests. A simple pile-supported structure was constructed in an excavated 3 m-deep pit. The test pit was backfilled with 100% water-saturated clean uniform sand. Accelerations were measured on the pile-supported structure, in the sand in the test pit, and in the adjacent free field. Excess pore water pressures in the test pit and strains of one pile were also measured. Vibration tests were performed with six different levels of input motions. The maximum horizontal acceleration recorded at the adjacent ground surface varied from 20 Gals to 1353 Gals. These alternations of acceleration provided different degrees of liquefaction in the test pit. Sand boiling phenomena were observed in the test pit with larger input motions. This paper outlines vibration tests and investigates the test results

  13. An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing

    Science.gov (United States)

    Xu, Jincheng; Liu, Wei; Wang, Jin; Liu, Linong; Zhang, Jianfeng

    2018-02-01

    De-absorption pre-stack time migration (QPSTM) compensates for the absorption and dispersion of seismic waves by introducing an effective Q parameter, thereby making it an effective tool for 3D, high-resolution imaging of seismic data. Although the optimal aperture obtained via stationary-phase migration reduces the computational cost of 3D QPSTM and yields 3D stationary-phase QPSTM, the associated computational efficiency is still the main problem in the processing of 3D, high-resolution images for real large-scale seismic data. In the current paper, we proposed a division method for large-scale, 3D seismic data to optimize the performance of stationary-phase QPSTM on clusters of graphics processing units (GPU). Then, we designed an imaging point parallel strategy to achieve an optimal parallel computing performance. Afterward, we adopted an asynchronous double buffering scheme for multi-stream to perform the GPU/CPU parallel computing. Moreover, several key optimization strategies of computation and storage based on the compute unified device architecture (CUDA) were adopted to accelerate the 3D stationary-phase QPSTM algorithm. Compared with the initial GPU code, the implementation of the key optimization steps, including thread optimization, shared memory optimization, register optimization and special function units (SFU), greatly improved the efficiency. A numerical example employing real large-scale, 3D seismic data showed that our scheme is nearly 80 times faster than the CPU-QPSTM algorithm. Our GPU/CPU heterogeneous parallel computing framework significant reduces the computational cost and facilitates 3D high-resolution imaging for large-scale seismic data.

  14. Icing Simulation Research Supporting the Ice-Accretion Testing of Large-Scale Swept-Wing Models

    Science.gov (United States)

    Yadlin, Yoram; Monnig, Jaime T.; Malone, Adam M.; Paul, Bernard P.

    2018-01-01

    The work summarized in this report is a continuation of NASA's Large-Scale, Swept-Wing Test Articles Fabrication; Research and Test Support for NASA IRT contract (NNC10BA05 -NNC14TA36T) performed by Boeing under the NASA Research and Technology for Aerospace Propulsion Systems (RTAPS) contract. In the study conducted under RTAPS, a series of icing tests in the Icing Research Tunnel (IRT) have been conducted to characterize ice formations on large-scale swept wings representative of modern commercial transport airplanes. The outcome of that campaign was a large database of ice-accretion geometries that can be used for subsequent aerodynamic evaluation in other experimental facilities and for validation of ice-accretion prediction codes.

  15. Full-color large-scaled computer-generated holograms using RGB color filters.

    Science.gov (United States)

    Tsuchiyama, Yasuhiro; Matsushima, Kyoji

    2017-02-06

    A technique using RGB color filters is proposed for creating high-quality full-color computer-generated holograms (CGHs). The fringe of these CGHs is composed of more than a billion pixels. The CGHs reconstruct full-parallax three-dimensional color images with a deep sensation of depth caused by natural motion parallax. The simulation technique as well as the principle and challenges of high-quality full-color reconstruction are presented to address the design of filter properties suitable for large-scaled CGHs. Optical reconstructions of actual fabricated full-color CGHs are demonstrated in order to verify the proposed techniques.

  16. Large scale and big data processing and management

    CERN Document Server

    Sakr, Sherif

    2014-01-01

    Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-bas

  17. Phylogenetic distribution of large-scale genome patchiness

    Directory of Open Access Journals (Sweden)

    Hackenberg Michael

    2008-04-01

    Full Text Available Abstract Background The phylogenetic distribution of large-scale genome structure (i.e. mosaic compositional patchiness has been explored mainly by analytical ultracentrifugation of bulk DNA. However, with the availability of large, good-quality chromosome sequences, and the recently developed computational methods to directly analyze patchiness on the genome sequence, an evolutionary comparative analysis can be carried out at the sequence level. Results The local variations in the scaling exponent of the Detrended Fluctuation Analysis are used here to analyze large-scale genome structure and directly uncover the characteristic scales present in genome sequences. Furthermore, through shuffling experiments of selected genome regions, computationally-identified, isochore-like regions were identified as the biological source for the uncovered large-scale genome structure. The phylogenetic distribution of short- and large-scale patchiness was determined in the best-sequenced genome assemblies from eleven eukaryotic genomes: mammals (Homo sapiens, Pan troglodytes, Mus musculus, Rattus norvegicus, and Canis familiaris, birds (Gallus gallus, fishes (Danio rerio, invertebrates (Drosophila melanogaster and Caenorhabditis elegans, plants (Arabidopsis thaliana and yeasts (Saccharomyces cerevisiae. We found large-scale patchiness of genome structure, associated with in silico determined, isochore-like regions, throughout this wide phylogenetic range. Conclusion Large-scale genome structure is detected by directly analyzing DNA sequences in a wide range of eukaryotic chromosome sequences, from human to yeast. In all these genomes, large-scale patchiness can be associated with the isochore-like regions, as directly detected in silico at the sequence level.

  18. Large Scale GW Calculations on the Cori System

    Science.gov (United States)

    Deslippe, Jack; Del Ben, Mauro; da Jornada, Felipe; Canning, Andrew; Louie, Steven

    The NERSC Cori system, powered by 9000+ Intel Xeon-Phi processors, represents one of the largest HPC systems for open-science in the United States and the world. We discuss the optimization of the GW methodology for this system, including both node level and system-scale optimizations. We highlight multiple large scale (thousands of atoms) case studies and discuss both absolute application performance and comparison to calculations on more traditional HPC architectures. We find that the GW method is particularly well suited for many-core architectures due to the ability to exploit a large amount of parallelism across many layers of the system. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program.

  19. HiQuant: Rapid Postquantification Analysis of Large-Scale MS-Generated Proteomics Data.

    Science.gov (United States)

    Bryan, Kenneth; Jarboui, Mohamed-Ali; Raso, Cinzia; Bernal-Llinares, Manuel; McCann, Brendan; Rauch, Jens; Boldt, Karsten; Lynn, David J

    2016-06-03

    Recent advances in mass-spectrometry-based proteomics are now facilitating ambitious large-scale investigations of the spatial and temporal dynamics of the proteome; however, the increasing size and complexity of these data sets is overwhelming current downstream computational methods, specifically those that support the postquantification analysis pipeline. Here we present HiQuant, a novel application that enables the design and execution of a postquantification workflow, including common data-processing steps, such as assay normalization and grouping, and experimental replicate quality control and statistical analysis. HiQuant also enables the interpretation of results generated from large-scale data sets by supporting interactive heatmap analysis and also the direct export to Cytoscape and Gephi, two leading network analysis platforms. HiQuant may be run via a user-friendly graphical interface and also supports complete one-touch automation via a command-line mode. We evaluate HiQuant's performance by analyzing a large-scale, complex interactome mapping data set and demonstrate a 200-fold improvement in the execution time over current methods. We also demonstrate HiQuant's general utility by analyzing proteome-wide quantification data generated from both a large-scale public tyrosine kinase siRNA knock-down study and an in-house investigation into the temporal dynamics of the KSR1 and KSR2 interactomes. Download HiQuant, sample data sets, and supporting documentation at http://hiquant.primesdb.eu .

  20. Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

    Directory of Open Access Journals (Sweden)

    Anjani Ragothaman

    2014-01-01

    Full Text Available While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

  1. Lightweight computational steering of very large scale molecular dynamics simulations

    International Nuclear Information System (INIS)

    Beazley, D.M.

    1996-01-01

    We present a computational steering approach for controlling, analyzing, and visualizing very large scale molecular dynamics simulations involving tens to hundreds of millions of atoms. Our approach relies on extensible scripting languages and an easy to use tool for building extensions and modules. The system is extremely easy to modify, works with existing C code, is memory efficient, and can be used from inexpensive workstations and networks. We demonstrate how we have used this system to manipulate data from production MD simulations involving as many as 104 million atoms running on the CM-5 and Cray T3D. We also show how this approach can be used to build systems that integrate common scripting languages (including Tcl/Tk, Perl, and Python), simulation code, user extensions, and commercial data analysis packages

  2. Topology Optimization of Large Scale Stokes Flow Problems

    DEFF Research Database (Denmark)

    Aage, Niels; Poulsen, Thomas Harpsøe; Gersborg-Hansen, Allan

    2008-01-01

    This note considers topology optimization of large scale 2D and 3D Stokes flow problems using parallel computations. We solve problems with up to 1.125.000 elements in 2D and 128.000 elements in 3D on a shared memory computer consisting of Sun UltraSparc IV CPUs.......This note considers topology optimization of large scale 2D and 3D Stokes flow problems using parallel computations. We solve problems with up to 1.125.000 elements in 2D and 128.000 elements in 3D on a shared memory computer consisting of Sun UltraSparc IV CPUs....

  3. The Convergence of High Performance Computing and Large Scale Data Analytics

    Science.gov (United States)

    Duffy, D.; Bowen, M. K.; Thompson, J. H.; Yang, C. P.; Hu, F.; Wills, B.

    2015-12-01

    As the combinations of remote sensing observations and model outputs have grown, scientists are increasingly burdened with both the necessity and complexity of large-scale data analysis. Scientists are increasingly applying traditional high performance computing (HPC) solutions to solve their "Big Data" problems. While this approach has the benefit of limiting data movement, the HPC system is not optimized to run analytics, which can create problems that permeate throughout the HPC environment. To solve these issues and to alleviate some of the strain on the HPC environment, the NASA Center for Climate Simulation (NCCS) has created the Advanced Data Analytics Platform (ADAPT), which combines both HPC and cloud technologies to create an agile system designed for analytics. Large, commonly used data sets are stored in this system in a write once/read many file system, such as Landsat, MODIS, MERRA, and NGA. High performance virtual machines are deployed and scaled according to the individual scientist's requirements specifically for data analysis. On the software side, the NCCS and GMU are working with emerging commercial technologies and applying them to structured, binary scientific data in order to expose the data in new ways. Native NetCDF data is being stored within a Hadoop Distributed File System (HDFS) enabling storage-proximal processing through MapReduce while continuing to provide accessibility of the data to traditional applications. Once the data is stored within HDFS, an additional indexing scheme is built on top of the data and placed into a relational database. This spatiotemporal index enables extremely fast mappings of queries to data locations to dramatically speed up analytics. These are some of the first steps toward a single unified platform that optimizes for both HPC and large-scale data analysis, and this presentation will elucidate the resulting and necessary exascale architectures required for future systems.

  4. Large-scale multimedia modeling applications

    International Nuclear Information System (INIS)

    Droppo, J.G. Jr.; Buck, J.W.; Whelan, G.; Strenge, D.L.; Castleton, K.J.; Gelston, G.M.

    1995-08-01

    Over the past decade, the US Department of Energy (DOE) and other agencies have faced increasing scrutiny for a wide range of environmental issues related to past and current practices. A number of large-scale applications have been undertaken that required analysis of large numbers of potential environmental issues over a wide range of environmental conditions and contaminants. Several of these applications, referred to here as large-scale applications, have addressed long-term public health risks using a holistic approach for assessing impacts from potential waterborne and airborne transport pathways. Multimedia models such as the Multimedia Environmental Pollutant Assessment System (MEPAS) were designed for use in such applications. MEPAS integrates radioactive and hazardous contaminants impact computations for major exposure routes via air, surface water, ground water, and overland flow transport. A number of large-scale applications of MEPAS have been conducted to assess various endpoints for environmental and human health impacts. These applications are described in terms of lessons learned in the development of an effective approach for large-scale applications

  5. Large-Scale, Multi-Sensor Atmospheric Data Fusion Using Hybrid Cloud Computing

    Science.gov (United States)

    Wilson, B. D.; Manipon, G.; Hua, H.; Fetzer, E. J.

    2015-12-01

    NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, MODIS, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over decades. Moving to multi-sensor, long-duration presents serious challenges for large-scale data mining and fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over 10 years of data. HySDS is a Hybrid-Cloud Science Data System that has been developed and applied under NASA AIST, MEaSUREs, and ACCESS grants. HySDS uses the SciFlow workflow engine to partition analysis workflows into parallel tasks (e.g. segmenting by time or space) that are pushed into a durable job queue. The tasks are "pulled" from the queue by worker Virtual Machines (VM's) and executed in an on-premise Cloud (Eucalyptus or OpenStack) or at Amazon in the public Cloud or govCloud. In this way, years of data (millions of files) can be processed in a massively parallel way. Input variables (arrays) are pulled on-demand into the Cloud using OPeNDAP URLs or other subsetting services, thereby minimizing the size of the transferred data. We are using HySDS to automate the production of multiple versions of a ten-year A-Train water vapor climatology under a MEASURES grant. We will present the architecture of HySDS, describe the achieved "clock time" speedups in fusing datasets on our own nodes and in the Amazon Cloud, and discuss the Cloud cost tradeoffs for storage, compute, and data transfer. Our system demonstrates how one can pull A-Train variables (Levels 2 & 3) on-demand into the Amazon Cloud, and cache only those variables that are heavily used, so that any number of compute jobs can be

  6. Computational intelligence for decision support in cyber-physical systems

    CERN Document Server

    Ali, A; Riaz, Zahid

    2014-01-01

    This book is dedicated to applied computational intelligence and soft computing techniques with special reference to decision support in Cyber Physical Systems (CPS), where the physical as well as the communication segment of the networked entities interact with each other. The joint dynamics of such systems result in a complex combination of computers, software, networks and physical processes all combined to establish a process flow at system level. This volume provides the audience with an in-depth vision about how to ensure dependability, safety, security and efficiency in real time by making use of computational intelligence in various CPS applications ranging from the nano-world to large scale wide area systems of systems. Key application areas include healthcare, transportation, energy, process control and robotics where intelligent decision support has key significance in establishing dynamic, ever-changing and high confidence future technologies. A recommended text for graduate students and researche...

  7. Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators

    International Nuclear Information System (INIS)

    Fonseca, R A; Vieira, J; Silva, L O; Fiuza, F; Davidson, A; Tsung, F S; Mori, W B

    2013-01-01

    A new generation of laser wakefield accelerators (LWFA), supported by the extreme accelerating fields generated in the interaction of PW-Class lasers and underdense targets, promises the production of high quality electron beams in short distances for multiple applications. Achieving this goal will rely heavily on numerical modelling to further understand the underlying physics and identify optimal regimes, but large scale modelling of these scenarios is computationally heavy and requires the efficient use of state-of-the-art petascale supercomputing systems. We discuss the main difficulties involved in running these simulations and the new developments implemented in the OSIRIS framework to address these issues, ranging from multi-dimensional dynamic load balancing and hybrid distributed/shared memory parallelism to the vectorization of the PIC algorithm. We present the results of the OASCR Joule Metric program on the issue of large scale modelling of LWFA, demonstrating speedups of over 1 order of magnitude on the same hardware. Finally, scalability to over ∼10 6 cores and sustained performance over ∼2 P Flops is demonstrated, opening the way for large scale modelling of LWFA scenarios. (paper)

  8. Artificial Intelligence Support for Computational Chemistry

    Science.gov (United States)

    Duch, Wlodzislaw

    Possible forms of artificial intelligence (AI) support for quantum chemistry are discussed. Questions addressed include: what kind of support is desirable, what kind of support is feasible, what can we expect in the coming years. Advantages and disadvantages of current AI techniques are presented and it is argued that at present the memory-based systems are the most effective for large scale applications. Such systems may be used to predict the accuracy of calculations and to select the least expensive methods and basis sets belonging to the same accuracy class. Advantages of the Feature Space Mapping as an improvement on the memory based systems are outlined and some results obtained in classification problems given. Relevance of such classification systems to computational chemistry is illustrated with two examples showing similarity of results obtained by different methods that take electron correlation into account.

  9. Computer-supported resolution of measurement conflicts: a case-study in materials science

    NARCIS (Netherlands)

    de Jong, Hidde; Mars, Nicolaas; van der Vet, P.E.

    1999-01-01

    Resolving conflicts between different measurements ofa property of a physical system may be a key step in a discovery process. With the emergence of large-scale databases and knowledge bases with property measurements, computer support for the task of conflict resolution has become highly desirable.

  10. Dynamic Modeling and Analysis of the Large-Scale Rotary Machine with Multi-Supporting

    Directory of Open Access Journals (Sweden)

    Xuejun Li

    2011-01-01

    Full Text Available The large-scale rotary machine with multi-supporting, such as rotary kiln and rope laying machine, is the key equipment in the architectural, chemistry, and agriculture industries. The body, rollers, wheels, and bearings constitute a chain multibody system. Axis line deflection is a vital parameter to determine mechanics state of rotary machine, thus body axial vibration needs to be studied for dynamic monitoring and adjusting of rotary machine. By using the Riccati transfer matrix method, the body system of rotary machine is divided into many subsystems composed of three elements, namely, rigid disk, elastic shaft, and linear spring. Multiple wheel-bearing structures are simplified as springs. The transfer matrices of the body system and overall transfer equation are developed, as well as the response overall motion equation. Taken a rotary kiln as an instance, natural frequencies, modal shape, and response vibration with certain exciting axis line deflection are obtained by numerical computing. The body vibration modal curves illustrate the cause of dynamical errors in the common axis line measurement methods. The displacement response can be used for further measurement dynamical error analysis and compensation. The response overall motion equation could be applied to predict the body motion under abnormal mechanics condition, and provide theory guidance for machine failure diagnosis.

  11. Deterministic methods for sensitivity and uncertainty analysis in large-scale computer models

    International Nuclear Information System (INIS)

    Worley, B.A.; Oblow, E.M.; Pin, F.G.; Maerker, R.E.; Horwedel, J.E.; Wright, R.Q.; Lucius, J.L.

    1987-01-01

    The fields of sensitivity and uncertainty analysis are dominated by statistical techniques when large-scale modeling codes are being analyzed. This paper reports on the development and availability of two systems, GRESS and ADGEN, that make use of computer calculus compilers to automate the implementation of deterministic sensitivity analysis capability into existing computer models. This automation removes the traditional limitation of deterministic sensitivity methods. The paper describes a deterministic uncertainty analysis method (DUA) that uses derivative information as a basis to propagate parameter probability distributions to obtain result probability distributions. The paper demonstrates the deterministic approach to sensitivity and uncertainty analysis as applied to a sample problem that models the flow of water through a borehole. The sample problem is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. The DUA method gives a more accurate result based upon only two model executions compared to fifty executions in the statistical case

  12. Comparing the performance of SIMD computers by running large air pollution models

    DEFF Research Database (Denmark)

    Brown, J.; Hansen, Per Christian; Wasniewski, J.

    1996-01-01

    To compare the performance and use of three massively parallel SIMD computers, we implemented a large air pollution model on these computers. Using a realistic large-scale model, we gained detailed insight about the performance of the computers involved when used to solve large-scale scientific...... problems that involve several types of numerical computations. The computers used in our study are the Connection Machines CM-200 and CM-5, and the MasPar MP-2216...

  13. Development and application of a computer model for large-scale flame acceleration experiments

    International Nuclear Information System (INIS)

    Marx, K.D.

    1987-07-01

    A new computational model for large-scale premixed flames is developed and applied to the simulation of flame acceleration experiments. The primary objective is to circumvent the necessity for resolving turbulent flame fronts; this is imperative because of the relatively coarse computational grids which must be used in engineering calculations. The essence of the model is to artificially thicken the flame by increasing the appropriate diffusivities and decreasing the combustion rate, but to do this in such a way that the burn velocity varies with pressure, temperature, and turbulence intensity according to prespecified phenomenological characteristics. The model is particularly aimed at implementation in computer codes which simulate compressible flows. To this end, it is applied to the two-dimensional simulation of hydrogen-air flame acceleration experiments in which the flame speeds and gas flow velocities attain or exceed the speed of sound in the gas. It is shown that many of the features of the flame trajectories and pressure histories in the experiments are simulated quite well by the model. Using the comparison of experimental and computational results as a guide, some insight is developed into the processes which occur in such experiments. 34 refs., 25 figs., 4 tabs

  14. Massive Cloud Computing Processing of P-SBAS Time Series for Displacement Analyses at Large Spatial Scale

    Science.gov (United States)

    Casu, F.; de Luca, C.; Lanari, R.; Manunta, M.; Zinno, I.

    2016-12-01

    A methodology for computing surface deformation time series and mean velocity maps of large areas is presented. Our approach relies on the availability of a multi-temporal set of Synthetic Aperture Radar (SAR) data collected from ascending and descending orbits over an area of interest, and also permits to estimate the vertical and horizontal (East-West) displacement components of the Earth's surface. The adopted methodology is based on an advanced Cloud Computing implementation of the Differential SAR Interferometry (DInSAR) Parallel Small Baseline Subset (P-SBAS) processing chain which allows the unsupervised processing of large SAR data volumes, from the raw data (level-0) imagery up to the generation of DInSAR time series and maps. The presented solution, which is highly scalable, has been tested on the ascending and descending ENVISAT SAR archives, which have been acquired over a large area of Southern California (US) that extends for about 90.000 km2. Such an input dataset has been processed in parallel by exploiting 280 computing nodes of the Amazon Web Services Cloud environment. Moreover, to produce the final mean deformation velocity maps of the vertical and East-West displacement components of the whole investigated area, we took also advantage of the information available from external GPS measurements that permit to account for possible regional trends not easily detectable by DInSAR and to refer the P-SBAS measurements to an external geodetic datum. The presented results clearly demonstrate the effectiveness of the proposed approach that paves the way to the extensive use of the available ERS and ENVISAT SAR data archives. Furthermore, the proposed methodology can be particularly suitable to deal with the very huge data flow provided by the Sentinel-1 constellation, thus permitting to extend the DInSAR analyses at a nearly global scale. This work is partially supported by: the DPC-CNR agreement, the EPOS-IP project and the ESA GEP project.

  15. Implementation of Grid-computing Framework for Simulation in Multi-scale Structural Analysis

    Directory of Open Access Journals (Sweden)

    Data Iranata

    2010-05-01

    Full Text Available A new grid-computing framework for simulation in multi-scale structural analysis is presented. Two levels of parallel processing will be involved in this framework: multiple local distributed computing environments connected by local network to form a grid-based cluster-to-cluster distributed computing environment. To successfully perform the simulation, a large-scale structural system task is decomposed into the simulations of a simplified global model and several detailed component models using various scales. These correlated multi-scale structural system tasks are distributed among clusters and connected together in a multi-level hierarchy and then coordinated over the internet. The software framework for supporting the multi-scale structural simulation approach is also presented. The program architecture design allows the integration of several multi-scale models as clients and servers under a single platform. To check its feasibility, a prototype software system has been designed and implemented to perform the proposed concept. The simulation results show that the software framework can increase the speedup performance of the structural analysis. Based on this result, the proposed grid-computing framework is suitable to perform the simulation of the multi-scale structural analysis.

  16. Political consultation and large-scale research

    International Nuclear Information System (INIS)

    Bechmann, G.; Folkers, H.

    1977-01-01

    Large-scale research and policy consulting have an intermediary position between sociological sub-systems. While large-scale research coordinates science, policy, and production, policy consulting coordinates science, policy and political spheres. In this very position, large-scale research and policy consulting lack of institutional guarantees and rational back-ground guarantee which are characteristic for their sociological environment. This large-scale research can neither deal with the production of innovative goods under consideration of rentability, nor can it hope for full recognition by the basis-oriented scientific community. Policy consulting knows neither the competence assignment of the political system to make decisions nor can it judge succesfully by the critical standards of the established social science, at least as far as the present situation is concerned. This intermediary position of large-scale research and policy consulting has, in three points, a consequence supporting the thesis which states that this is a new form of institutionalization of science: These are: 1) external control, 2) the organization form, 3) the theoretical conception of large-scale research and policy consulting. (orig.) [de

  17. Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

    Science.gov (United States)

    Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

    2015-09-01

    A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

  18. Amplification of large-scale magnetic field in nonhelical magnetohydrodynamics

    KAUST Repository

    Kumar, Rohit

    2017-08-11

    It is typically assumed that the kinetic and magnetic helicities play a crucial role in the growth of large-scale dynamo. In this paper, we demonstrate that helicity is not essential for the amplification of large-scale magnetic field. For this purpose, we perform nonhelical magnetohydrodynamic (MHD) simulation, and show that the large-scale magnetic field can grow in nonhelical MHD when random external forcing is employed at scale 1/10 the box size. The energy fluxes and shell-to-shell transfer rates computed using the numerical data show that the large-scale magnetic energy grows due to the energy transfers from the velocity field at the forcing scales.

  19. Grid Support in Large Scale PV Power Plants using Active Power Reserves

    DEFF Research Database (Denmark)

    Craciun, Bogdan-Ionut

    to validate the performance of the frequency support functions, a flexible grid model with IEEE 12 bus system characteristics has been developed and implemented in RTDS. A power hardware-in-the-loop (PHIL) system composed by 20 kW plant (2 x 10 kW inverters and PV linear simulator) and grid simulator (RTDS......Photovoltaic (PV) systems are in the 3rd place in the renewable energy market, after hydro and wind power. The increased penetration of PV within the electrical power system has led to stability issues of the entire grid in terms of its reliability, availability and security of the supply....... As a consequence, Large scale PV Power Plants (LPVPPs) operating in Maximum Power Point (MPP) are not supporting the electrical network, since several grid triggering events or the increased number of downward regulation procedures have forced European Network of Transmission System Operators for Electricity...

  20. Large-Scale Sentinel-1 Processing for Solid Earth Science and Urgent Response using Cloud Computing and Machine Learning

    Science.gov (United States)

    Hua, H.; Owen, S. E.; Yun, S. H.; Agram, P. S.; Manipon, G.; Starch, M.; Sacco, G. F.; Bue, B. D.; Dang, L. B.; Linick, J. P.; Malarout, N.; Rosen, P. A.; Fielding, E. J.; Lundgren, P.; Moore, A. W.; Liu, Z.; Farr, T.; Webb, F.; Simons, M.; Gurrola, E. M.

    2017-12-01

    With the increased availability of open SAR data (e.g. Sentinel-1 A/B), new challenges are being faced with processing and analyzing the voluminous SAR datasets to make geodetic measurements. Upcoming SAR missions such as NISAR are expected to generate close to 100TB per day. The Advanced Rapid Imaging and Analysis (ARIA) project can now generate geocoded unwrapped phase and coherence products from Sentinel-1 TOPS mode data in an automated fashion, using the ISCE software. This capability is currently being exercised on various study sites across the United States and around the globe, including Hawaii, Central California, Iceland and South America. The automated and large-scale SAR data processing and analysis capabilities use cloud computing techniques to speed the computations and provide scalable processing power and storage. Aspects such as how to processing these voluminous SLCs and interferograms at global scales, keeping up with the large daily SAR data volumes, and how to handle the voluminous data rates are being explored. Scene-partitioning approaches in the processing pipeline help in handling global-scale processing up to unwrapped interferograms with stitching done at a late stage. We have built an advanced science data system with rapid search functions to enable access to the derived data products. Rapid image processing of Sentinel-1 data to interferograms and time series is already being applied to natural hazards including earthquakes, floods, volcanic eruptions, and land subsidence due to fluid withdrawal. We will present the status of the ARIA science data system for generating science-ready data products and challenges that arise from being able to process SAR datasets to derived time series data products at large scales. For example, how do we perform large-scale data quality screening on interferograms? What approaches can be used to minimize compute, storage, and data movement costs for time series analysis in the cloud? We will also

  1. Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

    Science.gov (United States)

    Maskey, M.; Ramachandran, R.; Miller, J.

    2017-12-01

    Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

  2. Inferring Large-Scale Terrestrial Water Storage Through GRACE and GPS Data Fusion in Cloud Computing Environments

    Science.gov (United States)

    Rude, C. M.; Li, J. D.; Gowanlock, M.; Herring, T.; Pankratius, V.

    2016-12-01

    Surface subsidence due to depletion of groundwater can lead to permanent compaction of aquifers and damaged infrastructure. However, studies of such effects on a large scale are challenging and compute intensive because they involve fusing a variety of data sets beyond direct measurements from groundwater wells, such as gravity change measurements from the Gravity Recovery and Climate Experiment (GRACE) or surface displacements measured by GPS receivers. Our work therefore leverages Amazon cloud computing to enable these types of analyses spanning the entire continental US. Changes in groundwater storage are inferred from surface displacements measured by GPS receivers stationed throughout the country. Receivers located on bedrock are anti-correlated with changes in water levels from elastic deformation due to loading, while stations on aquifers correlate with groundwater changes due to poroelastic expansion and compaction. Correlating linearly detrended equivalent water thickness measurements from GRACE with linearly detrended and Kalman filtered vertical displacements of GPS stations located throughout the United States helps compensate for the spatial and temporal limitations of GRACE. Our results show that the majority of GPS stations are negatively correlated with GRACE in a statistically relevant way, as most GPS stations are located on bedrock in order to provide stable reference locations and measure geophysical processes such as tectonic deformations. Additionally, stations located on the Central Valley California aquifer show statistically significant positive correlations. Through the identification of positive and negative correlations, deformation phenomena can be classified as loading or poroelastic expansion due to changes in groundwater. This method facilitates further studies of terrestrial water storage on a global scale. This work is supported by NASA AIST-NNX15AG84G (PI: V. Pankratius) and Amazon.

  3. Computational Cosmology: from the Early Universe to the Large Scale Structure

    Directory of Open Access Journals (Sweden)

    Peter Anninos

    1998-09-01

    Full Text Available In order to account for the observable Universe, any comprehensive theory or model of cosmology must draw from many disciplines of physics, including gauge theories of strong and weak interactions, the hydrodynamics and microphysics of baryonic matter, electromagnetic fields, and spacetime curvature, for example. Although it is difficult to incorporate all these physical elements into a single complete model of our Universe, advances in computing methods and technologies have contributed significantly towards our understanding of cosmological models, the Universe, and astrophysical processes within them. A sample of numerical calculations addressing specific issues in cosmology are reviewed in this article: from the Big Bang singularity dynamics to the fundamental interactions of gravitational waves; from the quark--hadron phase transition to the large scale structure of the Universe. The emphasis, although not exclusively, is on thosecalculations designed to test different models of cosmology against the observed Universe.

  4. Computational Cosmology: From the Early Universe to the Large Scale Structure.

    Science.gov (United States)

    Anninos, Peter

    2001-01-01

    In order to account for the observable Universe, any comprehensive theory or model of cosmology must draw from many disciplines of physics, including gauge theories of strong and weak interactions, the hydrodynamics and microphysics of baryonic matter, electromagnetic fields, and spacetime curvature, for example. Although it is difficult to incorporate all these physical elements into a single complete model of our Universe, advances in computing methods and technologies have contributed significantly towards our understanding of cosmological models, the Universe, and astrophysical processes within them. A sample of numerical calculations (and numerical methods applied to specific issues in cosmology are reviewed in this article: from the Big Bang singularity dynamics to the fundamental interactions of gravitational waves; from the quark-hadron phase transition to the large scale structure of the Universe. The emphasis, although not exclusively, is on those calculations designed to test different models of cosmology against the observed Universe.

  5. Learning from large scale neural simulations

    DEFF Research Database (Denmark)

    Serban, Maria

    2017-01-01

    Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

  6. A large scale GIS geodatabase of soil parameters supporting the modeling of conservation practice alternatives in the United States

    Science.gov (United States)

    Water quality modeling requires across-scale support of combined digital soil elements and simulation parameters. This paper presents the unprecedented development of a large spatial scale (1:250,000) ArcGIS geodatabase coverage designed as a functional repository of soil-parameters for modeling an...

  7. WImpiBLAST: web interface for mpiBLAST to help biologists perform large-scale annotation using high performance computing.

    Directory of Open Access Journals (Sweden)

    Parichit Sharma

    Full Text Available The function of a newly sequenced gene can be discovered by determining its sequence homology with known proteins. BLAST is the most extensively used sequence analysis program for sequence similarity search in large databases of sequences. With the advent of next generation sequencing technologies it has now become possible to study genes and their expression at a genome-wide scale through RNA-seq and metagenome sequencing experiments. Functional annotation of all the genes is done by sequence similarity search against multiple protein databases. This annotation task is computationally very intensive and can take days to obtain complete results. The program mpiBLAST, an open-source parallelization of BLAST that achieves superlinear speedup, can be used to accelerate large-scale annotation by using supercomputers and high performance computing (HPC clusters. Although many parallel bioinformatics applications using the Message Passing Interface (MPI are available in the public domain, researchers are reluctant to use them due to lack of expertise in the Linux command line and relevant programming experience. With these limitations, it becomes difficult for biologists to use mpiBLAST for accelerating annotation. No web interface is available in the open-source domain for mpiBLAST. We have developed WImpiBLAST, a user-friendly open-source web interface for parallel BLAST searches. It is implemented in Struts 1.3 using a Java backbone and runs atop the open-source Apache Tomcat Server. WImpiBLAST supports script creation and job submission features and also provides a robust job management interface for system administrators. It combines script creation and modification features with job monitoring and management through the Torque resource manager on a Linux-based HPC cluster. Use case information highlights the acceleration of annotation analysis achieved by using WImpiBLAST. Here, we describe the WImpiBLAST web interface features and architecture

  8. WImpiBLAST: web interface for mpiBLAST to help biologists perform large-scale annotation using high performance computing.

    Science.gov (United States)

    Sharma, Parichit; Mantri, Shrikant S

    2014-01-01

    The function of a newly sequenced gene can be discovered by determining its sequence homology with known proteins. BLAST is the most extensively used sequence analysis program for sequence similarity search in large databases of sequences. With the advent of next generation sequencing technologies it has now become possible to study genes and their expression at a genome-wide scale through RNA-seq and metagenome sequencing experiments. Functional annotation of all the genes is done by sequence similarity search against multiple protein databases. This annotation task is computationally very intensive and can take days to obtain complete results. The program mpiBLAST, an open-source parallelization of BLAST that achieves superlinear speedup, can be used to accelerate large-scale annotation by using supercomputers and high performance computing (HPC) clusters. Although many parallel bioinformatics applications using the Message Passing Interface (MPI) are available in the public domain, researchers are reluctant to use them due to lack of expertise in the Linux command line and relevant programming experience. With these limitations, it becomes difficult for biologists to use mpiBLAST for accelerating annotation. No web interface is available in the open-source domain for mpiBLAST. We have developed WImpiBLAST, a user-friendly open-source web interface for parallel BLAST searches. It is implemented in Struts 1.3 using a Java backbone and runs atop the open-source Apache Tomcat Server. WImpiBLAST supports script creation and job submission features and also provides a robust job management interface for system administrators. It combines script creation and modification features with job monitoring and management through the Torque resource manager on a Linux-based HPC cluster. Use case information highlights the acceleration of annotation analysis achieved by using WImpiBLAST. Here, we describe the WImpiBLAST web interface features and architecture, explain design

  9. Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Chase Qishi [New Jersey Inst. of Technology, Newark, NJ (United States); Univ. of Memphis, TN (United States); Zhu, Michelle Mengxia [Southern Illinois Univ., Carbondale, IL (United States)

    2016-06-06

    The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models feature diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific

  10. Rucio - The next generation large scale distributed system for ATLAS Data Management

    CERN Document Server

    Beermann, T; The ATLAS collaboration; Lassnig, M; Barisits, M; Vigne, R; Serfon, C; Stewart, G A; Goossens, L; Nairz, A; Molfetas, A

    2014-01-01

    Rucio is the next-generation Distributed Data Management (DDM) system benefiting from recent advances in cloud and "Big Data" computing to address the ATLAS experiment scaling requirements. Rucio is an evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 150 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio will deal with these issues by relying on new technologies to ensure system scalability, address new user requirements and employ a new automation framework to reduce operational overheads.

  11. Computational Cosmology: from the Early Universe to the Large Scale Structure

    Directory of Open Access Journals (Sweden)

    Anninos Peter

    2001-01-01

    Full Text Available In order to account for the observable Universe, any comprehensive theory or model of cosmology must draw from many disciplines of physics, including gauge theories of strong and weak interactions, the hydrodynamics and microphysics of baryonic matter, electromagnetic fields, and spacetime curvature, for example. Although it is difficult to incorporate all these physical elements into a single complete model of our Universe, advances in computing methods and technologies have contributed significantly towards our understanding of cosmological models, the Universe, and astrophysical processes within them. A sample of numerical calculations (and numerical methods applied to specific issues in cosmology are reviewed in this article: from the Big Bang singularity dynamics to the fundamental interactions of gravitational waves; from the quark-hadron phase transition to the large scale structure of the Universe. The emphasis, although not exclusively, is on those calculations designed to test different models of cosmology against the observed Universe.

  12. Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

    Directory of Open Access Journals (Sweden)

    Koichi Kobayashi

    2013-01-01

    Full Text Available We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

  13. Large scale electrolysers

    International Nuclear Information System (INIS)

    B Bello; M Junker

    2006-01-01

    Hydrogen production by water electrolysis represents nearly 4 % of the world hydrogen production. Future development of hydrogen vehicles will require large quantities of hydrogen. Installation of large scale hydrogen production plants will be needed. In this context, development of low cost large scale electrolysers that could use 'clean power' seems necessary. ALPHEA HYDROGEN, an European network and center of expertise on hydrogen and fuel cells, has performed for its members a study in 2005 to evaluate the potential of large scale electrolysers to produce hydrogen in the future. The different electrolysis technologies were compared. Then, a state of art of the electrolysis modules currently available was made. A review of the large scale electrolysis plants that have been installed in the world was also realized. The main projects related to large scale electrolysis were also listed. Economy of large scale electrolysers has been discussed. The influence of energy prices on the hydrogen production cost by large scale electrolysis was evaluated. (authors)

  14. Large Spatial Scale Ground Displacement Mapping through the P-SBAS Processing of Sentinel-1 Data on a Cloud Computing Environment

    Science.gov (United States)

    Casu, F.; Bonano, M.; de Luca, C.; Lanari, R.; Manunta, M.; Manzo, M.; Zinno, I.

    2017-12-01

    Since its launch in 2014, the Sentinel-1 (S1) constellation has played a key role on SAR data availability and dissemination all over the World. Indeed, the free and open access data policy adopted by the European Copernicus program together with the global coverage acquisition strategy, make the Sentinel constellation as a game changer in the Earth Observation scenario. Being the SAR data become ubiquitous, the technological and scientific challenge is focused on maximizing the exploitation of such huge data flow. In this direction, the use of innovative processing algorithms and distributed computing infrastructures, such as the Cloud Computing platforms, can play a crucial role. In this work we present a Cloud Computing solution for the advanced interferometric (DInSAR) processing chain based on the Parallel SBAS (P-SBAS) approach, aimed at processing S1 Interferometric Wide Swath (IWS) data for the generation of large spatial scale deformation time series in efficient, automatic and systematic way. Such a DInSAR chain ingests Sentinel 1 SLC images and carries out several processing steps, to finally compute deformation time series and mean deformation velocity maps. Different parallel strategies have been designed ad hoc for each processing step of the P-SBAS S1 chain, encompassing both multi-core and multi-node programming techniques, in order to maximize the computational efficiency achieved within a Cloud Computing environment and cut down the relevant processing times. The presented P-SBAS S1 processing chain has been implemented on the Amazon Web Services platform and a thorough analysis of the attained parallel performances has been performed to identify and overcome the major bottlenecks to the scalability. The presented approach is used to perform national-scale DInSAR analyses over Italy, involving the processing of more than 3000 S1 IWS images acquired from both ascending and descending orbits. Such an experiment confirms the big advantage of

  15. Report of the Working Group on Large-Scale Computing in Aeronautics.

    Science.gov (United States)

    1984-06-01

    function and the use of drawings. In the hardware area, comtemporary large computer installations are quite powerful in terms of speed of computation as...critical to the competitive advantage of that member. He might then be willing to make them available to less advanced members under some business

  16. Full Scale Measurements of the Hydro-Elastic Response of Large Container Ships for Decision Support

    DEFF Research Database (Denmark)

    Andersen, Ingrid Marie Vincent

    scale measurements from four container ships of 4,400 TEU, 8,600 TEU, 9,400 TEU and 14,000 TEU Primarily, strains measured near the deck amidships are used. Furthermore, measurements of motions and the encountered sea state are available for one of the ships. The smallest ship is in operation...... frequency with the waves. Together with the relatively high design speed and often pronounced bow flare this makes large container ship more sensitive to slamming and, consequently, the effects of wave-induced hull girder vibrations. From full scale strain measurements of individual, measured hull girder......The overall topic of this thesis is decision support for operation of ships and several aspects are covered herein. However, the main focus is on the wave-induced hydro-elastic response of large container ships and its implications on the structural response. The analyses are based mainly on full...

  17. [Adverse Effect Predictions Based on Computational Toxicology Techniques and Large-scale Databases].

    Science.gov (United States)

    Uesawa, Yoshihiro

    2018-01-01

     Understanding the features of chemical structures related to the adverse effects of drugs is useful for identifying potential adverse effects of new drugs. This can be based on the limited information available from post-marketing surveillance, assessment of the potential toxicities of metabolites and illegal drugs with unclear characteristics, screening of lead compounds at the drug discovery stage, and identification of leads for the discovery of new pharmacological mechanisms. This present paper describes techniques used in computational toxicology to investigate the content of large-scale spontaneous report databases of adverse effects, and it is illustrated with examples. Furthermore, volcano plotting, a new visualization method for clarifying the relationships between drugs and adverse effects via comprehensive analyses, will be introduced. These analyses may produce a great amount of data that can be applied to drug repositioning.

  18. Challenges in Managing Trustworthy Large-scale Digital Science

    Science.gov (United States)

    Evans, B. J. K.

    2017-12-01

    The increased use of large-scale international digital science has opened a number of challenges for managing, handling, using and preserving scientific information. The large volumes of information are driven by three main categories - model outputs including coupled models and ensembles, data products that have been processing to a level of usability, and increasingly heuristically driven data analysis. These data products are increasingly the ones that are usable by the broad communities, and far in excess of the raw instruments data outputs. The data, software and workflows are then shared and replicated to allow broad use at an international scale, which places further demands of infrastructure to support how the information is managed reliably across distributed resources. Users necessarily rely on these underlying "black boxes" so that they are productive to produce new scientific outcomes. The software for these systems depend on computational infrastructure, software interconnected systems, and information capture systems. This ranges from the fundamentals of the reliability of the compute hardware, system software stacks and libraries, and the model software. Due to these complexities and capacity of the infrastructure, there is an increased emphasis of transparency of the approach and robustness of the methods over the full reproducibility. Furthermore, with large volume data management, it is increasingly difficult to store the historical versions of all model and derived data. Instead, the emphasis is on the ability to access the updated products and the reliability by which both previous outcomes are still relevant and can be updated for the new information. We will discuss these challenges and some of the approaches underway that are being used to address these issues.

  19. Progresses in application of computational ?uid dynamic methods to large scale wind turbine aerodynamics?

    Institute of Scientific and Technical Information of China (English)

    Zhenyu ZHANG; Ning ZHAO; Wei ZHONG; Long WANG; Bofeng XU

    2016-01-01

    The computational ?uid dynamics (CFD) methods are applied to aerody-namic problems for large scale wind turbines. The progresses including the aerodynamic analyses of wind turbine pro?les, numerical ?ow simulation of wind turbine blades, evalu-ation of aerodynamic performance, and multi-objective blade optimization are discussed. Based on the CFD methods, signi?cant improvements are obtained to predict two/three-dimensional aerodynamic characteristics of wind turbine airfoils and blades, and the vorti-cal structure in their wake ?ows is accurately captured. Combining with a multi-objective genetic algorithm, a 1.5 MW NH-1500 optimized blade is designed with high e?ciency in wind energy conversion.

  20. Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities.

    Science.gov (United States)

    Santangelo, Valerio

    2018-01-01

    implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

  1. Large-Scale Brain Networks Supporting Divided Attention across Spatial Locations and Sensory Modalities

    Directory of Open Access Journals (Sweden)

    Valerio Santangelo

    2018-02-01

    brain networks implicated during divided attention across spatial locations and sensory modalities, pointing out the importance of investigating effective connectivity of large-scale brain networks supporting complex behavior.

  2. Sensitivity analysis for large-scale problems

    Science.gov (United States)

    Noor, Ahmed K.; Whitworth, Sandra L.

    1987-01-01

    The development of efficient techniques for calculating sensitivity derivatives is studied. The objective is to present a computational procedure for calculating sensitivity derivatives as part of performing structural reanalysis for large-scale problems. The scope is limited to framed type structures. Both linear static analysis and free-vibration eigenvalue problems are considered.

  3. Large-scale simulations with distributed computing: Asymptotic scaling of ballistic deposition

    International Nuclear Information System (INIS)

    Farnudi, Bahman; Vvedensky, Dimitri D

    2011-01-01

    Extensive kinetic Monte Carlo simulations are reported for ballistic deposition (BD) in (1 + 1) dimensions. The large system sizes L observed for the onset of asymptotic scaling (L ≅ 2 12 ) explains the widespread discrepancies in previous reports for exponents of BD in one and likely in higher dimensions. The exponents obtained directly from our simulations, α = 0.499 ± 0.004 and β = 0.336 ± 0.004, capture the exact values α = 1/2 and β = 1/3 for the one-dimensional Kardar-Parisi-Zhang equation. An analysis of our simulations suggests a criterion for identifying the onset of true asymptotic scaling, which enables a more informed evaluation of exponents for BD in higher dimensions. These simulations were made possible by the Simulation through Social Networking project at the Institute for Advanced Studies in Basic Sciences in 2007, which was re-launched in November 2010.

  4. Large-scale restoration mitigate land degradation and support the establishment of green infrastructure

    Science.gov (United States)

    Tóthmérész, Béla; Mitchley, Jonathan; Jongepierová, Ivana; Baasch, Annett; Fajmon, Karel; Kirmer, Anita; Prach, Karel; Řehounková, Klára; Tischew, Sabine; Twiston-Davies, Grace; Dutoit, Thierry; Buisson, Elise; Jeunatre, Renaud; Valkó, Orsolya; Deák, Balázs; Török, Péter

    2017-04-01

    Sustaining the human well-being and the quality of life, it is essential to develop and support green infrastructure (strategically planned network of natural and semi-natural areas with other environmental features designed and managed to deliver a wide range of ecosystem services). For developing and sustaining green infrastructure the conservation and restoration of biodiversity in natural and traditionally managed habitats is essential. Species-rich landscapes in Europe have been maintained over centuries by various kinds of low-intensity use. Recently, they suffered by losses in extent and diversity due to land degradation by intensification or abandonment. Conservation of landscape-scale biodiversity requires the maintenance of species-rich habitats and the restoration of lost grasslands. We are focusing on landscape-level restoration studies including multiple sites in wide geographical scale (including Czech Republic, France, Germany, Hungary, and UK). In a European-wide perspective we aimed at to address four specific questions: (i) What were the aims and objectives of landscape-scale restoration? (ii) What results have been achieved? (iii) What are the costs of large-scale restoration? (iv) What policy tools are available for the restoration of landscape-scale biodiversity? We conclude that landscape-level restoration offers exciting new opportunities to reconnect long-disrupted ecological processes and to restore landscape connectivity. Generally, these measures enable to enhance the biodiversity at the landscape scale. The development of policy tools to achieve restoration at the landscape scale are essential for the achievement of the ambitious targets of the Convention on Biological Diversity and the European Biodiversity Strategy for ecosystem restoration.

  5. Using Agent Base Models to Optimize Large Scale Network for Large System Inventories

    Science.gov (United States)

    Shameldin, Ramez Ahmed; Bowling, Shannon R.

    2010-01-01

    The aim of this paper is to use Agent Base Models (ABM) to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. The models used in this paper either use computational algorithms or procedure implementations developed by Matlab to simulate agent based models in a principal programming language and mathematical theory using clusters, these clusters work as a high performance computational performance to run the program in parallel computational. In both cases, a model is defined as compilation of a set of structures and processes assumed to underlie the behavior of a network system.

  6. Large Scale Monte Carlo Simulation of Neutrino Interactions Using the Open Science Grid and Commercial Clouds

    International Nuclear Information System (INIS)

    Norman, A.; Boyd, J.; Davies, G.; Flumerfelt, E.; Herner, K.; Mayer, N.; Mhashilhar, P.; Tamsett, M.; Timm, S.

    2015-01-01

    Modern long baseline neutrino experiments like the NOvA experiment at Fermilab, require large scale, compute intensive simulations of their neutrino beam fluxes and backgrounds induced by cosmic rays. The amount of simulation required to keep the systematic uncertainties in the simulation from dominating the final physics results is often 10x to 100x that of the actual detector exposure. For the first physics results from NOvA this has meant the simulation of more than 2 billion cosmic ray events in the far detector and more than 200 million NuMI beam spill simulations. Performing these high statistics levels of simulation have been made possible for NOvA through the use of the Open Science Grid and through large scale runs on commercial clouds like Amazon EC2. We details the challenges in performing large scale simulation in these environments and how the computing infrastructure for the NOvA experiment has been adapted to seamlessly support the running of different simulation and data processing tasks on these resources. (paper)

  7. A PRACTICAL ONTOLOGY FOR THE LARGE-SCALE MODELING OF SCHOLARLY ARTIFACTS AND THEIR USAGE

    Energy Technology Data Exchange (ETDEWEB)

    RODRIGUEZ, MARKO A. [Los Alamos National Laboratory; BOLLEN, JOHAN [Los Alamos National Laboratory; VAN DE SOMPEL, HERBERT [Los Alamos National Laboratory

    2007-01-30

    The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scholarly ontology that was engineered to represent those classes for which large-scale bibliographic and usage data exists, supports usage research, and whose instantiation is scalable to the order of 50 million articles along with their associated artifacts (e.g. authors and journals) and an accompanying 1 billion usage events. The real world instantiation of the presented abstract ontology is a semantic network model of the scholarly community which lends the scholarly process to statistical analysis and computational support. They present the ontology, discuss its instantiation, and provide some example inference rules for calculating various scholarly artifact metrics.

  8. Design, development and integration of a large scale multiple source X-ray computed tomography system

    International Nuclear Information System (INIS)

    Malcolm, Andrew A.; Liu, Tong; Ng, Ivan Kee Beng; Teng, Wei Yuen; Yap, Tsi Tung; Wan, Siew Ping; Kong, Chun Jeng

    2013-01-01

    X-ray Computed Tomography (CT) allows visualisation of the physical structures in the interior of an object without physically opening or cutting it. This technology supports a wide range of applications in the non-destructive testing, failure analysis or performance evaluation of industrial products and components. Of the numerous factors that influence the performance characteristics of an X-ray CT system the energy level in the X-ray spectrum to be used is one of the most significant. The ability of the X-ray beam to penetrate a given thickness of a specific material is directly related to the maximum available energy level in the beam. Higher energy levels allow penetration of thicker components made of more dense materials. In response to local industry demand and in support of on-going research activity in the area of 3D X-ray imaging for industrial inspection the Singapore Institute of Manufacturing Technology (SIMTech) engaged in the design, development and integration of large scale multiple source X-ray computed tomography system based on X-ray sources operating at higher energies than previously available in the Institute. The system consists of a large area direct digital X-ray detector (410 x 410 mm), a multiple-axis manipulator system, a 225 kV open tube microfocus X-ray source and a 450 kV closed tube millifocus X-ray source. The 225 kV X-ray source can be operated in either transmission or reflection mode. The body of the 6-axis manipulator system is fabricated from heavy-duty steel onto which high precision linear and rotary motors have been mounted in order to achieve high accuracy, stability and repeatability. A source-detector distance of up to 2.5 m can be achieved. The system is controlled by a proprietary X-ray CT operating system developed by SIMTech. The system currently can accommodate samples up to 0.5 x 0.5 x 0.5 m in size with weight up to 50 kg. These specifications will be increased to 1.0 x 1.0 x 1.0 m and 100 kg in future

  9. Informational and emotional elements in online support groups: a Bayesian approach to large-scale content analysis.

    Science.gov (United States)

    Deetjen, Ulrike; Powell, John A

    2016-05-01

    This research examines the extent to which informational and emotional elements are employed in online support forums for 14 purposively sampled chronic medical conditions and the factors that influence whether posts are of a more informational or emotional nature. Large-scale qualitative data were obtained from Dailystrength.org. Based on a hand-coded training dataset, all posts were classified into informational or emotional using a Bayesian classification algorithm to generalize the findings. Posts that could not be classified with a probability of at least 75% were excluded. The overall tendency toward emotional posts differs by condition: mental health (depression, schizophrenia) and Alzheimer's disease consist of more emotional posts, while informational posts relate more to nonterminal physical conditions (irritable bowel syndrome, diabetes, asthma). There is no gender difference across conditions, although prostate cancer forums are oriented toward informational support, whereas breast cancer forums rather feature emotional support. Across diseases, the best predictors for emotional content are lower age and a higher number of overall posts by the support group member. The results are in line with previous empirical research and unify empirical findings from single/2-condition research. Limitations include the analytical restriction to predefined categories (informational, emotional) through the chosen machine-learning approach. Our findings provide an empirical foundation for building theory on informational versus emotional support across conditions, give insights for practitioners to better understand the role of online support groups for different patients, and show the usefulness of machine-learning approaches to analyze large-scale qualitative health data from online settings. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  10. Medium/small-scale computers HITACHI M-620, M-630, and M-640 systems: the aim of development and characteristics

    Energy Technology Data Exchange (ETDEWEB)

    Oshima, N; Saiki, Y; Sunaga, K [Hitachi, Ltd., Tokyo (Japan)

    1990-10-01

    The medium/small-scale HITACHI M-620, M-630, and M-640 computer systems are outlined. Every system is featured by the configuration usable as a medium or small-scale host computer in offices, the function connectable with large-scale host computers, the performance of 5-50 times those of conventional office computers, easy operation and fast processing. As features of the hardware, the one-board CPU and small integrated cubicle structure containing the CPU board, high-speed large-capacity magnetic disk storage device, various kinds of controllers and others are illustrated. As features of the software, the OS (VOS K) featured by the virtual data space control (VDSA) and relational database (RDB) functions, EAGLE/4GL (effective approach to achieving high level software productivity/4th generation language), STEP (self training environmental support program) and simple end user language ACE3/E2 are outlined. 7 figs.

  11. Penalized Estimation in Large-Scale Generalized Linear Array Models

    DEFF Research Database (Denmark)

    Lund, Adam; Vincent, Martin; Hansen, Niels Richard

    2017-01-01

    Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension...

  12. Proceedings of joint meeting of the 6th simulation science symposium and the NIFS collaboration research 'large scale computer simulation'

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2003-03-01

    Joint meeting of the 6th Simulation Science Symposium and the NIFS Collaboration Research 'Large Scale Computer Simulation' was held on December 12-13, 2002 at National Institute for Fusion Science, with the aim of promoting interdisciplinary collaborations in various fields of computer simulations. The present meeting attended by more than 40 people consists of the 11 invited and 22 contributed papers, of which topics were extended not only to fusion science but also to related fields such as astrophysics, earth science, fluid dynamics, molecular dynamics, computer science etc. (author)

  13. Extreme Scale Computing to Secure the Nation

    Energy Technology Data Exchange (ETDEWEB)

    Brown, D L; McGraw, J R; Johnson, J R; Frincke, D

    2009-11-10

    absence of nuclear testing, a progam to: (1) Support a focused, multifaceted program to increase the understanding of the enduring stockpile; (2) Predict, detect, and evaluate potential problems of the aging of the stockpile; (3) Refurbish and re-manufacture weapons and components, as required; and (4) Maintain the science and engineering institutions needed to support the nation's nuclear deterrent, now and in the future'. This program continues to fulfill its national security mission by adding significant new capabilities for producing scientific results through large-scale computational simulation coupled with careful experimentation, including sub-critical nuclear experiments permitted under the CTBT. To develop the computational science and the computational horsepower needed to support its mission, SBSS initiated the Accelerated Strategic Computing Initiative, later renamed the Advanced Simulation & Computing (ASC) program (sidebar: 'History of ASC Computing Program Computing Capability'). The modern 3D computational simulation capability of the ASC program supports the assessment and certification of the current nuclear stockpile through calibration with past underground test (UGT) data. While an impressive accomplishment, continued evolution of national security mission requirements will demand computing resources at a significantly greater scale than we have today. In particular, continued observance and potential Senate confirmation of the Comprehensive Test Ban Treaty (CTBT) together with the U.S administration's promise for a significant reduction in the size of the stockpile and the inexorable aging and consequent refurbishment of the stockpile all demand increasing refinement of our computational simulation capabilities. Assessment of the present and future stockpile with increased confidence of the safety and reliability without reliance upon calibration with past or future test data is a long-term goal of the ASC program. This

  14. Computational domain length and Reynolds number effects on large-scale coherent motions in turbulent pipe flow

    Science.gov (United States)

    Feldmann, Daniel; Bauer, Christian; Wagner, Claus

    2018-03-01

    We present results from direct numerical simulations (DNS) of turbulent pipe flow at shear Reynolds numbers up to Reτ = 1500 using different computational domains with lengths up to ?. The objectives are to analyse the effect of the finite size of the periodic pipe domain on large flow structures in dependency of Reτ and to assess a minimum ? required for relevant turbulent scales to be captured and a minimum Reτ for very large-scale motions (VLSM) to be analysed. Analysing one-point statistics revealed that the mean velocity profile is invariant for ?. The wall-normal location at which deviations occur in shorter domains changes strongly with increasing Reτ from the near-wall region to the outer layer, where VLSM are believed to live. The root mean square velocity profiles exhibit domain length dependencies for pipes shorter than 14R and 7R depending on Reτ. For all Reτ, the higher-order statistical moments show only weak dependencies and only for the shortest domain considered here. However, the analysis of one- and two-dimensional pre-multiplied energy spectra revealed that even for larger ?, not all physically relevant scales are fully captured, even though the aforementioned statistics are in good agreement with the literature. We found ? to be sufficiently large to capture VLSM-relevant turbulent scales in the considered range of Reτ based on our definition of an integral energy threshold of 10%. The requirement to capture at least 1/10 of the global maximum energy level is justified by a 14% increase of the streamwise turbulence intensity in the outer region between Reτ = 720 and 1500, which can be related to VLSM-relevant length scales. Based on this scaling anomaly, we found Reτ⪆1500 to be a necessary minimum requirement to investigate VLSM-related effects in pipe flow, even though the streamwise energy spectra does not yet indicate sufficient scale separation between the most energetic and the very long motions.

  15. Large-scale exact diagonalizations reveal low-momentum scales of nuclei

    Science.gov (United States)

    Forssén, C.; Carlsson, B. D.; Johansson, H. T.; Sääf, D.; Bansal, A.; Hagen, G.; Papenbrock, T.

    2018-03-01

    Ab initio methods aim to solve the nuclear many-body problem with controlled approximations. Virtually exact numerical solutions for realistic interactions can only be obtained for certain special cases such as few-nucleon systems. Here we extend the reach of exact diagonalization methods to handle model spaces with dimension exceeding 1010 on a single compute node. This allows us to perform no-core shell model (NCSM) calculations for 6Li in model spaces up to Nmax=22 and to reveal the 4He+d halo structure of this nucleus. Still, the use of a finite harmonic-oscillator basis implies truncations in both infrared (IR) and ultraviolet (UV) length scales. These truncations impose finite-size corrections on observables computed in this basis. We perform IR extrapolations of energies and radii computed in the NCSM and with the coupled-cluster method at several fixed UV cutoffs. It is shown that this strategy enables information gain also from data that is not fully UV converged. IR extrapolations improve the accuracy of relevant bound-state observables for a range of UV cutoffs, thus making them profitable tools. We relate the momentum scale that governs the exponential IR convergence to the threshold energy for the first open decay channel. Using large-scale NCSM calculations we numerically verify this small-momentum scale of finite nuclei.

  16. Parallel Computing in SCALE

    International Nuclear Information System (INIS)

    DeHart, Mark D.; Williams, Mark L.; Bowman, Stephen M.

    2010-01-01

    The SCALE computational architecture has remained basically the same since its inception 30 years ago, although constituent modules and capabilities have changed significantly. This SCALE concept was intended to provide a framework whereby independent codes can be linked to provide a more comprehensive capability than possible with the individual programs - allowing flexibility to address a wide variety of applications. However, the current system was designed originally for mainframe computers with a single CPU and with significantly less memory than today's personal computers. It has been recognized that the present SCALE computation system could be restructured to take advantage of modern hardware and software capabilities, while retaining many of the modular features of the present system. Preliminary work is being done to define specifications and capabilities for a more advanced computational architecture. This paper describes the state of current SCALE development activities and plans for future development. With the release of SCALE 6.1 in 2010, a new phase of evolutionary development will be available to SCALE users within the TRITON and NEWT modules. The SCALE (Standardized Computer Analyses for Licensing Evaluation) code system developed by Oak Ridge National Laboratory (ORNL) provides a comprehensive and integrated package of codes and nuclear data for a wide range of applications in criticality safety, reactor physics, shielding, isotopic depletion and decay, and sensitivity/uncertainty (S/U) analysis. Over the last three years, since the release of version 5.1 in 2006, several important new codes have been introduced within SCALE, and significant advances applied to existing codes. Many of these new features became available with the release of SCALE 6.0 in early 2009. However, beginning with SCALE 6.1, a first generation of parallel computing is being introduced. In addition to near-term improvements, a plan for longer term SCALE enhancement

  17. Tracking and computing

    International Nuclear Information System (INIS)

    Niederer, J.

    1983-01-01

    This note outlines several ways in which large scale simulation computing and programming support may be provided to the SSC design community. One aspect of the problem is getting supercomputer power without the high cost and long lead times of large scale institutional computing. Another aspect is the blending of modern programming practices with more conventional accelerator design programs in ways that do not also swamp designers with the details of complicated computer technology

  18. Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

    Directory of Open Access Journals (Sweden)

    Kun Qian

    2014-01-01

    Full Text Available Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.

  19. Large-scale pool fires

    Directory of Open Access Journals (Sweden)

    Steinhaus Thomas

    2007-01-01

    Full Text Available A review of research into the burning behavior of large pool fires and fuel spill fires is presented. The features which distinguish such fires from smaller pool fires are mainly associated with the fire dynamics at low source Froude numbers and the radiative interaction with the fire source. In hydrocarbon fires, higher soot levels at increased diameters result in radiation blockage effects around the perimeter of large fire plumes; this yields lower emissive powers and a drastic reduction in the radiative loss fraction; whilst there are simplifying factors with these phenomena, arising from the fact that soot yield can saturate, there are other complications deriving from the intermittency of the behavior, with luminous regions of efficient combustion appearing randomly in the outer surface of the fire according the turbulent fluctuations in the fire plume. Knowledge of the fluid flow instabilities, which lead to the formation of large eddies, is also key to understanding the behavior of large-scale fires. Here modeling tools can be effectively exploited in order to investigate the fluid flow phenomena, including RANS- and LES-based computational fluid dynamics codes. The latter are well-suited to representation of the turbulent motions, but a number of challenges remain with their practical application. Massively-parallel computational resources are likely to be necessary in order to be able to adequately address the complex coupled phenomena to the level of detail that is necessary.

  20. PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs

    Directory of Open Access Journals (Sweden)

    Di Jin

    2017-07-01

    Full Text Available Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is ‘important’? How can multiple researchers explore a new graph dataset collectively and “help” each other with their findings? In this article, we present Perseus-Hub, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. Perseus-Hub uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In Perseus-Hub, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution marked by other users or experts. Moreover, Perseus-Hub guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks.

  1. Large-scale structure of the Universe

    International Nuclear Information System (INIS)

    Doroshkevich, A.G.

    1978-01-01

    The problems, discussed at the ''Large-scale Structure of the Universe'' symposium are considered on a popular level. Described are the cell structure of galaxy distribution in the Universe, principles of mathematical galaxy distribution modelling. The images of cell structures, obtained after reprocessing with the computer are given. Discussed are three hypothesis - vortical, entropic, adiabatic, suggesting various processes of galaxy and galaxy clusters origin. A considerable advantage of the adiabatic hypothesis is recognized. The relict radiation, as a method of direct studying the processes taking place in the Universe is considered. The large-scale peculiarities and small-scale fluctuations of the relict radiation temperature enable one to estimate the turbance properties at the pre-galaxy stage. The discussion of problems, pertaining to studying the hot gas, contained in galaxy clusters, the interactions within galaxy clusters and with the inter-galaxy medium, is recognized to be a notable contribution into the development of theoretical and observational cosmology

  2. OffshoreDC DC grids for integration of large scale wind power

    DEFF Research Database (Denmark)

    Zeni, Lorenzo; Endegnanew, Atsede Gualu; Stamatiou, Georgios

    The present report summarizes the main findings of the Nordic Energy Research project “DC grids for large scale integration of offshore wind power – OffshoreDC”. The project is been funded by Nordic Energy Research through the TFI programme and was active between 2011 and 2016. The overall...... objective of the project was to drive the development of the VSC based HVDC technology for future large scale offshore grids, supporting a standardised and commercial development of the technology, and improving the opportunities for the technology to support power system integration of large scale offshore...

  3. An interactive display system for large-scale 3D models

    Science.gov (United States)

    Liu, Zijian; Sun, Kun; Tao, Wenbing; Liu, Liman

    2018-04-01

    With the improvement of 3D reconstruction theory and the rapid development of computer hardware technology, the reconstructed 3D models are enlarging in scale and increasing in complexity. Models with tens of thousands of 3D points or triangular meshes are common in practical applications. Due to storage and computing power limitation, it is difficult to achieve real-time display and interaction with large scale 3D models for some common 3D display software, such as MeshLab. In this paper, we propose a display system for large-scale 3D scene models. We construct the LOD (Levels of Detail) model of the reconstructed 3D scene in advance, and then use an out-of-core view-dependent multi-resolution rendering scheme to realize the real-time display of the large-scale 3D model. With the proposed method, our display system is able to render in real time while roaming in the reconstructed scene and 3D camera poses can also be displayed. Furthermore, the memory consumption can be significantly decreased via internal and external memory exchange mechanism, so that it is possible to display a large scale reconstructed scene with over millions of 3D points or triangular meshes in a regular PC with only 4GB RAM.

  4. Dynamic Reactive Power Compensation of Large Scale Wind Integrated Power System

    DEFF Research Database (Denmark)

    Rather, Zakir Hussain; Chen, Zhe; Thøgersen, Paul

    2015-01-01

    wind turbines especially wind farms with additional grid support functionalities like dynamic support (e,g dynamic reactive power support etc.) and ii) refurbishment of existing conventional central power plants to synchronous condensers could be one of the efficient, reliable and cost effective option......Due to progressive displacement of conventional power plants by wind turbines, dynamic security of large scale wind integrated power systems gets significantly compromised. In this paper we first highlight the importance of dynamic reactive power support/voltage security in large scale wind...... integrated power systems with least presence of conventional power plants. Then we propose a mixed integer dynamic optimization based method for optimal dynamic reactive power allocation in large scale wind integrated power systems. One of the important aspects of the proposed methodology is that unlike...

  5. Optimization of large-scale heterogeneous system-of-systems models.

    Energy Technology Data Exchange (ETDEWEB)

    Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)

    2012-01-01

    Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

  6. Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

    Science.gov (United States)

    Aji, Ablimit; Wang, Fusheng; Saltz, Joel H

    2012-11-06

    Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

  7. Sentinel-1 data massive processing for large scale DInSAR analyses within Cloud Computing environments through the P-SBAS approach

    Science.gov (United States)

    Lanari, Riccardo; Bonano, Manuela; Buonanno, Sabatino; Casu, Francesco; De Luca, Claudio; Fusco, Adele; Manunta, Michele; Manzo, Mariarosaria; Pepe, Antonio; Zinno, Ivana

    2017-04-01

    -core programming techniques. Currently, Cloud Computing environments make available large collections of computing resources and storage that can be effectively exploited through the presented S1 P-SBAS processing chain to carry out interferometric analyses at a very large scale, in reduced time. This allows us to deal also with the problems connected to the use of S1 P-SBAS chain in operational contexts, related to hazard monitoring and risk prevention and mitigation, where handling large amounts of data represents a challenging task. As a significant experimental result we performed a large spatial scale SBAS analysis relevant to the Central and Southern Italy by exploiting the Amazon Web Services Cloud Computing platform. In particular, we processed in parallel 300 S1 acquisitions covering the Italian peninsula from Lazio to Sicily through the presented S1 P-SBAS processing chain, generating 710 interferograms, thus finally obtaining the displacement time series of the whole processed area. This work has been partially supported by the CNR-DPC agreement, the H2020 EPOS-IP project (GA 676564) and the ESA GEP project.

  8. Simulation of large scale air detritiation operations by computer modeling and bench-scale experimentation

    International Nuclear Information System (INIS)

    Clemmer, R.G.; Land, R.H.; Maroni, V.A.; Mintz, J.M.

    1978-01-01

    Although some experience has been gained in the design and construction of 0.5 to 5 m 3 /s air-detritiation systems, little information is available on the performance of these systems under realistic conditions. Recently completed studies at ANL have attempted to provide some perspective on this subject. A time-dependent computer model was developed to study the effects of various reaction and soaking mechanisms that could occur in a typically-sized fusion reactor building (approximately 10 5 m 3 ) following a range of tritium releases (2 to 200 g). In parallel with the computer study, a small (approximately 50 liter) test chamber was set up to investigate cleanup characteristics under conditions which could also be simulated with the computer code. Whereas results of computer analyses indicated that only approximately 10 -3 percent of the tritium released to an ambient enclosure should be converted to tritiated water, the bench-scale experiments gave evidence of conversions to water greater than 1%. Furthermore, although the amounts (both calculated and observed) of soaked-in tritium are usually only a very small fraction of the total tritium release, the soaked tritium is significant, in that its continuous return to the enclosure extends the cleanup time beyond the predicted value in the absence of any soaking mechanisms

  9. Enabling Large-Scale Biomedical Analysis in the Cloud

    Directory of Open Access Journals (Sweden)

    Ying-Chih Lin

    2013-01-01

    Full Text Available Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.

  10. Stabilization Algorithms for Large-Scale Problems

    DEFF Research Database (Denmark)

    Jensen, Toke Koldborg

    2006-01-01

    The focus of the project is on stabilization of large-scale inverse problems where structured models and iterative algorithms are necessary for computing approximate solutions. For this purpose, we study various iterative Krylov methods and their abilities to produce regularized solutions. Some......-curve. This heuristic is implemented as a part of a larger algorithm which is developed in collaboration with G. Rodriguez and P. C. Hansen. Last, but not least, a large part of the project has, in different ways, revolved around the object-oriented Matlab toolbox MOORe Tools developed by PhD Michael Jacobsen. New...

  11. BFAST: an alignment tool for large scale genome resequencing.

    Directory of Open Access Journals (Sweden)

    Nils Homer

    2009-11-01

    Full Text Available The new generation of massively parallel DNA sequencers, combined with the challenge of whole human genome resequencing, result in the need for rapid and accurate alignment of billions of short DNA sequence reads to a large reference genome. Speed is obviously of great importance, but equally important is maintaining alignment accuracy of short reads, in the 25-100 base range, in the presence of errors and true biological variation.We introduce a new algorithm specifically optimized for this task, as well as a freely available implementation, BFAST, which can align data produced by any of current sequencing platforms, allows for user-customizable levels of speed and accuracy, supports paired end data, and provides for efficient parallel and multi-threaded computation on a computer cluster. The new method is based on creating flexible, efficient whole genome indexes to rapidly map reads to candidate alignment locations, with arbitrary multiple independent indexes allowed to achieve robustness against read errors and sequence variants. The final local alignment uses a Smith-Waterman method, with gaps to support the detection of small indels.We compare BFAST to a selection of large-scale alignment tools -- BLAT, MAQ, SHRiMP, and SOAP -- in terms of both speed and accuracy, using simulated and real-world datasets. We show BFAST can achieve substantially greater sensitivity of alignment in the context of errors and true variants, especially insertions and deletions, and minimize false mappings, while maintaining adequate speed compared to other current methods. We show BFAST can align the amount of data needed to fully resequence a human genome, one billion reads, with high sensitivity and accuracy, on a modest computer cluster in less than 24 hours. BFAST is available at (http://bfast.sourceforge.net.

  12. Large Scale Computing and Storage Requirements for Biological and Environmental Research

    Energy Technology Data Exchange (ETDEWEB)

    DOE Office of Science, Biological and Environmental Research Program Office (BER),

    2009-09-30

    In May 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of Biological and Environmental Research (BER) held a workshop to characterize HPC requirements for BER-funded research over the subsequent three to five years. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. Chief among them: scientific progress in BER-funded research is limited by current allocations of computational resources. Additionally, growth in mission-critical computing -- combined with new requirements for collaborative data manipulation and analysis -- will demand ever increasing computing, storage, network, visualization, reliability and service richness from NERSC. This report expands upon these key points and adds others. It also presents a number of"case studies" as significant representative samples of the needs of science teams within BER. Workshop participants were asked to codify their requirements in this"case study" format, summarizing their science goals, methods of solution, current and 3-5 year computing requirements, and special software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel,"multi-core" environment that is expected to dominate HPC architectures over the next few years.

  13. Fires in large scale ventilation systems

    International Nuclear Information System (INIS)

    Gregory, W.S.; Martin, R.A.; White, B.W.; Nichols, B.D.; Smith, P.R.; Leslie, I.H.; Fenton, D.L.; Gunaji, M.V.; Blythe, J.P.

    1991-01-01

    This paper summarizes the experience gained simulating fires in large scale ventilation systems patterned after ventilation systems found in nuclear fuel cycle facilities. The series of experiments discussed included: (1) combustion aerosol loading of 0.61x0.61 m HEPA filters with the combustion products of two organic fuels, polystyrene and polymethylemethacrylate; (2) gas dynamic and heat transport through a large scale ventilation system consisting of a 0.61x0.61 m duct 90 m in length, with dampers, HEPA filters, blowers, etc.; (3) gas dynamic and simultaneous transport of heat and solid particulate (consisting of glass beads with a mean aerodynamic diameter of 10μ) through the large scale ventilation system; and (4) the transport of heat and soot, generated by kerosene pool fires, through the large scale ventilation system. The FIRAC computer code, designed to predict fire-induced transients in nuclear fuel cycle facility ventilation systems, was used to predict the results of experiments (2) through (4). In general, the results of the predictions were satisfactory. The code predictions for the gas dynamics, heat transport, and particulate transport and deposition were within 10% of the experimentally measured values. However, the code was less successful in predicting the amount of soot generation from kerosene pool fires, probably due to the fire module of the code being a one-dimensional zone model. The experiments revealed a complicated three-dimensional combustion pattern within the fire room of the ventilation system. Further refinement of the fire module within FIRAC is needed. (orig.)

  14. Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses.

    Science.gov (United States)

    Liu, Bo; Madduri, Ravi K; Sotomayor, Borja; Chard, Kyle; Lacinski, Lukasz; Dave, Utpal J; Li, Jianqiang; Liu, Chunchen; Foster, Ian T

    2014-06-01

    Due to the upcoming data deluge of genome data, the need for storing and processing large-scale genome data, easy access to biomedical analyses tools, efficient data sharing and retrieval has presented significant challenges. The variability in data volume results in variable computing and storage requirements, therefore biomedical researchers are pursuing more reliable, dynamic and convenient methods for conducting sequencing analyses. This paper proposes a Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses, which enables reliable and highly scalable execution of sequencing analyses workflows in a fully automated manner. Our platform extends the existing Galaxy workflow system by adding data management capabilities for transferring large quantities of data efficiently and reliably (via Globus Transfer), domain-specific analyses tools preconfigured for immediate use by researchers (via user-specific tools integration), automatic deployment on Cloud for on-demand resource allocation and pay-as-you-go pricing (via Globus Provision), a Cloud provisioning tool for auto-scaling (via HTCondor scheduler), and the support for validating the correctness of workflows (via semantic verification tools). Two bioinformatics workflow use cases as well as performance evaluation are presented to validate the feasibility of the proposed approach. Copyright © 2014 Elsevier Inc. All rights reserved.

  15. Reliability in Warehouse-Scale Computing: Why Low Latency Matters

    DEFF Research Database (Denmark)

    Nannarelli, Alberto

    2015-01-01

    , the limiting factor of these warehouse-scale data centers is the power dissipation. Power is dissipated not only in the computation itself, but also in heat removal (fans, air conditioning, etc.) to keep the temperature of the devices within the operating ranges. The need to keep the temperature low within......Warehouse sized buildings are nowadays hosting several types of large computing systems: from supercomputers to large clusters of servers to provide the infrastructure to the cloud. Although the main target, especially for high-performance computing, is still to achieve high throughput...

  16. Computational Science Facility (CSF)

    Data.gov (United States)

    Federal Laboratory Consortium — PNNL Institutional Computing (PIC) is focused on meeting DOE's mission needs and is part of PNNL's overarching research computing strategy. PIC supports large-scale...

  17. Large Scale Document Inversion using a Multi-threaded Computing System.

    Science.gov (United States)

    Jung, Sungbo; Chang, Dar-Jen; Park, Juw Won

    2017-06-01

    Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel coprocessor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2-3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews. •Information systems➝Information retrieval • Computing methodologies➝Massively parallel and high-performance simulations.

  18. Proceedings of joint meeting of the 6th simulation science symposium and the NIFS collaboration research 'large scale computer simulation'

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2003-03-01

    Joint meeting of the 6th Simulation Science Symposium and the NIFS Collaboration Research 'Large Scale Computer Simulation' was held on December 12-13, 2002 at National Institute for Fusion Science, with the aim of promoting interdisciplinary collaborations in various fields of computer simulations. The present meeting attended by more than 40 people consists of the 11 invited and 22 contributed papers, of which topics were extended not only to fusion science but also to related fields such as astrophysics, earth science, fluid dynamics, molecular dynamics, computer science etc. (author)

  19. Large-scale fracture mechancis testing -- requirements and possibilities

    International Nuclear Information System (INIS)

    Brumovsky, M.

    1993-01-01

    Application of fracture mechanics to very important and/or complicated structures, like reactor pressure vessels, brings also some questions about the reliability and precision of such calculations. These problems become more pronounced in cases of elastic-plastic conditions of loading and/or in parts with non-homogeneous materials (base metal and austenitic cladding, property gradient changes through material thickness) or with non-homogeneous stress fields (nozzles, bolt threads, residual stresses etc.). For such special cases some verification by large-scale testing is necessary and valuable. This paper discusses problems connected with planning of such experiments with respect to their limitations, requirements to a good transfer of received results to an actual vessel. At the same time, an analysis of possibilities of small-scale model experiments is also shown, mostly in connection with application of results between standard, small-scale and large-scale experiments. Experience from 30 years of large-scale testing in SKODA is used as an example to support this analysis. 1 fig

  20. Large-scale stochasticity in Hamiltonian systems

    International Nuclear Information System (INIS)

    Escande, D.F.

    1982-01-01

    Large scale stochasticity (L.S.S.) in Hamiltonian systems is defined on the paradigm Hamiltonian H(v,x,t) =v 2 /2-M cos x-P cos k(x-t) which describes the motion of one particle in two electrostatic waves. A renormalization transformation Tsub(r) is described which acts as a microscope that focusses on a given KAM (Kolmogorov-Arnold-Moser) torus in phase space. Though approximate, Tsub(r) yields the threshold of L.S.S. in H with an error of 5-10%. The universal behaviour of KAM tori is predicted: for instance the scale invariance of KAM tori and the critical exponent of the Lyapunov exponent of Cantori. The Fourier expansion of KAM tori is computed and several conjectures by L. Kadanoff and S. Shenker are proved. Chirikov's standard mapping for stochastic layers is derived in a simpler way and the width of the layers is computed. A simpler renormalization scheme for these layers is defined. A Mathieu equation for describing the stability of a discrete family of cycles is derived. When combined with Tsub(r), it allows to prove the link between KAM tori and nearby cycles, conjectured by J. Greene and, in particular, to compute the mean residue of a torus. The fractal diagrams defined by G. Schmidt are computed. A sketch of a methodology for computing the L.S.S. threshold in any two-degree-of-freedom Hamiltonian system is given. (Auth.)

  1. Accelerating large-scale phase-field simulations with GPU

    Directory of Open Access Journals (Sweden)

    Xiaoming Shi

    2017-10-01

    Full Text Available A new package for accelerating large-scale phase-field simulations was developed by using GPU based on the semi-implicit Fourier method. The package can solve a variety of equilibrium equations with different inhomogeneity including long-range elastic, magnetostatic, and electrostatic interactions. Through using specific algorithm in Compute Unified Device Architecture (CUDA, Fourier spectral iterative perturbation method was integrated in GPU package. The Allen-Cahn equation, Cahn-Hilliard equation, and phase-field model with long-range interaction were solved based on the algorithm running on GPU respectively to test the performance of the package. From the comparison of the calculation results between the solver executed in single CPU and the one on GPU, it was found that the speed on GPU is enormously elevated to 50 times faster. The present study therefore contributes to the acceleration of large-scale phase-field simulations and provides guidance for experiments to design large-scale functional devices.

  2. Evaluation of Kirkwood-Buff integrals via finite size scaling: a large scale molecular dynamics study

    Science.gov (United States)

    Dednam, W.; Botha, A. E.

    2015-01-01

    Solvation of bio-molecules in water is severely affected by the presence of co-solvent within the hydration shell of the solute structure. Furthermore, since solute molecules can range from small molecules, such as methane, to very large protein structures, it is imperative to understand the detailed structure-function relationship on the microscopic level. For example, it is useful know the conformational transitions that occur in protein structures. Although such an understanding can be obtained through large-scale molecular dynamic simulations, it is often the case that such simulations would require excessively large simulation times. In this context, Kirkwood-Buff theory, which connects the microscopic pair-wise molecular distributions to global thermodynamic properties, together with the recently developed technique, called finite size scaling, may provide a better method to reduce system sizes, and hence also the computational times. In this paper, we present molecular dynamics trial simulations of biologically relevant low-concentration solvents, solvated by aqueous co-solvent solutions. In particular we compare two different methods of calculating the relevant Kirkwood-Buff integrals. The first (traditional) method computes running integrals over the radial distribution functions, which must be obtained from large system-size NVT or NpT simulations. The second, newer method, employs finite size scaling to obtain the Kirkwood-Buff integrals directly by counting the particle number fluctuations in small, open sub-volumes embedded within a larger reservoir that can be well approximated by a much smaller simulation cell. In agreement with previous studies, which made a similar comparison for aqueous co-solvent solutions, without the additional solvent, we conclude that the finite size scaling method is also applicable to the present case, since it can produce computationally more efficient results which are equivalent to the more costly radial distribution

  3. Evaluation of Kirkwood-Buff integrals via finite size scaling: a large scale molecular dynamics study

    International Nuclear Information System (INIS)

    Dednam, W; Botha, A E

    2015-01-01

    Solvation of bio-molecules in water is severely affected by the presence of co-solvent within the hydration shell of the solute structure. Furthermore, since solute molecules can range from small molecules, such as methane, to very large protein structures, it is imperative to understand the detailed structure-function relationship on the microscopic level. For example, it is useful know the conformational transitions that occur in protein structures. Although such an understanding can be obtained through large-scale molecular dynamic simulations, it is often the case that such simulations would require excessively large simulation times. In this context, Kirkwood-Buff theory, which connects the microscopic pair-wise molecular distributions to global thermodynamic properties, together with the recently developed technique, called finite size scaling, may provide a better method to reduce system sizes, and hence also the computational times. In this paper, we present molecular dynamics trial simulations of biologically relevant low-concentration solvents, solvated by aqueous co-solvent solutions. In particular we compare two different methods of calculating the relevant Kirkwood-Buff integrals. The first (traditional) method computes running integrals over the radial distribution functions, which must be obtained from large system-size NVT or NpT simulations. The second, newer method, employs finite size scaling to obtain the Kirkwood-Buff integrals directly by counting the particle number fluctuations in small, open sub-volumes embedded within a larger reservoir that can be well approximated by a much smaller simulation cell. In agreement with previous studies, which made a similar comparison for aqueous co-solvent solutions, without the additional solvent, we conclude that the finite size scaling method is also applicable to the present case, since it can produce computationally more efficient results which are equivalent to the more costly radial distribution

  4. GPU-Accelerated Large-Scale Electronic Structure Theory on Titan with a First-Principles All-Electron Code

    Science.gov (United States)

    Huhn, William Paul; Lange, Björn; Yu, Victor; Blum, Volker; Lee, Seyong; Yoon, Mina

    Density-functional theory has been well established as the dominant quantum-mechanical computational method in the materials community. Large accurate simulations become very challenging on small to mid-scale computers and require high-performance compute platforms to succeed. GPU acceleration is one promising approach. In this talk, we present a first implementation of all-electron density-functional theory in the FHI-aims code for massively parallel GPU-based platforms. Special attention is paid to the update of the density and to the integration of the Hamiltonian and overlap matrices, realized in a domain decomposition scheme on non-uniform grids. The initial implementation scales well across nodes on ORNL's Titan Cray XK7 supercomputer (8 to 64 nodes, 16 MPI ranks/node) and shows an overall speed up in runtime due to utilization of the K20X Tesla GPUs on each Titan node of 1.4x, with the charge density update showing a speed up of 2x. Further acceleration opportunities will be discussed. Work supported by the LDRD Program of ORNL managed by UT-Battle, LLC, for the U.S. DOE and by the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725.

  5. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Xiangyun Xiao

    Full Text Available The reconstruction of gene regulatory networks (GRNs from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM, experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  6. A new asynchronous parallel algorithm for inferring large-scale gene regulatory networks.

    Science.gov (United States)

    Xiao, Xiangyun; Zhang, Wei; Zou, Xiufen

    2015-01-01

    The reconstruction of gene regulatory networks (GRNs) from high-throughput experimental data has been considered one of the most important issues in systems biology research. With the development of high-throughput technology and the complexity of biological problems, we need to reconstruct GRNs that contain thousands of genes. However, when many existing algorithms are used to handle these large-scale problems, they will encounter two important issues: low accuracy and high computational cost. To overcome these difficulties, the main goal of this study is to design an effective parallel algorithm to infer large-scale GRNs based on high-performance parallel computing environments. In this study, we proposed a novel asynchronous parallel framework to improve the accuracy and lower the time complexity of large-scale GRN inference by combining splitting technology and ordinary differential equation (ODE)-based optimization. The presented algorithm uses the sparsity and modularity of GRNs to split whole large-scale GRNs into many small-scale modular subnetworks. Through the ODE-based optimization of all subnetworks in parallel and their asynchronous communications, we can easily obtain the parameters of the whole network. To test the performance of the proposed approach, we used well-known benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge (DREAM), experimentally determined GRN of Escherichia coli and one published dataset that contains more than 10 thousand genes to compare the proposed approach with several popular algorithms on the same high-performance computing environments in terms of both accuracy and time complexity. The numerical results demonstrate that our parallel algorithm exhibits obvious superiority in inferring large-scale GRNs.

  7. Large Scale Document Inversion using a Multi-threaded Computing System

    Science.gov (United States)

    Jung, Sungbo; Chang, Dar-Jen; Park, Juw Won

    2018-01-01

    Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel coprocessor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2-3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews. CCS Concepts •Information systems➝Information retrieval • Computing methodologies➝Massively parallel and high-performance simulations.

  8. The Schroedinger-Poisson equations as the large-N limit of the Newtonian N-body system. Applications to the large scale dark matter dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Briscese, Fabio [Northumbria University, Department of Mathematics, Physics and Electrical Engineering, Newcastle upon Tyne (United Kingdom); Citta Universitaria, Istituto Nazionale di Alta Matematica Francesco Severi, Gruppo Nazionale di Fisica Matematica, Rome (Italy)

    2017-09-15

    In this paper it is argued how the dynamics of the classical Newtonian N-body system can be described in terms of the Schroedinger-Poisson equations in the large N limit. This result is based on the stochastic quantization introduced by Nelson, and on the Calogero conjecture. According to the Calogero conjecture, the emerging effective Planck constant is computed in terms of the parameters of the N-body system as ℎ ∝ M{sup 5/3}G{sup 1/2}(N/ left angle ρ right angle){sup 1/6}, where is G the gravitational constant, N and M are the number and the mass of the bodies, and left angle ρ right angle is their average density. The relevance of this result in the context of large scale structure formation is discussed. In particular, this finding gives a further argument in support of the validity of the Schroedinger method as numerical double of the N-body simulations of dark matter dynamics at large cosmological scales. (orig.)

  9. Novel algorithm of large-scale simultaneous linear equations

    International Nuclear Information System (INIS)

    Fujiwara, T; Hoshi, T; Yamamoto, S; Sogabe, T; Zhang, S-L

    2010-01-01

    We review our recently developed methods of solving large-scale simultaneous linear equations and applications to electronic structure calculations both in one-electron theory and many-electron theory. This is the shifted COCG (conjugate orthogonal conjugate gradient) method based on the Krylov subspace, and the most important issue for applications is the shift equation and the seed switching method, which greatly reduce the computational cost. The applications to nano-scale Si crystals and the double orbital extended Hubbard model are presented.

  10. Large-scale inverse model analyses employing fast randomized data reduction

    Science.gov (United States)

    Lin, Youzuo; Le, Ellen B.; O'Malley, Daniel; Vesselinov, Velimir V.; Bui-Thanh, Tan

    2017-08-01

    When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 107 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called "sketching" matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.

  11. Rucio - The next generation of large scale distributed system for ATLAS Data Management

    CERN Document Server

    Garonne, V; The ATLAS collaboration; Beermann, T; Goossens, L; Lassnig, M; Nairz, A; Stewart, GA; Vigne, V; Serfon, C

    2013-01-01

    Rucio is the next-generation Distributed Data Management(DDM) system benefiting from recent advances in cloud and "Big Data" computing to address HEP experiments scaling requirements. Rucio is an evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 140 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio will address these issues by relying on a conceptual data model and new technology to ensure system scalability, address new user requirements and employ new automation framework to reduce operational overheads. We present the key concepts of Rucio, including its data organization/representation and a model of how ATLAS central group and user activities will be managed. The Rucio design, and the technology it employs, is described...

  12. Rucio - The next generation of large scale distributed system for ATLAS Data Management

    CERN Document Server

    Garonne, V; The ATLAS collaboration; Beermann, T; Goossens, L; Lassnig, M; Nairz, A; Stewart, GA; Vigne, V; Serfon, C

    2014-01-01

    Rucio is the next-generation Distributed Data Management(DDM) system benefiting from recent advances in cloud and ”Big Data” computing to address HEP experiments scaling requirements. Rucio is an evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 140 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio will address these issues by relying on a conceptual data model and new technology to ensure system scalability, address new user requirements and employ new automation framework to reduce operational overheads. We present the key concepts of Rucio, including its data organization/representation and a model of how ATLAS central group and user activities will be managed. The Rucio design, and the technology it employs, is descr...

  13. Large-scale solar purchasing

    International Nuclear Information System (INIS)

    1999-01-01

    The principal objective of the project was to participate in the definition of a new IEA task concerning solar procurement (''the Task'') and to assess whether involvement in the task would be in the interest of the UK active solar heating industry. The project also aimed to assess the importance of large scale solar purchasing to UK active solar heating market development and to evaluate the level of interest in large scale solar purchasing amongst potential large scale purchasers (in particular housing associations and housing developers). A further aim of the project was to consider means of stimulating large scale active solar heating purchasing activity within the UK. (author)

  14. Localization Algorithm Based on a Spring Model (LASM for Large Scale Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Shuai Li

    2008-03-01

    Full Text Available A navigation method for a lunar rover based on large scale wireless sensornetworks is proposed. To obtain high navigation accuracy and large exploration area, highnode localization accuracy and large network scale are required. However, thecomputational and communication complexity and time consumption are greatly increasedwith the increase of the network scales. A localization algorithm based on a spring model(LASM method is proposed to reduce the computational complexity, while maintainingthe localization accuracy in large scale sensor networks. The algorithm simulates thedynamics of physical spring system to estimate the positions of nodes. The sensor nodesare set as particles with masses and connected with neighbor nodes by virtual springs. Thevirtual springs will force the particles move to the original positions, the node positionscorrespondingly, from the randomly set positions. Therefore, a blind node position can bedetermined from the LASM algorithm by calculating the related forces with the neighbornodes. The computational and communication complexity are O(1 for each node, since thenumber of the neighbor nodes does not increase proportionally with the network scale size.Three patches are proposed to avoid local optimization, kick out bad nodes and deal withnode variation. Simulation results show that the computational and communicationcomplexity are almost constant despite of the increase of the network scale size. The time consumption has also been proven to remain almost constant since the calculation steps arealmost unrelated with the network scale size.

  15. Computational biology in the cloud: methods and new insights from computing at scale.

    Science.gov (United States)

    Kasson, Peter M

    2013-01-01

    The past few years have seen both explosions in the size of biological data sets and the proliferation of new, highly flexible on-demand computing capabilities. The sheer amount of information available from genomic and metagenomic sequencing, high-throughput proteomics, experimental and simulation datasets on molecular structure and dynamics affords an opportunity for greatly expanded insight, but it creates new challenges of scale for computation, storage, and interpretation of petascale data. Cloud computing resources have the potential to help solve these problems by offering a utility model of computing and storage: near-unlimited capacity, the ability to burst usage, and cheap and flexible payment models. Effective use of cloud computing on large biological datasets requires dealing with non-trivial problems of scale and robustness, since performance-limiting factors can change substantially when a dataset grows by a factor of 10,000 or more. New computing paradigms are thus often needed. The use of cloud platforms also creates new opportunities to share data, reduce duplication, and to provide easy reproducibility by making the datasets and computational methods easily available.

  16. Robust large-scale parallel nonlinear solvers for simulations.

    Energy Technology Data Exchange (ETDEWEB)

    Bader, Brett William; Pawlowski, Roger Patrick; Kolda, Tamara Gibson (Sandia National Laboratories, Livermore, CA)

    2005-11-01

    This report documents research to develop robust and efficient solution techniques for solving large-scale systems of nonlinear equations. The most widely used method for solving systems of nonlinear equations is Newton's method. While much research has been devoted to augmenting Newton-based solvers (usually with globalization techniques), little has been devoted to exploring the application of different models. Our research has been directed at evaluating techniques using different models than Newton's method: a lower order model, Broyden's method, and a higher order model, the tensor method. We have developed large-scale versions of each of these models and have demonstrated their use in important applications at Sandia. Broyden's method replaces the Jacobian with an approximation, allowing codes that cannot evaluate a Jacobian or have an inaccurate Jacobian to converge to a solution. Limited-memory methods, which have been successful in optimization, allow us to extend this approach to large-scale problems. We compare the robustness and efficiency of Newton's method, modified Newton's method, Jacobian-free Newton-Krylov method, and our limited-memory Broyden method. Comparisons are carried out for large-scale applications of fluid flow simulations and electronic circuit simulations. Results show that, in cases where the Jacobian was inaccurate or could not be computed, Broyden's method converged in some cases where Newton's method failed to converge. We identify conditions where Broyden's method can be more efficient than Newton's method. We also present modifications to a large-scale tensor method, originally proposed by Bouaricha, for greater efficiency, better robustness, and wider applicability. Tensor methods are an alternative to Newton-based methods and are based on computing a step based on a local quadratic model rather than a linear model. The advantage of Bouaricha's method is that it can use any

  17. Challenges in scaling NLO generators to leadership computers

    Science.gov (United States)

    Benjamin, D.; Childers, JT; Hoeche, S.; LeCompte, T.; Uram, T.

    2017-10-01

    Exascale computing resources are roughly a decade away and will be capable of 100 times more computing than current supercomputers. In the last year, Energy Frontier experiments crossed a milestone of 100 million core-hours used at the Argonne Leadership Computing Facility, Oak Ridge Leadership Computing Facility, and NERSC. The Fortran-based leading-order parton generator called Alpgen was successfully scaled to millions of threads to achieve this level of usage on Mira. Sherpa and MadGraph are next-to-leading order generators used heavily by LHC experiments for simulation. Integration times for high-multiplicity or rare processes can take a week or more on standard Grid machines, even using all 16-cores. We will describe our ongoing work to scale the Sherpa generator to thousands of threads on leadership-class machines and reduce run-times to less than a day. This work allows the experiments to leverage large-scale parallel supercomputers for event generation today, freeing tens of millions of grid hours for other work, and paving the way for future applications (simulation, reconstruction) on these and future supercomputers.

  18. Large-scale computation at PSI scientific achievements and future requirements

    International Nuclear Information System (INIS)

    Adelmann, A.; Markushin, V.

    2008-11-01

    ' (SNSP-HPCN) is discussing this complex. Scientific results which are made possible by PSI's engagement at CSCS (named Horizon) are summarised and PSI's future high-performance computing requirements are evaluated. The data collected shows the current situation and a 5 year extrapolation of the users' needs with respect to HPC resources is made. In consequence this report can serve as a basis for future strategic decisions with respect to a non-existing HPC road-map for PSI. PSI's institutional HPC area started hardware-wise approximately in 1999 with the assembly of a 32-processor LINUX cluster called Merlin. Merlin was upgraded several times, lastly in 2007. The Merlin cluster at PSI is used for small scale parallel jobs, and is the only general purpose computing system at PSI. Several dedicated small scale clusters followed the Merlin scheme. Many of the clusters are used to analyse data from experiments at PSI or CERN, because dedicated clusters are most efficient. The intellectual and financial involvement of the procurement (including a machine update in 2007) results in a PSI share of 25 % of the available computing resources at CSCS. The (over) usage of available computing resources by PSI scientists is demonstrated. We actually get more computing cycles than we have paid for. The reason is the fair share policy that is implemented on the Horizon machine. This policy allows us to get cycles, with a low priority, even when our bi-monthly share is used. Five important observations can be drawn from the analysis of the scientific output and the survey of future requirements of main PSI HPC users: (1) High Performance Computing is a main pillar in many important PSI research areas; (2) there is a lack in the order of 10 times the current computing resources (measured in available core-hours per year); (3) there is a trend to use in the order of 600 processors per average production run; (4) the disk and tape storage growth is dramatic; (5) small HPC clusters located

  19. Large-scale computation at PSI scientific achievements and future requirements

    Energy Technology Data Exchange (ETDEWEB)

    Adelmann, A.; Markushin, V

    2008-11-15

    and Networking' (SNSP-HPCN) is discussing this complex. Scientific results which are made possible by PSI's engagement at CSCS (named Horizon) are summarised and PSI's future high-performance computing requirements are evaluated. The data collected shows the current situation and a 5 year extrapolation of the users' needs with respect to HPC resources is made. In consequence this report can serve as a basis for future strategic decisions with respect to a non-existing HPC road-map for PSI. PSI's institutional HPC area started hardware-wise approximately in 1999 with the assembly of a 32-processor LINUX cluster called Merlin. Merlin was upgraded several times, lastly in 2007. The Merlin cluster at PSI is used for small scale parallel jobs, and is the only general purpose computing system at PSI. Several dedicated small scale clusters followed the Merlin scheme. Many of the clusters are used to analyse data from experiments at PSI or CERN, because dedicated clusters are most efficient. The intellectual and financial involvement of the procurement (including a machine update in 2007) results in a PSI share of 25 % of the available computing resources at CSCS. The (over) usage of available computing resources by PSI scientists is demonstrated. We actually get more computing cycles than we have paid for. The reason is the fair share policy that is implemented on the Horizon machine. This policy allows us to get cycles, with a low priority, even when our bi-monthly share is used. Five important observations can be drawn from the analysis of the scientific output and the survey of future requirements of main PSI HPC users: (1) High Performance Computing is a main pillar in many important PSI research areas; (2) there is a lack in the order of 10 times the current computing resources (measured in available core-hours per year); (3) there is a trend to use in the order of 600 processors per average production run; (4) the disk and tape storage growth

  20. Large scale access tests and online interfaces to ATLAS conditions databases

    International Nuclear Information System (INIS)

    Amorim, A; Lopes, L; Pereira, P; Simoes, J; Soloviev, I; Burckhart, D; Schmitt, J V D; Caprini, M; Kolos, S

    2008-01-01

    The access of the ATLAS Trigger and Data Acquisition (TDAQ) system to the ATLAS Conditions Databases sets strong reliability and performance requirements on the database storage and access infrastructures. Several applications were developed to support the integration of Conditions database access with the online services in TDAQ, including the interface to the Information Services (IS) and to the TDAQ Configuration Databases. The information storage requirements were the motivation for the ONline A Synchronous Interface to COOL (ONASIC) from the Information Service (IS) to LCG/COOL databases. ONASIC avoids the possible backpressure from Online Database servers by managing a local cache. In parallel, OKS2COOL was developed to store Configuration Databases into an Offline Database with history record. The DBStressor application was developed to test and stress the access to the Conditions database using the LCG/COOL interface while operating in an integrated way as a TDAQ application. The performance scaling of simultaneous Conditions database read accesses was studied in the context of the ATLAS High Level Trigger large computing farms. A large set of tests were performed involving up to 1000 computing nodes that simultaneously accessed the LCG central database server infrastructure at CERN

  1. TensorFlow: A system for large-scale machine learning

    OpenAIRE

    Abadi, Martín; Barham, Paul; Chen, Jianmin; Chen, Zhifeng; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Irving, Geoffrey; Isard, Michael; Kudlur, Manjunath; Levenberg, Josh; Monga, Rajat; Moore, Sherry; Murray, Derek G.

    2016-01-01

    TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexib...

  2. Large-Scale Optimization for Bayesian Inference in Complex Systems

    Energy Technology Data Exchange (ETDEWEB)

    Willcox, Karen [MIT; Marzouk, Youssef [MIT

    2013-11-12

    The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. The MIT--Sandia component of the SAGUARO Project addressed the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas--Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to-observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as ``reduce then sample'' and ``sample then reduce.'' In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to

  3. Computational challenges of large-scale, long-time, first-principles molecular dynamics

    International Nuclear Information System (INIS)

    Kent, P R C

    2008-01-01

    Plane wave density functional calculations have traditionally been able to use the largest available supercomputing resources. We analyze the scalability of modern projector-augmented wave implementations to identify the challenges in performing molecular dynamics calculations of large systems containing many thousands of electrons. Benchmark calculations on the Cray XT4 demonstrate that global linear-algebra operations are the primary reason for limited parallel scalability. Plane-wave related operations can be made sufficiently scalable. Improving parallel linear-algebra performance is an essential step to reaching longer timescales in future large-scale molecular dynamics calculations

  4. Unraveling The Connectome: Visualizing and Abstracting Large-Scale Connectomics Data

    KAUST Repository

    Al-Awami, Ali K.

    2017-01-01

    -user system seamlessly integrates a diverse set of tools. Our system provides support for the management, provenance, accountability, and auditing of large-scale segmentations. Finally, we present a novel architecture to render very large volumes interactively

  5. Multi-level discriminative dictionary learning with application to large scale image classification.

    Science.gov (United States)

    Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

    2015-10-01

    The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

  6. Large-scale retrieval for medical image analytics: A comprehensive review.

    Science.gov (United States)

    Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

    2018-01-01

    Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Networking for large-scale science: infrastructure, provisioning, transport and application mapping

    International Nuclear Information System (INIS)

    Rao, Nageswara S; Carter, Steven M; Wu Qishi; Wing, William R; Zhu Mengxia; Mezzacappa, Anthony; Veeraraghavan, Malathi; Blondin, John M

    2005-01-01

    Large-scale science computations and experiments require unprecedented network capabilities in the form of large bandwidth and dynamically stable connections to support data transfers, interactive visualizations, and monitoring and steering operations. A number of component technologies dealing with the infrastructure, provisioning, transport and application mappings must be developed and/or optimized to achieve these capabilities. We present a brief account of the following technologies that contribute toward achieving these network capabilities: (a) DOE UltraScienceNet and NSF CHEETAH network testbeds that provide on-demand and scheduled dedicated network connections; (b) experimental results on transport protocols that achieve close to 100% utilization on dedicated 1Gbps wide-area channels; (c) a scheme for optimally mapping a visualization pipeline onto a network to minimize the end-to-end delays; and (d) interconnect configuration and protocols that provides multiple Gbps flows from Cray X1 to external hosts

  8. Networking for large-scale science: infrastructure, provisioning, transport and application mapping

    Energy Technology Data Exchange (ETDEWEB)

    Rao, Nageswara S [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Carter, Steven M [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Wu Qishi [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Wing, William R [Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Zhu Mengxia [Department of Computer Science, Louisiana State University, Baton Rouge, LA 70803 (United States); Mezzacappa, Anthony [Physics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States); Veeraraghavan, Malathi [Department of Computer Science, University of Virginia, Charlottesville, VA 22904 (United States); Blondin, John M [Department of Physics, North Carolina State University, Raleigh, NC 27695 (United States)

    2005-01-01

    Large-scale science computations and experiments require unprecedented network capabilities in the form of large bandwidth and dynamically stable connections to support data transfers, interactive visualizations, and monitoring and steering operations. A number of component technologies dealing with the infrastructure, provisioning, transport and application mappings must be developed and/or optimized to achieve these capabilities. We present a brief account of the following technologies that contribute toward achieving these network capabilities: (a) DOE UltraScienceNet and NSF CHEETAH network testbeds that provide on-demand and scheduled dedicated network connections; (b) experimental results on transport protocols that achieve close to 100% utilization on dedicated 1Gbps wide-area channels; (c) a scheme for optimally mapping a visualization pipeline onto a network to minimize the end-to-end delays; and (d) interconnect configuration and protocols that provides multiple Gbps flows from Cray X1 to external hosts.

  9. Large-Scale Systems Control Design via LMI Optimization

    Czech Academy of Sciences Publication Activity Database

    Rehák, Branislav

    2015-01-01

    Roč. 44, č. 3 (2015), s. 247-253 ISSN 1392-124X Institutional support: RVO:67985556 Keywords : Combinatorial linear matrix inequalities * large-scale system * decentralized control Subject RIV: BC - Control Systems Theory Impact factor: 0.633, year: 2015

  10. Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems

    CERN Document Server

    Kołodziej, Joanna

    2012-01-01

    One of the most challenging issues in modelling today's large-scale computational systems is to effectively manage highly parametrised distributed environments such as computational grids, clouds, ad hoc networks and P2P networks. Next-generation computational grids must provide a wide range of services and high performance computing infrastructures. Various types of information and data processed in the large-scale dynamic grid environment may be incomplete, imprecise, and fragmented, which complicates the specification of proper evaluation criteria and which affects both the availability of resources and the final collective decisions of users. The complexity of grid architectures and grid management may also contribute towards higher energy consumption. All of these issues necessitate the development of intelligent resource management techniques, which are capable of capturing all of this complexity and optimising meaningful metrics for a wide range of grid applications.   This book covers hot topics in t...

  11. Less is more: regularization perspectives on large scale machine learning

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Deep learning based techniques provide a possible solution at the expanse of theoretical guidance and, especially, of computational requirements. It is then a key challenge for large scale machine learning to devise approaches guaranteed to be accurate and yet computationally efficient. In this talk, we will consider a regularization perspectives on machine learning appealing to classical ideas in linear algebra and inverse problems to scale-up dramatically nonparametric methods such as kernel methods, often dismissed because of prohibitive costs. Our analysis derives optimal theoretical guarantees while providing experimental results at par or out-performing state of the art approaches.

  12. Large-scale structures in turbulent Couette flow

    Science.gov (United States)

    Kim, Jung Hoon; Lee, Jae Hwa

    2016-11-01

    Direct numerical simulation of fully developed turbulent Couette flow is performed with a large computational domain in the streamwise and spanwise directions (40 πh and 6 πh) to investigate streamwise-scale growth mechanism of the streamwise velocity fluctuating structures in the core region, where h is the channel half height. It is shown that long streamwise-scale structures (> 3 h) are highly energetic and they contribute to more than 80% of the turbulent kinetic energy and Reynolds shear stress, compared to previous studies in canonical Poiseuille flows. Instantaneous and statistical analysis show that negative-u' structures on the bottom wall in the Couette flow continuously grow in the streamwise direction due to mean shear, and they penetrate to the opposite moving wall. The geometric center of the log layer is observed in the centerline with a dominant outer peak in streamwise spectrum, and the maximum streamwise extent for structure is found in the centerline, similar to previous observation in turbulent Poiseuille flows at high Reynolds number. Further inspection of time-evolving instantaneous fields clearly exhibits that adjacent long structures combine to form a longer structure in the centerline. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2057031).

  13. Evolution of scaling emergence in large-scale spatial epidemic spreading.

    Science.gov (United States)

    Wang, Lin; Li, Xiang; Zhang, Yi-Qing; Zhang, Yan; Zhang, Kan

    2011-01-01

    Zipf's law and Heaps' law are two representatives of the scaling concepts, which play a significant role in the study of complexity science. The coexistence of the Zipf's law and the Heaps' law motivates different understandings on the dependence between these two scalings, which has still hardly been clarified. In this article, we observe an evolution process of the scalings: the Zipf's law and the Heaps' law are naturally shaped to coexist at the initial time, while the crossover comes with the emergence of their inconsistency at the larger time before reaching a stable state, where the Heaps' law still exists with the disappearance of strict Zipf's law. Such findings are illustrated with a scenario of large-scale spatial epidemic spreading, and the empirical results of pandemic disease support a universal analysis of the relation between the two laws regardless of the biological details of disease. Employing the United States domestic air transportation and demographic data to construct a metapopulation model for simulating the pandemic spread at the U.S. country level, we uncover that the broad heterogeneity of the infrastructure plays a key role in the evolution of scaling emergence. The analyses of large-scale spatial epidemic spreading help understand the temporal evolution of scalings, indicating the coexistence of the Zipf's law and the Heaps' law depends on the collective dynamics of epidemic processes, and the heterogeneity of epidemic spread indicates the significance of performing targeted containment strategies at the early time of a pandemic disease.

  14. Problems of large-scale vertically-integrated aquaculture

    Energy Technology Data Exchange (ETDEWEB)

    Webber, H H; Riordan, P F

    1976-01-01

    The problems of vertically-integrated aquaculture are outlined; they are concerned with: species limitations (in the market, biological and technological); site selection, feed, manpower needs, and legal, institutional and financial requirements. The gaps in understanding of, and the constraints limiting, large-scale aquaculture are listed. Future action is recommended with respect to: types and diversity of species to be cultivated, marketing, biotechnology (seed supply, disease control, water quality and concerted effort), siting, feed, manpower, legal and institutional aids (granting of water rights, grants, tax breaks, duty-free imports, etc.), and adequate financing. The last of hard data based on experience suggests that large-scale vertically-integrated aquaculture is a high risk enterprise, and with the high capital investment required, banks and funding institutions are wary of supporting it. Investment in pilot projects is suggested to demonstrate that large-scale aquaculture can be a fully functional and successful business. Construction and operation of such pilot farms is judged to be in the interests of both the public and private sector.

  15. The cognitive dynamics of computer science cost-effective large scale software development

    CERN Document Server

    De Gyurky, Szabolcs Michael; John Wiley & Sons

    2006-01-01

    This book has three major objectives: To propose an ontology for computer software; To provide a methodology for development of large software systems to cost and schedule that is based on the ontology; To offer an alternative vision regarding the development of truly autonomous systems.

  16. Similitude and scaling of large structural elements: Case study

    Directory of Open Access Journals (Sweden)

    M. Shehadeh

    2015-06-01

    Full Text Available Scaled down models are widely used for experimental investigations of large structures due to the limitation in the capacities of testing facilities along with the expenses of the experimentation. The modeling accuracy depends upon the model material properties, fabrication accuracy and loading techniques. In the present work the Buckingham π theorem is used to develop the relations (i.e. geometry, loading and properties between the model and a large structural element as that is present in the huge existing petroleum oil drilling rigs. The model is to be designed, loaded and treated according to a set of similitude requirements that relate the model to the large structural element. Three independent scale factors which represent three fundamental dimensions, namely mass, length and time need to be selected for designing the scaled down model. Numerical prediction of the stress distribution within the model and its elastic deformation under steady loading is to be made. The results are compared with those obtained from the full scale structure numerical computations. The effect of scaled down model size and material on the accuracy of the modeling technique is thoroughly examined.

  17. A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem.

    Science.gov (United States)

    He, Qiang; Hu, Xiangtao; Ren, Hong; Zhang, Hongqi

    2015-11-01

    A novel artificial fish swarm algorithm (NAFSA) is proposed for solving large-scale reliability-redundancy allocation problem (RAP). In NAFSA, the social behaviors of fish swarm are classified in three ways: foraging behavior, reproductive behavior, and random behavior. The foraging behavior designs two position-updating strategies. And, the selection and crossover operators are applied to define the reproductive ability of an artificial fish. For the random behavior, which is essentially a mutation strategy, the basic cloud generator is used as the mutation operator. Finally, numerical results of four benchmark problems and a large-scale RAP are reported and compared. NAFSA shows good performance in terms of computational accuracy and computational efficiency for large scale RAP. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  18. On the use of Cloud Computing and Machine Learning for Large-Scale SAR Science Data Processing and Quality Assessment Analysi

    Science.gov (United States)

    Hua, H.

    2016-12-01

    Geodetic imaging is revolutionizing geophysics, but the scope of discovery has been limited by labor-intensive technological implementation of the analyses. The Advanced Rapid Imaging and Analysis (ARIA) project has proven capability to automate SAR data processing and analysis. Existing and upcoming SAR missions such as Sentinel-1A/B and NISAR are also expected to generate massive amounts of SAR data. This has brought to the forefront the need for analytical tools for SAR quality assessment (QA) on the large volumes of SAR data-a critical step before higher-level time series and velocity products can be reliably generated. Initially leveraging an advanced hybrid-cloud computing science data system for performing large-scale processing, machine learning approaches were augmented for automated analysis of various quality metrics. Machine learning-based user-training of features, cross-validation, prediction models were integrated into our cloud-based science data processing flow to enable large-scale and high-throughput QA analytics for enabling improvements to the production quality of geodetic data products.

  19. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment

    OpenAIRE

    Sanggoo Kang; Kiwon Lee

    2016-01-01

    Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-bas...

  20. Towards an integrated multiscale simulation of turbulent clouds on PetaScale computers

    International Nuclear Information System (INIS)

    Wang Lianping; Ayala, Orlando; Parishani, Hossein; Gao, Guang R; Kambhamettu, Chandra; Li Xiaoming; Rossi, Louis; Orozco, Daniel; Torres, Claudio; Grabowski, Wojciech W; Wyszogrodzki, Andrzej A; Piotrowski, Zbigniew

    2011-01-01

    The development of precipitating warm clouds is affected by several effects of small-scale air turbulence including enhancement of droplet-droplet collision rate by turbulence, entrainment and mixing at the cloud edges, and coupling of mechanical and thermal energies at various scales. Large-scale computation is a viable research tool for quantifying these multiscale processes. Specifically, top-down large-eddy simulations (LES) of shallow convective clouds typically resolve scales of turbulent energy-containing eddies while the effects of turbulent cascade toward viscous dissipation are parameterized. Bottom-up hybrid direct numerical simulations (HDNS) of cloud microphysical processes resolve fully the dissipation-range flow scales but only partially the inertial subrange scales. it is desirable to systematically decrease the grid length in LES and increase the domain size in HDNS so that they can be better integrated to address the full range of scales and their coupling. In this paper, we discuss computational issues and physical modeling questions in expanding the ranges of scales realizable in LES and HDNS, and in bridging LES and HDNS. We review our on-going efforts in transforming our simulation codes towards PetaScale computing, in improving physical representations in LES and HDNS, and in developing better methods to analyze and interpret the simulation results.

  1. Extreme Scale Computing for First-Principles Plasma Physics Research

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Choogn-Seock [Princeton University

    2011-10-12

    World superpowers are in the middle of the “Computnik” race. US Department of Energy (and National Nuclear Security Administration) wishes to launch exascale computer systems into the scientific (and national security) world by 2018. The objective is to solve important scientific problems and to predict the outcomes using the most fundamental scientific laws, which would not be possible otherwise. Being chosen into the next “frontier” group can be of great benefit to a scientific discipline. An extreme scale computer system requires different types of algorithms and programming philosophy from those we have been accustomed to. Only a handful of scientific codes are blessed to be capable of scalable usage of today’s largest computers in operation at petascale (using more than 100,000 cores concurrently). Fortunately, a few magnetic fusion codes are competing well in this race using the “first principles” gyrokinetic equations.These codes are beginning to study the fusion plasma dynamics in full-scale realistic diverted device geometry in natural nonlinear multiscale, including the large scale neoclassical and small scale turbulence physics, but excluding some ultra fast dynamics. In this talk, most of the above mentioned topics will be introduced at executive level. Representative properties of the extreme scale computers, modern programming exercises to take advantage of them, and different philosophies in the data flows and analyses will be presented. Examples of the multi-scale multi-physics scientific discoveries made possible by solving the gyrokinetic equations on extreme scale computers will be described. Future directions into “virtual tokamak experiments” will also be discussed.

  2. Large scale particle image velocimetry with helium filled soap bubbles

    Energy Technology Data Exchange (ETDEWEB)

    Bosbach, Johannes; Kuehn, Matthias; Wagner, Claus [German Aerospace Center (DLR), Institute of Aerodynamics and Flow Technology, Goettingen (Germany)

    2009-03-15

    The application of particle image velocimetry (PIV) to measurement of flows on large scales is a challenging necessity especially for the investigation of convective air flows. Combining helium filled soap bubbles as tracer particles with high power quality switched solid state lasers as light sources allows conducting PIV on scales of the order of several square meters. The technique was applied to mixed convection in a full scale double aisle aircraft cabin mock-up for validation of computational fluid dynamics simulations. (orig.)

  3. Large scale particle image velocimetry with helium filled soap bubbles

    Science.gov (United States)

    Bosbach, Johannes; Kühn, Matthias; Wagner, Claus

    2009-03-01

    The application of Particle Image Velocimetry (PIV) to measurement of flows on large scales is a challenging necessity especially for the investigation of convective air flows. Combining helium filled soap bubbles as tracer particles with high power quality switched solid state lasers as light sources allows conducting PIV on scales of the order of several square meters. The technique was applied to mixed convection in a full scale double aisle aircraft cabin mock-up for validation of Computational Fluid Dynamics simulations.

  4. Towards Process Support for Migrating Applications to Cloud Computing

    DEFF Research Database (Denmark)

    Chauhan, Muhammad Aufeef; Babar, Muhammad Ali

    2012-01-01

    Cloud computing is an active area of research for industry and academia. There are a large number of organizations providing cloud computing infrastructure and services. In order to utilize these infrastructure resources and services, existing applications need to be migrated to clouds. However...... for supporting migration to cloud computing based on our experiences from migrating an Open Source System (OSS), Hackystat, to two different cloud computing platforms. We explained the process by performing a comparative analysis of our efforts to migrate Hackystate to Amazon Web Services and Google App Engine....... We also report the potential challenges, suitable solutions, and lesson learned to support the presented process framework. We expect that the reported experiences can serve guidelines for those who intend to migrate software applications to cloud computing....

  5. Integration, Provenance, and Temporal Queries for Large-Scale Knowledge Bases

    OpenAIRE

    Gao, Shi

    2016-01-01

    Knowledge bases that summarize web information in RDF triples deliver many benefits, including support for natural language question answering and powerful structured queries that extract encyclopedic knowledge via SPARQL. Large scale knowledge bases grow rapidly in terms of scale and significance, and undergo frequent changes in both schema and content. Two critical problems have thus emerged: (i) how to support temporal queries that explore the history of knowledge bases or flash-back to th...

  6. Monitoring and Information Fusion for Search and Rescue Operations in Large-Scale Disasters

    National Research Council Canada - National Science Library

    Nardi, Daniele

    2002-01-01

    ... for information fusion with application to search-and-rescue and large scale disaster relief. The objective is to develop and to deploy tools to support the monitoring activities in an intervention caused by a large-scale disaster...

  7. Enabling Wide-Scale Computer Science Education through Improved Automated Assessment Tools

    Science.gov (United States)

    Boe, Bryce A.

    There is a proliferating demand for newly trained computer scientists as the number of computer science related jobs continues to increase. University programs will only be able to train enough new computer scientists to meet this demand when two things happen: when there are more primary and secondary school students interested in computer science, and when university departments have the resources to handle the resulting increase in enrollment. To meet these goals, significant effort is being made to both incorporate computational thinking into existing primary school education, and to support larger university computer science class sizes. We contribute to this effort through the creation and use of improved automated assessment tools. To enable wide-scale computer science education we do two things. First, we create a framework called Hairball to support the static analysis of Scratch programs targeted for fourth, fifth, and sixth grade students. Scratch is a popular building-block language utilized to pique interest in and teach the basics of computer science. We observe that Hairball allows for rapid curriculum alterations and thus contributes to wide-scale deployment of computer science curriculum. Second, we create a real-time feedback and assessment system utilized in university computer science classes to provide better feedback to students while reducing assessment time. Insights from our analysis of student submission data show that modifications to the system configuration support the way students learn and progress through course material, making it possible for instructors to tailor assignments to optimize learning in growing computer science classes.

  8. Development of Best Practices for Large-scale Data Management Infrastructure

    NARCIS (Netherlands)

    S. Stadtmüller; H.F. Mühleisen (Hannes); C. Bizer; M.L. Kersten (Martin); J.A. de Rijke (Arjen); F.E. Groffen (Fabian); Y. Zhang (Ying); G. Ladwig; A. Harth; M Trampus

    2012-01-01

    htmlabstractThe amount of available data for processing is constantly increasing and becomes more diverse. We collect our experiences on deploying large-scale data management tools on local-area clusters or cloud infrastructures and provide guidance to use these computing and storage

  9. First Mile Challenges for Large-Scale IoT

    KAUST Repository

    Bader, Ahmed

    2017-03-16

    The Internet of Things is large-scale by nature. This is not only manifested by the large number of connected devices, but also by the sheer scale of spatial traffic intensity that must be accommodated, primarily in the uplink direction. To that end, cellular networks are indeed a strong first mile candidate to accommodate the data tsunami to be generated by the IoT. However, IoT devices are required in the cellular paradigm to undergo random access procedures as a precursor to resource allocation. Such procedures impose a major bottleneck that hinders cellular networks\\' ability to support large-scale IoT. In this article, we shed light on the random access dilemma and present a case study based on experimental data as well as system-level simulations. Accordingly, a case is built for the latent need to revisit random access procedures. A call for action is motivated by listing a few potential remedies and recommendations.

  10. Energy transfers in large-scale and small-scale dynamos

    Science.gov (United States)

    Samtaney, Ravi; Kumar, Rohit; Verma, Mahendra

    2015-11-01

    We present the energy transfers, mainly energy fluxes and shell-to-shell energy transfers in small-scale dynamo (SSD) and large-scale dynamo (LSD) using numerical simulations of MHD turbulence for Pm = 20 (SSD) and for Pm = 0.2 on 10243 grid. For SSD, we demonstrate that the magnetic energy growth is caused by nonlocal energy transfers from the large-scale or forcing-scale velocity field to small-scale magnetic field. The peak of these energy transfers move towards lower wavenumbers as dynamo evolves, which is the reason for the growth of the magnetic fields at the large scales. The energy transfers U2U (velocity to velocity) and B2B (magnetic to magnetic) are forward and local. For LSD, we show that the magnetic energy growth takes place via energy transfers from large-scale velocity field to large-scale magnetic field. We observe forward U2U and B2B energy flux, similar to SSD.

  11. Large scale electronic structure calculations in the study of the condensed phase

    NARCIS (Netherlands)

    van Dam, H.J.J.; Guest, M.F.; Sherwood, P.; Thomas, J.M.H.; van Lenthe, J.H.; van Lingen, J.N.J.; Bailey, C.L.; Bush, I.J.

    2006-01-01

    We consider the role that large-scale electronic structure computations can now play in the modelling of the condensed phase. To structure our analysis, we consider four distict ways in which today's scientific targets can be re-scoped to take advantage of advances in computing resources: 1. time to

  12. Large-scale simulations of plastic neural networks on neuromorphic hardware

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-04-01

    Full Text Available SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 20000 neurons and 51200000 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.

  13. Rucio - The next generation of large scale distributed system for ATLAS Data Management

    Science.gov (United States)

    Garonne, V.; Vigne, R.; Stewart, G.; Barisits, M.; eermann, T. B.; Lassnig, M.; Serfon, C.; Goossens, L.; Nairz, A.; Atlas Collaboration

    2014-06-01

    Rucio is the next-generation Distributed Data Management (DDM) system benefiting from recent advances in cloud and "Big Data" computing to address HEP experiments scaling requirements. Rucio is an evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 140 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio will deal with these issues by relying on a conceptual data model and new technology to ensure system scalability, address new user requirements and employ new automation framework to reduce operational overheads. We present the key concepts of Rucio, including its data organization/representation and a model of how to manage central group and user activities. The Rucio design, and the technology it employs, is described, specifically looking at its RESTful architecture and the various software components it uses. We show also the performance of the system.

  14. Large scale CMB anomalies from thawing cosmic strings

    Energy Technology Data Exchange (ETDEWEB)

    Ringeval, Christophe [Centre for Cosmology, Particle Physics and Phenomenology, Institute of Mathematics and Physics, Louvain University, 2 Chemin du Cyclotron, 1348 Louvain-la-Neuve (Belgium); Yamauchi, Daisuke; Yokoyama, Jun' ichi [Research Center for the Early Universe (RESCEU), Graduate School of Science, The University of Tokyo, Tokyo 113-0033 (Japan); Bouchet, François R., E-mail: christophe.ringeval@uclouvain.be, E-mail: yamauchi@resceu.s.u-tokyo.ac.jp, E-mail: yokoyama@resceu.s.u-tokyo.ac.jp, E-mail: bouchet@iap.fr [Institut d' Astrophysique de Paris, UMR 7095-CNRS, Université Pierre et Marie Curie, 98bis boulevard Arago, 75014 Paris (France)

    2016-02-01

    Cosmic strings formed during inflation are expected to be either diluted over super-Hubble distances, i.e., invisible today, or to have crossed our past light cone very recently. We discuss the latter situation in which a few strings imprint their signature in the Cosmic Microwave Background (CMB) Anisotropies after recombination. Being almost frozen in the Hubble flow, these strings are quasi static and evade almost all of the previously derived constraints on their tension while being able to source large scale anisotropies in the CMB sky. Using a local variance estimator on thousand of numerically simulated Nambu-Goto all sky maps, we compute the expected signal and show that it can mimic a dipole modulation at large angular scales while being negligible at small angles. Interestingly, such a scenario generically produces one cold spot from the thawing of a cosmic string loop. Mixed with anisotropies of inflationary origin, we find that a few strings of tension GU = O(1) × 10{sup −6} match the amplitude of the dipole modulation reported in the Planck satellite measurements and could be at the origin of other large scale anomalies.

  15. Algebraic mesh generation for large scale viscous-compressible aerodynamic simulation

    International Nuclear Information System (INIS)

    Smith, R.E.

    1984-01-01

    Viscous-compressible aerodynamic simulation is the numerical solution of the compressible Navier-Stokes equations and associated boundary conditions. Boundary-fitted coordinate systems are well suited for the application of finite difference techniques to the Navier-Stokes equations. An algebraic approach to boundary-fitted coordinate systems is one where an explicit functional relation describes a mesh on which a solution is obtained. This approach has the advantage of rapid-precise mesh control. The basic mathematical structure of three algebraic mesh generation techniques is described. They are transfinite interpolation, the multi-surface method, and the two-boundary technique. The Navier-Stokes equations are transformed to a computational coordinate system where boundary-fitted coordinates can be applied. Large-scale computation implies that there is a large number of mesh points in the coordinate system. Computation of viscous compressible flow using boundary-fitted coordinate systems and the application of this computational philosophy on a vector computer are presented

  16. Some ecological guidelines for large-scale biomass plantations

    Energy Technology Data Exchange (ETDEWEB)

    Hoffman, W.; Cook, J.H.; Beyea, J. [National Audubon Society, Tavernier, FL (United States)

    1993-12-31

    The National Audubon Society sees biomass as an appropriate and necessary source of energy to help replace fossil fuels in the near future, but is concerned that large-scale biomass plantations could displace significant natural vegetation and wildlife habitat, and reduce national and global biodiversity. We support the development of an industry large enough to provide significant portions of our energy budget, but we see a critical need to ensure that plantations are designed and sited in ways that minimize ecological disruption, or even provide environmental benefits. We have been studying the habitat value of intensively managed short-rotation tree plantations. Our results show that these plantations support large populations of some birds, but not all of the species using the surrounding landscape, and indicate that their value as habitat can be increased greatly by including small areas of mature trees within them. We believe short-rotation plantations can benefit regional biodiversity if they can be deployed as buffers for natural forests, or as corridors connecting forest tracts. To realize these benefits, and to avoid habitat degradation, regional biomass plantation complexes (e.g., the plantations supplying all the fuel for a powerplant) need to be planned, sited, and developed as large-scale units in the context of the regional landscape mosaic.

  17. Computer-supported quality control in X-ray diagnosis

    International Nuclear Information System (INIS)

    Maier, W.; Klotz, E.

    1989-01-01

    Quality control of X-ray facilities in radiological departments of large hospitals is possible only if the instrumentation used for measurements is interfaced to a computer. The central computer helps to organize the measurements as well as analyse and record the results. It can also be connected to a densitometer and camera for evaluating radiographs of test devices. Other quality control tests are supported by a mobile station with equipment for non-invasive dosimetry measurements. Experience with a computer-supported system in quality control of film and film processing is described and the evaluation methods of ANSI and the German industrial standard DIN are compared. The disadvantage of these methods is the exclusion of film quality parameters, which can make processing control almost worthless. (author)

  18. Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

    Science.gov (United States)

    Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

    2014-01-01

    In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

  19. Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python

    Directory of Open Access Journals (Sweden)

    Nicolas eRey-Villamizar

    2014-04-01

    Full Text Available In this article, we describe use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis task, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral brain tissue images surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels, 6,000$times$10,000$times$500 voxels with 16 bits/voxel, implying image sizes exceeding 250GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analytics for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment consisting. Our Python script enables efficient data storage and movement between compute and storage servers, logging all processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

  20. Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction

    International Nuclear Information System (INIS)

    Yang, C L; Wei, H Y; Soleimani, M; Adler, A

    2013-01-01

    Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current–voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results. (paper)

  1. Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction.

    Science.gov (United States)

    Yang, C L; Wei, H Y; Adler, A; Soleimani, M

    2013-06-01

    Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

  2. Collaborative mining and interpretation of large-scale data for biomedical research insights.

    Directory of Open Access Journals (Sweden)

    Georgia Tsiliki

    Full Text Available Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to efficiently and effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. In line with related research directives revealing that, in spite of the recent advances in data mining and computational analysis, humans can easily detect patterns which computer algorithms may have difficulty in finding, this paper reports on the practical use of an innovative web-based collaboration support platform in a biomedical research context. Arguing that dealing with data-intensive and cognitively complex settings is not a technical problem alone, the proposed platform adopts a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. User experience shows that the platform enables more informed and quicker decisions, by displaying the aggregated information according to their needs, while also exploiting the associated human intelligence.

  3. Large scale solar district heating. Evaluation, modelling and designing - Appendices

    Energy Technology Data Exchange (ETDEWEB)

    Heller, A.

    2000-07-01

    The appendices present the following: A) Cad-drawing of the Marstal CSHP design. B) Key values - large-scale solar heating in Denmark. C) Monitoring - a system description. D) WMO-classification of pyranometers (solarimeters). E) The computer simulation model in TRNSYS. F) Selected papers from the author. (EHS)

  4. Dynamic Modeling, Optimization, and Advanced Control for Large Scale Biorefineries

    DEFF Research Database (Denmark)

    Prunescu, Remus Mihail

    with a complex conversion route. Computational fluid dynamics is used to model transport phenomena in large reactors capturing tank profiles, and delays due to plug flows. This work publishes for the first time demonstration scale real data for validation showing that the model library is suitable...

  5. Enabling systematic, harmonised and large-scale biofilms data computation: the Biofilms Experiment Workbench.

    Science.gov (United States)

    Pérez-Rodríguez, Gael; Glez-Peña, Daniel; Azevedo, Nuno F; Pereira, Maria Olívia; Fdez-Riverola, Florentino; Lourenço, Anália

    2015-03-01

    Biofilms are receiving increasing attention from the biomedical community. Biofilm-like growth within human body is considered one of the key microbial strategies to augment resistance and persistence during infectious processes. The Biofilms Experiment Workbench is a novel software workbench for the operation and analysis of biofilms experimental data. The goal is to promote the interchange and comparison of data among laboratories, providing systematic, harmonised and large-scale data computation. The workbench was developed with AIBench, an open-source Java desktop application framework for scientific software development in the domain of translational biomedicine. Implementation favours free and open-source third-parties, such as the R statistical package, and reaches for the Web services of the BiofOmics database to enable public experiment deposition. First, we summarise the novel, free, open, XML-based interchange format for encoding biofilms experimental data. Then, we describe the execution of common scenarios of operation with the new workbench, such as the creation of new experiments, the importation of data from Excel spreadsheets, the computation of analytical results, the on-demand and highly customised construction of Web publishable reports, and the comparison of results between laboratories. A considerable and varied amount of biofilms data is being generated, and there is a critical need to develop bioinformatics tools that expedite the interchange and comparison of microbiological and clinical results among laboratories. We propose a simple, open-source software infrastructure which is effective, extensible and easy to understand. The workbench is freely available for non-commercial use at http://sing.ei.uvigo.es/bew under LGPL license. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  6. Towards a Database System for Large-scale Analytics on Strings

    KAUST Repository

    Sahli, Majed A.

    2015-07-23

    Recent technological advances are causing an explosion in the production of sequential data. Biological sequences, web logs and time series are represented as strings. Currently, strings are stored, managed and queried in an ad-hoc fashion because they lack a standardized data model and query language. String queries are computationally demanding, especially when strings are long and numerous. Existing approaches cannot handle the growing number of strings produced by environmental, healthcare, bioinformatic, and space applications. There is a trade- off between performing analytics efficiently and scaling to thousands of cores to finish in reasonable times. In this thesis, we introduce a data model that unifies the input and output representations of core string operations. We define a declarative query language for strings where operators can be pipelined to form complex queries. A rich set of core string operators is described to support string analytics. We then demonstrate a database system for string analytics based on our model and query language. In particular, we propose the use of a novel data structure augmented by efficient parallel computation to strike a balance between preprocessing overheads and query execution times. Next, we delve into repeated motifs extraction as a core string operation for large-scale string analytics. Motifs are frequent patterns used, for example, to identify biological functionality, periodic trends, or malicious activities. Statistical approaches are fast but inexact while combinatorial methods are sound but slow. We introduce ACME, a combinatorial repeated motifs extractor. We study the spatial and temporal locality of motif extraction and devise a cache-aware search space traversal technique. ACME is the only method that scales to gigabyte- long strings, handles large alphabets, and supports interesting motif types with minimal overhead. While ACME is cache-efficient, it is limited by being serial. We devise a lightweight

  7. Collaborative Visualization for Large-Scale Accelerator Electromagnetic Modeling (Final Report)

    International Nuclear Information System (INIS)

    Schroeder, William J.

    2011-01-01

    This report contains the comprehensive summary of the work performed on the SBIR Phase II, Collaborative Visualization for Large-Scale Accelerator Electromagnetic Modeling at Kitware Inc. in collaboration with Stanford Linear Accelerator Center (SLAC). The goal of the work was to develop collaborative visualization tools for large-scale data as illustrated in the figure below. The solutions we proposed address the typical problems faced by geographicallyand organizationally-separated research and engineering teams, who produce large data (either through simulation or experimental measurement) and wish to work together to analyze and understand their data. Because the data is large, we expect that it cannot be easily transported to each team member's work site, and that the visualization server must reside near the data. Further, we also expect that each work site has heterogeneous resources: some with large computing clients, tiled (or large) displays and high bandwidth; others sites as simple as a team member on a laptop computer. Our solution is based on the open-source, widely used ParaView large-data visualization application. We extended this tool to support multiple collaborative clients who may locally visualize data, and then periodically rejoin and synchronize with the group to discuss their findings. Options for managing session control, adding annotation, and defining the visualization pipeline, among others, were incorporated. We also developed and deployed a Web visualization framework based on ParaView that enables the Web browser to act as a participating client in a collaborative session. The ParaView Web Visualization framework leverages various Web technologies including WebGL, JavaScript, Java and Flash to enable interactive 3D visualization over the web using ParaView as the visualization server. We steered the development of this technology by teaming with the SLAC National Accelerator Laboratory. SLAC has a computationally-intensive problem

  8. Collaborative Visualization for Large-Scale Accelerator Electromagnetic Modeling (Final Report)

    Energy Technology Data Exchange (ETDEWEB)

    William J. Schroeder

    2011-11-13

    This report contains the comprehensive summary of the work performed on the SBIR Phase II, Collaborative Visualization for Large-Scale Accelerator Electromagnetic Modeling at Kitware Inc. in collaboration with Stanford Linear Accelerator Center (SLAC). The goal of the work was to develop collaborative visualization tools for large-scale data as illustrated in the figure below. The solutions we proposed address the typical problems faced by geographicallyand organizationally-separated research and engineering teams, who produce large data (either through simulation or experimental measurement) and wish to work together to analyze and understand their data. Because the data is large, we expect that it cannot be easily transported to each team member's work site, and that the visualization server must reside near the data. Further, we also expect that each work site has heterogeneous resources: some with large computing clients, tiled (or large) displays and high bandwidth; others sites as simple as a team member on a laptop computer. Our solution is based on the open-source, widely used ParaView large-data visualization application. We extended this tool to support multiple collaborative clients who may locally visualize data, and then periodically rejoin and synchronize with the group to discuss their findings. Options for managing session control, adding annotation, and defining the visualization pipeline, among others, were incorporated. We also developed and deployed a Web visualization framework based on ParaView that enables the Web browser to act as a participating client in a collaborative session. The ParaView Web Visualization framework leverages various Web technologies including WebGL, JavaScript, Java and Flash to enable interactive 3D visualization over the web using ParaView as the visualization server. We steered the development of this technology by teaming with the SLAC National Accelerator Laboratory. SLAC has a computationally

  9. Large-scale data analytics

    CERN Document Server

    Gkoulalas-Divanis, Aris

    2014-01-01

    Provides cutting-edge research in large-scale data analytics from diverse scientific areas Surveys varied subject areas and reports on individual results of research in the field Shares many tips and insights into large-scale data analytics from authors and editors with long-term experience and specialization in the field

  10. Computer simulations for the nano-scale

    International Nuclear Information System (INIS)

    Stich, I.

    2007-01-01

    A review of methods for computations for the nano-scale is presented. The paper should provide a convenient starting point into computations for the nano-scale as well as a more in depth presentation for those already working in the field of atomic/molecular-scale modeling. The argument is divided in chapters covering the methods for description of the (i) electrons, (ii) ions, and (iii) techniques for efficient solving of the underlying equations. A fairly broad view is taken covering the Hartree-Fock approximation, density functional techniques and quantum Monte-Carlo techniques for electrons. The customary quantum chemistry methods, such as post Hartree-Fock techniques, are only briefly mentioned. Description of both classical and quantum ions is presented. The techniques cover Ehrenfest, Born-Oppenheimer, and Car-Parrinello dynamics. The strong and weak points of both principal and technical nature are analyzed. In the second part we introduce a number of applications to demonstrate the different approximations and techniques introduced in the first part. They cover a wide range of applications such as non-simple liquids, surfaces, molecule-surface interactions, applications in nano technology, etc. These more in depth presentations, while certainly not exhaustive, should provide information on technical aspects of the simulations, typical parameters used, and ways of analysis of the huge amounts of data generated in these large-scale supercomputer simulations. (author)

  11. Enabling High Performance Large Scale Dense Problems through KBLAS

    KAUST Repository

    Abdelfattah, Ahmad

    2014-05-04

    KBLAS (KAUST BLAS) is a small library that provides highly optimized BLAS routines on systems accelerated with GPUs. KBLAS is entirely written in CUDA C, and targets NVIDIA GPUs with compute capability 2.0 (Fermi) or higher. The current focus is on level-2 BLAS routines, namely the general matrix vector multiplication (GEMV) kernel, and the symmetric/hermitian matrix vector multiplication (SYMV/HEMV) kernel. KBLAS provides these two kernels in all four precisions (s, d, c, and z), with support to multi-GPU systems. Through advanced optimization techniques that target latency hiding and pushing memory bandwidth to the limit, KBLAS outperforms state-of-the-art kernels by 20-90% improvement. Competitors include CUBLAS-5.5, MAGMABLAS-1.4.0, and CULAR17. The SYMV/HEMV kernel from KBLAS has been adopted by NVIDIA, and should appear in CUBLAS-6.0. KBLAS has been used in large scale simulations of multi-object adaptive optics.

  12. Internationalization Measures in Large Scale Research Projects

    Science.gov (United States)

    Soeding, Emanuel; Smith, Nancy

    2017-04-01

    Internationalization measures in Large Scale Research Projects Large scale research projects (LSRP) often serve as flagships used by universities or research institutions to demonstrate their performance and capability to stakeholders and other interested parties. As the global competition among universities for the recruitment of the brightest brains has increased, effective internationalization measures have become hot topics for universities and LSRP alike. Nevertheless, most projects and universities are challenged with little experience on how to conduct these measures and make internationalization an cost efficient and useful activity. Furthermore, those undertakings permanently have to be justified with the Project PIs as important, valuable tools to improve the capacity of the project and the research location. There are a variety of measures, suited to support universities in international recruitment. These include e.g. institutional partnerships, research marketing, a welcome culture, support for science mobility and an effective alumni strategy. These activities, although often conducted by different university entities, are interlocked and can be very powerful measures if interfaced in an effective way. On this poster we display a number of internationalization measures for various target groups, identify interfaces between project management, university administration, researchers and international partners to work together, exchange information and improve processes in order to be able to recruit, support and keep the brightest heads to your project.

  13. VisualRank: applying PageRank to large-scale image search.

    Science.gov (United States)

    Jing, Yushi; Baluja, Shumeet

    2008-11-01

    Because of the relative ease in understanding and processing text, commercial image-search systems often rely on techniques that are largely indistinguishable from text-search. Recently, academic studies have demonstrated the effectiveness of employing image-based features to provide alternative or additional signals. However, it remains uncertain whether such techniques will generalize to a large number of popular web queries, and whether the potential improvement to search quality warrants the additional computational cost. In this work, we cast the image-ranking problem into the task of identifying "authority" nodes on an inferred visual similarity graph and propose VisualRank to analyze the visual link structures among images. The images found to be "authorities" are chosen as those that answer the image-queries well. To understand the performance of such an approach in a real system, we conducted a series of large-scale experiments based on the task of retrieving images for 2000 of the most popular products queries. Our experimental results show significant improvement, in terms of user satisfaction and relevancy, in comparison to the most recent Google Image Search results. Maintaining modest computational cost is vital to ensuring that this procedure can be used in practice; we describe the techniques required to make this system practical for large scale deployment in commercial search engines.

  14. Large Scale Landform Mapping Using Lidar DEM

    Directory of Open Access Journals (Sweden)

    Türkay Gökgöz

    2015-08-01

    Full Text Available In this study, LIDAR DEM data was used to obtain a primary landform map in accordance with a well-known methodology. This primary landform map was generalized using the Focal Statistics tool (Majority, considering the minimum area condition in cartographic generalization in order to obtain landform maps at 1:1000 and 1:5000 scales. Both the primary and the generalized landform maps were verified visually with hillshaded DEM and an orthophoto. As a result, these maps provide satisfactory visuals of the landforms. In order to show the effect of generalization, the area of each landform in both the primary and the generalized maps was computed. Consequently, landform maps at large scales could be obtained with the proposed methodology, including generalization using LIDAR DEM.

  15. Large-scale grid management

    International Nuclear Information System (INIS)

    Langdal, Bjoern Inge; Eggen, Arnt Ove

    2003-01-01

    The network companies in the Norwegian electricity industry now have to establish a large-scale network management, a concept essentially characterized by (1) broader focus (Broad Band, Multi Utility,...) and (2) bigger units with large networks and more customers. Research done by SINTEF Energy Research shows so far that the approaches within large-scale network management may be structured according to three main challenges: centralization, decentralization and out sourcing. The article is part of a planned series

  16. A testing facility for large scale models at 100 bar and 3000C to 10000C

    International Nuclear Information System (INIS)

    Zemann, H.

    1978-07-01

    A testing facility for large scale model tests is in construction under support of the Austrian Industry. It will contain a Prestressed Concrete Pressure Vessel (PCPV) with hot linear (300 0 C at 100 bar), an electrical heating system (1.2 MW, 1000 0 C), a gas supply system, and a cooling system for the testing space. The components themselves are models for advanced high temperature applications. The first main component which was tested successfully was the PCPV. Basic investigation of the building materials, improvements of concrete gauges, large scale model tests and measurements within the structural concrete and on the liner from the beginning of construction during the period of prestressing, the period of stabilization and the final pressurizing tests have been made. On the basis of these investigations a computer controlled safety surveillance system for long term high pressure, high temperature tests has been developed. (author)

  17. Large-Scale Compute-Intensive Analysis via a Combined In-situ and Co-scheduling Workflow Approach

    Energy Technology Data Exchange (ETDEWEB)

    Messer, Bronson [ORNL; Sewell, Christopher [Los Alamos National Laboratory (LANL); Heitmann, Katrin [ORNL; Finkel, Dr. Hal J [Argonne National Laboratory (ANL); Fasel, Patricia [Los Alamos National Laboratory (LANL); Zagaris, George [Lawrence Livermore National Laboratory (LLNL); Pope, Adrian [Los Alamos National Laboratory (LANL); Habib, Salman [ORNL; Parete-Koon, Suzanne T [ORNL

    2015-01-01

    Large-scale simulations can produce tens of terabytes of data per analysis cycle, complicating and limiting the efficiency of workflows. Traditionally, outputs are stored on the file system and analyzed in post-processing. With the rapidly increasing size and complexity of simulations, this approach faces an uncertain future. Trending techniques consist of performing the analysis in situ, utilizing the same resources as the simulation, and/or off-loading subsets of the data to a compute-intensive analysis system. We introduce an analysis framework developed for HACC, a cosmological N-body code, that uses both in situ and co-scheduling approaches for handling Petabyte-size outputs. An initial in situ step is used to reduce the amount of data to be analyzed, and to separate out the data-intensive tasks handled off-line. The analysis routines are implemented using the PISTON/VTK-m framework, allowing a single implementation of an algorithm that simultaneously targets a variety of GPU, multi-core, and many-core architectures.

  18. Scaling predictive modeling in drug development with cloud computing.

    Science.gov (United States)

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

    2015-01-26

    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

  19. Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

    KAUST Repository

    Zhang, Bohai; Sang, Huiyan; Huang, Jianhua Z.

    2014-01-01

    of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov

  20. Parallel clustering algorithm for large-scale biological data sets.

    Science.gov (United States)

    Wang, Minchao; Zhang, Wu; Ding, Wang; Dai, Dongbo; Zhang, Huiran; Xie, Hao; Chen, Luonan; Guo, Yike; Xie, Jiang

    2014-01-01

    Recent explosion of biological data brings a great challenge for the traditional clustering algorithms. With increasing scale of data sets, much larger memory and longer runtime are required for the cluster identification problems. The affinity propagation algorithm outperforms many other classical clustering algorithms and is widely applied into the biological researches. However, the time and space complexity become a great bottleneck when handling the large-scale data sets. Moreover, the similarity matrix, whose constructing procedure takes long runtime, is required before running the affinity propagation algorithm, since the algorithm clusters data sets based on the similarities between data pairs. Two types of parallel architectures are proposed in this paper to accelerate the similarity matrix constructing procedure and the affinity propagation algorithm. The memory-shared architecture is used to construct the similarity matrix, and the distributed system is taken for the affinity propagation algorithm, because of its large memory size and great computing capacity. An appropriate way of data partition and reduction is designed in our method, in order to minimize the global communication cost among processes. A speedup of 100 is gained with 128 cores. The runtime is reduced from serval hours to a few seconds, which indicates that parallel algorithm is capable of handling large-scale data sets effectively. The parallel affinity propagation also achieves a good performance when clustering large-scale gene data (microarray) and detecting families in large protein superfamilies.

  1. Extraction of drainage networks from large terrain datasets using high throughput computing

    Science.gov (United States)

    Gong, Jianya; Xie, Jibo

    2009-02-01

    Advanced digital photogrammetry and remote sensing technology produces large terrain datasets (LTD). How to process and use these LTD has become a big challenge for GIS users. Extracting drainage networks, which are basic for hydrological applications, from LTD is one of the typical applications of digital terrain analysis (DTA) in geographical information applications. Existing serial drainage algorithms cannot deal with large data volumes in a timely fashion, and few GIS platforms can process LTD beyond the GB size. High throughput computing (HTC), a distributed parallel computing mode, is proposed to improve the efficiency of drainage networks extraction from LTD. Drainage network extraction using HTC involves two key issues: (1) how to decompose the large DEM datasets into independent computing units and (2) how to merge the separate outputs into a final result. A new decomposition method is presented in which the large datasets are partitioned into independent computing units using natural watershed boundaries instead of using regular 1-dimensional (strip-wise) and 2-dimensional (block-wise) decomposition. Because the distribution of drainage networks is strongly related to watershed boundaries, the new decomposition method is more effective and natural. The method to extract natural watershed boundaries was improved by using multi-scale DEMs instead of single-scale DEMs. A HTC environment is employed to test the proposed methods with real datasets.

  2. RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

    Science.gov (United States)

    Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan

    2017-08-04

    This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

  3. Moditored unsaturated soil transport processes as a support for large scale soil and water management

    Science.gov (United States)

    Vanclooster, Marnik

    2010-05-01

    The current societal demand for sustainable soil and water management is very large. The drivers of global and climate change exert many pressures on the soil and water ecosystems, endangering appropriate ecosystem functioning. The unsaturated soil transport processes play a key role in soil-water system functioning as it controls the fluxes of water and nutrients from the soil to plants (the pedo-biosphere link), the infiltration flux of precipitated water to groundwater and the evaporative flux, and hence the feed back from the soil to the climate system. Yet, unsaturated soil transport processes are difficult to quantify since they are affected by huge variability of the governing properties at different space-time scales and the intrinsic non-linearity of the transport processes. The incompatibility of the scales between the scale at which processes reasonably can be characterized, the scale at which the theoretical process correctly can be described and the scale at which the soil and water system need to be managed, calls for further development of scaling procedures in unsaturated zone science. It also calls for a better integration of theoretical and modelling approaches to elucidate transport processes at the appropriate scales, compatible with the sustainable soil and water management objective. Moditoring science, i.e the interdisciplinary research domain where modelling and monitoring science are linked, is currently evolving significantly in the unsaturated zone hydrology area. In this presentation, a review of current moditoring strategies/techniques will be given and illustrated for solving large scale soil and water management problems. This will also allow identifying research needs in the interdisciplinary domain of modelling and monitoring and to improve the integration of unsaturated zone science in solving soil and water management issues. A focus will be given on examples of large scale soil and water management problems in Europe.

  4. Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction

    Science.gov (United States)

    Qiao, Baijie; Zhang, Xingwu; Gao, Jiawei; Liu, Ruonan; Chen, Xuefeng

    2017-01-01

    Most previous regularization methods for solving the inverse problem of force reconstruction are to minimize the l2-norm of the desired force. However, these traditional regularization methods such as Tikhonov regularization and truncated singular value decomposition, commonly fail to solve the large-scale ill-posed inverse problem in moderate computational cost. In this paper, taking into account the sparse characteristic of impact force, the idea of sparse deconvolution is first introduced to the field of impact force reconstruction and a general sparse deconvolution model of impact force is constructed. Second, a novel impact force reconstruction method based on the primal-dual interior point method (PDIPM) is proposed to solve such a large-scale sparse deconvolution model, where minimizing the l2-norm is replaced by minimizing the l1-norm. Meanwhile, the preconditioned conjugate gradient algorithm is used to compute the search direction of PDIPM with high computational efficiency. Finally, two experiments including the small-scale or medium-scale single impact force reconstruction and the relatively large-scale consecutive impact force reconstruction are conducted on a composite wind turbine blade and a shell structure to illustrate the advantage of PDIPM. Compared with Tikhonov regularization, PDIPM is more efficient, accurate and robust whether in the single impact force reconstruction or in the consecutive impact force reconstruction.

  5. BILGO: Bilateral greedy optimization for large scale semidefinite programming

    KAUST Repository

    Hao, Zhifeng; Yuan, Ganzhao; Ghanem, Bernard

    2013-01-01

    Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.

  6. BILGO: Bilateral greedy optimization for large scale semidefinite programming

    KAUST Repository

    Hao, Zhifeng

    2013-10-03

    Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.

  7. Large scale statistics for computational verification of grain growth simulations with experiments

    International Nuclear Information System (INIS)

    Demirel, Melik C.; Kuprat, Andrew P.; George, Denise C.; Straub, G.K.; Misra, Amit; Alexander, Kathleen B.; Rollett, Anthony D.

    2002-01-01

    It is known that by controlling microstructural development, desirable properties of materials can be achieved. The main objective of our research is to understand and control interface dominated material properties, and finally, to verify experimental results with computer simulations. We have previously showed a strong similarity between small-scale grain growth experiments and anisotropic three-dimensional simulations obtained from the Electron Backscattered Diffraction (EBSD) measurements. Using the same technique, we obtained 5170-grain data from an Aluminum-film (120 (micro)m thick) with a columnar grain structure. Experimentally obtained starting microstructure and grain boundary properties are input for the three-dimensional grain growth simulation. In the computational model, minimization of the interface energy is the driving force for the grain boundary motion. The computed evolved microstructure is compared with the final experimental microstructure, after annealing at 550 C. Characterization of the structures and properties of grain boundary networks (GBN) to produce desirable microstructures is one of the fundamental problems in interface science. There is an ongoing research for the development of new experimental and analytical techniques in order to obtain and synthesize information related to GBN. The grain boundary energy and mobility data were characterized by Electron Backscattered Diffraction (EBSD) technique and Atomic Force Microscopy (AFM) observations (i.e., for ceramic MgO and for the metal Al). Grain boundary energies are extracted from triple junction (TJ) geometry considering the local equilibrium condition at TJ's. Relative boundary mobilities were also extracted from TJ's through a statistical/multiscale analysis. Additionally, there are recent theoretical developments of grain boundary evolution in microstructures. In this paper, a new technique for three-dimensional grain growth simulations was used to simulate interface migration

  8. An assessment of future computer system needs for large-scale computation

    Science.gov (United States)

    Lykos, P.; White, J.

    1980-01-01

    Data ranging from specific computer capability requirements to opinions about the desirability of a national computer facility are summarized. It is concluded that considerable attention should be given to improving the user-machine interface. Otherwise, increased computer power may not improve the overall effectiveness of the machine user. Significant improvement in throughput requires highly concurrent systems plus the willingness of the user community to develop problem solutions for that kind of architecture. An unanticipated result was the expression of need for an on-going cross-disciplinary users group/forum in order to share experiences and to more effectively communicate needs to the manufacturers.

  9. The composing technique of fast and large scale nuclear data acquisition and control system with single chip microcomputers and PC computers

    International Nuclear Information System (INIS)

    Xu Zurun; Wu Shiying; Liu Haitao; Yao Yangsen; Wang Yingguan; Yang Chaowen

    1998-01-01

    The technique of employing single-chip microcomputers and PC computers to compose a fast and large scale nuclear data acquisition and control system was discussed in detail. The optimum composition mode of this kind of system, the acquisition and control circuit unit based on single-chip microcomputers, the real-time communication methods and the software composition under the Windows 3.2 were also described. One, two and three dimensional spectra measured by this system were demonstrated

  10. The composing technique of fast and large scale nuclear data acquisition and control system with single chip microcomputers and PC computers

    International Nuclear Information System (INIS)

    Xu Zurun; Wu Shiying; Liu Haitao; Yao Yangsen; Wang Yingguan; Yang Chaowen

    1997-01-01

    The technique of employing single-chip microcomputers and PC computers to compose a fast and large scale nuclear data acquisition and control system was discussed in detail. The optimum composition mode of this kind of system, the acquisition and control circuit unit based on single-chip microcomputers, the real-time communication methods and the software composition under the Windows 3.2 were also described. One, two and three dimensional spectra measured by this system were demonstrated

  11. Large scale computing in the Energy Research Programs

    International Nuclear Information System (INIS)

    1991-05-01

    The Energy Research Supercomputer Users Group (ERSUG) comprises all investigators using resources of the Department of Energy Office of Energy Research supercomputers. At the December 1989 meeting held at Florida State University (FSU), the ERSUG executive committee determined that the continuing rapid advances in computational sciences and computer technology demanded a reassessment of the role computational science should play in meeting DOE's commitments. Initial studies were to be performed for four subdivisions: (1) Basic Energy Sciences (BES) and Applied Mathematical Sciences (AMS), (2) Fusion Energy, (3) High Energy and Nuclear Physics, and (4) Health and Environmental Research. The first two subgroups produced formal subreports that provided a basis for several sections of this report. Additional information provided in the AMS/BES is included as Appendix C in an abridged form that eliminates most duplication. Additionally, each member of the executive committee was asked to contribute area-specific assessments; these assessments are included in the next section. In the following sections, brief assessments are given for specific areas, a conceptual model is proposed that the entire computational effort for energy research is best viewed as one giant nation-wide computer, and then specific recommendations are made for the appropriate evolution of the system

  12. NONLINEAR FORCE-FREE FIELD EXTRAPOLATION OF A CORONAL MAGNETIC FLUX ROPE SUPPORTING A LARGE-SCALE SOLAR FILAMENT FROM A PHOTOSPHERIC VECTOR MAGNETOGRAM

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Chaowei; Wu, S. T.; Hu, Qiang [Center for Space Plasma and Aeronomic Research, The University of Alabama in Huntsville, Huntsville, AL 35899 (United States); Feng, Xueshang, E-mail: cwjiang@spaceweather.ac.cn, E-mail: wus@uah.edu, E-mail: qh0001@uah.edu, E-mail: fengx@spaceweather.ac.cn [SIGMA Weather Group, State Key Laboratory for Space Weather, Center for Space Science and Applied Research, Chinese Academy of Sciences, Beijing 100190 (China)

    2014-05-10

    Solar filaments are commonly thought to be supported in magnetic dips, in particular, in those of magnetic flux ropes (FRs). In this Letter, based on the observed photospheric vector magnetogram, we implement a nonlinear force-free field (NLFFF) extrapolation of a coronal magnetic FR that supports a large-scale intermediate filament between an active region and a weak polarity region. This result is a first, in the sense that current NLFFF extrapolations including the presence of FRs are limited to relatively small-scale filaments that are close to sunspots and along main polarity inversion lines (PILs) with strong transverse field and magnetic shear, and the existence of an FR is usually predictable. In contrast, the present filament lies along the weak-field region (photospheric field strength ≲ 100 G), where the PIL is very fragmented due to small parasitic polarities on both sides of the PIL and the transverse field has a low signal-to-noise ratio. Thus, extrapolating a large-scale FR in such a case represents a far more difficult challenge. We demonstrate that our CESE-MHD-NLFFF code is sufficient for the challenge. The numerically reproduced magnetic dips of the extrapolated FR match observations of the filament and its barbs very well, which strongly supports the FR-dip model for filaments. The filament is stably sustained because the FR is weakly twisted and strongly confined by the overlying closed arcades.

  13. Commercial applications of large-scale Research and Development computer simulation technologies

    International Nuclear Information System (INIS)

    Kuok Mee Ling; Pascal Chen; Wen Ho Lee

    1998-01-01

    The potential commercial applications of two large-scale R and D computer simulation technologies are presented. One such technology is based on the numerical solution of the hydrodynamics equations, and is embodied in the two-dimensional Eulerian code EULE2D, which solves the hydrodynamic equations with various models for the equation of state (EOS), constitutive relations and fracture mechanics. EULE2D is an R and D code originally developed to design and analyze conventional munitions for anti-armor penetrations such as shaped charges, explosive formed projectiles, and kinetic energy rods. Simulated results agree very well with actual experiments. A commercial application presented here is the design and simulation of shaped charges for oil and gas well bore perforation. The other R and D simulation technology is based on the numerical solution of Maxwell's partial differential equations of electromagnetics in space and time, and is implemented in the three-dimensional code FDTD-SPICE, which solves Maxwell's equations in the time domain with finite-differences in the three spatial dimensions and calls SPICE for information when nonlinear active devices are involved. The FDTD method has been used in the radar cross-section modeling of military aircrafts and many other electromagnetic phenomena. The coupling of FDTD method with SPICE, a popular circuit and device simulation program, provides a powerful tool for the simulation and design of microwave and millimeter-wave circuits containing nonlinear active semiconductor devices. A commercial application of FDTD-SPICE presented here is the simulation of a two-element active antenna system. The simulation results and the experimental measurements are in excellent agreement. (Author)

  14. Large scale Brownian dynamics of confined suspensions of rigid particles

    Science.gov (United States)

    Sprinkle, Brennan; Balboa Usabiaga, Florencio; Patankar, Neelesh A.; Donev, Aleksandar

    2017-12-01

    We introduce methods for large-scale Brownian Dynamics (BD) simulation of many rigid particles of arbitrary shape suspended in a fluctuating fluid. Our method adds Brownian motion to the rigid multiblob method [F. Balboa Usabiaga et al., Commun. Appl. Math. Comput. Sci. 11(2), 217-296 (2016)] at a cost comparable to the cost of deterministic simulations. We demonstrate that we can efficiently generate deterministic and random displacements for many particles using preconditioned Krylov iterative methods, if kernel methods to efficiently compute the action of the Rotne-Prager-Yamakawa (RPY) mobility matrix and its "square" root are available for the given boundary conditions. These kernel operations can be computed with near linear scaling for periodic domains using the positively split Ewald method. Here we study particles partially confined by gravity above a no-slip bottom wall using a graphical processing unit implementation of the mobility matrix-vector product, combined with a preconditioned Lanczos iteration for generating Brownian displacements. We address a major challenge in large-scale BD simulations, capturing the stochastic drift term that arises because of the configuration-dependent mobility. Unlike the widely used Fixman midpoint scheme, our methods utilize random finite differences and do not require the solution of resistance problems or the computation of the action of the inverse square root of the RPY mobility matrix. We construct two temporal schemes which are viable for large-scale simulations, an Euler-Maruyama traction scheme and a trapezoidal slip scheme, which minimize the number of mobility problems to be solved per time step while capturing the required stochastic drift terms. We validate and compare these schemes numerically by modeling suspensions of boomerang-shaped particles sedimented near a bottom wall. Using the trapezoidal scheme, we investigate the steady-state active motion in dense suspensions of confined microrollers, whose

  15. Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

    Science.gov (United States)

    Penas, David R; González, Patricia; Egea, Jose A; Doallo, Ramón; Banga, Julio R

    2017-01-21

    The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

  16. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    OpenAIRE

    Abadi, Martín; Agarwal, Ashish; Barham, Paul; Brevdo, Eugene; Chen, Zhifeng; Citro, Craig; Corrado, Greg S.; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Goodfellow, Ian; Harp, Andrew; Irving, Geoffrey; Isard, Michael

    2016-01-01

    TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algo...

  17. Improving Large-scale Storage System Performance via Topology-aware and Balanced Data Placement

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Feiyi [ORNL; Oral, H Sarp [ORNL; Vazhkudai, Sudharshan S [ORNL

    2014-01-01

    With the advent of big data, the I/O subsystems of large-scale compute clusters are becoming a center of focus, with more applications putting greater demands on end-to-end I/O performance. These subsystems are often complex in design. They comprise of multiple hardware and software layers to cope with the increasing capacity, capability and scalability requirements of data intensive applications. The sharing nature of storage resources and the intrinsic interactions across these layers make it to realize user-level, end-to-end performance gains a great challenge. We propose a topology-aware resource load balancing strategy to improve per-application I/O performance. We demonstrate the effectiveness of our algorithm on an extreme-scale compute cluster, Titan, at the Oak Ridge Leadership Computing Facility (OLCF). Our experiments with both synthetic benchmarks and a real-world application show that, even under congestion, our proposed algorithm can improve large-scale application I/O performance significantly, resulting in both the reduction of application run times and higher resolution simulation runs.

  18. Unraveling The Connectome: Visualizing and Abstracting Large-Scale Connectomics Data

    KAUST Repository

    Al-Awami, Ali K.

    2017-04-30

    We explore visualization and abstraction approaches to represent neuronal data. Neuroscientists acquire electron microscopy volumes to reconstruct a complete wiring diagram of the neurons in the brain, called the connectome. This will be crucial to understanding brains and their development. However, the resulting data is complex and large, posing a big challenge to existing visualization techniques in terms of clarity and scalability. We describe solutions to tackle the problems of scalability and cluttered presentation. We first show how a query-guided interactive approach to visual exploration can reduce the clutter and help neuroscientists explore their data dynamically. We use a knowledge-based query algebra that facilitates the interactive creation of queries. This allows neuroscientists to pose domain-specific questions related to their research. Simple queries can be combined to form complex queries to answer more sophisticated questions. We then show how visual abstractions from 3D to 2D can significantly reduce the visual clutter and add clarity to the visualization so that scientists can focus more on the analysis. We abstract the topology of 3D neurons into a multi-scale, relative distance-preserving subway map visualization that allows scientists to interactively explore the morphological and connectivity features of neuronal cells. We then focus on the process of acquisition, where neuroscientists segment electron microscopy images to reconstruct neurons. The segmentation process of such data is tedious, time-intensive, and usually performed using a diverse set of tools. We present a novel web-based visualization system for tracking the state, progress, and evolution of segmentation data in neuroscience. Our multi-user system seamlessly integrates a diverse set of tools. Our system provides support for the management, provenance, accountability, and auditing of large-scale segmentations. Finally, we present a novel architecture to render very large

  19. Ethics of large-scale change

    OpenAIRE

    Arler, Finn

    2006-01-01

      The subject of this paper is long-term large-scale changes in human society. Some very significant examples of large-scale change are presented: human population growth, human appropriation of land and primary production, the human use of fossil fuels, and climate change. The question is posed, which kind of attitude is appropriate when dealing with large-scale changes like these from an ethical point of view. Three kinds of approaches are discussed: Aldo Leopold's mountain thinking, th...

  20. Self-* and Adaptive Mechanisms for Large Scale Distributed Systems

    Science.gov (United States)

    Fragopoulou, P.; Mastroianni, C.; Montero, R.; Andrjezak, A.; Kondo, D.

    Large-scale distributed computing systems and infrastructure, such as Grids, P2P systems and desktop Grid platforms, are decentralized, pervasive, and composed of a large number of autonomous entities. The complexity of these systems is such that human administration is nearly impossible and centralized or hierarchical control is highly inefficient. These systems need to run on highly dynamic environments, where content, network topologies and workloads are continuously changing. Moreover, they are characterized by the high degree of volatility of their components and the need to provide efficient service management and to handle efficiently large amounts of data. This paper describes some of the areas for which adaptation emerges as a key feature, namely, the management of computational Grids, the self-management of desktop Grid platforms and the monitoring and healing of complex applications. It also elaborates on the use of bio-inspired algorithms to achieve self-management. Related future trends and challenges are described.

  1. Eliciting and receiving online support: using computer-aided content analysis to examine the dynamics of online social support.

    Science.gov (United States)

    Wang, Yi-Chia; Kraut, Robert E; Levine, John M

    2015-04-20

    Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, Ponline support communities.

  2. Mixing Metaphors: Building Infrastructure for Large Scale School Turnaround

    Science.gov (United States)

    Peurach, Donald J.; Neumerski, Christine M.

    2015-01-01

    The purpose of this analysis is to increase understanding of the possibilities and challenges of building educational infrastructure--the basic, foundational structures, systems, and resources--to support large-scale school turnaround. Building educational infrastructure often exceeds the capacity of schools, districts, and state education…

  3. Computer-based data acquisition system in the Large Coil Test Facility

    International Nuclear Information System (INIS)

    Gould, S.S.; Layman, L.R.; Million, D.L.

    1983-01-01

    The utilization of computers for data acquisition and control is of paramount importance on large-scale fusion experiments because they feature the ability to acquire data from a large number of sensors at various sample rates and provide for flexible data interpretation, presentation, reduction, and analysis. In the Large Coil Test Facility (LCTF) a Digital Equipment Corporation (DEC) PDP-11/60 host computer with the DEC RSX-11M operating system coordinates the activities of five DEC LSI-11/23 front-end processors (FEPs) via direct memory access (DMA) communication links. This provides host control of scheduled data acquisition and FEP event-triggered data collection tasks. Four of the five FEPs have no operating system

  4. Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

    Directory of Open Access Journals (Sweden)

    Lixiong Xu

    2017-01-01

    Full Text Available As one of the most effective function mining algorithms, Gene Expression Programming (GEP algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.

  5. Large-scale ground motion simulation using GPGPU

    Science.gov (United States)

    Aoi, S.; Maeda, T.; Nishizawa, N.; Aoki, T.

    2012-12-01

    Huge computation resources are required to perform large-scale ground motion simulations using 3-D finite difference method (FDM) for realistic and complex models with high accuracy. Furthermore, thousands of various simulations are necessary to evaluate the variability of the assessment caused by uncertainty of the assumptions of the source models for future earthquakes. To conquer the problem of restricted computational resources, we introduced the use of GPGPU (General purpose computing on graphics processing units) which is the technique of using a GPU as an accelerator of the computation which has been traditionally conducted by the CPU. We employed the CPU version of GMS (Ground motion Simulator; Aoi et al., 2004) as the original code and implemented the function for GPU calculation using CUDA (Compute Unified Device Architecture). GMS is a total system for seismic wave propagation simulation based on 3-D FDM scheme using discontinuous grids (Aoi&Fujiwara, 1999), which includes the solver as well as the preprocessor tools (parameter generation tool) and postprocessor tools (filter tool, visualization tool, and so on). The computational model is decomposed in two horizontal directions and each decomposed model is allocated to a different GPU. We evaluated the performance of our newly developed GPU version of GMS on the TSUBAME2.0 which is one of the Japanese fastest supercomputer operated by the Tokyo Institute of Technology. First we have performed a strong scaling test using the model with about 22 million grids and achieved 3.2 and 7.3 times of the speed-up by using 4 and 16 GPUs. Next, we have examined a weak scaling test where the model sizes (number of grids) are increased in proportion to the degree of parallelism (number of GPUs). The result showed almost perfect linearity up to the simulation with 22 billion grids using 1024 GPUs where the calculation speed reached to 79.7 TFlops and about 34 times faster than the CPU calculation using the same number

  6. Policy support for large scale demonstration projects for hydrogen use in transport. Deliverable D 5.1 (Part B)

    International Nuclear Information System (INIS)

    Ros, M.E.; Jeeninga, H.; Godfroij, P.

    2007-06-01

    This research addresses the possible policy support mechanisms for hydrogen use in transport to answer the question which policy support mechanism potentially is most effective to stimulate hydrogen in transport and especially for large scale demonstrations. This is done by investigating two approaches. First, the possible policy support mechanisms for energy innovations. Second, by relating these to the different technology development stages (R and D, early market and mass market stage) and reviewing their effect on different parts of the hydrogen energy chain (production, distribution and end-use). Additionally, a comparison of the currently policy support mechanisms used in Europe (on EU level) with the United States (National and State level) is made. The analysis shows that in principle various policy support mechanisms can be used to stimulate hydrogen. The choice for a policy support mechanism should depend on the need to reduce the investment cost (euros/MW), production/use cost (euros/GJ) or increase performance (euros/kg CO2 avoided) of a technology during its development. Careful thought has to be put into the design and choice of a policy support mechanism because it can have effects on other parts of the hydrogen energy chain, mostly how hydrogen is produced. The effectiveness of a policy support mechanism greatly depends on the ability to adapt to the developments of the technology and the changing requirements which come with technological progress. In time different policy support mechanisms have to be applied. For demonstration projects there is currently the tendency to apply R and D subsidies in Europe, while the United States applies a variety of policy support mechanisms. The United States not only has higher and more support for demonstration projects but also has stronger incentives to prepare early market demand (for instance requiring public procurement and sales obligations). In order to re-establish the level playing field, Europe may

  7. Large-scale mHealth professional support for health workers in rural Maharashtra, India.

    Science.gov (United States)

    Hegde, Shailendra Kumar B; Saride, Sriranga Prasad; Kuruganty, Sudha; Banker, Niraja; Patil, Chetan; Phanse, Vishal

    2018-04-01

    Expanding mobile telephony in India has prompted interest in the potential of mobile-telephone health (mHealth) in linking health workers in rural areas with specialist medical advice and other professional services. In 2012, a toll-free helpline offering specialist medical advice to community-based health workers throughout Maharashtra was launched. Calls are handled via a 24 h centre in Pune, staffed by health advisory officers and medical specialists. Health advisory officers handle general queries, which include medical advice via validated algorithms; blood on-call services; grievance issues; and mental health support - the latter calls are transferred to a qualified counsellor. Calls requiring more specialist advice are transferred to the appropriate medical specialist. This paper describes the experience of the first 4 years of this helpline, in terms of the services used, callers, nature of calls, types of queries serviced and lessons learnt. In the first 4 years of the helpline, 669 265 calls were serviced. Of these calls, 453 373 (67.74%) needed medical advice and were handled by health advisory officers. Specialist services were required to address 199 226 (29.77%) calls. Blood-bank-related services accounted for 7919 (1.18%) calls, while 2462 (0.37%) were grievance calls. Counselling for mental health issues accounted for 6285 (0.94%) calls. The large-scale mHealth professional support provided by this helpline in Maharashtra has reached many health workers serving rural communities. Future work is required to explore ways to expand the reach of the helpline further and to measure its effectiveness in improving health outcomes.

  8. Accelerating large-scale protein structure alignments with graphics processing units

    Directory of Open Access Journals (Sweden)

    Pang Bin

    2012-02-01

    Full Text Available Abstract Background Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons. Findings We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs. As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH. Conclusions ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU.

  9. Neural networks supporting audiovisual integration for speech: A large-scale lesion study.

    Science.gov (United States)

    Hickok, Gregory; Rogalsky, Corianne; Matchin, William; Basilakos, Alexandra; Cai, Julia; Pillay, Sara; Ferrill, Michelle; Mickelsen, Soren; Anderson, Steven W; Love, Tracy; Binder, Jeffrey; Fridriksson, Julius

    2018-06-01

    Auditory and visual speech information are often strongly integrated resulting in perceptual enhancements for audiovisual (AV) speech over audio alone and sometimes yielding compelling illusory fusion percepts when AV cues are mismatched, the McGurk-MacDonald effect. Previous research has identified three candidate regions thought to be critical for AV speech integration: the posterior superior temporal sulcus (STS), early auditory cortex, and the posterior inferior frontal gyrus. We assess the causal involvement of these regions (and others) in the first large-scale (N = 100) lesion-based study of AV speech integration. Two primary findings emerged. First, behavioral performance and lesion maps for AV enhancement and illusory fusion measures indicate that classic metrics of AV speech integration are not necessarily measuring the same process. Second, lesions involving superior temporal auditory, lateral occipital visual, and multisensory zones in the STS are the most disruptive to AV speech integration. Further, when AV speech integration fails, the nature of the failure-auditory vs visual capture-can be predicted from the location of the lesions. These findings show that AV speech processing is supported by unimodal auditory and visual cortices as well as multimodal regions such as the STS at their boundary. Motor related frontal regions do not appear to play a role in AV speech integration. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. Large-Scale Testing and High-Fidelity Simulation Capabilities at Sandia National Laboratories to Support Space Power and Propulsion

    International Nuclear Information System (INIS)

    Dobranich, Dean; Blanchat, Thomas K.

    2008-01-01

    Sandia National Laboratories, as a Department of Energy, National Nuclear Security Agency, has major responsibility to ensure the safety and security needs of nuclear weapons. As such, with an experienced research staff, Sandia maintains a spectrum of modeling and simulation capabilities integrated with experimental and large-scale test capabilities. This expertise and these capabilities offer considerable resources for addressing issues of interest to the space power and propulsion communities. This paper presents Sandia's capability to perform thermal qualification (analysis, test, modeling and simulation) using a representative weapon system as an example demonstrating the potential to support NASA's Lunar Reactor System

  11. Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems

    Energy Technology Data Exchange (ETDEWEB)

    Ghattas, Omar [The University of Texas at Austin

    2013-10-15

    The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUARO Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.

  12. Dose monitoring in large-scale flowing aqueous media

    International Nuclear Information System (INIS)

    Kuruca, C.N.

    1995-01-01

    The Miami Electron Beam Research Facility (EBRF) has been in operation for six years. The EBRF houses a 1.5 MV, 75 KW DC scanned electron beam. Experiments have been conducted to evaluate the effectiveness of high-energy electron irradiation in the removal of toxic organic chemicals from contaminated water and the disinfection of various wastewater streams. The large-scale plant operates at approximately 450 L/min (120 gal/min). The radiation dose absorbed by the flowing aqueous streams is estimated by measuring the difference in water temperature before and after it passes in front of the beam. Temperature measurements are made using resistance temperature devices (RTDs) and recorded by computer along with other operating parameters. Estimated dose is obtained from the measured temperature differences using the specific heat of water. This presentation will discuss experience with this measurement system, its application to different water presentation devices, sources of error, and the advantages and disadvantages of its use in large-scale process applications

  13. A Novel Spatial-Temporal Voronoi Diagram-Based Heuristic Approach for Large-Scale Vehicle Routing Optimization with Time Constraints

    Directory of Open Access Journals (Sweden)

    Wei Tu

    2015-10-01

    Full Text Available Vehicle routing optimization (VRO designs the best routes to reduce travel cost, energy consumption, and carbon emission. Due to non-deterministic polynomial-time hard (NP-hard complexity, many VROs involved in real-world applications require too much computing effort. Shortening computing time for VRO is a great challenge for state-of-the-art spatial optimization algorithms. From a spatial-temporal perspective, this paper presents a spatial-temporal Voronoi diagram-based heuristic approach for large-scale vehicle routing problems with time windows (VRPTW. Considering time constraints, a spatial-temporal Voronoi distance is derived from the spatial-temporal Voronoi diagram to find near neighbors in the space-time searching context. A Voronoi distance decay strategy that integrates a time warp operation is proposed to accelerate local search procedures. A spatial-temporal feature-guided search is developed to improve unpromising micro route structures. Experiments on VRPTW benchmarks and real-world instances are conducted to verify performance. The results demonstrate that the proposed approach is competitive with state-of-the-art heuristics and achieves high-quality solutions for large-scale instances of VRPTWs in a short time. This novel approach will contribute to spatial decision support community by developing an effective vehicle routing optimization method for large transportation applications in both public and private sectors.

  14. Large-scale simulation of ductile fracture process of microstructured materials

    International Nuclear Information System (INIS)

    Tian Rong; Wang Chaowei

    2011-01-01

    The promise of computational science in the extreme-scale computing era is to reduce and decompose macroscopic complexities into microscopic simplicities with the expense of high spatial and temporal resolution of computing. In materials science and engineering, the direct combination of 3D microstructure data sets and 3D large-scale simulations provides unique opportunity for the development of a comprehensive understanding of nano/microstructure-property relationships in order to systematically design materials with specific desired properties. In the paper, we present a framework simulating the ductile fracture process zone in microstructural detail. The experimentally reconstructed microstructural data set is directly embedded into a FE mesh model to improve the simulation fidelity of microstructure effects on fracture toughness. To the best of our knowledge, it is for the first time that the linking of fracture toughness to multiscale microstructures in a realistic 3D numerical model in a direct manner is accomplished. (author)

  15. High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

    Science.gov (United States)

    Maly, K.

    1998-01-01

    Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated

  16. Large scale hydro-economic modelling for policy support

    Science.gov (United States)

    de Roo, Ad; Burek, Peter; Bouraoui, Faycal; Reynaud, Arnaud; Udias, Angel; Pistocchi, Alberto; Lanzanova, Denis; Trichakis, Ioannis; Beck, Hylke; Bernhard, Jeroen

    2014-05-01

    To support European Union water policy making and policy monitoring, a hydro-economic modelling environment has been developed to assess optimum combinations of water retention measures, water savings measures, and nutrient reduction measures for continental Europe. This modelling environment consists of linking the agricultural CAPRI model, the LUMP land use model, the LISFLOOD water quantity model, the EPIC water quality model, the LISQUAL combined water quantity, quality and hydro-economic model, and a multi-criteria optimisation routine. With this modelling environment, river basin scale simulations are carried out to assess the effects of water-retention measures, water-saving measures, and nutrient-reduction measures on several hydro-chemical indicators, such as the Water Exploitation Index (WEI), Nitrate and Phosphate concentrations in rivers, the 50-year return period river discharge as an indicator for flooding, and economic losses due to water scarcity for the agricultural sector, the manufacturing-industry sector, the energy-production sector and the domestic sector, as well as the economic loss due to flood damage. Recently, this model environment is being extended with a groundwater model to evaluate the effects of measures on the average groundwater table and available resources. Also, water allocation rules are addressed, while having environmental flow included as a minimum requirement for the environment. Economic functions are currently being updated as well. Recent development and examples will be shown and discussed, as well as open challenges.

  17. Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

    Directory of Open Access Journals (Sweden)

    C. M. R. Mateo

    2017-10-01

    Full Text Available Global-scale river models (GRMs are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC is assumed, simulation results deteriorate with finer spatial resolution; Nash–Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

  18. Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

    Science.gov (United States)

    Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

    2017-10-01

    Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction, the suitability of GRMs for application to finer resolutions needs to be assessed. This study investigates the impacts of spatial resolution and flow connectivity representation on the predictive capability of a GRM, CaMa-Flood, in simulating the 2011 extreme flood in Thailand. Analyses show that when single downstream connectivity (SDC) is assumed, simulation results deteriorate with finer spatial resolution; Nash-Sutcliffe efficiency coefficients decreased by more than 50 % between simulation results at 10 km resolution and 1 km resolution. When multiple downstream connectivity (MDC) is represented, simulation results slightly improve with finer spatial resolution. The SDC simulations result in excessive backflows on very flat floodplains due to the restrictive flow directions at finer resolutions. MDC channels attenuated these effects by maintaining flow connectivity and flow capacity between floodplains in varying spatial resolutions. While a regional-scale flood was chosen as a test case, these findings should be universal and may have significant impacts on large- to global-scale simulations, especially in regions where mega deltas exist.These results demonstrate that a GRM can be used for higher resolution simulations of large-scale floods, provided that MDC in rivers and floodplains is adequately represented in the model structure.

  19. Fast selection of miRNA candidates based on large-scale pre-computed MFE sets of randomized sequences.

    Science.gov (United States)

    Warris, Sven; Boymans, Sander; Muiser, Iwe; Noback, Michiel; Krijnen, Wim; Nap, Jan-Peter

    2014-01-13

    Small RNAs are important regulators of genome function, yet their prediction in genomes is still a major computational challenge. Statistical analyses of pre-miRNA sequences indicated that their 2D structure tends to have a minimal free energy (MFE) significantly lower than MFE values of equivalently randomized sequences with the same nucleotide composition, in contrast to other classes of non-coding RNA. The computation of many MFEs is, however, too intensive to allow for genome-wide screenings. Using a local grid infrastructure, MFE distributions of random sequences were pre-calculated on a large scale. These distributions follow a normal distribution and can be used to determine the MFE distribution for any given sequence composition by interpolation. It allows on-the-fly calculation of the normal distribution for any candidate sequence composition. The speedup achieved makes genome-wide screening with this characteristic of a pre-miRNA sequence practical. Although this particular property alone will not be able to distinguish miRNAs from other sequences sufficiently discriminative, the MFE-based P-value should be added to the parameters of choice to be included in the selection of potential miRNA candidates for experimental verification.

  20. Large scale analysis of signal reachability.

    Science.gov (United States)

    Todor, Andrei; Gabr, Haitham; Dobra, Alin; Kahveci, Tamer

    2014-06-15

    Major disorders, such as leukemia, have been shown to alter the transcription of genes. Understanding how gene regulation is affected by such aberrations is of utmost importance. One promising strategy toward this objective is to compute whether signals can reach to the transcription factors through the transcription regulatory network (TRN). Due to the uncertainty of the regulatory interactions, this is a #P-complete problem and thus solving it for very large TRNs remains to be a challenge. We develop a novel and scalable method to compute the probability that a signal originating at any given set of source genes can arrive at any given set of target genes (i.e., transcription factors) when the topology of the underlying signaling network is uncertain. Our method tackles this problem for large networks while providing a provably accurate result. Our method follows a divide-and-conquer strategy. We break down the given network into a sequence of non-overlapping subnetworks such that reachability can be computed autonomously and sequentially on each subnetwork. We represent each interaction using a small polynomial. The product of these polynomials express different scenarios when a signal can or cannot reach to target genes from the source genes. We introduce polynomial collapsing operators for each subnetwork. These operators reduce the size of the resulting polynomial and thus the computational complexity dramatically. We show that our method scales to entire human regulatory networks in only seconds, while the existing methods fail beyond a few tens of genes and interactions. We demonstrate that our method can successfully characterize key reachability characteristics of the entire transcriptions regulatory networks of patients affected by eight different subtypes of leukemia, as well as those from healthy control samples. All the datasets and code used in this article are available at bioinformatics.cise.ufl.edu/PReach/scalable.htm. © The Author 2014

  1. GPU-based large-scale visualization

    KAUST Repository

    Hadwiger, Markus

    2013-11-19

    Recent advances in image and volume acquisition as well as computational advances in simulation have led to an explosion of the amount of data that must be visualized and analyzed. Modern techniques combine the parallel processing power of GPUs with out-of-core methods and data streaming to enable the interactive visualization of giga- and terabytes of image and volume data. A major enabler for interactivity is making both the computational and the visualization effort proportional to the amount of data that is actually visible on screen, decoupling it from the full data size. This leads to powerful display-aware multi-resolution techniques that enable the visualization of data of almost arbitrary size. The course consists of two major parts: An introductory part that progresses from fundamentals to modern techniques, and a more advanced part that discusses details of ray-guided volume rendering, novel data structures for display-aware visualization and processing, and the remote visualization of large online data collections. You will learn how to develop efficient GPU data structures and large-scale visualizations, implement out-of-core strategies and concepts such as virtual texturing that have only been employed recently, as well as how to use modern multi-resolution representations. These approaches reduce the GPU memory requirements of extremely large data to a working set size that fits into current GPUs. You will learn how to perform ray-casting of volume data of almost arbitrary size and how to render and process gigapixel images using scalable, display-aware techniques. We will describe custom virtual texturing architectures as well as recent hardware developments in this area. We will also describe client/server systems for distributed visualization, on-demand data processing and streaming, and remote visualization. We will describe implementations using OpenGL as well as CUDA, exploiting parallelism on GPUs combined with additional asynchronous

  2. Lattice QCD - a challenge in large scale computing

    International Nuclear Information System (INIS)

    Schilling, K.

    1987-01-01

    The computation of the hadron spectrum within the framework of lattice QCD sets a demanding goal for the application of supercomputers in basic science. It requires both big computer capacities and clever algorithms to fight all the numerical evils that one encounters in the Euclidean space-time-world. The talk will attempt to introduce to the present state of the art of spectrum calculations by lattice simulations. (orig.)

  3. A quasi-Newton algorithm for large-scale nonlinear equations

    Directory of Open Access Journals (Sweden)

    Linghua Huang

    2017-02-01

    Full Text Available Abstract In this paper, the algorithm for large-scale nonlinear equations is designed by the following steps: (i a conjugate gradient (CG algorithm is designed as a sub-algorithm to obtain the initial points of the main algorithm, where the sub-algorithm’s initial point does not have any restrictions; (ii a quasi-Newton algorithm with the initial points given by sub-algorithm is defined as main algorithm, where a new nonmonotone line search technique is presented to get the step length α k $\\alpha_{k}$ . The given nonmonotone line search technique can avoid computing the Jacobian matrix. The global convergence and the 1 + q $1+q$ -order convergent rate of the main algorithm are established under suitable conditions. Numerical results show that the proposed method is competitive with a similar method for large-scale problems.

  4. [A large-scale accident in Alpine terrain].

    Science.gov (United States)

    Wildner, M; Paal, P

    2015-02-01

    Due to the geographical conditions, large-scale accidents amounting to mass casualty incidents (MCI) in Alpine terrain regularly present rescue teams with huge challenges. Using an example incident, specific conditions and typical problems associated with such a situation are presented. The first rescue team members to arrive have the elementary tasks of qualified triage and communication to the control room, which is required to dispatch the necessary additional support. Only with a clear "concept", to which all have to adhere, can the subsequent chaos phase be limited. In this respect, a time factor confounded by adverse weather conditions or darkness represents enormous pressure. Additional hazards are frostbite and hypothermia. If priorities can be established in terms of urgency, then treatment and procedure algorithms have proven successful. For evacuation of causalities, a helicopter should be strived for. Due to the low density of hospitals in Alpine regions, it is often necessary to distribute the patients over a wide area. Rescue operations in Alpine terrain have to be performed according to the particular conditions and require rescue teams to have specific knowledge and expertise. The possibility of a large-scale accident should be considered when planning events. With respect to optimization of rescue measures, regular training and exercises are rational, as is the analysis of previous large-scale Alpine accidents.

  5. Findings and Challenges in Fine-Resolution Large-Scale Hydrological Modeling

    Science.gov (United States)

    Her, Y. G.

    2017-12-01

    Fine-resolution large-scale (FL) modeling can provide the overall picture of the hydrological cycle and transport while taking into account unique local conditions in the simulation. It can also help develop water resources management plans consistent across spatial scales by describing the spatial consequences of decisions and hydrological events extensively. FL modeling is expected to be common in the near future as global-scale remotely sensed data are emerging, and computing resources have been advanced rapidly. There are several spatially distributed models available for hydrological analyses. Some of them rely on numerical methods such as finite difference/element methods (FDM/FEM), which require excessive computing resources (implicit scheme) to manipulate large matrices or small simulation time intervals (explicit scheme) to maintain the stability of the solution, to describe two-dimensional overland processes. Others make unrealistic assumptions such as constant overland flow velocity to reduce the computational loads of the simulation. Thus, simulation efficiency often comes at the expense of precision and reliability in FL modeling. Here, we introduce a new FL continuous hydrological model and its application to four watersheds in different landscapes and sizes from 3.5 km2 to 2,800 km2 at the spatial resolution of 30 m on an hourly basis. The model provided acceptable accuracy statistics in reproducing hydrological observations made in the watersheds. The modeling outputs including the maps of simulated travel time, runoff depth, soil water content, and groundwater recharge, were animated, visualizing the dynamics of hydrological processes occurring in the watersheds during and between storm events. Findings and challenges were discussed in the context of modeling efficiency, accuracy, and reproducibility, which we found can be improved by employing advanced computing techniques and hydrological understandings, by using remotely sensed hydrological

  6. A Combined Eulerian-Lagrangian Data Representation for Large-Scale Applications.

    Science.gov (United States)

    Sauer, Franz; Xie, Jinrong; Ma, Kwan-Liu

    2017-10-01

    The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.

  7. Nonlinear Force-free Field Extrapolation of a Coronal Magnetic Flux Rope Supporting a Large-scale Solar Filament from a Photospheric Vector Magnetogram

    Science.gov (United States)

    Jiang, Chaowei; Wu, S. T.; Feng, Xueshang; Hu, Qiang

    2014-05-01

    Solar filaments are commonly thought to be supported in magnetic dips, in particular, in those of magnetic flux ropes (FRs). In this Letter, based on the observed photospheric vector magnetogram, we implement a nonlinear force-free field (NLFFF) extrapolation of a coronal magnetic FR that supports a large-scale intermediate filament between an active region and a weak polarity region. This result is a first, in the sense that current NLFFF extrapolations including the presence of FRs are limited to relatively small-scale filaments that are close to sunspots and along main polarity inversion lines (PILs) with strong transverse field and magnetic shear, and the existence of an FR is usually predictable. In contrast, the present filament lies along the weak-field region (photospheric field strength barbs very well, which strongly supports the FR-dip model for filaments. The filament is stably sustained because the FR is weakly twisted and strongly confined by the overlying closed arcades.

  8. Financial return for government support of large-scale thin-film solar photovoltaic manufacturing in Canada

    International Nuclear Information System (INIS)

    Branker, K.; Pearce, J.M.

    2010-01-01

    As the Ontario government has recognized that solar photovoltaic (PV) energy conversion is a solution to satisfying energy demands while reducing the adverse anthropogenic impacts on the global environment that compromise social welfare, it has begun to generate policy to support financial incentives for PV. This paper provides a financial analysis for investment in a 1 GW per year turnkey amorphous silicon PV manufacturing plant. The financial benefits for both the provincial and federal governments were quantified for: (i) full construction subsidy, (ii) construction subsidy and sale, (iii) partially subsidize construction, (iv) a publicly owned plant, (v) loan guarantee for construction, and (vi) an income tax holiday. Revenues for the governments are derived from: taxation (personal, corporate, and sales), sales of panels in Ontario, and saved health, environmental and economic costs associated with offsetting coal-fired electricity. Both governments enjoyed positive cash flows from these investments in less than 12 years and in many of the scenarios both governments earned well over 8% on investments from 100 s of millions to $2.4 billion. The results showed that it is in the financial best interest of both the Ontario and Canadian federal governments to implement aggressive fiscal policy to support large-scale PV manufacturing.

  9. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Sanggoo Kang

    2016-08-01

    Full Text Available Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-based image processing algorithms by comparing the performance of a single virtual server and multiple auto-scaled virtual servers under identical experimental conditions. In this study, the cloud computing environment is built with OpenStack, and four algorithms from the Orfeo toolbox are used for practical geo-based image processing experiments. The auto-scaling results from all experimental performance tests demonstrate applicable significance with respect to cloud utilization concerning response time. Auto-scaling contributes to the development of web-based satellite image application services using cloud-based technologies.

  10. Large Data at Small Universities: Astronomical processing using a computer classroom

    Science.gov (United States)

    Fuller, Nathaniel James; Clarkson, William I.; Fluharty, Bill; Belanger, Zach; Dage, Kristen

    2016-06-01

    The use of large computing clusters for astronomy research is becoming more commonplace as datasets expand, but access to these required resources is sometimes difficult for research groups working at smaller Universities. As an alternative to purchasing processing time on an off-site computing cluster, or purchasing dedicated hardware, we show how one can easily build a crude on-site cluster by utilizing idle cycles on instructional computers in computer-lab classrooms. Since these computers are maintained as part of the educational mission of the University, the resource impact on the investigator is generally low.By using open source Python routines, it is possible to have a large number of desktop computers working together via a local network to sort through large data sets. By running traditional analysis routines in an “embarrassingly parallel” manner, gains in speed are accomplished without requiring the investigator to learn how to write routines using highly specialized methodology. We demonstrate this concept here applied to 1. photometry of large-format images and 2. Statistical significance-tests for X-ray lightcurve analysis. In these scenarios, we see a speed-up factor which scales almost linearly with the number of cores in the cluster. Additionally, we show that the usage of the cluster does not severely limit performance for a local user, and indeed the processing can be performed while the computers are in use for classroom purposes.

  11. Success Factors of Large Scale ERP Implementation in Thailand

    OpenAIRE

    Rotchanakitumnuai; Siriluck

    2010-01-01

    The objectives of the study are to examine the determinants of ERP implementation success factors of ERP implementation. The result indicates that large scale ERP implementation success consist of eight factors: project management competence, knowledge sharing, ERP system quality , understanding, user involvement, business process re-engineering, top management support, organization readiness.

  12. The Emergence of Large-Scale Computer Assisted Summative Examination Facilities in Higher Education

    NARCIS (Netherlands)

    Draaijer, S.; Warburton, W. I.

    2014-01-01

    A case study is presented of VU University Amsterdam where a dedicated large-scale CAA examination facility was established. In the facility, 385 students can take an exam concurrently. The case study describes the change factors and processes leading up to the decision by the institution to

  13. An Axiomatic Analysis Approach for Large-Scale Disaster-Tolerant Systems Modeling

    Directory of Open Access Journals (Sweden)

    Theodore W. Manikas

    2011-02-01

    Full Text Available Disaster tolerance in computing and communications systems refers to the ability to maintain a degree of functionality throughout the occurrence of a disaster. We accomplish the incorporation of disaster tolerance within a system by simulating various threats to the system operation and identifying areas for system redesign. Unfortunately, extremely large systems are not amenable to comprehensive simulation studies due to the large computational complexity requirements. To address this limitation, an axiomatic approach that decomposes a large-scale system into smaller subsystems is developed that allows the subsystems to be independently modeled. This approach is implemented using a data communications network system example. The results indicate that the decomposition approach produces simulation responses that are similar to the full system approach, but with greatly reduced simulation time.

  14. Exploiting large-scale correlations to detect continuous gravitational waves.

    Science.gov (United States)

    Pletsch, Holger J; Allen, Bruce

    2009-10-30

    Fully coherent searches (over realistic ranges of parameter space and year-long observation times) for unknown sources of continuous gravitational waves are computationally prohibitive. Less expensive hierarchical searches divide the data into shorter segments which are analyzed coherently, then detection statistics from different segments are combined incoherently. The novel method presented here solves the long-standing problem of how best to do the incoherent combination. The optimal solution exploits large-scale parameter-space correlations in the coherent detection statistic. Application to simulated data shows dramatic sensitivity improvements compared with previously available (ad hoc) methods, increasing the spatial volume probed by more than 2 orders of magnitude at lower computational cost.

  15. Adsorption and diffusion of Ru adatoms on Ru(0001)-supported graphene: Large-scale first-principles calculations

    Energy Technology Data Exchange (ETDEWEB)

    Han, Yong; Evans, James W. [Department of Physics and Astronomy, Iowa State University, Ames, Iowa 50011, USA and Ames Laboratory—U.S. Department of Energy, Iowa State University, Ames, Iowa 50011 (United States)

    2015-10-28

    Large-scale first-principles density functional theory calculations are performed to investigate the adsorption and diffusion of Ru adatoms on monolayer graphene (G) supported on Ru(0001). The G sheet exhibits a periodic moiré-cell superstructure due to lattice mismatch. Within a moiré cell, there are three distinct regions: fcc, hcp, and mound, in which the C{sub 6}-ring center is above a fcc site, a hcp site, and a surface Ru atom of Ru(0001), respectively. The adsorption energy of a Ru adatom is evaluated at specific sites in these distinct regions. We find the strongest binding at an adsorption site above a C atom in the fcc region, next strongest in the hcp region, then the fcc-hcp boundary (ridge) between these regions, and the weakest binding in the mound region. Behavior is similar to that observed from small-unit-cell calculations of Habenicht et al. [Top. Catal. 57, 69 (2014)], which differ from previous large-scale calculations. We determine the minimum-energy path for local diffusion near the center of the fcc region and obtain a local diffusion barrier of ∼0.48 eV. We also estimate a significantly lower local diffusion barrier in the ridge region. These barriers and information on the adsorption energy variation facilitate development of a realistic model for the global potential energy surface for Ru adatoms. This in turn enables simulation studies elucidating diffusion-mediated directed-assembly of Ru nanoclusters during deposition of Ru on G/Ru(0001)

  16. A method of orbital analysis for large-scale first-principles simulations

    Energy Technology Data Exchange (ETDEWEB)

    Ohwaki, Tsukuru [Advanced Materials Laboratory, Nissan Research Center, Nissan Motor Co., Ltd., 1 Natsushima-cho, Yokosuka, Kanagawa 237-8523 (Japan); Otani, Minoru [Nanosystem Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568 (Japan); Ozaki, Taisuke [Research Center for Simulation Science (RCSS), Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi, Ishikawa 923-1292 (Japan)

    2014-06-28

    An efficient method of calculating the natural bond orbitals (NBOs) based on a truncation of the entire density matrix of a whole system is presented for large-scale density functional theory calculations. The method recovers an orbital picture for O(N) electronic structure methods which directly evaluate the density matrix without using Kohn-Sham orbitals, thus enabling quantitative analysis of chemical reactions in large-scale systems in the language of localized Lewis-type chemical bonds. With the density matrix calculated by either an exact diagonalization or O(N) method, the computational cost is O(1) for the calculation of NBOs associated with a local region where a chemical reaction takes place. As an illustration of the method, we demonstrate how an electronic structure in a local region of interest can be analyzed by NBOs in a large-scale first-principles molecular dynamics simulation for a liquid electrolyte bulk model (propylene carbonate + LiBF{sub 4})

  17. A method of orbital analysis for large-scale first-principles simulations

    International Nuclear Information System (INIS)

    Ohwaki, Tsukuru; Otani, Minoru; Ozaki, Taisuke

    2014-01-01

    An efficient method of calculating the natural bond orbitals (NBOs) based on a truncation of the entire density matrix of a whole system is presented for large-scale density functional theory calculations. The method recovers an orbital picture for O(N) electronic structure methods which directly evaluate the density matrix without using Kohn-Sham orbitals, thus enabling quantitative analysis of chemical reactions in large-scale systems in the language of localized Lewis-type chemical bonds. With the density matrix calculated by either an exact diagonalization or O(N) method, the computational cost is O(1) for the calculation of NBOs associated with a local region where a chemical reaction takes place. As an illustration of the method, we demonstrate how an electronic structure in a local region of interest can be analyzed by NBOs in a large-scale first-principles molecular dynamics simulation for a liquid electrolyte bulk model (propylene carbonate + LiBF 4 )

  18. Psychometric Properties of the Perceived Wellness Culture and Environment Support Scale.

    Science.gov (United States)

    Melnyk, Bernadette Mazurek; Szalacha, Laura A; Amaya, Megan

    2018-05-01

    This study reports on the psychometric properties of the 11-item Perceived Wellness Culture and Environment Support Scale (PWCESS) and its relationship with employee healthy lifestyle beliefs and behaviors. Faculty and staff (N = 3959) at a large public university in the United States mid-west completed the PWCESS along with healthy lifestyle beliefs and behaviors scales. Data were randomly split into 2 halves to explore the PWCESS' validity and reliability and the second half to confirm findings. Principal components analysis indicated a unidimensional construct. The PWCESS was positively related to healthy lifestyle beliefs and behaviors supporting the scale's validity. Confirmatory factor analysis supported the unidimensional construct (Cronbach's α = .92). Strong evidence supports the validity and reliability of the PWCESS. Future use of this scale could guide workplace intervention strategies to improve organizational wellness culture and employee health outcomes.

  19. Large Scale Solar Heating

    DEFF Research Database (Denmark)

    Heller, Alfred

    2001-01-01

    The main objective of the research was to evaluate large-scale solar heating connected to district heating (CSDHP), to build up a simulation tool and to demonstrate the application of the simulation tool for design studies and on a local energy planning case. The evaluation was mainly carried out...... model is designed and validated on the Marstal case. Applying the Danish Reference Year, a design tool is presented. The simulation tool is used for proposals for application of alternative designs, including high-performance solar collector types (trough solar collectors, vaccum pipe collectors......). Simulation programs are proposed as control supporting tool for daily operation and performance prediction of central solar heating plants. Finaly the CSHP technolgy is put into persepctive with respect to alternatives and a short discussion on the barries and breakthrough of the technology are given....

  20. Planning under uncertainty solving large-scale stochastic linear programs

    Energy Technology Data Exchange (ETDEWEB)

    Infanger, G. [Stanford Univ., CA (United States). Dept. of Operations Research]|[Technische Univ., Vienna (Austria). Inst. fuer Energiewirtschaft

    1992-12-01

    For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.

  1. Solving Large-Scale Computational Problems Using Insights from Statistical Physics

    Energy Technology Data Exchange (ETDEWEB)

    Selman, Bart [Cornell University

    2012-02-29

    Many challenging problems in computer science and related fields can be formulated as constraint satisfaction problems. Such problems consist of a set of discrete variables and a set of constraints between those variables, and represent a general class of so-called NP-complete problems. The goal is to find a value assignment to the variables that satisfies all constraints, generally requiring a search through and exponentially large space of variable-value assignments. Models for disordered systems, as studied in statistical physics, can provide important new insights into the nature of constraint satisfaction problems. Recently, work in this area has resulted in the discovery of a new method for solving such problems, called the survey propagation (SP) method. With SP, we can solve problems with millions of variables and constraints, an improvement of two orders of magnitude over previous methods.

  2. 3D large-scale calculations using the method of characteristics

    International Nuclear Information System (INIS)

    Dahmani, M.; Roy, R.; Koclas, J.

    2004-01-01

    An overview of the computational requirements and the numerical developments made in order to be able to solve 3D large-scale problems using the characteristics method will be presented. To accelerate the MCI solver, efficient acceleration techniques were implemented and parallelization was performed. However, for the very large problems, the size of the tracking file used to store the tracks can still become prohibitive and exceed the capacity of the machine. The new 3D characteristics solver MCG will now be introduced. This methodology is dedicated to solve very large 3D problems (a part or a whole core) without spatial homogenization. In order to eliminate the input/output problems occurring when solving these large problems, we define a new computing scheme that requires more CPU resources than the usual one, based on sweeps over large tracking files. The huge capacity of storage needed in some problems and the related I/O queries needed by the characteristics solver are replaced by on-the-fly recalculation of tracks at each iteration step. Using this technique, large 3D problems are no longer I/O-bound, and distributed CPU resources can be efficiently used. (author)

  3. Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis

    Science.gov (United States)

    Moore, R. T.; Hansen, M. C.

    2011-12-01

    Google Earth Engine is a new technology platform that enables monitoring and measurement of changes in the earth's environment, at planetary scale, on a large catalog of earth observation data. The platform offers intrinsically-parallel computational access to thousands of computers in Google's data centers. Initial efforts have focused primarily on global forest monitoring and measurement, in support of REDD+ activities in the developing world. The intent is to put this platform into the hands of scientists and developing world nations, in order to advance the broader operational deployment of existing scientific methods, and strengthen the ability for public institutions and civil society to better understand, manage and report on the state of their natural resources. Earth Engine currently hosts online nearly the complete historical Landsat archive of L5 and L7 data collected over more than twenty-five years. Newly-collected Landsat imagery is downloaded from USGS EROS Center into Earth Engine on a daily basis. Earth Engine also includes a set of historical and current MODIS data products. The platform supports generation, on-demand, of spatial and temporal mosaics, "best-pixel" composites (for example to remove clouds and gaps in satellite imagery), as well as a variety of spectral indices. Supervised learning methods are available over the Landsat data catalog. The platform also includes a new application programming framework, or "API", that allows scientists access to these computational and data resources, to scale their current algorithms or develop new ones. Under the covers of the Google Earth Engine API is an intrinsically-parallel image-processing system. Several forest monitoring applications powered by this API are currently in development and expected to be operational in 2011. Combining science with massive data and technology resources in a cloud-computing framework can offer advantages of computational speed, ease-of-use and collaboration, as

  4. Decentralized Large-Scale Power Balancing

    DEFF Research Database (Denmark)

    Halvgaard, Rasmus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad

    2013-01-01

    problem is formulated as a centralized large-scale optimization problem but is then decomposed into smaller subproblems that are solved locally by each unit connected to an aggregator. For large-scale systems the method is faster than solving the full problem and can be distributed to include an arbitrary...

  5. Large-Scale Traveling Weather Systems in Mars’ Southern Extratropics

    Science.gov (United States)

    Hollingsworth, Jeffery L.; Kahre, Melinda A.

    2017-10-01

    Between late fall and early spring, Mars’ middle- and high-latitude atmosphere supports strong mean equator-to-pole temperature contrasts and an accompanying mean westerly polar vortex. Observations from both the MGS Thermal Emission Spectrometer (TES) and the MRO Mars Climate Sounder (MCS) indicate that a mean baroclinicity-barotropicity supports intense, large-scale eastward traveling weather systems (i.e., transient synoptic-period waves). Such extratropical weather disturbances are critical components of the global circulation as they serve as agents in the transport of heat and momentum, and generalized scalar/tracer quantities (e.g., atmospheric dust, water-vapor and ice clouds). The character of such traveling extratropical synoptic disturbances in Mars' southern hemisphere during late winter through early spring is investigated using a moderately high-resolution Mars global climate model (Mars GCM). This Mars GCM imposes interactively-lifted and radiatively-active dust based on a threshold value of the surface stress. The model exhibits a reasonable "dust cycle" (i.e., globally averaged, a dustier atmosphere during southern spring and summer occurs). Compared to the northern-hemisphere counterparts, the southern synoptic-period weather disturbances and accompanying frontal waves have smaller meridional and zonal scales, and are far less intense. Influences of the zonally asymmetric (i.e., east-west varying) topography on southern large-scale weather are investigated, in addition to large-scale up-slope/down-slope flows and the diurnal cycle. A southern storm zone in late winter and early spring presents in the western hemisphere via orographic influences from the Tharsis highlands, and the Argyre and Hellas impact basins. Geographically localized transient-wave activity diagnostics are constructed that illuminate dynamical differences amongst the simulations and these are presented.

  6. Large-Scale Traveling Weather Systems in Mars Southern Extratropics

    Science.gov (United States)

    Hollingsworth, Jeffery L.; Kahre, Melinda A.

    2017-01-01

    Between late fall and early spring, Mars' middle- and high-latitude atmosphere supports strong mean equator-to-pole temperature contrasts and an accompanying mean westerly polar vortex. Observations from both the MGS Thermal Emission Spectrometer (TES) and the MRO Mars Climate Sounder (MCS) indicate that a mean baroclinicity-barotropicity supports intense, large-scale eastward traveling weather systems (i.e., transient synoptic-period waves). Such extratropical weather disturbances are critical components of the global circulation as they serve as agents in the transport of heat and momentum, and generalized scalar/tracer quantities (e.g., atmospheric dust, water-vapor and ice clouds). The character of such traveling extratropical synoptic disturbances in Mars' southern hemisphere during late winter through early spring is investigated using a moderately high-resolution Mars global climate model (Mars GCM). This Mars GCM imposes interactively-lifted and radiatively-active dust based on a threshold value of the surface stress. The model exhibits a reasonable "dust cycle" (i.e., globally averaged, a dustier atmosphere during southern spring and summer occurs). Compared to the northern-hemisphere counterparts, the southern synoptic-period weather disturbances and accompanying frontal waves have smaller meridional and zonal scales, and are far less intense. Influences of the zonally asymmetric (i.e., east-west varying) topography on southern large-scale weather are investigated, in addition to large-scale up-slope/down-slope flows and the diurnal cycle. A southern storm zone in late winter and early spring presents in the western hemisphere via orographic influences from the Tharsis highlands, and the Argyre and Hellas impact basins. Geographically localized transient-wave activity diagnostics are constructed that illuminate dynamical differences amongst the simulations and these are presented.

  7. A Classification Framework for Large-Scale Face Recognition Systems

    OpenAIRE

    Zhou, Ziheng; Deravi, Farzin

    2009-01-01

    This paper presents a generic classification framework for large-scale face recognition systems. Within the framework, a data sampling strategy is proposed to tackle the data imbalance when image pairs are sampled from thousands of face images for preparing a training dataset. A modified kernel Fisher discriminant classifier is proposed to make it computationally feasible to train the kernel-based classification method using tens of thousands of training samples. The framework is tested in an...

  8. Rucio – The next generation of large scale distributed system for ATLAS data management

    International Nuclear Information System (INIS)

    Garonne, V; Vigne, R; Stewart, G; Barisits, M; Eermann, T B; Lassnig, M; Serfon, C; Goossens, L; Nairz, A

    2014-01-01

    Rucio is the next-generation Distributed Data Management (DDM) system benefiting from recent advances in cloud and 'Big Data' computing to address HEP experiments scaling requirements. Rucio is an evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 140 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio will deal with these issues by relying on a conceptual data model and new technology to ensure system scalability, address new user requirements and employ new automation framework to reduce operational overheads. We present the key concepts of Rucio, including its data organization/representation and a model of how to manage central group and user activities. The Rucio design, and the technology it employs, is described, specifically looking at its RESTful architecture and the various software components it uses. We show also the performance of the system.

  9. Automating large-scale reactor systems

    International Nuclear Information System (INIS)

    Kisner, R.A.

    1985-01-01

    This paper conveys a philosophy for developing automated large-scale control systems that behave in an integrated, intelligent, flexible manner. Methods for operating large-scale systems under varying degrees of equipment degradation are discussed, and a design approach that separates the effort into phases is suggested. 5 refs., 1 fig

  10. Streaming Parallel GPU Acceleration of Large-Scale filter-based Spiking Neural Networks

    NARCIS (Netherlands)

    L.P. Slazynski (Leszek); S.M. Bohte (Sander)

    2012-01-01

    htmlabstractThe arrival of graphics processing (GPU) cards suitable for massively parallel computing promises a↵ordable large-scale neural network simulation previously only available at supercomputing facil- ities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of

  11. Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses

    Science.gov (United States)

    Rosé, Carolyn Penstein; Ferschke, Oliver

    2016-01-01

    This article offers a vision for technology supported collaborative and discussion-based learning at scale. It begins with historical work in the area of tutorial dialogue systems. It traces the history of that area of the field of Artificial Intelligence in Education as it has made an impact on the field of Computer-Supported Collaborative…

  12. Power suppression at large scales in string inflation

    Energy Technology Data Exchange (ETDEWEB)

    Cicoli, Michele [Dipartimento di Fisica ed Astronomia, Università di Bologna, via Irnerio 46, Bologna, 40126 (Italy); Downes, Sean; Dutta, Bhaskar, E-mail: mcicoli@ictp.it, E-mail: sddownes@physics.tamu.edu, E-mail: dutta@physics.tamu.edu [Mitchell Institute for Fundamental Physics and Astronomy, Department of Physics and Astronomy, Texas A and M University, College Station, TX, 77843-4242 (United States)

    2013-12-01

    We study a possible origin of the anomalous suppression of the power spectrum at large angular scales in the cosmic microwave background within the framework of explicit string inflationary models where inflation is driven by a closed string modulus parameterizing the size of the extra dimensions. In this class of models the apparent power loss at large scales is caused by the background dynamics which involves a sharp transition from a fast-roll power law phase to a period of Starobinsky-like slow-roll inflation. An interesting feature of this class of string inflationary models is that the number of e-foldings of inflation is inversely proportional to the string coupling to a positive power. Therefore once the string coupling is tuned to small values in order to trust string perturbation theory, enough e-foldings of inflation are automatically obtained without the need of extra tuning. Moreover, in the less tuned cases the sharp transition responsible for the power loss takes place just before the last 50-60 e-foldings of inflation. We illustrate these general claims in the case of Fibre Inflation where we study the strength of this transition in terms of the attractor dynamics, finding that it induces a pivot from a blue to a redshifted power spectrum which can explain the apparent large scale power loss. We compute the effects of this pivot for example cases and demonstrate how magnitude and duration of this effect depend on model parameters.

  13. Cross-flow turbines: progress report on physical and numerical model studies at large laboratory scale

    Science.gov (United States)

    Wosnik, Martin; Bachant, Peter

    2016-11-01

    Cross-flow turbines show potential in marine hydrokinetic (MHK) applications. A research focus is on accurately predicting device performance and wake evolution to improve turbine array layouts for maximizing overall power output, i.e., minimizing wake interference, or taking advantage of constructive wake interaction. Experiments were carried with large laboratory-scale cross-flow turbines D O (1 m) using a turbine test bed in a large cross-section tow tank, designed to achieve sufficiently high Reynolds numbers for the results to be Reynolds number independent with respect to turbine performance and wake statistics, such that they can be reliably extrapolated to full scale and used for model validation. Several turbines of varying solidity were employed, including the UNH Reference Vertical Axis Turbine (RVAT) and a 1:6 scale model of the DOE-Sandia Reference Model 2 (RM2) turbine. To improve parameterization in array simulations, an actuator line model (ALM) was developed to provide a computationally feasible method for simulating full turbine arrays inside Navier-Stokes models. Results are presented for the simulation of performance and wake dynamics of cross-flow turbines and compared with experiments and body-fitted mesh, blade-resolving CFD. Supported by NSF-CBET Grant 1150797, Sandia National Laboratories.

  14. Energy efficiency supervision strategy selection of Chinese large-scale public buildings

    International Nuclear Information System (INIS)

    Jin Zhenxing; Wu Yong; Li Baizhan; Gao Yafeng

    2009-01-01

    This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China.

  15. Energy efficiency supervision strategy selection of Chinese large-scale public buildings

    Energy Technology Data Exchange (ETDEWEB)

    Jin, Zhenxing; Li, Baizhan; Gao, Yafeng [The Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China); Wu, Yong [The Department of Science and Technology, Ministry of Construction, Beijing 100835 (China)

    2009-06-15

    This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China. (author)

  16. Energy efficiency supervision strategy selection of Chinese large-scale public buildings

    Energy Technology Data Exchange (ETDEWEB)

    Jin Zhenxing [Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China)], E-mail: jinzhenxing33@sina.com; Wu Yong [Department of Science and Technology, Ministry of Construction, Beijing 100835 (China); Li Baizhan; Gao Yafeng [Faculty of Urban Construction and Environmental Engineering, Chongqing University, Chongqing (China); Key Laboratory of the Three Gorges Reservoir Region' s Eco-Environment, Ministry of Education, Chongqing 400045 (China)

    2009-06-15

    This paper discusses energy consumption, building development and building energy consumption in China, and points that energy efficiency management and maintenance of large-scale public buildings is the breakthrough point of building energy saving in China. Three obstacles are lack of basic statistics data, lack of service market for building energy saving, and lack of effective management measures account for the necessity of energy efficiency supervision for large-scale public buildings. And then the paper introduces the supervision aims, the supervision system and the five basic systems' role in the supervision system, and analyzes the working mechanism of the five basic systems. The energy efficiency supervision system of large-scale public buildings takes energy consumption statistics as a data basis, Energy auditing as a technical support, energy consumption ration as a benchmark of energy saving and price increase beyond ration as a price lever, and energy efficiency public-noticing as an amplifier. The supervision system promotes energy efficiency operation and maintenance of large-scale public building, and drives a comprehensive building energy saving in China.

  17. Multidimensional scaling for large genomic data sets

    Directory of Open Access Journals (Sweden)

    Lu Henry

    2008-04-01

    Full Text Available Abstract Background Multi-dimensional scaling (MDS is aimed to represent high dimensional data in a low dimensional space with preservation of the similarities between data points. This reduction in dimensionality is crucial for analyzing and revealing the genuine structure hidden in the data. For noisy data, dimension reduction can effectively reduce the effect of noise on the embedded structure. For large data set, dimension reduction can effectively reduce information retrieval complexity. Thus, MDS techniques are used in many applications of data mining and gene network research. However, although there have been a number of studies that applied MDS techniques to genomics research, the number of analyzed data points was restricted by the high computational complexity of MDS. In general, a non-metric MDS method is faster than a metric MDS, but it does not preserve the true relationships. The computational complexity of most metric MDS methods is over O(N2, so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data. Results We developed a new rapid metric MDS method with a low computational complexity, making metric MDS applicable for large data sets. Computer simulation showed that the new method of split-and-combine MDS (SC-MDS is fast, accurate and efficient. Our empirical studies using microarray data on the yeast cell cycle showed that the performance of K-means in the reduced dimensional space is similar to or slightly better than that of K-means in the original space, but about three times faster to obtain the clustering results. Our clustering results using SC-MDS are more stable than those in the original space. Hence, the proposed SC-MDS is useful for analyzing whole genome data. Conclusion Our new method reduces the computational complexity from O(N3 to O(N when the dimension of the feature space is far less than the number of genes N, and it successfully

  18. The Effect of Large Scale Salinity Gradient on Langmuir Turbulence

    Science.gov (United States)

    Fan, Y.; Jarosz, E.; Yu, Z.; Jensen, T.; Sullivan, P. P.; Liang, J.

    2017-12-01

    Langmuir circulation (LC) is believed to be one of the leading order causes of turbulent mixing in the upper ocean. It is important for momentum and heat exchange across the mixed layer (ML) and directly impact the dynamics and thermodynamics in the upper ocean and lower atmosphere including the vertical distributions of chemical, biological, optical, and acoustic properties. Based on Craik and Leibovich (1976) theory, large eddy simulation (LES) models have been developed to simulate LC in the upper ocean, yielding new insights that could not be obtained from field observations and turbulent closure models. Due its high computational cost, LES models are usually limited to small domain sizes and cannot resolve large-scale flows. Furthermore, most LES models used in the LC simulations use periodic boundary conditions in the horizontal direction, which assumes the physical properties (i.e. temperature and salinity) and expected flow patterns in the area of interest are of a periodically repeating nature so that the limited small LES domain is representative for the larger area. Using periodic boundary condition can significantly reduce computational effort in problems, and it is a good assumption for isotropic shear turbulence. However, LC is anisotropic (McWilliams et al 1997) and was observed to be modulated by crosswind tidal currents (Kukulka et al 2011). Using symmetrical domains, idealized LES studies also indicate LC could interact with oceanic fronts (Hamlington et al 2014) and standing internal waves (Chini and Leibovich, 2005). The present study expands our previous LES modeling investigations of Langmuir turbulence to the real ocean conditions with large scale environmental motion that features fresh water inflow into the study region. Large scale gradient forcing is introduced to the NCAR LES model through scale separation analysis. The model is applied to a field observation in the Gulf of Mexico in July, 2016 when the measurement site was impacted by

  19. Measuring Students' Writing Ability on a Computer-Analytic Developmental Scale: An Exploratory Validity Study

    Science.gov (United States)

    Burdick, Hal; Swartz, Carl W.; Stenner, A. Jackson; Fitzgerald, Jill; Burdick, Don; Hanlon, Sean T.

    2013-01-01

    The purpose of the study was to explore the validity of a novel computer-analytic developmental scale, the Writing Ability Developmental Scale. On the whole, collective results supported the validity of the scale. It was sensitive to writing ability differences across grades and sensitive to within-grade variability as compared to human-rated…

  20. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    Science.gov (United States)

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  1. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    Directory of Open Access Journals (Sweden)

    Lorenzo L. Pesce

    2013-01-01

    Full Text Available Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons and processor pool sizes (1 to 256 processors. Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.

  2. Theoretical science and the future of large scale computing

    International Nuclear Information System (INIS)

    Wilson, K.G.

    1983-01-01

    The author describes the application of computer simulation to physical problems. In this connection the FORTRAN language is considered. Furthermore the application of computer networks is described whereby the processing of experimental data is considered. (HSI).

  3. The Software Reliability of Large Scale Integration Circuit and Very Large Scale Integration Circuit

    OpenAIRE

    Artem Ganiyev; Jan Vitasek

    2010-01-01

    This article describes evaluation method of faultless function of large scale integration circuits (LSI) and very large scale integration circuits (VLSI). In the article there is a comparative analysis of factors which determine faultless of integrated circuits, analysis of already existing methods and model of faultless function evaluation of LSI and VLSI. The main part describes a proposed algorithm and program for analysis of fault rate in LSI and VLSI circuits.

  4. Large scale parallel FEM computations of far/near stress field changes in rocks

    Czech Academy of Sciences Publication Activity Database

    Blaheta, Radim; Byczanski, Petr; Jakl, Ondřej; Kohut, Roman; Kolcun, Alexej; Krečmer, Karel; Starý, Jiří

    2006-01-01

    Roč. 22, č. 4 (2006), s. 449-459 ISSN 0167-739X R&D Projects: GA ČR(CZ) GA105/02/0492; GA AV ČR(CZ) 1ET400300415 Institutional research plan: CEZ:AV0Z30860518 Keywords : large scale finite element analysis Subject RIV: BA - General Mathematics Impact factor: 0.722, year: 2006

  5. Distributed simulation of large computer systems

    International Nuclear Information System (INIS)

    Marzolla, M.

    2001-01-01

    Sequential simulation of large complex physical systems is often regarded as a computationally expensive task. In order to speed-up complex discrete-event simulations, the paradigm of Parallel and Distributed Discrete Event Simulation (PDES) has been introduced since the late 70s. The authors analyze the applicability of PDES to the modeling and analysis of large computer system; such systems are increasingly common in the area of High Energy and Nuclear Physics, because many modern experiments make use of large 'compute farms'. Some feasibility tests have been performed on a prototype distributed simulator

  6. An industrial perspective on bioreactor scale-down: what we can learn from combined large-scale bioprocess and model fluid studies.

    Science.gov (United States)

    Noorman, Henk

    2011-08-01

    For industrial bioreactor design, operation, control and optimization, the scale-down approach is often advocated to efficiently generate data on a small scale, and effectively apply suggested improvements to the industrial scale. In all cases it is important to ensure that the scale-down conditions are representative of the real large-scale bioprocess. Progress is hampered by limited detailed and local information from large-scale bioprocesses. Complementary to real fermentation studies, physical aspects of model fluids such as air-water in large bioreactors provide useful information with limited effort and cost. Still, in industrial practice, investments of time, capital and resources often prohibit systematic work, although, in the end, savings obtained in this way are trivial compared to the expenses that result from real process disturbances, batch failures, and non-flyers with loss of business opportunity. Here we try to highlight what can be learned from real large-scale bioprocess in combination with model fluid studies, and to provide suitable computation tools to overcome data restrictions. Focus is on a specific well-documented case for a 30-m(3) bioreactor. Areas for further research from an industrial perspective are also indicated. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Implicit solvers for large-scale nonlinear problems

    International Nuclear Information System (INIS)

    Keyes, David E; Reynolds, Daniel R; Woodward, Carol S

    2006-01-01

    Computational scientists are grappling with increasingly complex, multi-rate applications that couple such physical phenomena as fluid dynamics, electromagnetics, radiation transport, chemical and nuclear reactions, and wave and material propagation in inhomogeneous media. Parallel computers with large storage capacities are paving the way for high-resolution simulations of coupled problems; however, hardware improvements alone will not prove enough to enable simulations based on brute-force algorithmic approaches. To accurately capture nonlinear couplings between dynamically relevant phenomena, often while stepping over rapid adjustments to quasi-equilibria, simulation scientists are increasingly turning to implicit formulations that require a discrete nonlinear system to be solved for each time step or steady state solution. Recent advances in iterative methods have made fully implicit formulations a viable option for solution of these large-scale problems. In this paper, we overview one of the most effective iterative methods, Newton-Krylov, for nonlinear systems and point to software packages with its implementation. We illustrate the method with an example from magnetically confined plasma fusion and briefly survey other areas in which implicit methods have bestowed important advantages, such as allowing high-order temporal integration and providing a pathway to sensitivity analyses and optimization. Lastly, we overview algorithm extensions under development motivated by current SciDAC applications

  8. Integration and segregation of large-scale brain networks during short-term task automatization.

    Science.gov (United States)

    Mohr, Holger; Wolfensteller, Uta; Betzel, Richard F; Mišić, Bratislav; Sporns, Olaf; Richiardi, Jonas; Ruge, Hannes

    2016-11-03

    The human brain is organized into large-scale functional networks that can flexibly reconfigure their connectivity patterns, supporting both rapid adaptive control and long-term learning processes. However, it has remained unclear how short-term network dynamics support the rapid transformation of instructions into fluent behaviour. Comparing fMRI data of a learning sample (N=70) with a control sample (N=67), we find that increasingly efficient task processing during short-term practice is associated with a reorganization of large-scale network interactions. Practice-related efficiency gains are facilitated by enhanced coupling between the cingulo-opercular network and the dorsal attention network. Simultaneously, short-term task automatization is accompanied by decreasing activation of the fronto-parietal network, indicating a release of high-level cognitive control, and a segregation of the default mode network from task-related networks. These findings suggest that short-term task automatization is enabled by the brain's ability to rapidly reconfigure its large-scale network organization involving complementary integration and segregation processes.

  9. Effect of Variable Spatial Scales on USLE-GIS Computations

    Science.gov (United States)

    Patil, R. J.; Sharma, S. K.

    2017-12-01

    Use of appropriate spatial scale is very important in Universal Soil Loss Equation (USLE) based spatially distributed soil erosion modelling. This study aimed at assessment of annual rates of soil erosion at different spatial scales/grid sizes and analysing how changes in spatial scales affect USLE-GIS computations using simulation and statistical variabilities. Efforts have been made in this study to recommend an optimum spatial scale for further USLE-GIS computations for management and planning in the study area. The present research study was conducted in Shakkar River watershed, situated in Narsinghpur and Chhindwara districts of Madhya Pradesh, India. Remote Sensing and GIS techniques were integrated with Universal Soil Loss Equation (USLE) to predict spatial distribution of soil erosion in the study area at four different spatial scales viz; 30 m, 50 m, 100 m, and 200 m. Rainfall data, soil map, digital elevation model (DEM) and an executable C++ program, and satellite image of the area were used for preparation of the thematic maps for various USLE factors. Annual rates of soil erosion were estimated for 15 years (1992 to 2006) at four different grid sizes. The statistical analysis of four estimated datasets showed that sediment loss dataset at 30 m spatial scale has a minimum standard deviation (2.16), variance (4.68), percent deviation from observed values (2.68 - 18.91 %), and highest coefficient of determination (R2 = 0.874) among all the four datasets. Thus, it is recommended to adopt this spatial scale for USLE-GIS computations in the study area due to its minimum statistical variability and better agreement with the observed sediment loss data. This study also indicates large scope for use of finer spatial scales in spatially distributed soil erosion modelling.

  10. Managing large-scale models: DBS

    International Nuclear Information System (INIS)

    1981-05-01

    A set of fundamental management tools for developing and operating a large scale model and data base system is presented. Based on experience in operating and developing a large scale computerized system, the only reasonable way to gain strong management control of such a system is to implement appropriate controls and procedures. Chapter I discusses the purpose of the book. Chapter II classifies a broad range of generic management problems into three groups: documentation, operations, and maintenance. First, system problems are identified then solutions for gaining management control are disucssed. Chapters III, IV, and V present practical methods for dealing with these problems. These methods were developed for managing SEAS but have general application for large scale models and data bases

  11. Remote collaboration system based on large scale simulation

    International Nuclear Information System (INIS)

    Kishimoto, Yasuaki; Sugahara, Akihiro; Li, J.Q.

    2008-01-01

    Large scale simulation using super-computer, which generally requires long CPU time and produces large amount of data, has been extensively studied as a third pillar in various advanced science fields in parallel to theory and experiment. Such a simulation is expected to lead new scientific discoveries through elucidation of various complex phenomena, which are hardly identified only by conventional theoretical and experimental approaches. In order to assist such large simulation studies for which many collaborators working at geographically different places participate and contribute, we have developed a unique remote collaboration system, referred to as SIMON (simulation monitoring system), which is based on client-server system control introducing an idea of up-date processing, contrary to that of widely used post-processing. As a key ingredient, we have developed a trigger method, which transmits various requests for the up-date processing from the simulation (client) running on a super-computer to a workstation (server). Namely, the simulation running on a super-computer actively controls the timing of up-date processing. The server that has received the requests from the ongoing simulation such as data transfer, data analyses, and visualizations, etc. starts operations according to the requests during the simulation. The server makes the latest results available to web browsers, so that the collaborators can monitor the results at any place and time in the world. By applying the system to a specific simulation project of laser-matter interaction, we have confirmed that the system works well and plays an important role as a collaboration platform on which many collaborators work with one another

  12. Large Scale Self-Organizing Information Distribution System

    National Research Council Canada - National Science Library

    Low, Steven

    2005-01-01

    This project investigates issues in "large-scale" networks. Here "large-scale" refers to networks with large number of high capacity nodes and transmission links, and shared by a large number of users...

  13. Distributed system for large-scale remote research

    International Nuclear Information System (INIS)

    Ueshima, Yutaka

    2002-01-01

    In advanced photon research, large-scale simulations and high-resolution observations are powerfull tools. In numerical and real experiments, the real-time visualization and steering system is considered as a hopeful method of data analysis. This approach is valid in the typical analysis at one time or low cost experiment and simulation. In research of an unknown problem, it is necessary that the output data be analyzed many times because conclusive analysis is difficult at one time. Consequently, output data should be filed to refer and analyze at any time. To support research, we need the automatic functions, transporting data files from data generator to data storage, analyzing data, tracking history of data handling, and so on. The supporting system will be a functionally distributed system. (author)

  14. USE OF RFID AT LARGE-SCALE EVENTS

    Directory of Open Access Journals (Sweden)

    Yuusuke KAWAKITA

    2005-01-01

    Full Text Available Radio Frequency Identification (RFID devices and related technologies have received a great deal of attention for their ability to perform non-contact object identification. Systems incorporating RFID have been evaluated from a variety of perspectives. The authors constructed a networked RFID system to support event management at NetWorld+Interop 2004 Tokyo, an event that received 150,000 visitors. The system used multiple RFID readers installed at the venue and RFID tags carried by each visitor to provide a platform for running various management and visitor support applications. This paper presents the results of this field trial of RFID readability rates. It further addresses the applicability of RFID systems to visitor management, a problematic aspect of large-scale events.

  15. Large-Scale Cubic-Scaling Random Phase Approximation Correlation Energy Calculations Using a Gaussian Basis.

    Science.gov (United States)

    Wilhelm, Jan; Seewald, Patrick; Del Ben, Mauro; Hutter, Jürg

    2016-12-13

    We present an algorithm for computing the correlation energy in the random phase approximation (RPA) in a Gaussian basis requiring [Formula: see text] operations and [Formula: see text] memory. The method is based on the resolution of the identity (RI) with the overlap metric, a reformulation of RI-RPA in the Gaussian basis, imaginary time, and imaginary frequency integration techniques, and the use of sparse linear algebra. Additional memory reduction without extra computations can be achieved by an iterative scheme that overcomes the memory bottleneck of canonical RPA implementations. We report a massively parallel implementation that is the key for the application to large systems. Finally, cubic-scaling RPA is applied to a thousand water molecules using a correlation-consistent triple-ζ quality basis.

  16. A large-scale evaluation of computational protein function prediction

    NARCIS (Netherlands)

    Radivojac, P.; Clark, W.T.; Oron, T.R.; Schnoes, A.M.; Wittkop, T.; Kourmpetis, Y.A.I.; Dijk, van A.D.J.; Friedberg, I.

    2013-01-01

    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be

  17. A Dynamic Optimization Strategy for the Operation of Large Scale Seawater Reverses Osmosis System

    Directory of Open Access Journals (Sweden)

    Aipeng Jiang

    2014-01-01

    Full Text Available In this work, an efficient strategy was proposed for efficient solution of the dynamic model of SWRO system. Since the dynamic model is formulated by a set of differential-algebraic equations, simultaneous strategies based on collocations on finite element were used to transform the DAOP into large scale nonlinear programming problem named Opt2. Then, simulation of RO process and storage tanks was carried element by element and step by step with fixed control variables. All the obtained values of these variables then were used as the initial value for the optimal solution of SWRO system. Finally, in order to accelerate the computing efficiency and at the same time to keep enough accuracy for the solution of Opt2, a simple but efficient finite element refinement rule was used to reduce the scale of Opt2. The proposed strategy was applied to a large scale SWRO system with 8 RO plants and 4 storage tanks as case study. Computing result shows that the proposed strategy is quite effective for optimal operation of the large scale SWRO system; the optimal problem can be successfully solved within decades of iterations and several minutes when load and other operating parameters fluctuate.

  18. Multigrid preconditioned conjugate-gradient method for large-scale wave-front reconstruction.

    Science.gov (United States)

    Gilles, Luc; Vogel, Curtis R; Ellerbroek, Brent L

    2002-09-01

    We introduce a multigrid preconditioned conjugate-gradient (MGCG) iterative scheme for computing open-loop wave-front reconstructors for extreme adaptive optics systems. We present numerical simulations for a 17-m class telescope with n = 48756 sensor measurement grid points within the aperture, which indicate that our MGCG method has a rapid convergence rate for a wide range of subaperture average slope measurement signal-to-noise ratios. The total computational cost is of order n log n. Hence our scheme provides for fast wave-front simulation and control in large-scale adaptive optics systems.

  19. Inference of functional properties from large-scale analysis of enzyme superfamilies.

    Science.gov (United States)

    Brown, Shoshana D; Babbitt, Patricia C

    2012-01-02

    As increasingly large amounts of data from genome and other sequencing projects become available, new approaches are needed to determine the functions of the proteins these genes encode. We show how large-scale computational analysis can help to address this challenge by linking functional information to sequence and structural similarities using protein similarity networks. Network analyses using three functionally diverse enzyme superfamilies illustrate the use of these approaches for facile updating and comparison of available structures for a large superfamily, for creation of functional hypotheses for metagenomic sequences, and to summarize the limits of our functional knowledge about even well studied superfamilies.

  20. Large scale structure and baryogenesis

    International Nuclear Information System (INIS)

    Kirilova, D.P.; Chizhov, M.V.

    2001-08-01

    We discuss a possible connection between the large scale structure formation and the baryogenesis in the universe. An update review of the observational indications for the presence of a very large scale 120h -1 Mpc in the distribution of the visible matter of the universe is provided. The possibility to generate a periodic distribution with the characteristic scale 120h -1 Mpc through a mechanism producing quasi-periodic baryon density perturbations during inflationary stage, is discussed. The evolution of the baryon charge density distribution is explored in the framework of a low temperature boson condensate baryogenesis scenario. Both the observed very large scale of a the visible matter distribution in the universe and the observed baryon asymmetry value could naturally appear as a result of the evolution of a complex scalar field condensate, formed at the inflationary stage. Moreover, for some model's parameters a natural separation of matter superclusters from antimatter ones can be achieved. (author)

  1. Automatic management software for large-scale cluster system

    International Nuclear Information System (INIS)

    Weng Yunjian; Chinese Academy of Sciences, Beijing; Sun Gongxing

    2007-01-01

    At present, the large-scale cluster system faces to the difficult management. For example the manager has large work load. It needs to cost much time on the management and the maintenance of large-scale cluster system. The nodes in large-scale cluster system are very easy to be chaotic. Thousands of nodes are put in big rooms so that some managers are very easy to make the confusion with machines. How do effectively carry on accurate management under the large-scale cluster system? The article introduces ELFms in the large-scale cluster system. Furthermore, it is proposed to realize the large-scale cluster system automatic management. (authors)

  2. A frequency-domain implementation of a sliding-window traffic sign detector for large scale panoramic datasets

    NARCIS (Netherlands)

    Creusen, I.M.; Hazelhoff, L.; With, de P.H.N.

    2013-01-01

    In large-scale automatic traffic sign surveying systems, the primary computational effort is concentrated at the traffic sign detection stage. This paper focuses on reducing the computational load of particularly the sliding window object detection algorithm which is employed for traffic sign

  3. Using Practitioner Inquiry within and against Large-Scale Educational Reform

    Science.gov (United States)

    Hines, Mary Beth; Conner-Zachocki, Jennifer

    2015-01-01

    This research study examines the impact of teacher research on participants in a large-scale educational reform initiative in the United States, No Child Left Behind, and its strand for reading teachers, Reading First. Reading First supported professional development for teachers in order to increase student scores on standardized tests. The…

  4. Large-scale agent-based social simulation : A study on epidemic prediction and control

    NARCIS (Netherlands)

    Zhang, M.

    2016-01-01

    Large-scale agent-based social simulation is gradually proving to be a versatile methodological approach for studying human societies, which could make contributions from policy making in social science, to distributed artificial intelligence and agent technology in computer science, and to theory

  5. Non-parametric co-clustering of large scale sparse bipartite networks on the GPU

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai

    2011-01-01

    of row and column clusters from a hypothesis space of an infinite number of clusters. To reach large scale applications of co-clustering we exploit that parameter inference for co-clustering is well suited for parallel computing. We develop a generic GPU framework for efficient inference on large scale...... sparse bipartite networks and achieve a speedup of two orders of magnitude compared to estimation based on conventional CPUs. In terms of scalability we find for networks with more than 100 million links that reliable inference can be achieved in less than an hour on a single GPU. To efficiently manage...

  6. PathlinesExplorer — Image-based exploration of large-scale pathline fields

    KAUST Repository

    Nagoor, Omniah H.

    2015-10-25

    PathlinesExplorer is a novel image-based tool, which has been designed to visualize large scale pathline fields on a single computer [7]. PathlinesExplorer integrates explorable images (EI) technique [4] with order-independent transparency (OIT) method [2]. What makes this method different is that it allows users to handle large data on a single workstation. Although it is a view-dependent method, PathlinesExplorer combines both exploration and modification of visual aspects without re-accessing the original huge data. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathline segments. With this view-dependent method, it is possible to filter, color-code, and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.

  7. Development of integrated platform for computational material design

    Energy Technology Data Exchange (ETDEWEB)

    Kiyoshi, Matsubara; Kumi, Itai; Nobutaka, Nishikawa; Akifumi, Kato [Center for Computational Science and Engineering, Fuji Research Institute Corporation (Japan); Hideaki, Koike [Advance Soft Corporation (Japan)

    2003-07-01

    The goal of our project is to design and develop a problem-solving environment (PSE) that will help computational scientists and engineers develop large complicated application software and simulate complex phenomena by using networking and parallel computing. The integrated platform, which is designed for PSE in the Japanese national project of Frontier Simulation Software for Industrial Science, is defined by supporting the entire range of problem solving activity from program formulation and data setup to numerical simulation, data management, and visualization. A special feature of our integrated platform is based on a new architecture called TASK FLOW. It integrates the computational resources such as hardware and software on the network and supports complex and large-scale simulation. This concept is applied to computational material design and the project 'comprehensive research for modeling, analysis, control, and design of large-scale complex system considering properties of human being'. Moreover this system will provide the best solution for developing large and complicated software and simulating complex and large-scaled phenomena in computational science and engineering. A prototype has already been developed and the validation and verification of an integrated platform will be scheduled by using the prototype in 2003. In the validation and verification, fluid-structure coupling analysis system for designing an industrial machine will be developed on the integrated platform. As other examples of validation and verification, integrated platform for quantum chemistry and bio-mechanical system are planned.

  8. Development of integrated platform for computational material design

    International Nuclear Information System (INIS)

    Kiyoshi, Matsubara; Kumi, Itai; Nobutaka, Nishikawa; Akifumi, Kato; Hideaki, Koike

    2003-01-01

    The goal of our project is to design and develop a problem-solving environment (PSE) that will help computational scientists and engineers develop large complicated application software and simulate complex phenomena by using networking and parallel computing. The integrated platform, which is designed for PSE in the Japanese national project of Frontier Simulation Software for Industrial Science, is defined by supporting the entire range of problem solving activity from program formulation and data setup to numerical simulation, data management, and visualization. A special feature of our integrated platform is based on a new architecture called TASK FLOW. It integrates the computational resources such as hardware and software on the network and supports complex and large-scale simulation. This concept is applied to computational material design and the project 'comprehensive research for modeling, analysis, control, and design of large-scale complex system considering properties of human being'. Moreover this system will provide the best solution for developing large and complicated software and simulating complex and large-scaled phenomena in computational science and engineering. A prototype has already been developed and the validation and verification of an integrated platform will be scheduled by using the prototype in 2003. In the validation and verification, fluid-structure coupling analysis system for designing an industrial machine will be developed on the integrated platform. As other examples of validation and verification, integrated platform for quantum chemistry and bio-mechanical system are planned

  9. Solving large scale structure in ten easy steps with COLA

    Energy Technology Data Exchange (ETDEWEB)

    Tassev, Svetlin [Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544 (United States); Zaldarriaga, Matias [School of Natural Sciences, Institute for Advanced Study, Olden Lane, Princeton, NJ 08540 (United States); Eisenstein, Daniel J., E-mail: stassev@cfa.harvard.edu, E-mail: matiasz@ias.edu, E-mail: deisenstein@cfa.harvard.edu [Center for Astrophysics, Harvard University, 60 Garden Street, Cambridge, MA 02138 (United States)

    2013-06-01

    We present the COmoving Lagrangian Acceleration (COLA) method: an N-body method for solving for Large Scale Structure (LSS) in a frame that is comoving with observers following trajectories calculated in Lagrangian Perturbation Theory (LPT). Unlike standard N-body methods, the COLA method can straightforwardly trade accuracy at small-scales in order to gain computational speed without sacrificing accuracy at large scales. This is especially useful for cheaply generating large ensembles of accurate mock halo catalogs required to study galaxy clustering and weak lensing, as those catalogs are essential for performing detailed error analysis for ongoing and future surveys of LSS. As an illustration, we ran a COLA-based N-body code on a box of size 100 Mpc/h with particles of mass ≈ 5 × 10{sup 9}M{sub s}un/h. Running the code with only 10 timesteps was sufficient to obtain an accurate description of halo statistics down to halo masses of at least 10{sup 11}M{sub s}un/h. This is only at a modest speed penalty when compared to mocks obtained with LPT. A standard detailed N-body run is orders of magnitude slower than our COLA-based code. The speed-up we obtain with COLA is due to the fact that we calculate the large-scale dynamics exactly using LPT, while letting the N-body code solve for the small scales, without requiring it to capture exactly the internal dynamics of halos. Achieving a similar level of accuracy in halo statistics without the COLA method requires at least 3 times more timesteps than when COLA is employed.

  10. HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing.

    Science.gov (United States)

    Wan, Shixiang; Zou, Quan

    2017-01-01

    Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types. Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction. The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource. THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

  11. Development and testing of a scale to assess physician attitudes about handheld computers with decision support.

    Science.gov (United States)

    Ray, Midge N; Houston, Thomas K; Yu, Feliciano B; Menachemi, Nir; Maisiak, Richard S; Allison, Jeroan J; Berner, Eta S

    2006-01-01

    The authors developed and evaluated a rating scale, the Attitudes toward Handheld Decision Support Software Scale (H-DSS), to assess physician attitudes about handheld decision support systems. The authors conducted a prospective assessment of psychometric characteristics of the H-DSS including reliability, validity, and responsiveness. Participants were 82 Internal Medicine residents. A higher score on each of the 14 five-point Likert scale items reflected a more positive attitude about handheld DSS. The H-DSS score is the mean across the fourteen items. Attitudes toward the use of the handheld DSS were assessed prior to and six months after receiving the handheld device. Cronbach's Alpha was used to assess internal consistency reliability. Pearson correlations were used to estimate and detect significant associations between scale scores and other measures (validity). Paired sample t-tests were used to test for changes in the mean attitude scale score (responsiveness) and for differences between groups. Internal consistency reliability for the scale was alpha = 0.73. In testing validity, moderate correlations were noted between the attitude scale scores and self-reported Personal Digital Assistant (PDA) usage in the hospital (correlation coefficient = 0.55) and clinic (0.48), p DSS scale was reliable, valid, and responsive. The scale can be used to guide future handheld DSS development and implementation.

  12. CMS computing support at JINR

    International Nuclear Information System (INIS)

    Golutvin, I.; Koren'kov, V.; Lavrent'ev, A.; Pose, R.; Tikhonenko, E.

    1998-01-01

    Participation of JINR specialists in the CMS experiment at LHC requires a wide use of computer resources. In the context of JINR activities in the CMS Project hardware and software resources have been provided for full participation of JINR specialists in the CMS experiment; the JINR computer infrastructure was made closer to the CERN one. JINR also provides the informational support for the CMS experiment (web-server http://sunct2.jinr.dubna.su). Plans for further CMS computing support at JINR are stated

  13. Large scale biomimetic membrane arrays

    DEFF Research Database (Denmark)

    Hansen, Jesper Søndergaard; Perry, Mark; Vogel, Jörg

    2009-01-01

    To establish planar biomimetic membranes across large scale partition aperture arrays, we created a disposable single-use horizontal chamber design that supports combined optical-electrical measurements. Functional lipid bilayers could easily and efficiently be established across CO2 laser micro......-structured 8 x 8 aperture partition arrays with average aperture diameters of 301 +/- 5 mu m. We addressed the electro-physical properties of the lipid bilayers established across the micro-structured scaffold arrays by controllable reconstitution of biotechnological and physiological relevant membrane...... peptides and proteins. Next, we tested the scalability of the biomimetic membrane design by establishing lipid bilayers in rectangular 24 x 24 and hexagonal 24 x 27 aperture arrays, respectively. The results presented show that the design is suitable for further developments of sensitive biosensor assays...

  14. ORNL Pre-test Analyses of A Large-scale Experiment in STYLE

    International Nuclear Information System (INIS)

    Williams, Paul T.; Yin, Shengjun; Klasky, Hilda B.; Bass, Bennett Richard

    2011-01-01

    Oak Ridge National Laboratory (ORNL) is conducting a series of numerical analyses to simulate a large scale mock-up experiment planned within the European Network for Structural Integrity for Lifetime Management non-RPV Components (STYLE). STYLE is a European cooperative effort to assess the structural integrity of (non-reactor pressure vessel) reactor coolant pressure boundary components relevant to ageing and life-time management and to integrate the knowledge created in the project into mainstream nuclear industry assessment codes. ORNL contributes work-in-kind support to STYLE Work Package 2 (Numerical Analysis/Advanced Tools) and Work Package 3 (Engineering Assessment Methods/LBB Analyses). This paper summarizes the current status of ORNL analyses of the STYLE Mock-Up3 large-scale experiment to simulate and evaluate crack growth in a cladded ferritic pipe. The analyses are being performed in two parts. In the first part, advanced fracture mechanics models are being developed and performed to evaluate several experiment designs taking into account the capabilities of the test facility while satisfying the test objectives. Then these advanced fracture mechanics models will be utilized to simulate the crack growth in the large scale mock-up test. For the second part, the recently developed ORNL SIAM-PFM open-source, cross-platform, probabilistic computational tool will be used to generate an alternative assessment for comparison with the advanced fracture mechanics model results. The SIAM-PFM probabilistic analysis of the Mock-Up3 experiment will utilize fracture modules that are installed into a general probabilistic framework. The probabilistic results of the Mock-Up3 experiment obtained from SIAM-PFM will be compared to those results generated using the deterministic 3D nonlinear finite-element modeling approach. The objective of the probabilistic analysis is to provide uncertainty bounds that will assist in assessing the more detailed 3D finite

  15. Large-Scale Outflows in Seyfert Galaxies

    Science.gov (United States)

    Colbert, E. J. M.; Baum, S. A.

    1995-12-01

    \\catcode`\\@=11 \\ialign{m @th#1hfil ##hfil \\crcr#2\\crcr\\sim\\crcr}}} \\catcode`\\@=12 Highly collimated outflows extend out to Mpc scales in many radio-loud active galaxies. In Seyfert galaxies, which are radio-quiet, the outflows extend out to kpc scales and do not appear to be as highly collimated. In order to study the nature of large-scale (>~1 kpc) outflows in Seyferts, we have conducted optical, radio and X-ray surveys of a distance-limited sample of 22 edge-on Seyfert galaxies. Results of the optical emission-line imaging and spectroscopic survey imply that large-scale outflows are present in >~{{1} /{4}} of all Seyferts. The radio (VLA) and X-ray (ROSAT) surveys show that large-scale radio and X-ray emission is present at about the same frequency. Kinetic luminosities of the outflows in Seyferts are comparable to those in starburst-driven superwinds. Large-scale radio sources in Seyferts appear diffuse, but do not resemble radio halos found in some edge-on starburst galaxies (e.g. M82). We discuss the feasibility of the outflows being powered by the active nucleus (e.g. a jet) or a circumnuclear starburst.

  16. FFTLasso: Large-Scale LASSO in the Fourier Domain

    KAUST Repository

    Bibi, Adel Aamer

    2017-11-09

    In this paper, we revisit the LASSO sparse representation problem, which has been studied and used in a variety of different areas, ranging from signal processing and information theory to computer vision and machine learning. In the vision community, it found its way into many important applications, including face recognition, tracking, super resolution, image denoising, to name a few. Despite advances in efficient sparse algorithms, solving large-scale LASSO problems remains a challenge. To circumvent this difficulty, people tend to downsample and subsample the problem (e.g. via dimensionality reduction) to maintain a manageable sized LASSO, which usually comes at the cost of losing solution accuracy. This paper proposes a novel circulant reformulation of the LASSO that lifts the problem to a higher dimension, where ADMM can be efficiently applied to its dual form. Because of this lifting, all optimization variables are updated using only basic element-wise operations, the most computationally expensive of which is a 1D FFT. In this way, there is no need for a linear system solver nor matrix-vector multiplication. Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e.g. on a GPU). The attractive computational properties of FFTLasso are verified by extensive experiments on synthetic and real data and on the face recognition task. They demonstrate that FFTLasso scales much more effectively than a state-of-the-art solver.

  17. FFTLasso: Large-Scale LASSO in the Fourier Domain

    KAUST Repository

    Bibi, Adel Aamer; Itani, Hani; Ghanem, Bernard

    2017-01-01

    In this paper, we revisit the LASSO sparse representation problem, which has been studied and used in a variety of different areas, ranging from signal processing and information theory to computer vision and machine learning. In the vision community, it found its way into many important applications, including face recognition, tracking, super resolution, image denoising, to name a few. Despite advances in efficient sparse algorithms, solving large-scale LASSO problems remains a challenge. To circumvent this difficulty, people tend to downsample and subsample the problem (e.g. via dimensionality reduction) to maintain a manageable sized LASSO, which usually comes at the cost of losing solution accuracy. This paper proposes a novel circulant reformulation of the LASSO that lifts the problem to a higher dimension, where ADMM can be efficiently applied to its dual form. Because of this lifting, all optimization variables are updated using only basic element-wise operations, the most computationally expensive of which is a 1D FFT. In this way, there is no need for a linear system solver nor matrix-vector multiplication. Since all operations in our FFTLasso method are element-wise, the subproblems are completely independent and can be trivially parallelized (e.g. on a GPU). The attractive computational properties of FFTLasso are verified by extensive experiments on synthetic and real data and on the face recognition task. They demonstrate that FFTLasso scales much more effectively than a state-of-the-art solver.

  18. Experimental simulation of microinteractions in large scale explosions

    Energy Technology Data Exchange (ETDEWEB)

    Chen, X.; Luo, R.; Yuen, W.W.; Theofanous, T.G. [California Univ., Santa Barbara, CA (United States). Center for Risk Studies and Safety

    1998-01-01

    This paper presents data and analysis of recent experiments conducted in the SIGMA-2000 facility to simulate microinteractions in large scale explosions. Specifically, the fragmentation behavior of a high temperature molten steel drop under high pressure (beyond critical) conditions are investigated. The current data demonstrate, for the first time, the effect of high pressure in suppressing the thermal effect of fragmentation under supercritical conditions. The results support the microinteractions idea, and the ESPROSE.m prediction of fragmentation rate. (author)

  19. SCALE INTERACTION IN A MIXING LAYER. THE ROLE OF THE LARGE-SCALE GRADIENTS

    KAUST Repository

    Fiscaletti, Daniele

    2015-08-23

    The interaction between scales is investigated in a turbulent mixing layer. The large-scale amplitude modulation of the small scales already observed in other works depends on the crosswise location. Large-scale positive fluctuations correlate with a stronger activity of the small scales on the low speed-side of the mixing layer, and a reduced activity on the high speed-side. However, from physical considerations we would expect the scales to interact in a qualitatively similar way within the flow and across different turbulent flows. Therefore, instead of the large-scale fluctuations, the large-scale gradients modulation of the small scales has been additionally investigated.

  20. Tracking of large-scale structures in turbulent channel with direct numerical simulation of low Prandtl number passive scalar

    Science.gov (United States)

    Tiselj, Iztok

    2014-12-01

    Channel flow DNS (Direct Numerical Simulation) at friction Reynolds number 180 and with passive scalars of Prandtl numbers 1 and 0.01 was performed in various computational domains. The "normal" size domain was ˜2300 wall units long and ˜750 wall units wide; size taken from the similar DNS of Moser et al. The "large" computational domain, which is supposed to be sufficient to describe the largest structures of the turbulent flows was 3 times longer and 3 times wider than the "normal" domain. The "very large" domain was 6 times longer and 6 times wider than the "normal" domain. All simulations were performed with the same spatial and temporal resolution. Comparison of the standard and large computational domains shows the velocity field statistics (mean velocity, root-mean-square (RMS) fluctuations, and turbulent Reynolds stresses) that are within 1%-2%. Similar agreement is observed for Pr = 1 temperature fields and can be observed also for the mean temperature profiles at Pr = 0.01. These differences can be attributed to the statistical uncertainties of the DNS. However, second-order moments, i.e., RMS temperature fluctuations of standard and large computational domains at Pr = 0.01 show significant differences of up to 20%. Stronger temperature fluctuations in the "large" and "very large" domains confirm the existence of the large-scale structures. Their influence is more or less invisible in the main velocity field statistics or in the statistics of the temperature fields at Prandtl numbers around 1. However, these structures play visible role in the temperature fluctuations at low Prandtl number, where high temperature diffusivity effectively smears the small-scale structures in the thermal field and enhances the relative contribution of large-scales. These large thermal structures represent some kind of an echo of the large scale velocity structures: the highest temperature-velocity correlations are not observed between the instantaneous temperatures and

  1. An efficient method based on the uniformity principle for synthesis of large-scale heat exchanger networks

    International Nuclear Information System (INIS)

    Zhang, Chunwei; Cui, Guomin; Chen, Shang

    2016-01-01

    Highlights: • Two dimensionless uniformity factors are presented to heat exchange network. • The grouping of process streams reduces the computational complexity of large-scale HENS problems. • The optimal sub-network can be obtained by Powell particle swarm optimization algorithm. • The method is illustrated by a case study involving 39 process streams, with a better solution. - Abstract: The optimal design of large-scale heat exchanger networks is a difficult task due to the inherent non-linear characteristics and the combinatorial nature of heat exchangers. To solve large-scale heat exchanger network synthesis (HENS) problems, two dimensionless uniformity factors to describe the heat exchanger network (HEN) uniformity in terms of the temperature difference and the accuracy of process stream grouping are deduced. Additionally, a novel algorithm that combines deterministic and stochastic optimizations to obtain an optimal sub-network with a suitable heat load for a given group of streams is proposed, and is named the Powell particle swarm optimization (PPSO). As a result, the synthesis of large-scale heat exchanger networks is divided into two corresponding sub-parts, namely, the grouping of process streams and the optimization of sub-networks. This approach reduces the computational complexity and increases the efficiency of the proposed method. The robustness and effectiveness of the proposed method are demonstrated by solving a large-scale HENS problem involving 39 process streams, and the results obtained are better than those previously published in the literature.

  2. Particle physics and polyedra proximity calculation for hazard simulations in large-scale industrial plants

    Science.gov (United States)

    Plebe, Alice; Grasso, Giorgio

    2016-12-01

    This paper describes a system developed for the simulation of flames inside an open-source 3D computer graphic software, Blender, with the aim of analyzing in virtual reality scenarios of hazards in large-scale industrial plants. The advantages of Blender are of rendering at high resolution the very complex structure of large industrial plants, and of embedding a physical engine based on smoothed particle hydrodynamics. This particle system is used to evolve a simulated fire. The interaction of this fire with the components of the plant is computed using polyhedron separation distance, adopting a Voronoi-based strategy that optimizes the number of feature distance computations. Results on a real oil and gas refining industry are presented.

  3. Large-scale machine learning and evaluation platform for real-time traffic surveillance

    Science.gov (United States)

    Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

    2016-09-01

    In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

  4. Dissecting the large-scale galactic conformity

    Science.gov (United States)

    Seo, Seongu

    2018-01-01

    Galactic conformity is an observed phenomenon that galaxies located in the same region have similar properties such as star formation rate, color, gas fraction, and so on. The conformity was first observed among galaxies within in the same halos (“one-halo conformity”). The one-halo conformity can be readily explained by mutual interactions among galaxies within a halo. Recent observations however further witnessed a puzzling connection among galaxies with no direct interaction. In particular, galaxies located within a sphere of ~5 Mpc radius tend to show similarities, even though the galaxies do not share common halos with each other ("two-halo conformity" or “large-scale conformity”). Using a cosmological hydrodynamic simulation, Illustris, we investigate the physical origin of the two-halo conformity and put forward two scenarios. First, back-splash galaxies are likely responsible for the large-scale conformity. They have evolved into red galaxies due to ram-pressure stripping in a given galaxy cluster and happen to reside now within a ~5 Mpc sphere. Second, galaxies in strong tidal field induced by large-scale structure also seem to give rise to the large-scale conformity. The strong tides suppress star formation in the galaxies. We discuss the importance of the large-scale conformity in the context of galaxy evolution.

  5. An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs.

    Directory of Open Access Journals (Sweden)

    Graham Cormode

    Full Text Available Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users' queries from commercial search engines, computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH methods and evaluate four variants in a distributed computing environment (specifically, Hadoop. We identify several optimizations which improve performance, suitable for deployment in very large scale settings. The experimental results demonstrate our variants of LSH achieve the robust performance with better recall compared with "vanilla" LSH, even when using the same amount of space.

  6. Inference of Functional Properties from Large-scale Analysis of Enzyme Superfamilies*

    Science.gov (United States)

    Brown, Shoshana D.; Babbitt, Patricia C.

    2012-01-01

    As increasingly large amounts of data from genome and other sequencing projects become available, new approaches are needed to determine the functions of the proteins these genes encode. We show how large-scale computational analysis can help to address this challenge by linking functional information to sequence and structural similarities using protein similarity networks. Network analyses using three functionally diverse enzyme superfamilies illustrate the use of these approaches for facile updating and comparison of available structures for a large superfamily, for creation of functional hypotheses for metagenomic sequences, and to summarize the limits of our functional knowledge about even well studied superfamilies. PMID:22069325

  7. Algorithm 873: LSTRS: MATLAB Software for Large-Scale Trust-Region Subproblems and Regularization

    DEFF Research Database (Denmark)

    Rojas Larrazabal, Marielba de la Caridad; Santos, Sandra A.; Sorensen, Danny C.

    2008-01-01

    A MATLAB 6.0 implementation of the LSTRS method is resented. LSTRS was described in Rojas, M., Santos, S.A., and Sorensen, D.C., A new matrix-free method for the large-scale trust-region subproblem, SIAM J. Optim., 11(3):611-646, 2000. LSTRS is designed for large-scale quadratic problems with one...... at each step. LSTRS relies on matrix-vector products only and has low and fixed storage requirements, features that make it suitable for large-scale computations. In the MATLAB implementation, the Hessian matrix of the quadratic objective function can be specified either explicitly, or in the form...... of a matrix-vector multiplication routine. Therefore, the implementation preserves the matrix-free nature of the method. A description of the LSTRS method and of the MATLAB software, version 1.2, is presented. Comparisons with other techniques and applications of the method are also included. A guide...

  8. Efficient algorithms for collaborative decision making for large scale settings

    DEFF Research Database (Denmark)

    Assent, Ira

    2011-01-01

    to bring about more effective and more efficient retrieval systems that support the users' decision making process. We sketch promising research directions for more efficient algorithms for collaborative decision making, especially for large scale systems.......Collaborative decision making is a successful approach in settings where data analysis and querying can be done interactively. In large scale systems with huge data volumes or many users, collaboration is often hindered by impractical runtimes. Existing work on improving collaboration focuses...... on avoiding redundancy for users working on the same task. While this improves the effectiveness of the user work process, the underlying query processing engine is typically considered a "black box" and left unchanged. Research in multiple query processing, on the other hand, ignores the application...

  9. Solving Large Scale Nonlinear Eigenvalue Problem in Next-Generation Accelerator Design

    Energy Technology Data Exchange (ETDEWEB)

    Liao, Ben-Shan; Bai, Zhaojun; /UC, Davis; Lee, Lie-Quan; Ko, Kwok; /SLAC

    2006-09-28

    A number of numerical methods, including inverse iteration, method of successive linear problem and nonlinear Arnoldi algorithm, are studied in this paper to solve a large scale nonlinear eigenvalue problem arising from finite element analysis of resonant frequencies and external Q{sub e} values of a waveguide loaded cavity in the next-generation accelerator design. They present a nonlinear Rayleigh-Ritz iterative projection algorithm, NRRIT in short and demonstrate that it is the most promising approach for a model scale cavity design. The NRRIT algorithm is an extension of the nonlinear Arnoldi algorithm due to Voss. Computational challenges of solving such a nonlinear eigenvalue problem for a full scale cavity design are outlined.

  10. Secure File Allocation and Caching in Large-scale Distributed Systems

    DEFF Research Database (Denmark)

    Di Mauro, Alessio; Mei, Alessandro; Jajodia, Sushil

    2012-01-01

    In this paper, we present a file allocation and caching scheme that guarantees high assurance, availability, and load balancing in a large-scale distributed file system that can support dynamic updates of authorization policies. The scheme uses fragmentation and replication to store files with hi......-balancing, and reducing delay of read operations. The system offers a trade-off-between performance and security that is dynamically tunable according to the current level of threat. We validate our mechanisms with extensive simulations in an Internet-like network.......In this paper, we present a file allocation and caching scheme that guarantees high assurance, availability, and load balancing in a large-scale distributed file system that can support dynamic updates of authorization policies. The scheme uses fragmentation and replication to store files with high...... security requirements in a system composed of a majority of low-security servers. We develop mechanisms to fragment files, to allocate them into multiple servers, and to cache them as close as possible to their readers while preserving the security requirement of the files, providing load...

  11. Resolving the three-dimensional microstructure of polymer electrolyte fuel cell electrodes using nanometer-scale X-ray computed tomography

    Energy Technology Data Exchange (ETDEWEB)

    Epting, William K.; Gelb, Jeff; Litster, Shawn

    2012-02-08

    The electrodes of a polymer electrolyte fuel cell (PEFC) are composite porous layers consisting of carbon and platinum nanoparticles and a polymer electrolyte binder. The proper composition and arrangement of these materials for fast reactant transport and high electrochemical activity is crucial to achieving high performance, long lifetimes, and low costs. Here, the microstructure of a PEFC electrode using nanometer-scale X-ray computed tomography (nano-CT) with a resolution of 50 nm is investigated. The nano-CT instrument obtains this resolution for the low-atomic-number catalyst support and binder using a combination of a Fresnel zone plate objective and Zernike phase contrast imaging. High-resolution, non-destructive imaging of the three-dimensional (3D) microstructures provides important new information on the size and form of the catalyst particle agglomerates and pore spaces. Transmission electron microscopy (TEM) and mercury intrusion porosimetry (MIP) is applied to evaluate the limits of the resolution and to verify the 3D reconstructions. The computational reconstructions and size distributions obtained with nano-CT can be used for evaluating electrode preparation, performing pore-scale simulations, and extracting effective morphological parameters for large-scale computational models. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  12. Watchdog - a workflow management system for the distributed analysis of large-scale experimental data.

    Science.gov (United States)

    Kluge, Michael; Friedel, Caroline C

    2018-03-13

    The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated execution of corresponding analysis workflows. Major advantages of WMSs are the easy reproducibility of results as well as the reusability of workflows or their components. In this article, we present Watchdog, a WMS for the automated analysis of large-scale experimental data. Main features include straightforward processing of replicate data, support for distributed computer systems, customizable error detection and manual intervention into workflow execution. Watchdog is implemented in Java and thus platform-independent and allows easy sharing of workflows and corresponding program modules. It provides a graphical user interface (GUI) for workflow construction using pre-defined modules as well as a helper script for creating new module definitions. Execution of workflows is possible using either the GUI or a command-line interface and a web-interface is provided for monitoring the execution status and intervening in case of errors. To illustrate its potentials on a real-life example, a comprehensive workflow and modules for the analysis of RNA-seq experiments were implemented and are provided with the software in addition to simple test examples. Watchdog is a powerful and flexible WMS for the analysis of large-scale high-throughput experiments. We believe it will greatly benefit both users with and without programming skills who want to develop and apply bioinformatical workflows with reasonable overhead. The software, example workflows and a comprehensive documentation are freely

  13. Efficient Computation of Sparse Matrix Functions for Large-Scale Electronic Structure Calculations: The CheSS Library.

    Science.gov (United States)

    Mohr, Stephan; Dawson, William; Wagner, Michael; Caliste, Damien; Nakajima, Takahito; Genovese, Luigi

    2017-10-10

    We present CheSS, the "Chebyshev Sparse Solvers" library, which has been designed to solve typical problems arising in large-scale electronic structure calculations using localized basis sets. The library is based on a flexible and efficient expansion in terms of Chebyshev polynomials and presently features the calculation of the density matrix, the calculation of matrix powers for arbitrary powers, and the extraction of eigenvalues in a selected interval. CheSS is able to exploit the sparsity of the matrices and scales linearly with respect to the number of nonzero entries, making it well-suited for large-scale calculations. The approach is particularly adapted for setups leading to small spectral widths of the involved matrices and outperforms alternative methods in this regime. By coupling CheSS to the DFT code BigDFT, we show that such a favorable setup is indeed possible in practice. In addition, the approach based on Chebyshev polynomials can be massively parallelized, and CheSS exhibits excellent scaling up to thousands of cores even for relatively small matrix sizes.

  14. A multiple-scaling method of the computation of threaded structures

    International Nuclear Information System (INIS)

    Andrieux, S.; Leger, A.

    1989-01-01

    The numerical computation of threaded structures usually leads to very large finite elements problems. It was therefore very difficult to carry out some parametric studies, especially in non-linear cases involving plasticity or unilateral contact conditions. Nevertheless, these parametric studies are essential in many industrial problems, for instance for the evaluation of various repairing processes of the closure studs of PWR. It is well known that such repairing generally involves several modifications of the thread geometry, of the number of active threads, of the flange clamping conditions, and so on. This paper is devoted to the description of a two-scale method, which easily allows parametric studies. The main idea of this method consists of dividing the problem into a global part, and a local part. The local problem is solved by F.E.M. on the precise geometry of the thread of some elementary loadings. The global one is formulated on the gudgeon scale and is reduced to a monodimensional one. The resolution of this global problem leads to the unsignificant computational cost. Then, a post-processing gives the stress field at the thread scale anywhere in the assembly. After recalling some principles of the two-scales approach, the method is described. The validation by comparison with a direct F.E. computation and some further applications are presented

  15. Large-scale perspective as a challenge

    NARCIS (Netherlands)

    Plomp, M.G.A.

    2012-01-01

    1. Scale forms a challenge for chain researchers: when exactly is something ‘large-scale’? What are the underlying factors (e.g. number of parties, data, objects in the chain, complexity) that determine this? It appears to be a continuum between small- and large-scale, where positioning on that

  16. Hydrologic test plans for large-scale, multiple-well tests in support of site characterization at Hanford, Washington

    International Nuclear Information System (INIS)

    Rogers, P.M.; Stone, R.; Lu, A.H.

    1985-01-01

    The Basalt Waste Isolation Project is preparing plans for tests and has begun work on some tests that will provide the data necessary for the hydrogeologic characterization of a site located on a United States government reservation at Hanford, Washington. This site is being considered for the Nation's first geologic repository of high level nuclear waste. Hydrogeologic characterization of this site requires several lines of investigation which include: surface-based small-scale tests, testing performed at depth from an exploratory shaft, geochemistry investigations, regional studies, and site-specific investigations using large-scale, multiple-well hydraulic tests. The large-scale multiple-well tests are planned for several locations in and around the site. These tests are being designed to provide estimates of hydraulic parameter values of the geologic media, chemical properties of the groundwater, and hydrogeologic boundary conditions at a scale appropriate for evaluating repository performance with respect to potential radionuclide transport

  17. Algorithm 896: LSA: Algorithms for Large-Scale Optimization

    Czech Academy of Sciences Publication Activity Database

    Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan

    2009-01-01

    Roč. 36, č. 3 (2009), 16-1-16-29 ISSN 0098-3500 R&D Pro jects: GA AV ČR IAA1030405; GA ČR GP201/06/P397 Institutional research plan: CEZ:AV0Z10300504 Keywords : algorithms * design * large-scale optimization * large-scale nonsmooth optimization * large-scale nonlinear least squares * large-scale nonlinear minimax * large-scale systems of nonlinear equations * sparse pro blems * partially separable pro blems * limited-memory methods * discrete Newton methods * quasi-Newton methods * primal interior-point methods Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.904, year: 2009

  18. Scale interactions in a mixing layer – the role of the large-scale gradients

    KAUST Repository

    Fiscaletti, D.

    2016-02-15

    © 2016 Cambridge University Press. The interaction between the large and the small scales of turbulence is investigated in a mixing layer, at a Reynolds number based on the Taylor microscale of , via direct numerical simulations. The analysis is performed in physical space, and the local vorticity root-mean-square (r.m.s.) is taken as a measure of the small-scale activity. It is found that positive large-scale velocity fluctuations correspond to large vorticity r.m.s. on the low-speed side of the mixing layer, whereas, they correspond to low vorticity r.m.s. on the high-speed side. The relationship between large and small scales thus depends on position if the vorticity r.m.s. is correlated with the large-scale velocity fluctuations. On the contrary, the correlation coefficient is nearly constant throughout the mixing layer and close to unity if the vorticity r.m.s. is correlated with the large-scale velocity gradients. Therefore, the small-scale activity appears closely related to large-scale gradients, while the correlation between the small-scale activity and the large-scale velocity fluctuations is shown to reflect a property of the large scales. Furthermore, the vorticity from unfiltered (small scales) and from low pass filtered (large scales) velocity fields tend to be aligned when examined within vortical tubes. These results provide evidence for the so-called \\'scale invariance\\' (Meneveau & Katz, Annu. Rev. Fluid Mech., vol. 32, 2000, pp. 1-32), and suggest that some of the large-scale characteristics are not lost at the small scales, at least at the Reynolds number achieved in the present simulation.

  19. Distributed weighted least-squares estimation with fast convergence for large-scale systems☆

    Science.gov (United States)

    Marelli, Damián Edgardo; Fu, Minyue

    2015-01-01

    In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods. PMID:25641976

  20. Distributed weighted least-squares estimation with fast convergence for large-scale systems.

    Science.gov (United States)

    Marelli, Damián Edgardo; Fu, Minyue

    2015-01-01

    In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods.

  1. The Roles of Sparse Direct Methods in Large-scale Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.

    2005-06-27

    Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research.

  2. The Roles of Sparse Direct Methods in Large-scale Simulations

    International Nuclear Information System (INIS)

    Li, Xiaoye S.; Gao, Weiguo; Husbands, Parry J.R.; Yang, Chao; Ng, Esmond G.

    2005-01-01

    Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergence deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together to take full advantage of terascale computing systems and new algorithms research

  3. Quantum Monte Carlo for large chemical systems: implementing efficient strategies for peta scale platforms and beyond

    International Nuclear Information System (INIS)

    Scemama, Anthony; Caffarel, Michel; Oseret, Emmanuel; Jalby, William

    2013-01-01

    Various strategies to implement efficiently quantum Monte Carlo (QMC) simulations for large chemical systems are presented. These include: (i) the introduction of an efficient algorithm to calculate the computationally expensive Slater matrices. This novel scheme is based on the use of the highly localized character of atomic Gaussian basis functions (not the molecular orbitals as usually done), (ii) the possibility of keeping the memory footprint minimal, (iii) the important enhancement of single-core performance when efficient optimization tools are used, and (iv) the definition of a universal, dynamic, fault-tolerant, and load-balanced framework adapted to all kinds of computational platforms (massively parallel machines, clusters, or distributed grids). These strategies have been implemented in the QMC-Chem code developed at Toulouse and illustrated with numerical applications on small peptides of increasing sizes (158, 434, 1056, and 1731 electrons). Using 10-80 k computing cores of the Curie machine (GENCI-TGCC-CEA, France), QMC-Chem has been shown to be capable of running at the peta scale level, thus demonstrating that for this machine a large part of the peak performance can be achieved. Implementation of large-scale QMC simulations for future exa scale platforms with a comparable level of efficiency is expected to be feasible. (authors)

  4. Large-scale matrix-handling subroutines 'ATLAS'

    International Nuclear Information System (INIS)

    Tsunematsu, Toshihide; Takeda, Tatsuoki; Fujita, Keiichi; Matsuura, Toshihiko; Tahara, Nobuo

    1978-03-01

    Subroutine package ''ATLAS'' has been developed for handling large-scale matrices. The package is composed of four kinds of subroutines, i.e., basic arithmetic routines, routines for solving linear simultaneous equations and for solving general eigenvalue problems and utility routines. The subroutines are useful in large scale plasma-fluid simulations. (auth.)

  5. Implementation of highly parallel and large scale GW calculations within the OpenAtom software

    Science.gov (United States)

    Ismail-Beigi, Sohrab

    The need to describe electronic excitations with better accuracy than provided by band structures produced by Density Functional Theory (DFT) has been a long-term enterprise for the computational condensed matter and materials theory communities. In some cases, appropriate theoretical frameworks have existed for some time but have been difficult to apply widely due to computational cost. For example, the GW approximation incorporates a great deal of important non-local and dynamical electronic interaction effects but has been too computationally expensive for routine use in large materials simulations. OpenAtom is an open source massively parallel ab initiodensity functional software package based on plane waves and pseudopotentials (http://charm.cs.uiuc.edu/OpenAtom/) that takes advantage of the Charm + + parallel framework. At present, it is developed via a three-way collaboration, funded by an NSF SI2-SSI grant (ACI-1339804), between Yale (Ismail-Beigi), IBM T. J. Watson (Glenn Martyna) and the University of Illinois at Urbana Champaign (Laxmikant Kale). We will describe the project and our current approach towards implementing large scale GW calculations with OpenAtom. Potential applications of large scale parallel GW software for problems involving electronic excitations in semiconductor and/or metal oxide systems will be also be pointed out.

  6. Large-scale solar heat

    Energy Technology Data Exchange (ETDEWEB)

    Tolonen, J.; Konttinen, P.; Lund, P. [Helsinki Univ. of Technology, Otaniemi (Finland). Dept. of Engineering Physics and Mathematics

    1998-12-31

    In this project a large domestic solar heating system was built and a solar district heating system was modelled and simulated. Objectives were to improve the performance and reduce costs of a large-scale solar heating system. As a result of the project the benefit/cost ratio can be increased by 40 % through dimensioning and optimising the system at the designing stage. (orig.)

  7. Large Scale EOF Analysis of Climate Data

    Science.gov (United States)

    Prabhat, M.; Gittens, A.; Kashinath, K.; Cavanaugh, N. R.; Mahoney, M.

    2016-12-01

    We present a distributed approach towards extracting EOFs from 3D climate data. We implement the method in Apache Spark, and process multi-TB sized datasets on O(1000-10,000) cores. We apply this method to latitude-weighted ocean temperature data from CSFR, a 2.2 terabyte-sized data set comprising ocean and subsurface reanalysis measurements collected at 41 levels in the ocean, at 6 hour intervals over 31 years. We extract the first 100 EOFs of this full data set and compare to the EOFs computed simply on the surface temperature field. Our analyses provide evidence of Kelvin and Rossy waves and components of large-scale modes of oscillation including the ENSO and PDO that are not visible in the usual SST EOFs. Further, they provide information on the the most influential parts of the ocean, such as the thermocline, that exist below the surface. Work is ongoing to understand the factors determining the depth-varying spatial patterns observed in the EOFs. We will experiment with weighting schemes to appropriately account for the differing depths of the observations. We also plan to apply the same distributed approach to analysis of analysis of 3D atmospheric climatic data sets, including multiple variables. Because the atmosphere changes on a quicker time-scale than the ocean, we expect that the results will demonstrate an even greater advantage to computing 3D EOFs in lieu of 2D EOFs.

  8. Distributed computing and nuclear reactor analysis

    International Nuclear Information System (INIS)

    Brown, F.B.; Derstine, K.L.; Blomquist, R.N.

    1994-01-01

    Large-scale scientific and engineering calculations for nuclear reactor analysis can now be carried out effectively in a distributed computing environment, at costs far lower than for traditional mainframes. The distributed computing environment must include support for traditional system services, such as a queuing system for batch work, reliable filesystem backups, and parallel processing capabilities for large jobs. All ANL computer codes for reactor analysis have been adapted successfully to a distributed system based on workstations and X-terminals. Distributed parallel processing has been demonstrated to be effective for long-running Monte Carlo calculations

  9. LEMON - LHC Era Monitoring for Large-Scale Infrastructures

    International Nuclear Information System (INIS)

    Babik, Marian; Hook, Nicholas; Lansdale, Thomas Hector; Lenkes, Daniel; Siket, Miroslav; Waldron, Denis; Fedorko, Ivan

    2011-01-01

    At the present time computer centres are facing a massive rise in virtualization and cloud computing as these solutions bring advantages to service providers and consolidate the computer centre resources. However, as a result the monitoring complexity is increasing. Computer centre management requires not only to monitor servers, network equipment and associated software but also to collect additional environment and facilities data (e.g. temperature, power consumption, cooling efficiency, etc.) to have also a good overview of the infrastructure performance. The LHC Era Monitoring (Lemon) system is addressing these requirements for a very large scale infrastructure. The Lemon agent that collects data on every client and forwards the samples to the central measurement repository provides a flexible interface that allows rapid development of new sensors. The system allows also to report on behalf of remote devices such as switches and power supplies. Online and historical data can be visualized via a web-based interface or retrieved via command-line tools. The Lemon Alarm System component can be used for notifying the operator about error situations. In this article, an overview of the Lemon monitoring is provided together with a description of the CERN LEMON production instance. No direct comparison is made with other monitoring tool.

  10. Large-scale modelling of neuronal systems

    International Nuclear Information System (INIS)

    Castellani, G.; Verondini, E.; Giampieri, E.; Bersani, F.; Remondini, D.; Milanesi, L.; Zironi, I.

    2009-01-01

    The brain is, without any doubt, the most, complex system of the human body. Its complexity is also due to the extremely high number of neurons, as well as the huge number of synapses connecting them. Each neuron is capable to perform complex tasks, like learning and memorizing a large class of patterns. The simulation of large neuronal systems is challenging for both technological and computational reasons, and can open new perspectives for the comprehension of brain functioning. A well-known and widely accepted model of bidirectional synaptic plasticity, the BCM model, is stated by a differential equation approach based on bistability and selectivity properties. We have modified the BCM model extending it from a single-neuron to a whole-network model. This new model is capable to generate interesting network topologies starting from a small number of local parameters, describing the interaction between incoming and outgoing links from each neuron. We have characterized this model in terms of complex network theory, showing how this, learning rule can be a support For network generation.

  11. Molecular computational elements encode large populations of small objects

    Science.gov (United States)

    Prasanna de Silva, A.; James, Mark R.; McKinney, Bernadine O. F.; Pears, David A.; Weir, Sheenagh M.

    2006-10-01

    Since the introduction of molecular computation, experimental molecular computational elements have grown to encompass small-scale integration, arithmetic and games, among others. However, the need for a practical application has been pressing. Here we present molecular computational identification (MCID), a demonstration that molecular logic and computation can be applied to a widely relevant issue. Examples of populations that need encoding in the microscopic world are cells in diagnostics or beads in combinatorial chemistry (tags). Taking advantage of the small size (about 1nm) and large `on/off' output ratios of molecular logic gates and using the great variety of logic types, input chemical combinations, switching thresholds and even gate arrays in addition to colours, we produce unique identifiers for members of populations of small polymer beads (about 100μm) used for synthesis of combinatorial libraries. Many millions of distinguishable tags become available. This method should be extensible to far smaller objects, with the only requirement being a `wash and watch' protocol. Our focus on converting molecular science into technology concerning analog sensors, turns to digital logic devices in the present work.

  12. Probes of large-scale structure in the Universe

    International Nuclear Information System (INIS)

    Suto, Yasushi; Gorski, K.; Juszkiewicz, R.; Silk, J.

    1988-01-01

    Recent progress in observational techniques has made it possible to confront quantitatively various models for the large-scale structure of the Universe with detailed observational data. We develop a general formalism to show that the gravitational instability theory for the origin of large-scale structure is now capable of critically confronting observational results on cosmic microwave background radiation angular anisotropies, large-scale bulk motions and large-scale clumpiness in the galaxy counts. (author)

  13. Energy Conservation Using Dynamic Voltage Frequency Scaling for Computational Cloud

    Directory of Open Access Journals (Sweden)

    A. Paulin Florence

    2016-01-01

    Full Text Available Cloud computing is a new technology which supports resource sharing on a “Pay as you go” basis around the world. It provides various services such as SaaS, IaaS, and PaaS. Computation is a part of IaaS and the entire computational requests are to be served efficiently with optimal power utilization in the cloud. Recently, various algorithms are developed to reduce power consumption and even Dynamic Voltage and Frequency Scaling (DVFS scheme is also used in this perspective. In this paper we have devised methodology which analyzes the behavior of the given cloud request and identifies the associated type of algorithm. Once the type of algorithm is identified, using their asymptotic notations, its time complexity is calculated. Using best fit strategy the appropriate host is identified and the incoming job is allocated to the victimized host. Using the measured time complexity the required clock frequency of the host is measured. According to that CPU frequency is scaled up or down using DVFS scheme, enabling energy to be saved up to 55% of total Watts consumption.

  14. Computer-Administered Interviews and Rating Scales

    Science.gov (United States)

    Garb, Howard N.

    2007-01-01

    To evaluate the value of computer-administered interviews and rating scales, the following topics are reviewed in the present article: (a) strengths and weaknesses of structured and unstructured assessment instruments, (b) advantages and disadvantages of computer administration, and (c) the validity and utility of computer-administered interviews…

  15. Hierarchical approach to optimization of parallel matrix multiplication on large-scale platforms

    KAUST Repository

    Hasanov, Khalid

    2014-03-04

    © 2014, Springer Science+Business Media New York. Many state-of-the-art parallel algorithms, which are widely used in scientific applications executed on high-end computing systems, were designed in the twentieth century with relatively small-scale parallelism in mind. Indeed, while in 1990s a system with few hundred cores was considered a powerful supercomputer, modern top supercomputers have millions of cores. In this paper, we present a hierarchical approach to optimization of message-passing parallel algorithms for execution on large-scale distributed-memory systems. The idea is to reduce the communication cost by introducing hierarchy and hence more parallelism in the communication scheme. We apply this approach to SUMMA, the state-of-the-art parallel algorithm for matrix–matrix multiplication, and demonstrate both theoretically and experimentally that the modified Hierarchical SUMMA significantly improves the communication cost and the overall performance on large-scale platforms.

  16. Pervasive Computing Support for Hospitals: An Overview of the Activity-Based Computing Project

    DEFF Research Database (Denmark)

    Christensen, Henrik Bærbak; Bardram, Jakob E

    2007-01-01

    The activity-based computing project researched pervasive computing support for clinical hospital work. Such technologies have potential for supporting the mobile, collaborative, and disruptive use of heterogeneous embedded devices in a hospital......The activity-based computing project researched pervasive computing support for clinical hospital work. Such technologies have potential for supporting the mobile, collaborative, and disruptive use of heterogeneous embedded devices in a hospital...

  17. Detonation and fragmentation modeling for the description of large scale vapor explosions

    International Nuclear Information System (INIS)

    Buerger, M.; Carachalios, C.; Unger, H.

    1985-01-01

    The thermal detonation modeling of large-scale vapor explosions is shown to be indispensable for realistic safety evaluations. A steady-state as well as transient detonation model have been developed including detailed descriptions of the dynamics as well as the fragmentation processes inside a detonation wave. Strong restrictions for large-scale vapor explosions are obtained from this modeling and they indicate that the reactor pressure vessel would even withstand explosions with unrealistically high masses of corium involved. The modeling is supported by comparisons with a detonation experiment and - concerning its key part - hydronamic fragmentation experiments. (orig.) [de

  18. Large-scale grid management; Storskala Nettforvaltning

    Energy Technology Data Exchange (ETDEWEB)

    Langdal, Bjoern Inge; Eggen, Arnt Ove

    2003-07-01

    The network companies in the Norwegian electricity industry now have to establish a large-scale network management, a concept essentially characterized by (1) broader focus (Broad Band, Multi Utility,...) and (2) bigger units with large networks and more customers. Research done by SINTEF Energy Research shows so far that the approaches within large-scale network management may be structured according to three main challenges: centralization, decentralization and out sourcing. The article is part of a planned series.

  19. Numerically modelling the large scale coronal magnetic field

    Science.gov (United States)

    Panja, Mayukh; Nandi, Dibyendu

    2016-07-01

    The solar corona spews out vast amounts of magnetized plasma into the heliosphere which has a direct impact on the Earth's magnetosphere. Thus it is important that we develop an understanding of the dynamics of the solar corona. With our present technology it has not been possible to generate 3D magnetic maps of the solar corona; this warrants the use of numerical simulations to study the coronal magnetic field. A very popular method of doing this, is to extrapolate the photospheric magnetic field using NLFF or PFSS codes. However the extrapolations at different time intervals are completely independent of each other and do not capture the temporal evolution of magnetic fields. On the other hand full MHD simulations of the global coronal field, apart from being computationally very expensive would be physically less transparent, owing to the large number of free parameters that are typically used in such codes. This brings us to the Magneto-frictional model which is relatively simpler and computationally more economic. We have developed a Magnetofrictional Model, in 3D spherical polar co-ordinates to study the large scale global coronal field. Here we present studies of changing connectivities between active regions, in response to photospheric motions.

  20. Computational investigation of large-scale vortex interaction with flexible bodies

    Science.gov (United States)

    Connell, Benjamin; Yue, Dick K. P.

    2003-11-01

    The interaction of large-scale vortices with flexible bodies is examined with particular interest paid to the energy and momentum budgets of the system. Finite difference direct numerical simulation of the Navier-Stokes equations on a moving curvilinear grid is coupled with a finite difference structural solver of both a linear membrane under tension and linear Euler-Bernoulli beam. The hydrodynamics and structural dynamics are solved simultaneously using an iterative procedure with the external structural forcing calculated from the hydrodynamics at the surface and the flow-field velocity boundary condition given by the structural motion. We focus on an investigation into the canonical problem of a vortex-dipole impinging on a flexible membrane. It is discovered that the structural properties of the membrane direct the interaction in terms of the flow evolution and the energy budget. Pressure gradients associated with resonant membrane response are shown to sustain the oscillatory motion of the vortex pair. Understanding how the key mechanisms in vortex-body interactions are guided by the structural properties of the body is a prerequisite to exploiting these mechanisms.

  1. Large Scale Flutter Data for Design of Rotating Blades Using Navier-Stokes Equations

    Science.gov (United States)

    Guruswamy, Guru P.

    2012-01-01

    A procedure to compute flutter boundaries of rotating blades is presented; a) Navier-Stokes equations. b) Frequency domain method compatible with industry practice. Procedure is initially validated: a) Unsteady loads with flapping wing experiment. b) Flutter boundary with fixed wing experiment. Large scale flutter computation is demonstrated for rotating blade: a) Single job submission script. b) Flutter boundary in 24 hour wall clock time with 100 cores. c) Linearly scalable with number of cores. Tested with 1000 cores that produced data in 25 hrs for 10 flutter boundaries. Further wall-clock speed-up is possible by performing parallel computations within each case.

  2. Japanese large-scale interferometers

    CERN Document Server

    Kuroda, K; Miyoki, S; Ishizuka, H; Taylor, C T; Yamamoto, K; Miyakawa, O; Fujimoto, M K; Kawamura, S; Takahashi, R; Yamazaki, T; Arai, K; Tatsumi, D; Ueda, A; Fukushima, M; Sato, S; Shintomi, T; Yamamoto, A; Suzuki, T; Saitô, Y; Haruyama, T; Sato, N; Higashi, Y; Uchiyama, T; Tomaru, T; Tsubono, K; Ando, M; Takamori, A; Numata, K; Ueda, K I; Yoneda, H; Nakagawa, K; Musha, M; Mio, N; Moriwaki, S; Somiya, K; Araya, A; Kanda, N; Telada, S; Sasaki, M; Tagoshi, H; Nakamura, T; Tanaka, T; Ohara, K

    2002-01-01

    The objective of the TAMA 300 interferometer was to develop advanced technologies for kilometre scale interferometers and to observe gravitational wave events in nearby galaxies. It was designed as a power-recycled Fabry-Perot-Michelson interferometer and was intended as a step towards a final interferometer in Japan. The present successful status of TAMA is presented. TAMA forms a basis for LCGT (large-scale cryogenic gravitational wave telescope), a 3 km scale cryogenic interferometer to be built in the Kamioka mine in Japan, implementing cryogenic mirror techniques. The plan of LCGT is schematically described along with its associated R and D.

  3. Automatic Installation and Configuration for Large Scale Farms

    CERN Document Server

    Novák, J

    2005-01-01

    Since the early appearance of commodity hardware, the utilization of computers rose rapidly, and they became essential in all areas of life. Soon it was realized that nodes are able to work cooperatively, in order to solve new, more complex tasks. This conception got materialized in coherent aggregations of computers called farms and clusters. Collective application of nodes, being efficient and economical, was adopted in education, research and industry before long. But maintainance, especially in large scale, appeared as a problem to be resolved. New challenges needed new methods and tools. Development work has been started to build farm management applications and frameworks. In the first part of the thesis, these systems are introduced. After a general description of the matter, a comparative analysis of different approaches and tools illustrates the practical aspects of the theoretical discussion. CERN, the European Organization of Nuclear Research is the largest Particle Physics laboratory in the world....

  4. Report of the Workshop on Petascale Systems Integration for LargeScale Facilities

    Energy Technology Data Exchange (ETDEWEB)

    Kramer, William T.C.; Walter, Howard; New, Gary; Engle, Tom; Pennington, Rob; Comes, Brad; Bland, Buddy; Tomlison, Bob; Kasdorf, Jim; Skinner, David; Regimbal, Kevin

    2007-10-01

    There are significant issues regarding Large Scale System integration that are not being addressed in other forums such as current research portfolios or vendor user groups. Unfortunately, the issues in the area of large-scale system integration often fall into a netherworld; not research, not facilities, not procurement, not operations, not user services. Taken together, these issues along with the impact of sub-optimal integration technology means the time required to deploy, integrate and stabilize large scale system may consume up to 20 percent of the useful life of such systems. Improving the state of the art for large scale systems integration has potential to increase the scientific productivity of these systems. Sites have significant expertise, but there are no easy ways to leverage this expertise among them . Many issues inhibit the sharing of information, including available time and effort, as well as issues with sharing proprietary information. Vendors also benefit in the long run from the solutions to issues detected during site testing and integration. There is a great deal of enthusiasm for making large scale system integration a full-fledged partner along with the other major thrusts supported by funding agencies in the definition, design, and use of a petascale systems. Integration technology and issues should have a full 'seat at the table' as petascale and exascale initiatives and programs are planned. The workshop attendees identified a wide range of issues and suggested paths forward. Pursuing these with funding opportunities and innovation offers the opportunity to dramatically improve the state of large scale system integration.

  5. Scale-up and optimization of biohydrogen production reactor from laboratory-scale to industrial-scale on the basis of computational fluid dynamics simulation

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Xu; Ding, Jie; Guo, Wan-Qian; Ren, Nan-Qi [State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 202 Haihe Road, Nangang District, Harbin, Heilongjiang 150090 (China)

    2010-10-15

    The objective of conducting experiments in a laboratory is to gain data that helps in designing and operating large-scale biological processes. However, the scale-up and design of industrial-scale biohydrogen production reactors is still uncertain. In this paper, an established and proven Eulerian-Eulerian computational fluid dynamics (CFD) model was employed to perform hydrodynamics assessments of an industrial-scale continuous stirred-tank reactor (CSTR) for biohydrogen production. The merits of the laboratory-scale CSTR and industrial-scale CSTR were compared and analyzed on the basis of CFD simulation. The outcomes demonstrated that there are many parameters that need to be optimized in the industrial-scale reactor, such as the velocity field and stagnation zone. According to the results of hydrodynamics evaluation, the structure of industrial-scale CSTR was optimized and the results are positive in terms of advancing the industrialization of biohydrogen production. (author)

  6. Large-Scale Astrophysical Visualization on Smartphones

    Science.gov (United States)

    Becciani, U.; Massimino, P.; Costa, A.; Gheller, C.; Grillo, A.; Krokos, M.; Petta, C.

    2011-07-01

    Nowadays digital sky surveys and long-duration, high-resolution numerical simulations using high performance computing and grid systems produce multidimensional astrophysical datasets in the order of several Petabytes. Sharing visualizations of such datasets within communities and collaborating research groups is of paramount importance for disseminating results and advancing astrophysical research. Moreover educational and public outreach programs can benefit greatly from novel ways of presenting these datasets by promoting understanding of complex astrophysical processes, e.g., formation of stars and galaxies. We have previously developed VisIVO Server, a grid-enabled platform for high-performance large-scale astrophysical visualization. This article reviews the latest developments on VisIVO Web, a custom designed web portal wrapped around VisIVO Server, then introduces VisIVO Smartphone, a gateway connecting VisIVO Web and data repositories for mobile astrophysical visualization. We discuss current work and summarize future developments.

  7. Large-Scale Environment Properties of Narrow-Line Seyfert 1 Galaxies at z < 0.4

    Energy Technology Data Exchange (ETDEWEB)

    Järvelä, Emilia [Metsähovi Radio Observatory, Aalto University, Espoo (Finland); Department of Electronics and Nanoengineering, Aalto University, Espoo (Finland); Lähteenmäki, A. [Metsähovi Radio Observatory, Aalto University, Espoo (Finland); Department of Electronics and Nanoengineering, Aalto University, Espoo (Finland); Tartu Observatory, Tõravere (Estonia); Lietzen, H., E-mail: emilia.jarvela@aalto.fi [Tartu Observatory, Tõravere (Estonia)

    2017-11-30

    The large-scale environment is believed to affect the evolution and intrinsic properties of galaxies. It offers a new perspective on narrow-line Seyfert 1 galaxies (NLS1) which have not been extensively studied in this context before. We study a large and diverse sample of 960 NLS1 galaxies using a luminosity-density field constructed using Sloan Digital Sky Survey. We investigate how the large-scale environment is connected to the properties of NLS1 galaxies, especially their radio loudness. Furthermore, we compare the large-scale environment properties of NLS1 galaxies with other active galactic nuclei (AGN) classes, for example, other jetted AGN and broad-line Seyfert 1 (BLS1) galaxies, to shed light on their possible relations. In general NLS1 galaxies reside in less dense large-scale environments than any of our comparison samples, thus supporting their young age. The average luminosity-density and distribution to different luminosity-density regions of NLS1 sources is significantly different compared to BLS1 galaxies. This contradicts the simple orientation-based unification of NLS1 and BLS1 galaxies, and weakens the hypothesis that BLS1 galaxies are the parent population of NLS1 galaxies. The large-scale environment density also has an impact on the intrinsic properties of NLS1 galaxies; the radio loudness increases with the increasing luminosity-density. However, our results suggest that the NLS1 population is indeed heterogeneous, and that a considerable fraction of them are misclassified. We support a suggested description that the traditional classification based on the radio loudness should be replaced with the division to jetted and non-jetted sources.

  8. Large scale anisotropy studies with the Auger Observatory

    International Nuclear Information System (INIS)

    Santos, E.M.; Letessier-Selvon, A.

    2006-01-01

    With the increasing Auger surface array data sample of the highest energy cosmic rays, large scale anisotropy studies at this part of the spectrum become a promising path towards the understanding of the origin of ultra-high energy cosmic particles. We describe the methods underlying the search for distortions in the cosmic rays arrival directions over large angular scales, that is, bigger than those commonly employed in the search for correlations with point-like sources. The widely used tools, known as coverage maps, are described and some of the issues involved in their calculations are presented through Monte Carlo based studies. Coverage computation requires a deep knowledge on the local detection efficiency, including the influence of weather parameters like temperature and pressure. Particular attention is devoted to a new proposed method to extract the coverage, based upon the assumption of time factorization of an extensive air shower detector acceptance. We use Auger monitoring data to test the goodness of such a hypothesis. We finally show the necessity of using more than one coverage to extract any possible anisotropic pattern on the sky, by pointing to some of the biases present in commonly used methods based, for example, on the scrambling of the UTC arrival times for each event. (author)

  9. Large scale model testing

    International Nuclear Information System (INIS)

    Brumovsky, M.; Filip, R.; Polachova, H.; Stepanek, S.

    1989-01-01

    Fracture mechanics and fatigue calculations for WWER reactor pressure vessels were checked by large scale model testing performed using large testing machine ZZ 8000 (with a maximum load of 80 MN) at the SKODA WORKS. The results are described from testing the material resistance to fracture (non-ductile). The testing included the base materials and welded joints. The rated specimen thickness was 150 mm with defects of a depth between 15 and 100 mm. The results are also presented of nozzles of 850 mm inner diameter in a scale of 1:3; static, cyclic, and dynamic tests were performed without and with surface defects (15, 30 and 45 mm deep). During cyclic tests the crack growth rate in the elastic-plastic region was also determined. (author). 6 figs., 2 tabs., 5 refs

  10. Why small-scale cannabis growers stay small: five mechanisms that prevent small-scale growers from going large scale.

    Science.gov (United States)

    Hammersvik, Eirik; Sandberg, Sveinung; Pedersen, Willy

    2012-11-01

    Over the past 15-20 years, domestic cultivation of cannabis has been established in a number of European countries. New techniques have made such cultivation easier; however, the bulk of growers remain small-scale. In this study, we explore the factors that prevent small-scale growers from increasing their production. The study is based on 1 year of ethnographic fieldwork and qualitative interviews conducted with 45 Norwegian cannabis growers, 10 of whom were growing on a large-scale and 35 on a small-scale. The study identifies five mechanisms that prevent small-scale indoor growers from going large-scale. First, large-scale operations involve a number of people, large sums of money, a high work-load and a high risk of detection, and thus demand a higher level of organizational skills than for small growing operations. Second, financial assets are needed to start a large 'grow-site'. Housing rent, electricity, equipment and nutrients are expensive. Third, to be able to sell large quantities of cannabis, growers need access to an illegal distribution network and knowledge of how to act according to black market norms and structures. Fourth, large-scale operations require advanced horticultural skills to maximize yield and quality, which demands greater skills and knowledge than does small-scale cultivation. Fifth, small-scale growers are often embedded in the 'cannabis culture', which emphasizes anti-commercialism, anti-violence and ecological and community values. Hence, starting up large-scale production will imply having to renegotiate or abandon these values. Going from small- to large-scale cannabis production is a demanding task-ideologically, technically, economically and personally. The many obstacles that small-scale growers face and the lack of interest and motivation for going large-scale suggest that the risk of a 'slippery slope' from small-scale to large-scale growing is limited. Possible political implications of the findings are discussed. Copyright

  11. Distributed large-scale dimensional metrology new insights

    CERN Document Server

    Franceschini, Fiorenzo; Maisano, Domenico

    2011-01-01

    Focuses on the latest insights into and challenges of distributed large scale dimensional metrology Enables practitioners to study distributed large scale dimensional metrology independently Includes specific examples of the development of new system prototypes

  12. Applicability of vector processing to large-scale nuclear codes

    International Nuclear Information System (INIS)

    Ishiguro, Misako; Harada, Hiroo; Matsuura, Toshihiko; Okuda, Motoi; Ohta, Fumio; Umeya, Makoto.

    1982-03-01

    To meet the growing trend of computational requirements in JAERI, introduction of a high-speed computer with vector processing faculty (a vector processor) is desirable in the near future. To make effective use of a vector processor, appropriate optimization of nuclear codes to pipelined-vector architecture is vital, which will pose new problems concerning code development and maintenance. In this report, vector processing efficiency is assessed with respect to large-scale nuclear codes by examining the following items: 1) The present feature of computational load in JAERI is analyzed by compiling the computer utilization statistics. 2) Vector processing efficiency is estimated for the ten heavily-used nuclear codes by analyzing their dynamic behaviors run on a scalar machine. 3) Vector processing efficiency is measured for the other five nuclear codes by using the current vector processors, FACOM 230-75 APU and CRAY-1. 4) Effectiveness of applying a high-speed vector processor to nuclear codes is evaluated by taking account of the characteristics in JAERI jobs. Problems of vector processors are also discussed from the view points of code performance and ease of use. (author)

  13. Scaling of the Urban Water Footprint: An Analysis of 65 Mid- to Large-Sized U.S. Metropolitan Areas

    Science.gov (United States)

    Mahjabin, T.; Garcia, S.; Grady, C.; Mejia, A.

    2017-12-01

    Scaling laws have been shown to be relevant to a range of disciplines including biology, ecology, hydrology, and physics, among others. Recently, scaling was shown to be important for understanding and characterizing cities. For instance, it was found that urban infrastructure (water supply pipes and electrical wires) tends to scale sublinearly with city population, implying that large cities are more efficient. In this study, we explore the scaling of the water footprint of cities. The water footprint is a measure of water appropriation that considers both the direct and indirect (virtual) water use of a consumer or producer. Here we compute the water footprint of 65 mid- to large-sized U.S. metropolitan areas, accounting for direct and indirect water uses associated with agricultural and industrial commodities, and residential and commercial water uses. We find that the urban water footprint, computed as the sum of the water footprint of consumption and production, exhibits sublinear scaling with an exponent of 0.89. This suggests the possibility of large cities being more water-efficient than small ones. To further assess this result, we conduct additional analysis by accounting for international flows, and the effects of green water and city boundary definition on the scaling. The analysis confirms the scaling and provides additional insight about its interpretation.

  14. Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

    KAUST Repository

    Jamour, Fuad Tarek

    2017-10-17

    Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose iCentral; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. iCentral is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, iCentral scales to large graphs. We demonstrate with real datasets that the serial implementation of iCentral is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.

  15. Human visual system automatically represents large-scale sequential regularities.

    Science.gov (United States)

    Kimura, Motohiro; Widmann, Andreas; Schröger, Erich

    2010-03-04

    Our brain recordings reveal that large-scale sequential regularities defined across non-adjacent stimuli can be automatically represented in visual sensory memory. To show that, we adopted an auditory paradigm developed by Sussman, E., Ritter, W., and Vaughan, H. G. Jr. (1998). Predictability of stimulus deviance and the mismatch negativity. NeuroReport, 9, 4167-4170, Sussman, E., and Gumenyuk, V. (2005). Organization of sequential sounds in auditory memory. NeuroReport, 16, 1519-1523 to the visual domain by presenting task-irrelevant infrequent luminance-deviant stimuli (D, 20%) inserted among task-irrelevant frequent stimuli being of standard luminance (S, 80%) in randomized (randomized condition, SSSDSSSSSDSSSSD...) and fixed manners (fixed condition, SSSSDSSSSDSSSSD...). Comparing the visual mismatch negativity (visual MMN), an event-related brain potential (ERP) index of memory-mismatch processes in human visual sensory system, revealed that visual MMN elicited by deviant stimuli was reduced in the fixed compared to the randomized condition. Thus, the large-scale sequential regularity being present in the fixed condition (SSSSD) must have been represented in visual sensory memory. Interestingly, this effect did not occur in conditions with stimulus-onset asynchronies (SOAs) of 480 and 800 ms but was confined to the 160-ms SOA condition supporting the hypothesis that large-scale regularity extraction was based on perceptual grouping of the five successive stimuli defining the regularity. 2010 Elsevier B.V. All rights reserved.

  16. IP over optical multicasting for large-scale video delivery

    Science.gov (United States)

    Jin, Yaohui; Hu, Weisheng; Sun, Weiqiang; Guo, Wei

    2007-11-01

    In the IPTV systems, multicasting will play a crucial role in the delivery of high-quality video services, which can significantly improve bandwidth efficiency. However, the scalability and the signal quality of current IPTV can barely compete with the existing broadcast digital TV systems since it is difficult to implement large-scale multicasting with end-to-end guaranteed quality of service (QoS) in packet-switched IP network. China 3TNet project aimed to build a high performance broadband trial network to support large-scale concurrent streaming media and interactive multimedia services. The innovative idea of 3TNet is that an automatic switched optical networks (ASON) with the capability of dynamic point-to-multipoint (P2MP) connections replaces the conventional IP multicasting network in the transport core, while the edge remains an IP multicasting network. In this paper, we will introduce the network architecture and discuss challenges in such IP over Optical multicasting for video delivery.

  17. Support system for ATLAS distributed computing operations

    CERN Document Server

    Kishimoto, Tomoe; The ATLAS collaboration

    2018-01-01

    The ATLAS distributed computing system has allowed the experiment to successfully meet the challenges of LHC Run 2. In order for distributed computing to operate smoothly and efficiently, several support teams are organized in the ATLAS experiment. The ADCoS (ATLAS Distributed Computing Operation Shifts) is a dedicated group of shifters who follow and report failing jobs, failing data transfers between sites, degradation of ATLAS central computing services, and more. The DAST (Distributed Analysis Support Team) provides user support to resolve issues related to running distributed analysis on the grid. The CRC (Computing Run Coordinator) maintains a global view of the day-to-day operations. In this presentation, the status and operational experience of the support system for ATLAS distributed computing in LHC Run 2 will be reported. This report also includes operations experience from the grid site point of view, and an analysis of the errors that create the biggest waste of wallclock time. The report of oper...

  18. Computational Biology Support: RECOMB Conference Series (Conference Support)

    Energy Technology Data Exchange (ETDEWEB)

    Michael Waterman

    2006-06-15

    This funding was support for student and postdoctoral attendance at the Annual Recomb Conference from 2001 to 2005. The RECOMB Conference series was founded in 1997 to provide a scientific forum for theoretical advances in computational biology and their applications in molecular biology and medicine. The conference series aims at attracting research contributions in all areas of computational molecular biology. Typical, but not exclusive, the topics of interest are: Genomics, Molecular sequence analysis, Recognition of genes and regulatory elements, Molecular evolution, Protein structure, Structural genomics, Gene Expression, Gene Networks, Drug Design, Combinatorial libraries, Computational proteomics, and Structural and functional genomics. The origins of the conference came from the mathematical and computational side of the field, and there remains to be a certain focus on computational advances. However, the effective use of computational techniques to biological innovation is also an important aspect of the conference. The conference had a growing number of attendees, topping 300 in recent years and often exceeding 500. The conference program includes between 30 and 40 contributed papers, that are selected by a international program committee with around 30 experts during a rigorous review process rivaling the editorial procedure for top-rate scientific journals. In previous years papers selection has been made from up to 130--200 submissions from well over a dozen countries. 10-page extended abstracts of the contributed papers are collected in a volume published by ACM Press and Springer, and are available at the conference. Full versions of a selection of the papers are published annually in a special issue of the Journal of Computational Biology devoted to the RECOMB Conference. A further point in the program is a lively poster session. From 120-300 posters have been presented each year at RECOMB 2000. One of the highlights of each RECOMB conference is a

  19. Open Problems in Network-aware Data Management in Exa-scale Computing and Terabit Networking Era

    Energy Technology Data Exchange (ETDEWEB)

    Balman, Mehmet; Byna, Surendra

    2011-12-06

    Accessing and managing large amounts of data is a great challenge in collaborative computing environments where resources and users are geographically distributed. Recent advances in network technology led to next-generation high-performance networks, allowing high-bandwidth connectivity. Efficient use of the network infrastructure is necessary in order to address the increasing data and compute requirements of large-scale applications. We discuss several open problems, evaluate emerging trends, and articulate our perspectives in network-aware data management.

  20. Large-scale sequential quadratic programming algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Eldersveld, S.K.

    1992-09-01

    The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.

  1. SCALE INTERACTION IN A MIXING LAYER. THE ROLE OF THE LARGE-SCALE GRADIENTS

    KAUST Repository

    Fiscaletti, Daniele; Attili, Antonio; Bisetti, Fabrizio; Elsinga, Gerrit E.

    2015-01-01

    from physical considerations we would expect the scales to interact in a qualitatively similar way within the flow and across different turbulent flows. Therefore, instead of the large-scale fluctuations, the large-scale gradients modulation of the small scales has been additionally investigated.

  2. A Ranking Approach on Large-Scale Graph With Multidimensional Heterogeneous Information.

    Science.gov (United States)

    Wei, Wei; Gao, Bin; Liu, Tie-Yan; Wang, Taifeng; Li, Guohui; Li, Hang

    2016-04-01

    Graph-based ranking has been extensively studied and frequently applied in many applications, such as webpage ranking. It aims at mining potentially valuable information from the raw graph-structured data. Recently, with the proliferation of rich heterogeneous information (e.g., node/edge features and prior knowledge) available in many real-world graphs, how to effectively and efficiently leverage all information to improve the ranking performance becomes a new challenging problem. Previous methods only utilize part of such information and attempt to rank graph nodes according to link-based methods, of which the ranking performances are severely affected by several well-known issues, e.g., over-fitting or high computational complexity, especially when the scale of graph is very large. In this paper, we address the large-scale graph-based ranking problem and focus on how to effectively exploit rich heterogeneous information of the graph to improve the ranking performance. Specifically, we propose an innovative and effective semi-supervised PageRank (SSP) approach to parameterize the derived information within a unified semi-supervised learning framework (SSLF-GR), then simultaneously optimize the parameters and the ranking scores of graph nodes. Experiments on the real-world large-scale graphs demonstrate that our method significantly outperforms the algorithms that consider such graph information only partially.

  3. A global classification of coastal flood hazard climates associated with large-scale oceanographic forcing.

    Science.gov (United States)

    Rueda, Ana; Vitousek, Sean; Camus, Paula; Tomás, Antonio; Espejo, Antonio; Losada, Inigo J; Barnard, Patrick L; Erikson, Li H; Ruggiero, Peter; Reguero, Borja G; Mendez, Fernando J

    2017-07-11

    Coastal communities throughout the world are exposed to numerous and increasing threats, such as coastal flooding and erosion, saltwater intrusion and wetland degradation. Here, we present the first global-scale analysis of the main drivers of coastal flooding due to large-scale oceanographic factors. Given the large dimensionality of the problem (e.g. spatiotemporal variability in flood magnitude and the relative influence of waves, tides and surge levels), we have performed a computer-based classification to identify geographical areas with homogeneous climates. Results show that 75% of coastal regions around the globe have the potential for very large flooding events with low probabilities (unbounded tails), 82% are tide-dominated, and almost 49% are highly susceptible to increases in flooding frequency due to sea-level rise.

  4. An inertia-free filter line-search algorithm for large-scale nonlinear programming

    Energy Technology Data Exchange (ETDEWEB)

    Chiang, Nai-Yuan; Zavala, Victor M.

    2016-02-15

    We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.

  5. Computer-based tools for decision support at the Hanford Site

    International Nuclear Information System (INIS)

    Doctor, P.G.; Mahaffey, J.A.; Cowley, P.J.; Freshley, M.D.; Hassig, N.L.; Brothers, J.W.; Glantz, C.S.; Strachan, D.M.

    1992-11-01

    To help integrate activities in the environmental restoration and waste management mission of the Hanford Site, the Hanford Integrated Planning Project (HIPP) was established and funded by the US Department of Energy. The project is divided into three key program elements, the first focusing on an explicit, defensible and comprehensive method for evaluating technical options. Based on the premise that computer technology can be used to support the decision-making process and facilitate integration among programs and activities, the Decision Support Tools Task was charged with assessing the status of computer technology for those purposes at the Site. The task addressed two types of tools: tools need to provide technical information and management support tools. Technical tools include performance and risk assessment models, information management systems, data and the computer infrastructure to supports models, data, and information management systems. Management decision support tools are used to synthesize information at a high' level to assist with making decisions. The major conclusions resulting from the assessment are that there is much technical information available, but it is not reaching the decision-makers in a form to be used. Many existing tools provide components that are needed to integrate site activities; however, some components are missing and, more importantly, the ''glue'' or connections to tie the components together to answer decision-makers questions is largely absent. Top priority should be given to decision support tools that support activities given in the TPA. Other decision tools are needed to facilitate and support the environmental restoration and waste management mission

  6. Computer-based tools for decision support at the Hanford Site

    Energy Technology Data Exchange (ETDEWEB)

    Doctor, P.G.; Mahaffey, J.A.; Cowley, P.J.; Freshley, M.D.; Hassig, N.L.; Brothers, J.W.; Glantz, C.S.; Strachan, D.M.

    1992-11-01

    To help integrate activities in the environmental restoration and waste management mission of the Hanford Site, the Hanford Integrated Planning Project (HIPP) was established and funded by the US Department of Energy. The project is divided into three key program elements, the first focusing on an explicit, defensible and comprehensive method for evaluating technical options. Based on the premise that computer technology can be used to support the decision-making process and facilitate integration among programs and activities, the Decision Support Tools Task was charged with assessing the status of computer technology for those purposes at the Site. The task addressed two types of tools: tools need to provide technical information and management support tools. Technical tools include performance and risk assessment models, information management systems, data and the computer infrastructure to supports models, data, and information management systems. Management decision support tools are used to synthesize information at a high' level to assist with making decisions. The major conclusions resulting from the assessment are that there is much technical information available, but it is not reaching the decision-makers in a form to be used. Many existing tools provide components that are needed to integrate site activities; however, some components are missing and, more importantly, the glue'' or connections to tie the components together to answer decision-makers questions is largely absent. Top priority should be given to decision support tools that support activities given in the TPA. Other decision tools are needed to facilitate and support the environmental restoration and waste management mission.

  7. Computer-based tools for decision support at the Hanford Site

    Energy Technology Data Exchange (ETDEWEB)

    Doctor, P.G.; Mahaffey, J.A.; Cowley, P.J.; Freshley, M.D.; Hassig, N.L.; Brothers, J.W.; Glantz, C.S.; Strachan, D.M.

    1992-11-01

    To help integrate activities in the environmental restoration and waste management mission of the Hanford Site, the Hanford Integrated Planning Project (HIPP) was established and funded by the US Department of Energy. The project is divided into three key program elements, the first focusing on an explicit, defensible and comprehensive method for evaluating technical options. Based on the premise that computer technology can be used to support the decision-making process and facilitate integration among programs and activities, the Decision Support Tools Task was charged with assessing the status of computer technology for those purposes at the Site. The task addressed two types of tools: tools need to provide technical information and management support tools. Technical tools include performance and risk assessment models, information management systems, data and the computer infrastructure to supports models, data, and information management systems. Management decision support tools are used to synthesize information at a high` level to assist with making decisions. The major conclusions resulting from the assessment are that there is much technical information available, but it is not reaching the decision-makers in a form to be used. Many existing tools provide components that are needed to integrate site activities; however, some components are missing and, more importantly, the ``glue`` or connections to tie the components together to answer decision-makers questions is largely absent. Top priority should be given to decision support tools that support activities given in the TPA. Other decision tools are needed to facilitate and support the environmental restoration and waste management mission.

  8. Secure multiparty computation goes live

    NARCIS (Netherlands)

    Bogetoft, P.; Christensen, D.L.; Damgard, Ivan; Geisler, M.; Jakobsen, T.; Kroigaard, M.; Nielsen, J.D.; Nielsen, J.B.; Nielsen, K.; Pagter, J.; Schwartzbach, M.; Toft, T.; Dingledine, R.; Golle, Ph.

    2009-01-01

    In this note, we report on the first large-scale and practical application of secure multiparty computation, which took place in January 2008. We also report on the novel cryptographic protocols that were used. This work was supported by the Danish Strategic Research Council and the European

  9. Secure multiparty computation goes live

    DEFF Research Database (Denmark)

    Bogetoft, Peter; Christensen, Dan Lund; Damgård, Ivan Bjerre

    2009-01-01

    In this note, we report on the first large-scale and practical application of secure multiparty computation, which took place in January 2008. We also report on the novel cryptographic protocols that were used. This work was supported by the Danish Strategic Research Council and the European...

  10. Upscaling of Large-Scale Transport in Spatially Heterogeneous Porous Media Using Wavelet Transformation

    Science.gov (United States)

    Moslehi, M.; de Barros, F.; Ebrahimi, F.; Sahimi, M.

    2015-12-01

    Modeling flow and solute transport in large-scale heterogeneous porous media involves substantial computational burdens. A common approach to alleviate this complexity is to utilize upscaling methods. These processes generate upscaled models with less complexity while attempting to preserve the hydrogeological properties comparable to the original fine-scale model. We use Wavelet Transformations (WT) of the spatial distribution of aquifer's property to upscale the hydrogeological models and consequently transport processes. In particular, we apply the technique to a porous formation with broadly distributed and correlated transmissivity to verify the performance of the WT. First, transmissivity fields are coarsened using WT in such a way that the high transmissivity zones, in which more important information is embedded, mostly remain the same, while the low transmissivity zones are averaged out since they contain less information about the hydrogeological formation. Next, flow and non-reactive transport are simulated in both fine-scale and upscaled models to predict both the concentration breakthrough curves at a control location and the large-scale spreading of the plume around its centroid. The results reveal that the WT of the fields generates non-uniform grids with an average of 2.1% of the number of grid blocks in the original fine-scale models, which eventually leads to a significant reduction in the computational costs. We show that the upscaled model obtained through the WT reconstructs the concentration breakthrough curves and the spreading of the plume at different times accurately. Furthermore, the impacts of the Hurst coefficient, size of the flow domain and the orders of magnitude difference in transmissivity values on the results have been investigated. It is observed that as the heterogeneity and the size of the domain increase, better agreement between the results of fine-scale and upscaled models can be achieved. Having this framework at hand aids

  11. Examining Agencies' Satisfaction with Electronic Record Management Systems in e-Government: A Large-Scale Survey Study

    Science.gov (United States)

    Hsu, Fang-Ming; Hu, Paul Jen-Hwa; Chen, Hsinchun; Hu, Han-Fen

    While e-government is propelling and maturing steadily, advanced technological capabilities alone cannot guarantee agencies’ realizing the full benefits of the enabling computer-based systems. This study analyzes information systems in e-government settings by examining agencies’ satisfaction with an electronic record management system (ERMS). Specifically, we investigate key satisfaction determinants that include regulatory compliance, job relevance, and satisfaction with support services for using the ERMS. We test our model and the hypotheses in it, using a large-scale survey that involves a total of 1,652 government agencies in Taiwan. Our results show significant effects of regulatory compliance on job relevance and satisfaction with support services, which in turn determine government agencies’ satisfaction with an ERMS. Our data exhibit a reasonably good fit to our model, which can explain a significant portion of the variance in agencies’ satisfaction with an ERMS. Our findings have several important implications to research and practice, which are also discussed.

  12. Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

    OpenAIRE

    C. M. R. Mateo; C. M. R. Mateo; D. Yamazaki; D. Yamazaki; H. Kim; A. Champathong; J. Vaze; T. Oki; T. Oki

    2017-01-01

    Global-scale river models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representations of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development...

  13. Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

    Directory of Open Access Journals (Sweden)

    Mohsen Alavash

    2017-06-01

    Full Text Available Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations. The speed at which we make perceptual decisions varies. This translation of sensory information into perceptual decisions hinges on dynamic changes in neural oscillatory activity. However, the large-scale neural-network embodiment supporting perceptual decision-making is unclear. We addressed this question by experimenting two auditory perceptual decision-making situations. Using graph-theoretical network discovery, we traced the large-scale network

  14. Radiations: large scale monitoring in Japan

    International Nuclear Information System (INIS)

    Linton, M.; Khalatbari, A.

    2011-01-01

    As the consequences of radioactive leaks on their health are a matter of concern for Japanese people, a large scale epidemiological study has been launched by the Fukushima medical university. It concerns the two millions inhabitants of the Fukushima Prefecture. On the national level and with the support of public funds, medical care and follow-up, as well as systematic controls are foreseen, notably to check the thyroid of 360.000 young people less than 18 year old and of 20.000 pregnant women in the Fukushima Prefecture. Some measurements have already been performed on young children. Despite the sometimes rather low measures, and because they know that some parts of the area are at least as much contaminated as it was the case around Chernobyl, some people are reluctant to go back home

  15. Gas Generators and Their Potential to Support Human-Scale HIADS (Hypersonic Inflatable Aerodynamic Decelerators)

    Science.gov (United States)

    Bodkin, Richard J.; Cheatwood, F. M.; Dillman, Robert A; Dinonno, John M.; Hughes, Stephen J.; Lucy, Melvin H.

    2016-01-01

    As HIAD technology progresses from 3-m diameter experimental scale to as large as 20-m diameter for human Mars entry, the mass penalties of carrying compressed gas has led the HIAD team to research current state-of-the-art gas generator approaches. Summarized below are several technologies identified in this survey, along with some of the pros and cons with respect to supporting large-scale HIAD applications.

  16. Conference on Large Scale Optimization

    CERN Document Server

    Hearn, D; Pardalos, P

    1994-01-01

    On February 15-17, 1993, a conference on Large Scale Optimization, hosted by the Center for Applied Optimization, was held at the University of Florida. The con­ ference was supported by the National Science Foundation, the U. S. Army Research Office, and the University of Florida, with endorsements from SIAM, MPS, ORSA and IMACS. Forty one invited speakers presented papers on mathematical program­ ming and optimal control topics with an emphasis on algorithm development, real world applications and numerical results. Participants from Canada, Japan, Sweden, The Netherlands, Germany, Belgium, Greece, and Denmark gave the meeting an important international component. At­ tendees also included representatives from IBM, American Airlines, US Air, United Parcel Serice, AT & T Bell Labs, Thinking Machines, Army High Performance Com­ puting Research Center, and Argonne National Laboratory. In addition, the NSF sponsored attendance of thirteen graduate students from universities in the United States and abro...

  17. Designing and developing portable large-scale JavaScript web applications within the Experiment Dashboard framework

    Science.gov (United States)

    Andreeva, J.; Dzhunov, I.; Karavakis, E.; Kokoszkiewicz, L.; Nowotka, M.; Saiz, P.; Tuckett, D.

    2012-12-01

    Improvements in web browser performance and web standards compliance, as well as the availability of comprehensive JavaScript libraries, provides an opportunity to develop functionally rich yet intuitive web applications that allow users to access, render and analyse data in novel ways. However, the development of such large-scale JavaScript web applications presents new challenges, in particular with regard to code sustainability and team-based work. We present an approach that meets the challenges of large-scale JavaScript web application design and development, including client-side model-view-controller architecture, design patterns, and JavaScript libraries. Furthermore, we show how the approach leads naturally to the encapsulation of the data source as a web API, allowing applications to be easily ported to new data sources. The Experiment Dashboard framework is used for the development of applications for monitoring the distributed computing activities of virtual organisations on the Worldwide LHC Computing Grid. We demonstrate the benefits of the approach for large-scale JavaScript web applications in this context by examining the design of several Experiment Dashboard applications for data processing, data transfer and site status monitoring, and by showing how they have been ported for different virtual organisations and technologies.

  18. Designing and developing portable large-scale JavaScript web applications within the Experiment Dashboard framework

    International Nuclear Information System (INIS)

    Andreeva, J; Dzhunov, I; Karavakis, E; Kokoszkiewicz, L; Nowotka, M; Saiz, P; Tuckett, D

    2012-01-01

    Improvements in web browser performance and web standards compliance, as well as the availability of comprehensive JavaScript libraries, provides an opportunity to develop functionally rich yet intuitive web applications that allow users to access, render and analyse data in novel ways. However, the development of such large-scale JavaScript web applications presents new challenges, in particular with regard to code sustainability and team-based work. We present an approach that meets the challenges of large-scale JavaScript web application design and development, including client-side model-view-controller architecture, design patterns, and JavaScript libraries. Furthermore, we show how the approach leads naturally to the encapsulation of the data source as a web API, allowing applications to be easily ported to new data sources. The Experiment Dashboard framework is used for the development of applications for monitoring the distributed computing activities of virtual organisations on the Worldwide LHC Computing Grid. We demonstrate the benefits of the approach for large-scale JavaScript web applications in this context by examining the design of several Experiment Dashboard applications for data processing, data transfer and site status monitoring, and by showing how they have been ported for different virtual organisations and technologies.

  19. Exploring Hardware Support For Scaling Irregular Applications on Multi-node Multi-core Architectures

    Energy Technology Data Exchange (ETDEWEB)

    Secchi, Simone; Ceriani, Marco; Tumeo, Antonino; Villa, Oreste; Palermo, Gianluca; Raffo, Luigi

    2013-06-05

    With the recent emergence of large-scale knowledge dis- covery, data mining and social network analysis, irregular applications have gained renewed interest. Classic cache-based high-performance architectures do not provide optimal performances with such kind of workloads, mainly due to the very low spatial and temporal locality of the irregular control and memory access patterns. In this paper, we present a multi-node, multi-core, fine-grained multi-threaded shared-memory system architecture specifically designed for the execution of large-scale irregular applications, and built on top of three pillars, that we believe are fundamental to support these workloads. First, we offer transparent hardware support for Partitioned Global Address Space (PGAS) to provide a large globally-shared address space with no software library overhead. Second, we employ multi-threaded multi-core processing nodes to achieve the necessary latency tolerance required by accessing global memory, which potentially resides in a remote node. Finally, we devise hardware support for inter-thread synchronization on the whole global address space. We first model the performances by using an analytical model that takes into account the main architecture and application characteristics. We describe the hardware design of the proposed cus- tom architectural building blocks that provide support for the above- mentioned three pillars. Finally, we present a limited-scale evaluation of the system on a multi-board FPGA prototype with typical irregular kernels and benchmarks. The experimental evaluation demonstrates the architecture performance scalability for different configurations of the whole system.

  20. Large-Scale 3D Printing: The Way Forward

    Science.gov (United States)

    Jassmi, Hamad Al; Najjar, Fady Al; Ismail Mourad, Abdel-Hamid

    2018-03-01

    Research on small-scale 3D printing has rapidly evolved, where numerous industrial products have been tested and successfully applied. Nonetheless, research on large-scale 3D printing, directed to large-scale applications such as construction and automotive manufacturing, yet demands a great a great deal of efforts. Large-scale 3D printing is considered an interdisciplinary topic and requires establishing a blended knowledge base from numerous research fields including structural engineering, materials science, mechatronics, software engineering, artificial intelligence and architectural engineering. This review article summarizes key topics of relevance to new research trends on large-scale 3D printing, particularly pertaining (1) technological solutions of additive construction (i.e. the 3D printers themselves), (2) materials science challenges, and (3) new design opportunities.

  1. Workflow management in large distributed systems

    International Nuclear Information System (INIS)

    Legrand, I; Newman, H; Voicu, R; Dobre, C; Grigoras, C

    2011-01-01

    The MonALISA (Monitoring Agents using a Large Integrated Services Architecture) framework provides a distributed service system capable of controlling and optimizing large-scale, data-intensive applications. An essential part of managing large-scale, distributed data-processing facilities is a monitoring system for computing facilities, storage, networks, and the very large number of applications running on these systems in near realtime. All this monitoring information gathered for all the subsystems is essential for developing the required higher-level services—the components that provide decision support and some degree of automated decisions—and for maintaining and optimizing workflow in large-scale distributed systems. These management and global optimization functions are performed by higher-level agent-based services. We present several applications of MonALISA's higher-level services including optimized dynamic routing, control, data-transfer scheduling, distributed job scheduling, dynamic allocation of storage resource to running jobs and automated management of remote services among a large set of grid facilities.

  2. Workflow management in large distributed systems

    Science.gov (United States)

    Legrand, I.; Newman, H.; Voicu, R.; Dobre, C.; Grigoras, C.

    2011-12-01

    The MonALISA (Monitoring Agents using a Large Integrated Services Architecture) framework provides a distributed service system capable of controlling and optimizing large-scale, data-intensive applications. An essential part of managing large-scale, distributed data-processing facilities is a monitoring system for computing facilities, storage, networks, and the very large number of applications running on these systems in near realtime. All this monitoring information gathered for all the subsystems is essential for developing the required higher-level services—the components that provide decision support and some degree of automated decisions—and for maintaining and optimizing workflow in large-scale distributed systems. These management and global optimization functions are performed by higher-level agent-based services. We present several applications of MonALISA's higher-level services including optimized dynamic routing, control, data-transfer scheduling, distributed job scheduling, dynamic allocation of storage resource to running jobs and automated management of remote services among a large set of grid facilities.

  3. Large-scale research in the Federal Republic of Germany. Pt. 4

    International Nuclear Information System (INIS)

    Mock, W.

    1986-01-01

    The name is misleading: in the biggest of 13 large-scale research institutions, the KFA Nuclear Research Centre Juelich, nuclear research is now only one sphere of activities among many, besides other areas of research such as computer science, materials, and environmental research. This change in the areas of main emphasis constitutes the successful attempt - or so it seems up to now - of a 'research dinosaur' to answer to the necessities of an altered 'research landscape'. (orig.) [de

  4. Large-Scale Image Analytics Using Deep Learning

    Science.gov (United States)

    Ganguly, S.; Nemani, R. R.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Votava, P.

    2014-12-01

    High resolution land cover classification maps are needed to increase the accuracy of current Land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) land cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agricultural Imaging Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~100 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. In order to perform image analytics in such a granular system, it is mandatory to devise an intelligent archiving and query system for image retrieval, file structuring, metadata processing and filtering of all available image scenes. Using the Open NASA Earth Exchange (NEX) initiative, which is a partnership with Amazon Web Services (AWS), we have developed an end-to-end architecture for designing the database and the deep belief network (following the distbelief computing model) to solve a grand challenge of scaling this process across quarter million NAIP tiles that cover the entire Continental United States. The

  5. Growth Limits in Large Scale Networks

    DEFF Research Database (Denmark)

    Knudsen, Thomas Phillip

    limitations. The rising complexity of network management with the convergence of communications platforms is shown as problematic for both automatic management feasibility and for manpower resource management. In the fourth step the scope is extended to include the present society with the DDN project as its......The Subject of large scale networks is approached from the perspective of the network planner. An analysis of the long term planning problems is presented with the main focus on the changing requirements for large scale networks and the potential problems in meeting these requirements. The problems...... the fundamental technological resources in network technologies are analysed for scalability. Here several technological limits to continued growth are presented. The third step involves a survey of major problems in managing large scale networks given the growth of user requirements and the technological...

  6. Accelerating sustainability in large-scale facilities

    CERN Multimedia

    Marina Giampietro

    2011-01-01

    Scientific research centres and large-scale facilities are intrinsically energy intensive, but how can big science improve its energy management and eventually contribute to the environmental cause with new cleantech? CERN’s commitment to providing tangible answers to these questions was sealed in the first workshop on energy management for large scale scientific infrastructures held in Lund, Sweden, on the 13-14 October.   Participants at the energy management for large scale scientific infrastructures workshop. The workshop, co-organised with the European Spallation Source (ESS) and  the European Association of National Research Facilities (ERF), tackled a recognised need for addressing energy issues in relation with science and technology policies. It brought together more than 150 representatives of Research Infrastrutures (RIs) and energy experts from Europe and North America. “Without compromising our scientific projects, we can ...

  7. THE DECAY OF A WEAK LARGE-SCALE MAGNETIC FIELD IN TWO-DIMENSIONAL TURBULENCE

    Energy Technology Data Exchange (ETDEWEB)

    Kondić, Todor; Hughes, David W.; Tobias, Steven M., E-mail: t.kondic@leeds.ac.uk [Department of Applied Mathematics, University of Leeds, Leeds LS2 9JT (United Kingdom)

    2016-06-01

    We investigate the decay of a large-scale magnetic field in the context of incompressible, two-dimensional magnetohydrodynamic turbulence. It is well established that a very weak mean field, of strength significantly below equipartition value, induces a small-scale field strong enough to inhibit the process of turbulent magnetic diffusion. In light of ever-increasing computer power, we revisit this problem to investigate fluids and magnetic Reynolds numbers that were previously inaccessible. Furthermore, by exploiting the relation between the turbulent diffusion of the magnetic potential and that of the magnetic field, we are able to calculate the turbulent magnetic diffusivity extremely accurately through the imposition of a uniform mean magnetic field. We confirm the strong dependence of the turbulent diffusivity on the product of the magnetic Reynolds number and the energy of the large-scale magnetic field. We compare our findings with various theoretical descriptions of this process.

  8. Large scale reflood test

    International Nuclear Information System (INIS)

    Hirano, Kemmei; Murao, Yoshio

    1980-01-01

    The large-scale reflood test with a view to ensuring the safety of light water reactors was started in fiscal 1976 based on the special account act for power source development promotion measures by the entrustment from the Science and Technology Agency. Thereafter, to establish the safety of PWRs in loss-of-coolant accidents by joint international efforts, the Japan-West Germany-U.S. research cooperation program was started in April, 1980. Thereupon, the large-scale reflood test is now included in this program. It consists of two tests using a cylindrical core testing apparatus for examining the overall system effect and a plate core testing apparatus for testing individual effects. Each apparatus is composed of the mock-ups of pressure vessel, primary loop, containment vessel and ECCS. The testing method, the test results and the research cooperation program are described. (J.P.N.)

  9. The Saskatchewan River Basin - a large scale observatory for water security research (Invited)

    Science.gov (United States)

    Wheater, H. S.

    2013-12-01

    The 336,000 km2 Saskatchewan River Basin (SaskRB) in Western Canada illustrates many of the issues of Water Security faced world-wide. It poses globally-important science challenges due to the diversity in its hydro-climate and ecological zones. With one of the world's more extreme climates, it embodies environments of global significance, including the Rocky Mountains (source of the major rivers in Western Canada), the Boreal Forest (representing 30% of Canada's land area) and the Prairies (home to 80% of Canada's agriculture). Management concerns include: provision of water resources to more than three million inhabitants, including indigenous communities; balancing competing needs for water between different uses, such as urban centres, industry, agriculture, hydropower and environmental flows; issues of water allocation between upstream and downstream users in the three prairie provinces; managing the risks of flood and droughts; and assessing water quality impacts of discharges from major cities and intensive agricultural production. Superimposed on these issues is the need to understand and manage uncertain water futures, including effects of economic growth and environmental change, in a highly fragmented water governance environment. Key science questions focus on understanding and predicting the effects of land and water management and environmental change on water quantity and quality. To address the science challenges, observational data are necessary across multiple scales. This requires focussed research at intensively monitored sites and small watersheds to improve process understanding and fine-scale models. To understand large-scale effects on river flows and quality, land-atmosphere feedbacks, and regional climate, integrated monitoring, modelling and analysis is needed at large basin scale. And to support water management, new tools are needed for operational management and scenario-based planning that can be implemented across multiple scales and

  10. Large Atmospheric Computation on the Earth Simulator: The LACES Project

    Directory of Open Access Journals (Sweden)

    Michel Desgagné

    2006-01-01

    Full Text Available The Large Atmospheric Computation on the Earth Simulator (LACES project is a joint initiative between Canadian and Japanese meteorological services and academic institutions that focuses on the high resolution simulation of Hurricane Earl (1998. The unique aspect of this effort is the extent of the computational domain, which covers all of North America and Europe with a grid spacing of 1 km. The Canadian Mesoscale Compressible Community (MC2 model is shown to parallelize effectively on the Japanese Earth Simulator (ES supercomputer; however, even using the extensive computing resources of the ES Center (ESC, the full simulation for the majority of Hurricane Earl's lifecycle takes over eight days to perform and produces over 5.2 TB of raw data. Preliminary diagnostics show that the results of the LACES simulation for the tropical stage of Hurricane Earl's lifecycle compare well with available observations for the storm. Further studies involving advanced diagnostics have commenced, taking advantage of the uniquely large spatial extent of the high resolution LACES simulation to investigate multiscale interactions in the hurricane and its environment. It is hoped that these studies will enhance our understanding of processes occurring within the hurricane and between the hurricane and its planetary-scale environment.

  11. Impacts of spatial resolution and representation of flow connectivity on large-scale simulation of floods

    OpenAIRE

    Mateo, Cherry May R.; Yamazaki, Dai; Kim, Hyungjun; Champathong, Adisorn; Vaze, Jai; Oki, Taikan

    2017-01-01

    Global-scale River Models (GRMs) are core tools for providing consistent estimates of global flood hazard, especially in data-scarce regions. Due to former limitations in computational power and input datasets, most GRMs have been developed to use simplified representation of flow physics and run at coarse spatial resolutions. With increasing computational power and improved datasets, the application of GRMs to finer resolutions is becoming a reality. To support development in this direction,...

  12. A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

    OpenAIRE

    Guan, Xiangmin; Zhang, Xuejun; Lv, Renli; Chen, Jun; Weiszer, Michal

    2017-01-01

    Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, the current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide the controllers with variety of optional solutions, representing different trade-offs. Furthermore, the flight track error is often overlooked i...

  13. Large Scale Cosmological Anomalies and Inhomogeneous Dark Energy

    Directory of Open Access Journals (Sweden)

    Leandros Perivolaropoulos

    2014-01-01

    Full Text Available A wide range of large scale observations hint towards possible modifications on the standard cosmological model which is based on a homogeneous and isotropic universe with a small cosmological constant and matter. These observations, also known as “cosmic anomalies” include unexpected Cosmic Microwave Background perturbations on large angular scales, large dipolar peculiar velocity flows of galaxies (“bulk flows”, the measurement of inhomogenous values of the fine structure constant on cosmological scales (“alpha dipole” and other effects. The presence of the observational anomalies could either be a large statistical fluctuation in the context of ΛCDM or it could indicate a non-trivial departure from the cosmological principle on Hubble scales. Such a departure is very much constrained by cosmological observations for matter. For dark energy however there are no significant observational constraints for Hubble scale inhomogeneities. In this brief review I discuss some of the theoretical models that can naturally lead to inhomogeneous dark energy, their observational constraints and their potential to explain the large scale cosmic anomalies.

  14. Large-scale patterns in Rayleigh-Benard convection

    International Nuclear Information System (INIS)

    Hardenberg, J. von; Parodi, A.; Passoni, G.; Provenzale, A.; Spiegel, E.A.

    2008-01-01

    Rayleigh-Benard convection at large Rayleigh number is characterized by the presence of intense, vertically moving plumes. Both laboratory and numerical experiments reveal that the rising and descending plumes aggregate into separate clusters so as to produce large-scale updrafts and downdrafts. The horizontal scales of the aggregates reported so far have been comparable to the horizontal extent of the containers, but it has not been clear whether that represents a limitation imposed by domain size. In this work, we present numerical simulations of convection at sufficiently large aspect ratio to ascertain whether there is an intrinsic saturation scale for the clustering process when that ratio is large enough. From a series of simulations of Rayleigh-Benard convection with Rayleigh numbers between 10 5 and 10 8 and with aspect ratios up to 12π, we conclude that the clustering process has a finite horizontal saturation scale with at most a weak dependence on Rayleigh number in the range studied

  15. Concepts and Plans towards fast large scale Monte Carlo production for the ATLAS Experiment

    Science.gov (United States)

    Ritsch, E.; Atlas Collaboration

    2014-06-01

    The huge success of the physics program of the ATLAS experiment at the Large Hadron Collider (LHC) during Run 1 relies upon a great number of simulated Monte Carlo events. This Monte Carlo production takes the biggest part of the computing resources being in use by ATLAS as of now. In this document we describe the plans to overcome the computing resource limitations for large scale Monte Carlo production in the ATLAS Experiment for Run 2, and beyond. A number of fast detector simulation, digitization and reconstruction techniques are being discussed, based upon a new flexible detector simulation framework. To optimally benefit from these developments, a redesigned ATLAS MC production chain is presented at the end of this document.

  16. Development of Large-Scale Spacecraft Fire Safety Experiments

    DEFF Research Database (Denmark)

    Ruff, Gary A.; Urban, David L.; Fernandez-Pello, A. Carlos

    2013-01-01

    exploration missions outside of low-earth orbit and accordingly, more complex in terms of operations, logistics, and safety. This will increase the challenge of ensuring a fire-safe environment for the crew throughout the mission. Based on our fundamental uncertainty of the behavior of fires in low...... of the spacecraft fire safety risk. The activity of this project is supported by an international topical team of fire experts from other space agencies who conduct research that is integrated into the overall experiment design. The large-scale space flight experiment will be conducted in an Orbital Sciences...

  17. The large hadron computer

    CERN Multimedia

    Hirstius, Andreas

    2008-01-01

    Plans for dealing with the torrent of data from the Large Hadron Collider's detectors have made the CERN particle-phycis lab, yet again, a pioneer in computing as well as physics. The author describes the challenges of processing and storing data in the age of petabyt science. (4 pages)

  18. Neighborhood Discriminant Hashing for Large-Scale Image Retrieval.

    Science.gov (United States)

    Tang, Jinhui; Li, Zechao; Wang, Meng; Zhao, Ruizhen

    2015-09-01

    With the proliferation of large-scale community-contributed images, hashing-based approximate nearest neighbor search in huge databases has aroused considerable interest from the fields of computer vision and multimedia in recent years because of its computational and memory efficiency. In this paper, we propose a novel hashing method named neighborhood discriminant hashing (NDH) (for short) to implement approximate similarity search. Different from the previous work, we propose to learn a discriminant hashing function by exploiting local discriminative information, i.e., the labels of a sample can be inherited from the neighbor samples it selects. The hashing function is expected to be orthogonal to avoid redundancy in the learned hashing bits as much as possible, while an information theoretic regularization is jointly exploited using maximum entropy principle. As a consequence, the learned hashing function is compact and nonredundant among bits, while each bit is highly informative. Extensive experiments are carried out on four publicly available data sets and the comparison results demonstrate the outperforming performance of the proposed NDH method over state-of-the-art hashing techniques.

  19. Grid computing in pakistan and: opening to large hadron collider experiments

    International Nuclear Information System (INIS)

    Batool, N.; Osman, A.; Mahmood, A.; Rana, M.A.

    2009-01-01

    A grid computing facility was developed at sister institutes Pakistan Institute of Nuclear Science and Technology (PINSTECH) and Pakistan Institute of Engineering and Applied Sciences (PIEAS) in collaboration with Large Hadron Collider (LHC) Computing Grid during early years of the present decade. The Grid facility PAKGRID-LCG2 as one of the grid node in Pakistan was developed employing mainly local means and is capable of supporting local and international research and computational tasks in the domain of LHC Computing Grid. Functional status of the facility is presented in terms of number of jobs performed. The facility developed provides a forum to local researchers in the field of high energy physics to participate in the LHC experiments and related activities at European particle physics research laboratory (CERN), which is one of the best physics laboratories in the world. It also provides a platform of an emerging computing technology (CT). (author)

  20. Scaling strength distributions in quasi-brittle materials from micro-to macro-scales: A computational approach to modeling Nature-inspired structural ceramics

    International Nuclear Information System (INIS)

    Genet, Martin; Couegnat, Guillaume; Tomsia, Antoni P.; Ritchie, Robert O.

    2014-01-01

    This paper presents an approach to predict the strength distribution of quasi-brittle materials across multiple length-scales, with emphasis on Nature-inspired ceramic structures. It permits the computation of the failure probability of any structure under any mechanical load, solely based on considerations of the microstructure and its failure properties by naturally incorporating the statistical and size-dependent aspects of failure. We overcome the intrinsic limitations of single periodic unit-based approaches by computing the successive failures of the material components and associated stress redistributions on arbitrary numbers of periodic units. For large size samples, the microscopic cells are replaced by a homogenized continuum with equivalent stochastic and damaged constitutive behavior. After establishing the predictive capabilities of the method, and illustrating its potential relevance to several engineering problems, we employ it in the study of the shape and scaling of strength distributions across differing length-scales for a particular quasi-brittle system. We find that the strength distributions display a Weibull form for samples of size approaching the periodic unit; however, these distributions become closer to normal with further increase in sample size before finally reverting to a Weibull form for macroscopic sized samples. In terms of scaling, we find that the weakest link scaling applies only to microscopic, and not macroscopic scale, samples. These findings are discussed in relation to failure patterns computed at different size-scales. (authors)

  1. Web tools for large-scale 3D biological images and atlases

    Directory of Open Access Journals (Sweden)

    Husz Zsolt L

    2012-06-01

    Full Text Available Abstract Background Large-scale volumetric biomedical image data of three or more dimensions are a significant challenge for distributed browsing and visualisation. Many images now exceed 10GB which for most users is too large to handle in terms of computer RAM and network bandwidth. This is aggravated when users need to access tens or hundreds of such images from an archive. Here we solve the problem for 2D section views through archive data delivering compressed tiled images enabling users to browse through very-large volume data in the context of a standard web-browser. The system provides an interactive visualisation for grey-level and colour 3D images including multiple image layers and spatial-data overlay. Results The standard Internet Imaging Protocol (IIP has been extended to enable arbitrary 2D sectioning of 3D data as well a multi-layered images and indexed overlays. The extended protocol is termed IIP3D and we have implemented a matching server to deliver the protocol and a series of Ajax/Javascript client codes that will run in an Internet browser. We have tested the server software on a low-cost linux-based server for image volumes up to 135GB and 64 simultaneous users. The section views are delivered with response times independent of scale and orientation. The exemplar client provided multi-layer image views with user-controlled colour-filtering and overlays. Conclusions Interactive browsing of arbitrary sections through large biomedical-image volumes is made possible by use of an extended internet protocol and efficient server-based image tiling. The tools open the possibility of enabling fast access to large image archives without the requirement of whole image download and client computers with very large memory configurations. The system was demonstrated using a range of medical and biomedical image data extending up to 135GB for a single image volume.

  2. Development and psychometric evaluation of supportive leadership scales.

    Science.gov (United States)

    McGilton, Katherine S

    2003-12-01

    The purpose of this study was to develop and evaluate the psychometric properties of 2 supportive leadership scales, the Charge Nurse Support Scale and the Unit Manager Support Scale, designed for long-term-care environments. These 6-item self-report scales were administered to 70 nursing staff and their internal consistency reliability, test-retest reliability, content validity, factor structure, and construct validity investigated. Content validity was established with the assistance of experts. Both scales were deemed reliable. As hypothesized, a significant relationship was found between the measure of how nursing staff related to residents and measures of charge nurses' supportive behaviours (r = .42, p = .05). Reliable and valid measures of supportive leadership could be developed for use in identifying the quality of support provided to staff in long-term-care environments.

  3. Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

    Science.gov (United States)

    Zhou, Zhiwei; Tu, Jia; Zhu, Zheng-Jiang

    2018-02-01

    Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Computational applications of DNA physical scales

    DEFF Research Database (Denmark)

    Baldi, Pierre; Chauvin, Yves; Brunak, Søren

    1998-01-01

    that these scales provide an alternative or complementary compact representation of DNA sequences. As an example we construct a strand invariant representation of DNA sequences. The scales can also be used to analyze and discover new DNA structural patterns, especially in combinations with hidden Markov models......The authors study from a computational standpoint several different physical scales associated with structural features of DNA sequences, including dinucleotide scales such as base stacking energy and propellor twist, and trinucleotide scales such as bendability and nucleosome positioning. We show...

  5. Computational applications of DNA structural scales

    DEFF Research Database (Denmark)

    Baldi, P.; Chauvin, Y.; Brunak, Søren

    1998-01-01

    that these scales provide an alternative or complementary compact representation of DNA sequences. As an example, we construct a strand-invariant representation of DNA sequences. The scales can also be used to analyze and discover new DNA structural patterns, especially in combination with hidden Markov models......Studies several different physical scales associated with the structural features of DNA sequences from a computational standpoint, including dinucleotide scales, such as base stacking energy and propeller twist, and trinucleotide scales, such as bendability and nucleosome positioning. We show...

  6. Visual Data-Analytics of Large-Scale Parallel Discrete-Event Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Ross, Caitlin; Carothers, Christopher D.; Mubarak, Misbah; Carns, Philip; Ross, Robert; Li, Jianping Kelvin; Ma, Kwan-Liu

    2016-11-13

    Parallel discrete-event simulation (PDES) is an important tool in the codesign of extreme-scale systems because PDES provides a cost-effective way to evaluate designs of highperformance computing systems. Optimistic synchronization algorithms for PDES, such as Time Warp, allow events to be processed without global synchronization among the processing elements. A rollback mechanism is provided when events are processed out of timestamp order. Although optimistic synchronization protocols enable the scalability of large-scale PDES, the performance of the simulations must be tuned to reduce the number of rollbacks and provide an improved simulation runtime. To enable efficient large-scale optimistic simulations, one has to gain insight into the factors that affect the rollback behavior and simulation performance. We developed a tool for ROSS model developers that gives them detailed metrics on the performance of their large-scale optimistic simulations at varying levels of simulation granularity. Model developers can use this information for parameter tuning of optimistic simulations in order to achieve better runtime and fewer rollbacks. In this work, we instrument the ROSS optimistic PDES framework to gather detailed statistics about the simulation engine. We have also developed an interactive visualization interface that uses the data collected by the ROSS instrumentation to understand the underlying behavior of the simulation engine. The interface connects real time to virtual time in the simulation and provides the ability to view simulation data at different granularities. We demonstrate the usefulness of our framework by performing a visual analysis of the dragonfly network topology model provided by the CODES simulation framework built on top of ROSS. The instrumentation needs to minimize overhead in order to accurately collect data about the simulation performance. To ensure that the instrumentation does not introduce unnecessary overhead, we perform a

  7. Manufacturing test of large scale hollow capsule and long length cladding in the large scale oxide dispersion strengthened (ODS) martensitic steel

    International Nuclear Information System (INIS)

    Narita, Takeshi; Ukai, Shigeharu; Kaito, Takeji; Ohtsuka, Satoshi; Fujiwara, Masayuki

    2004-04-01

    Mass production capability of oxide dispersion strengthened (ODS) martensitic steel cladding (9Cr) has being evaluated in the Phase II of the Feasibility Studies on Commercialized Fast Reactor Cycle System. The cost for manufacturing mother tube (raw materials powder production, mechanical alloying (MA) by ball mill, canning, hot extrusion, and machining) is a dominant factor in the total cost for manufacturing ODS ferritic steel cladding. In this study, the large-sale 9Cr-ODS martensitic steel mother tube which is made with a large-scale hollow capsule, and long length claddings were manufactured, and the applicability of these processes was evaluated. Following results were obtained in this study. (1) Manufacturing the large scale mother tube in the dimension of 32 mm OD, 21 mm ID, and 2 m length has been successfully carried out using large scale hollow capsule. This mother tube has a high degree of accuracy in size. (2) The chemical composition and the micro structure of the manufactured mother tube are similar to the existing mother tube manufactured by a small scale can. And the remarkable difference between the bottom and top sides in the manufactured mother tube has not been observed. (3) The long length cladding has been successfully manufactured from the large scale mother tube which was made using a large scale hollow capsule. (4) For reducing the manufacturing cost of the ODS steel claddings, manufacturing process of the mother tubes using a large scale hollow capsules is promising. (author)

  8. Large Scale Chromosome Folding Is Stable against Local Changes in Chromatin Structure.

    Directory of Open Access Journals (Sweden)

    Ana-Maria Florescu

    2016-06-01

    Full Text Available Characterizing the link between small-scale chromatin structure and large-scale chromosome folding during interphase is a prerequisite for understanding transcription. Yet, this link remains poorly investigated. Here, we introduce a simple biophysical model where interphase chromosomes are described in terms of the folding of chromatin sequences composed of alternating blocks of fibers with different thicknesses and flexibilities, and we use it to study the influence of sequence disorder on chromosome behaviors in space and time. By employing extensive computer simulations, we thus demonstrate that chromosomes undergo noticeable conformational changes only on length-scales smaller than 105 basepairs and time-scales shorter than a few seconds, and we suggest there might exist effective upper bounds to the detection of chromosome reorganization in eukaryotes. We prove the relevance of our framework by modeling recent experimental FISH data on murine chromosomes.

  9. Risk Management Challenges in Large-scale Energy PSS

    DEFF Research Database (Denmark)

    Tegeltija, Miroslava; Oehmen, Josef; Kozin, Igor

    2017-01-01

    Probabilistic risk management approaches have a long tradition in engineering. A large variety of tools and techniques based on the probabilistic view of risk is available and applied in PSS practice. However, uncertainties that arise due to lack of knowledge and information are still missing...... adequate representations. We focus on a large-scale energy company in Denmark as one case of current product/servicesystems risk management best practices. We analyze their risk management process and investigate the tools they use in order to support decision making processes within the company. First, we...... identify the following challenges in the current risk management practices that are in line with literature: (1) current methods are not appropriate for the situations dominated by weak knowledge and information; (2) quality of traditional models in such situations is open to debate; (3) quality of input...

  10. Management Needs for Computer Support.

    Science.gov (United States)

    Irby, Alice J.

    University management has many and varied needs for effective computer services in support of their processing and information functions. The challenge for the computer center managers is to better understand these needs and assist in the development of effective and timely solutions. Management needs can range from accounting and payroll to…

  11. Hydrometeorological variability on a large french catchment and its relation to large-scale circulation across temporal scales

    Science.gov (United States)

    Massei, Nicolas; Dieppois, Bastien; Fritier, Nicolas; Laignel, Benoit; Debret, Maxime; Lavers, David; Hannah, David

    2015-04-01

    In the present context of global changes, considerable efforts have been deployed by the hydrological scientific community to improve our understanding of the impacts of climate fluctuations on water resources. Both observational and modeling studies have been extensively employed to characterize hydrological changes and trends, assess the impact of climate variability or provide future scenarios of water resources. In the aim of a better understanding of hydrological changes, it is of crucial importance to determine how and to what extent trends and long-term oscillations detectable in hydrological variables are linked to global climate oscillations. In this work, we develop an approach associating large-scale/local-scale correlation, enmpirical statistical downscaling and wavelet multiresolution decomposition of monthly precipitation and streamflow over the Seine river watershed, and the North Atlantic sea level pressure (SLP) in order to gain additional insights on the atmospheric patterns associated with the regional hydrology. We hypothesized that: i) atmospheric patterns may change according to the different temporal wavelengths defining the variability of the signals; and ii) definition of those hydrological/circulation relationships for each temporal wavelength may improve the determination of large-scale predictors of local variations. The results showed that the large-scale/local-scale links were not necessarily constant according to time-scale (i.e. for the different frequencies characterizing the signals), resulting in changing spatial patterns across scales. This was then taken into account by developing an empirical statistical downscaling (ESD) modeling approach which integrated discrete wavelet multiresolution analysis for reconstructing local hydrometeorological processes (predictand : precipitation and streamflow on the Seine river catchment) based on a large-scale predictor (SLP over the Euro-Atlantic sector) on a monthly time-step. This approach

  12. Coordination processes in computer supported collaborative writing

    NARCIS (Netherlands)

    Kanselaar, G.; Erkens, Gijsbert; Jaspers, Jos; Prangsma, M.E.

    2005-01-01

    In the COSAR-project a computer-supported collaborative learning environment enables students to collaborate in writing an argumentative essay. The TC3 groupware environment (TC3: Text Composer, Computer supported and Collaborative) offers access to relevant information sources, a private notepad, a

  13. Superconducting materials for large scale applications

    International Nuclear Information System (INIS)

    Dew-Hughes, D.

    1975-01-01

    Applications of superconductors capable of carrying large current densities in large-scale electrical devices are examined. Discussions are included on critical current density, superconducting materials available, and future prospects for improved superconducting materials. (JRD)

  14. Computer model for large-scale offshore wind-power systems

    Energy Technology Data Exchange (ETDEWEB)

    Dambolena, I G [Bucknell Univ., Lewisburg, PA; Rikkers, R F; Kaminsky, F C

    1977-01-01

    A computer-based planning model has been developed to evaluate the cost and simulate the performance of offshore wind-power systems. In these systems, the electricity produced by wind generators either satisfies directly demand or produces hydrogen by water electrolysis. The hydrogen is stored and later used to produce electricity in fuel cells. Using as inputs basic characteristics of the system and historical or computer-generated time series for wind speed and electricity demand, the model simulates system performance over time. A history of the energy produced and the discounted annual cost of the system are used to evaluate alternatives. The output also contains information which is useful in pointing towards more favorable design alternatives. Use of the model to analyze a specific wind-power system for New England indicates that electric energy could perhaps be generated at a competitive cost.

  15. Large-scale influences in near-wall turbulence.

    Science.gov (United States)

    Hutchins, Nicholas; Marusic, Ivan

    2007-03-15

    Hot-wire data acquired in a high Reynolds number facility are used to illustrate the need for adequate scale separation when considering the coherent structure in wall-bounded turbulence. It is found that a large-scale motion in the log region becomes increasingly comparable in energy to the near-wall cycle as the Reynolds number increases. Through decomposition of fluctuating velocity signals, it is shown that this large-scale motion has a distinct modulating influence on the small-scale energy (akin to amplitude modulation). Reassessment of DNS data, in light of these results, shows similar trends, with the rate and intensity of production due to the near-wall cycle subject to a modulating influence from the largest-scale motions.

  16. Parallel supercomputing: Advanced methods, algorithms, and software for large-scale linear and nonlinear problems

    Energy Technology Data Exchange (ETDEWEB)

    Carey, G.F.; Young, D.M.

    1993-12-31

    The program outlined here is directed to research on methods, algorithms, and software for distributed parallel supercomputers. Of particular interest are finite element methods and finite difference methods together with sparse iterative solution schemes for scientific and engineering computations of very large-scale systems. Both linear and nonlinear problems will be investigated. In the nonlinear case, applications with bifurcation to multiple solutions will be considered using continuation strategies. The parallelizable numerical methods of particular interest are a family of partitioning schemes embracing domain decomposition, element-by-element strategies, and multi-level techniques. The methods will be further developed incorporating parallel iterative solution algorithms with associated preconditioners in parallel computer software. The schemes will be implemented on distributed memory parallel architectures such as the CRAY MPP, Intel Paragon, the NCUBE3, and the Connection Machine. We will also consider other new architectures such as the Kendall-Square (KSQ) and proposed machines such as the TERA. The applications will focus on large-scale three-dimensional nonlinear flow and reservoir problems with strong convective transport contributions. These are legitimate grand challenge class computational fluid dynamics (CFD) problems of significant practical interest to DOE. The methods developed and algorithms will, however, be of wider interest.

  17. 8th International Symposium on Intelligent Distributed Computing & Workshop on Cyber Security and Resilience of Large-Scale Systems & 6th International Workshop on Multi-Agent Systems Technology and Semantics

    CERN Document Server

    Braubach, Lars; Venticinque, Salvatore; Badica, Costin

    2015-01-01

    This book represents the combined peer-reviewed proceedings of the Eight International Symposium on Intelligent Distributed Computing - IDC'2014, of the Workshop on Cyber Security and Resilience of Large-Scale Systems - WSRL-2014, and of the Sixth International Workshop on Multi-Agent Systems Technology and Semantics- MASTS-2014. All the events were held in Madrid, Spain, during September 3-5, 2014. The 47 contributions published in this book address several topics related to theory and applications of the intelligent distributed computing and multi-agent systems, including: agent-based data processing, ambient intelligence, collaborative systems, cryptography and security, distributed algorithms, grid and cloud computing, information extraction, knowledge management, big data and ontologies, social networks, swarm intelligence or videogames amongst others.

  18. Local scale decision support systems - actual situation and trends for the future

    International Nuclear Information System (INIS)

    Govaerts, P.

    1993-01-01

    Based on the communications presented in the session on local scale decision support systems, some common trends for those models have been identified. During the last decade the evolutionary change of those models is related with the better insight in decisions to be taken with respect to interventions, the acceptance of large uncertainties, the perceived importance of social and economic factors and shift of the identity of the user. A more revolutionary change is predicted for the near future, putting most emphasis on the predictive mode, extending the integration of monitoring data in the decision support system, and the use of pre-established scenarios. The local scale decision support system will become the key module of the off-site emergency control room. (author)

  19. Supporting collaborative computing and interaction

    International Nuclear Information System (INIS)

    Agarwal, Deborah; McParland, Charles; Perry, Marcia

    2002-01-01

    To enable collaboration on the daily tasks involved in scientific research, collaborative frameworks should provide lightweight and ubiquitous components that support a wide variety of interaction modes. We envision a collaborative environment as one that provides a persistent space within which participants can locate each other, exchange synchronous and asynchronous messages, share documents and applications, share workflow, and hold videoconferences. We are developing the Pervasive Collaborative Computing Environment (PCCE) as such an environment. The PCCE will provide integrated tools to support shared computing and task control and monitoring. This paper describes the PCCE and the rationale for its design

  20. On the Soft Limit of the Large Scale Structure Power Spectrum: UV Dependence

    CERN Document Server

    Garny, Mathias; Porto, Rafael A; Sagunski, Laura

    2015-01-01

    We derive a non-perturbative equation for the large scale structure power spectrum of long-wavelength modes. Thereby, we use an operator product expansion together with relations between the three-point function and power spectrum in the soft limit. The resulting equation encodes the coupling to ultraviolet (UV) modes in two time-dependent coefficients, which may be obtained from response functions to (anisotropic) parameters, such as spatial curvature, in a modified cosmology. We argue that both depend weakly on fluctuations deep in the UV. As a byproduct, this implies that the renormalized leading order coefficient(s) in the effective field theory (EFT) of large scale structures receive most of their contribution from modes close to the non-linear scale. Consequently, the UV dependence found in explicit computations within standard perturbation theory stems mostly from counter-term(s). We confront a simplified version of our non-perturbative equation against existent numerical simulations, and find good agr...

  1. Fan-out Estimation in Spin-based Quantum Computer Scale-up.

    Science.gov (United States)

    Nguyen, Thien; Hill, Charles D; Hollenberg, Lloyd C L; James, Matthew R

    2017-10-17

    Solid-state spin-based qubits offer good prospects for scaling based on their long coherence times and nexus to large-scale electronic scale-up technologies. However, high-threshold quantum error correction requires a two-dimensional qubit array operating in parallel, posing significant challenges in fabrication and control. While architectures incorporating distributed quantum control meet this challenge head-on, most designs rely on individual control and readout of all qubits with high gate densities. We analysed the fan-out routing overhead of a dedicated control line architecture, basing the analysis on a generalised solid-state spin qubit platform parameterised to encompass Coulomb confined (e.g. donor based spin qubits) or electrostatically confined (e.g. quantum dot based spin qubits) implementations. The spatial scalability under this model is estimated using standard electronic routing methods and present-day fabrication constraints. Based on reasonable assumptions for qubit control and readout we estimate 10 2 -10 5 physical qubits, depending on the quantum interconnect implementation, can be integrated and fanned-out independently. Assuming relatively long control-free interconnects the scalability can be extended. Ultimately, the universal quantum computation may necessitate a much higher number of integrated qubits, indicating that higher dimensional electronics fabrication and/or multiplexed distributed control and readout schemes may be the preferredstrategy for large-scale implementation.

  2. Full-Scale Approximations of Spatio-Temporal Covariance Models for Large Datasets

    KAUST Repository

    Zhang, Bohai

    2014-01-01

    Various continuously-indexed spatio-temporal process models have been constructed to characterize spatio-temporal dependence structures, but the computational complexity for model fitting and predictions grows in a cubic order with the size of dataset and application of such models is not feasible for large datasets. This article extends the full-scale approximation (FSA) approach by Sang and Huang (2012) to the spatio-temporal context to reduce computational complexity. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed to select knots automatically from a discrete set of spatio-temporal points. Our approach is applicable to nonseparable and nonstationary spatio-temporal covariance models. We illustrate the effectiveness of our method through simulation experiments and application to an ozone measurement dataset.

  3. Large-scale, high-performance and cloud-enabled multi-model analytics experiments in the context of the Earth System Grid Federation

    Science.gov (United States)

    Fiore, S.; Płóciennik, M.; Doutriaux, C.; Blanquer, I.; Barbera, R.; Williams, D. N.; Anantharaj, V. G.; Evans, B. J. K.; Salomoni, D.; Aloisio, G.

    2017-12-01

    The increased models resolution in the development of comprehensive Earth System Models is rapidly leading to very large climate simulations output that pose significant scientific data management challenges in terms of data sharing, processing, analysis, visualization, preservation, curation, and archiving.Large scale global experiments for Climate Model Intercomparison Projects (CMIP) have led to the development of the Earth System Grid Federation (ESGF), a federated data infrastructure which has been serving the CMIP5 experiment, providing access to 2PB of data for the IPCC Assessment Reports. In such a context, running a multi-model data analysis experiment is very challenging, as it requires the availability of a large amount of data related to multiple climate models simulations and scientific data management tools for large-scale data analytics. To address these challenges, a case study on climate models intercomparison data analysis has been defined and implemented in the context of the EU H2020 INDIGO-DataCloud project. The case study has been tested and validated on CMIP5 datasets, in the context of a large scale, international testbed involving several ESGF sites (LLNL, ORNL and CMCC), one orchestrator site (PSNC) and one more hosting INDIGO PaaS services (UPV). Additional ESGF sites, such as NCI (Australia) and a couple more in Europe, are also joining the testbed. The added value of the proposed solution is summarized in the following: it implements a server-side paradigm which limits data movement; it relies on a High-Performance Data Analytics (HPDA) stack to address performance; it exploits the INDIGO PaaS layer to support flexible, dynamic and automated deployment of software components; it provides user-friendly web access based on the INDIGO Future Gateway; and finally it integrates, complements and extends the support currently available through ESGF. Overall it provides a new "tool" for climate scientists to run multi-model experiments. At the

  4. Application of simplified models to CO2 migration and immobilization in large-scale geological systems

    KAUST Repository

    Gasda, Sarah E.

    2012-07-01

    Long-term stabilization of injected carbon dioxide (CO 2) is an essential component of risk management for geological carbon sequestration operations. However, migration and trapping phenomena are inherently complex, involving processes that act over multiple spatial and temporal scales. One example involves centimeter-scale density instabilities in the dissolved CO 2 region leading to large-scale convective mixing that can be a significant driver for CO 2 dissolution. Another example is the potentially important effect of capillary forces, in addition to buoyancy and viscous forces, on the evolution of mobile CO 2. Local capillary effects lead to a capillary transition zone, or capillary fringe, where both fluids are present in the mobile state. This small-scale effect may have a significant impact on large-scale plume migration as well as long-term residual and dissolution trapping. Computational models that can capture both large and small-scale effects are essential to predict the role of these processes on the long-term storage security of CO 2 sequestration operations. Conventional modeling tools are unable to resolve sufficiently all of these relevant processes when modeling CO 2 migration in large-scale geological systems. Herein, we present a vertically-integrated approach to CO 2 modeling that employs upscaled representations of these subgrid processes. We apply the model to the Johansen formation, a prospective site for sequestration of Norwegian CO 2 emissions, and explore the sensitivity of CO 2 migration and trapping to subscale physics. Model results show the relative importance of different physical processes in large-scale simulations. The ability of models such as this to capture the relevant physical processes at large spatial and temporal scales is important for prediction and analysis of CO 2 storage sites. © 2012 Elsevier Ltd.

  5. Multi-VO support in IHEP's distributed computing environment

    International Nuclear Information System (INIS)

    Yan, T; Suo, B; Zhao, X H; Zhang, X M; Ma, Z T; Yan, X F; Lin, T; Deng, Z Y; Li, W D; Belov, S; Pelevanyuk, I; Zhemchugov, A; Cai, H

    2015-01-01

    Inspired by the success of BESDIRAC, the distributed computing environment based on DIRAC for BESIII experiment, several other experiments operated by Institute of High Energy Physics (IHEP), such as Circular Electron Positron Collider (CEPC), Jiangmen Underground Neutrino Observatory (JUNO), Large High Altitude Air Shower Observatory (LHAASO) and Hard X-ray Modulation Telescope (HXMT) etc, are willing to use DIRAC to integrate the geographically distributed computing resources available by their collaborations. In order to minimize manpower and hardware cost, we extended the BESDIRAC platform to support multi-VO scenario, instead of setting up a self-contained distributed computing environment for each VO. This makes DIRAC as a service for the community of those experiments. To support multi-VO, the system architecture of BESDIRAC is adjusted for scalability. The VOMS and DIRAC servers are reconfigured to manage users and groups belong to several VOs. A lightweight storage resource manager StoRM is employed as the central SE to integrate local and grid data. A frontend system is designed for user's massive job splitting, submission and management, with plugins to support new VOs. A monitoring and accounting system is also considered to easy the system administration and VO related resources usage accounting. (paper)

  6. PKI security in large-scale healthcare networks.

    Science.gov (United States)

    Mantas, Georgios; Lymberopoulos, Dimitrios; Komninos, Nikos

    2012-06-01

    During the past few years a lot of PKI (Public Key Infrastructures) infrastructures have been proposed for healthcare networks in order to ensure secure communication services and exchange of data among healthcare professionals. However, there is a plethora of challenges in these healthcare PKI infrastructures. Especially, there are a lot of challenges for PKI infrastructures deployed over large-scale healthcare networks. In this paper, we propose a PKI infrastructure to ensure security in a large-scale Internet-based healthcare network connecting a wide spectrum of healthcare units geographically distributed within a wide region. Furthermore, the proposed PKI infrastructure facilitates the trust issues that arise in a large-scale healthcare network including multi-domain PKI infrastructures.

  7. Large-scale ab initio configuration interaction calculations for light nuclei

    International Nuclear Information System (INIS)

    Maris, Pieter; Potter, Hugh; Vary, James P; Aktulga, H Metin; Ng, Esmond G; Yang Chao; Caprio, Mark A; Çatalyürek, Ümit V; Saule, Erik; Oryspayev, Dossay; Sosonkina, Masha; Zhou Zheng

    2012-01-01

    In ab-initio Configuration Interaction calculations, the nuclear wavefunction is expanded in Slater determinants of single-nucleon wavefunctions and the many-body Schrodinger equation becomes a large sparse matrix problem. The challenge is to reach numerical convergence to within quantified numerical uncertainties for physical observables using finite truncations of the infinite-dimensional basis space. We discuss strategies for constructing and solving the resulting large sparse matrix eigenvalue problems on current multicore computer architectures. Several of these strategies have been implemented in the code MFDn, a hybrid MPI/OpenMP Fortran code for ab-initio nuclear structure calculations that can scale to 100,000 cores and more. Finally, we will conclude with some recent results for 12 C including emerging collective phenomena such as rotational band structures using SRG evolved chiral N3LO interactions.

  8. Large Scale Solar Power Integration in Distribution Grids : PV Modelling, Voltage Support and Aggregation Studies

    NARCIS (Netherlands)

    Samadi, A.

    2014-01-01

    Long term supporting schemes for photovoltaic (PV) system installation have led to accommodating large numbers of PV systems within load pockets in distribution grids. High penetrations of PV systems can cause new technical challenges, such as voltage rise due to reverse power flow during light load

  9. Emerging large-scale solar heating applications

    International Nuclear Information System (INIS)

    Wong, W.P.; McClung, J.L.

    2009-01-01

    Currently the market for solar heating applications in Canada is dominated by outdoor swimming pool heating, make-up air pre-heating and domestic water heating in homes, commercial and institutional buildings. All of these involve relatively small systems, except for a few air pre-heating systems on very large buildings. Together these applications make up well over 90% of the solar thermal collectors installed in Canada during 2007. These three applications, along with the recent re-emergence of large-scale concentrated solar thermal for generating electricity, also dominate the world markets. This paper examines some emerging markets for large scale solar heating applications, with a focus on the Canadian climate and market. (author)

  10. Emerging large-scale solar heating applications

    Energy Technology Data Exchange (ETDEWEB)

    Wong, W.P.; McClung, J.L. [Science Applications International Corporation (SAIC Canada), Ottawa, Ontario (Canada)

    2009-07-01

    Currently the market for solar heating applications in Canada is dominated by outdoor swimming pool heating, make-up air pre-heating and domestic water heating in homes, commercial and institutional buildings. All of these involve relatively small systems, except for a few air pre-heating systems on very large buildings. Together these applications make up well over 90% of the solar thermal collectors installed in Canada during 2007. These three applications, along with the recent re-emergence of large-scale concentrated solar thermal for generating electricity, also dominate the world markets. This paper examines some emerging markets for large scale solar heating applications, with a focus on the Canadian climate and market. (author)

  11. WAMS Based Intelligent Operation and Control of Modern Power System with large Scale Renewable Energy Penetration

    DEFF Research Database (Denmark)

    Rather, Zakir Hussain

    security limits. Under such scenario, progressive displacement of conventional generation by wind generation is expected to eventually lead a complex power system with least presence of central power plants. Consequently the support from conventional power plants is expected to reach its all-time low...... system voltage control responsibility from conventional power plants to wind turbines. With increased wind penetration and displaced conventional central power plants, dynamic voltage security has been identified as one of the challenging issue for large scale wind integration. To address the dynamic...... security issue, a WAMS based systematic voltage control scheme for large scale wind integrated power system has been proposed. Along with the optimal reactive power compensation, the proposed scheme considers voltage support from wind farms (equipped with voltage support functionality) and refurbished...

  12. Manual of a suite of computer codes, EXPRESS (EXact PREparedness Supporting System)

    International Nuclear Information System (INIS)

    Chino, Masamichi

    1992-06-01

    The emergency response supporting system EXPRESS (EXact PREparedness Supporting System) is constructed in JAERI for low cost engineering work stations under the UNIX operation. The purpose of this system is real-time predictions of affected areas due to radioactivities discharged into atmosphere from nuclear facilities. The computational models in EXPRESS are the mass-consistent wind field model EXPRESS-I and the particle dispersion model EXPRESS-II for atmospheric dispersions. In order to attain the quick response even when the codes are used in a small-scale computer, a high-speed iteration method MILUCR (Modified Incomplete Linear Unitary Conjugate Residual) is applied to EXPRESS-I and kernel density method is to EXPRESS-II. This manual describes the model configurations, code structures, related files, namelists and sample outputs of EXPRESS-I and -II. (author)

  13. Properties Important To Mixing For WTP Large Scale Integrated Testing

    International Nuclear Information System (INIS)

    Koopman, D.; Martino, C.; Poirier, M.

    2012-01-01

    Large Scale Integrated Testing (LSIT) is being planned by Bechtel National, Inc. to address uncertainties in the full scale mixing performance of the Hanford Waste Treatment and Immobilization Plant (WTP). Testing will use simulated waste rather than actual Hanford waste. Therefore, the use of suitable simulants is critical to achieving the goals of the test program. External review boards have raised questions regarding the overall representativeness of simulants used in previous mixing tests. Accordingly, WTP requested the Savannah River National Laboratory (SRNL) to assist with development of simulants for use in LSIT. Among the first tasks assigned to SRNL was to develop a list of waste properties that matter to pulse-jet mixer (PJM) mixing of WTP tanks. This report satisfies Commitment 5.2.3.1 of the Department of Energy Implementation Plan for Defense Nuclear Facilities Safety Board Recommendation 2010-2: physical properties important to mixing and scaling. In support of waste simulant development, the following two objectives are the focus of this report: (1) Assess physical and chemical properties important to the testing and development of mixing scaling relationships; (2) Identify the governing properties and associated ranges for LSIT to achieve the Newtonian and non-Newtonian test objectives. This includes the properties to support testing of sampling and heel management systems. The test objectives for LSIT relate to transfer and pump out of solid particles, prototypic integrated operations, sparger operation, PJM controllability, vessel level/density measurement accuracy, sampling, heel management, PJM restart, design and safety margin, Computational Fluid Dynamics (CFD) Verification and Validation (V and V) and comparison, performance testing and scaling, and high temperature operation. The slurry properties that are most important to Performance Testing and Scaling depend on the test objective and rheological classification of the slurry (i

  14. PROPERTIES IMPORTANT TO MIXING FOR WTP LARGE SCALE INTEGRATED TESTING

    Energy Technology Data Exchange (ETDEWEB)

    Koopman, D.; Martino, C.; Poirier, M.

    2012-04-26

    Large Scale Integrated Testing (LSIT) is being planned by Bechtel National, Inc. to address uncertainties in the full scale mixing performance of the Hanford Waste Treatment and Immobilization Plant (WTP). Testing will use simulated waste rather than actual Hanford waste. Therefore, the use of suitable simulants is critical to achieving the goals of the test program. External review boards have raised questions regarding the overall representativeness of simulants used in previous mixing tests. Accordingly, WTP requested the Savannah River National Laboratory (SRNL) to assist with development of simulants for use in LSIT. Among the first tasks assigned to SRNL was to develop a list of waste properties that matter to pulse-jet mixer (PJM) mixing of WTP tanks. This report satisfies Commitment 5.2.3.1 of the Department of Energy Implementation Plan for Defense Nuclear Facilities Safety Board Recommendation 2010-2: physical properties important to mixing and scaling. In support of waste simulant development, the following two objectives are the focus of this report: (1) Assess physical and chemical properties important to the testing and development of mixing scaling relationships; (2) Identify the governing properties and associated ranges for LSIT to achieve the Newtonian and non-Newtonian test objectives. This includes the properties to support testing of sampling and heel management systems. The test objectives for LSIT relate to transfer and pump out of solid particles, prototypic integrated operations, sparger operation, PJM controllability, vessel level/density measurement accuracy, sampling, heel management, PJM restart, design and safety margin, Computational Fluid Dynamics (CFD) Verification and Validation (V and V) and comparison, performance testing and scaling, and high temperature operation. The slurry properties that are most important to Performance Testing and Scaling depend on the test objective and rheological classification of the slurry (i

  15. Concepts and Plans towards fast large scale Monte Carlo production for the ATLAS Experiment

    CERN Document Server

    Chapman, J; Duehrssen, M; Elsing, M; Froidevaux, D; Harrington, R; Jansky, R; Langenberg, R; Mandrysch, R; Marshall, Z; Ritsch, E; Salzburger, A

    2014-01-01

    The huge success of the physics program of the ATLAS experiment at the Large Hadron Collider (LHC) during run I relies upon a great number of simulated Monte Carlo events. This Monte Carlo production takes the biggest part of the computing resources being in use by ATLAS as of now. In this document we describe the plans to overcome the computing resource limitations for large scale Monte Carlo production in the ATLAS Experiment for run II, and beyond. A number of fast detector simulation, digitization and reconstruction techniques and are being discussed, based upon a new flexible detector simulation framework. To optimally benefit from these developments, a redesigned ATLAS MC production chain is presented at the end of this document.

  16. Implementation of a solution Cloud Computing with MapReduce model

    International Nuclear Information System (INIS)

    Baya, Chalabi

    2014-01-01

    In recent years, large scale computer systems have emerged to meet the demands of high storage, supercomputing, and applications using very large data sets. The emergence of Cloud Computing offers the potentiel for analysis and processing of large data sets. Mapreduce is the most popular programming model which is used to support the development of such applications. It was initially designed by Google for building large datacenters on a large scale, to provide Web search services with rapid response and high availability. In this paper we will test the clustering algorithm K-means Clustering in a Cloud Computing. This algorithm is implemented on MapReduce. It has been chosen for its characteristics that are representative of many iterative data analysis algorithms. Then, we modify the framework CloudSim to simulate the MapReduce execution of K-means Clustering on different Cloud Computing, depending on their size and characteristics of target platforms. The experiment show that the implementation of K-means Clustering gives good results especially for large data set and the Cloud infrastructure has an influence on these results

  17. Large Scale Simulations of the Euler Equations on GPU Clusters

    KAUST Repository

    Liebmann, Manfred

    2010-08-01

    The paper investigates the scalability of a parallel Euler solver, using the Vijayasundaram method, on a GPU cluster with 32 Nvidia Geforce GTX 295 boards. The aim of this research is to enable large scale fluid dynamics simulations with up to one billion elements. We investigate communication protocols for the GPU cluster to compensate for the slow Gigabit Ethernet network between the GPU compute nodes and to maintain overall efficiency. A diesel engine intake-port and a nozzle, meshed in different resolutions, give good real world examples for the scalability tests on the GPU cluster. © 2010 IEEE.

  18. Large-scale regions of antimatter

    International Nuclear Information System (INIS)

    Grobov, A. V.; Rubin, S. G.

    2015-01-01

    Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era

  19. Large-scale regions of antimatter

    Energy Technology Data Exchange (ETDEWEB)

    Grobov, A. V., E-mail: alexey.grobov@gmail.com; Rubin, S. G., E-mail: sgrubin@mephi.ru [National Research Nuclear University MEPhI (Russian Federation)

    2015-07-15

    Amodified mechanism of the formation of large-scale antimatter regions is proposed. Antimatter appears owing to fluctuations of a complex scalar field that carries a baryon charge in the inflation era.

  20. Large-scale atomistic simulations of nanostructured materials based on divide-and-conquer density functional theory

    Directory of Open Access Journals (Sweden)

    Vashishta P.

    2011-05-01

    Full Text Available A linear-scaling algorithm based on a divide-and-conquer (DC scheme is designed to perform large-scale molecular-dynamics simulations, in which interatomic forces are computed quantum mechanically in the framework of the density functional theory (DFT. This scheme is applied to the thermite reaction at an Al/Fe2O3 interface. It is found that mass diffusion and reaction rate at the interface are enhanced by a concerted metal-oxygen flip mechanism. Preliminary simulations are carried out for an aluminum particle in water based on the conventional DFT, as a target system for large-scale DC-DFT simulations. A pair of Lewis acid and base sites on the aluminum surface preferentially catalyzes hydrogen production in a low activation-barrier mechanism found in the simulations