GPU computing and applications
See, Simon
2015-01-01
This book presents a collection of state of the art research on GPU Computing and Application. The major part of this book is selected from the work presented at the 2013 Symposium on GPU Computing and Applications held in Nanyang Technological University, Singapore (Oct 9, 2013). Three major domains of GPU application are covered in the book including (1) Engineering design and simulation; (2) Biomedical Sciences; and (3) Interactive & Digital Media. The book also addresses the fundamental issues in GPU computing with a focus on big data processing. Researchers and developers in GPU Computing and Applications will benefit from this book. Training professionals and educators can also benefit from this book to learn the possible application of GPU technology in various areas.
GPU applications for data processing
Vladymyrov, Mykhailo, E-mail: mykhailo.vladymyrov@cern.ch [LPI - Lebedev Physical Institute of the Russian Academy of Sciences, RUS-119991 Moscow (Russian Federation); Aleksandrov, Andrey [LPI - Lebedev Physical Institute of the Russian Academy of Sciences, RUS-119991 Moscow (Russian Federation); INFN sezione di Napoli, I-80125 Napoli (Italy); Tioukov, Valeri [INFN sezione di Napoli, I-80125 Napoli (Italy)
2015-12-31
Modern experiments that use nuclear photoemulsion imply fast and efficient data acquisition from the emulsion can be performed. The new approaches in developing scanning systems require real-time processing of large amount of data. Methods that use Graphical Processing Unit (GPU) computing power for emulsion data processing are presented here. It is shown how the GPU-accelerated emulsion processing helped us to rise the scanning speed by factor of nine.
Parallelization and checkpointing of GPU applications through program transformation
Solano-Quinde, Lizandro Damian [Iowa State Univ., Ames, IA (United States)
2012-01-01
GPUs have emerged as a powerful tool for accelerating general-purpose applications. The availability of programming languages that makes writing general-purpose applications for running on GPUs tractable have consolidated GPUs as an alternative for accelerating general purpose applications. Among the areas that have benefited from GPU acceleration are: signal and image processing, computational fluid dynamics, quantum chemistry, and, in general, the High Performance Computing (HPC) Industry. In order to continue to exploit higher levels of parallelism with GPUs, multi-GPU systems are gaining popularity. In this context, single-GPU applications are parallelized for running in multi-GPU systems. Furthermore, multi-GPU systems help to solve the GPU memory limitation for applications with large application memory footprint. Parallelizing single-GPU applications has been approached by libraries that distribute the workload at runtime, however, they impose execution overhead and are not portable. On the other hand, on traditional CPU systems, parallelization has been approached through application transformation at pre-compile time, which enhances the application to distribute the workload at application level and does not have the issues of library-based approaches. Hence, a parallelization scheme for GPU systems based on application transformation is needed. Like any computing engine of today, reliability is also a concern in GPUs. GPUs are vulnerable to transient and permanent failures. Current checkpoint/restart techniques are not suitable for systems with GPUs. Checkpointing for GPU systems present new and interesting challenges, primarily due to the natural differences imposed by the hardware design, the memory subsystem architecture, the massive number of threads, and the limited amount of synchronization among threads. Therefore, a checkpoint/restart technique suitable for GPU systems is needed. The goal of this work is to exploit higher levels of parallelism and
GPU-based Parallel Application Design for Emerging Mobile Devices
Gupta, Kshitij
A revolution is underway in the computing world that is causing a fundamental paradigm shift in device capabilities and form-factor, with a move from well-established legacy desktop/laptop computers to mobile devices in varying sizes and shapes. Amongst all the tasks these devices must support, graphics has emerged as the 'killer app' for providing a fluid user interface and high-fidelity game rendering, effectively making the graphics processor (GPU) one of the key components in (present and future) mobile systems. By utilizing the GPU as a general-purpose parallel processor, this dissertation explores the GPU computing design space from an applications standpoint, in the mobile context, by focusing on key challenges presented by these devices---limited compute, memory bandwidth, and stringent power consumption requirements---while improving the overall application efficiency of the increasingly important speech recognition workload for mobile user interaction. We broadly partition trends in GPU computing into four major categories. We analyze hardware and programming model limitations in current-generation GPUs and detail an alternate programming style called Persistent Threads, identify four use case patterns, and propose minimal modifications that would be required for extending native support. We show how by manually extracting data locality and altering the speech recognition pipeline, we are able to achieve significant savings in memory bandwidth while simultaneously reducing the compute burden on GPU-like parallel processors. As we foresee GPU computing to evolve from its current 'co-processor' model into an independent 'applications processor' that is capable of executing complex work independently, we create an alternate application framework that enables the GPU to handle all control-flow dependencies autonomously at run-time while minimizing host involvement to just issuing commands, that facilitates an efficient application implementation. Finally, as
VISMASHUP: streamlining the creation of custom visualization applications
Ahrens, James P [Los Alamos National Laboratory; Santos, Emanuele [UNIV OF UTAH; Lins, Lauro [UNIV OF UTAH; Freire, Juliana [UNIV OF UTAH; Silva, Cl' audio T [UNIV OF UTAH
2010-01-01
Visualization is essential for understanding the increasing volumes of digital data. However, the process required to create insightful visualizations is involved and time consuming. Although several visualization tools are available, including tools with sophisticated visual interfaces, they are out of reach for users who have little or no knowledge of visualization techniques and/or who do not have programming expertise. In this paper, we propose VISMASHUP, a new framework for streamlining the creation of customized visualization applications. Because these applications can be customized for very specific tasks, they can hide much of the complexity in a visualization specification and make it easier for users to explore visualizations by manipulating a small set of parameters. We describe the framework and how it supports the various tasks a designer needs to carry out to develop an application, from mining and exploring a set of visualization specifications (pipelines), to the creation of simplified views of the pipelines, and the automatic generation of the application and its interface. We also describe the implementation of the system and demonstrate its use in two real application scenarios.
GPU Lossless Hyperspectral Data Compression System for Space Applications
Keymeulen, Didier; Aranki, Nazeeh; Hopson, Ben; Kiely, Aaron; Klimesh, Matthew; Benkrid, Khaled
2012-01-01
On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. At JPL, a novel, adaptive and predictive technique for lossless compression of hyperspectral data, named the Fast Lossless (FL) algorithm, was recently developed. This technique uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. Because of its outstanding performance and suitability for real-time onboard hardware implementation, the FL compressor is being formalized as the emerging CCSDS Standard for Lossless Multispectral & Hyperspectral image compression. The FL compressor is well-suited for parallel hardware implementation. A GPU hardware implementation was developed for FL targeting the current state-of-the-art GPUs from NVIDIA(Trademark). The GPU implementation on a NVIDIA(Trademark) GeForce(Trademark) GTX 580 achieves a throughput performance of 583.08 Mbits/sec (44.85 MSamples/sec) and an acceleration of at least 6 times a software implementation running on a 3.47 GHz single core Intel(Trademark) Xeon(Trademark) processor. This paper describes the design and implementation of the FL algorithm on the GPU. The massively parallel implementation will provide in the future a fast and practical real-time solution for airborne and space applications.
Miki, T; Wang, X; Aoki, T; Imai, Y; Ishikawa, T; Takase, K; Yamaguchi, T
2012-01-01
In this paper, we propose a novel patient-specific method of modelling pulmonary airflow using graphics processing unit (GPU) computation that can be applied in medical practice. To overcome the barriers imposed by computation speed, installation price and footprint to the application of computational fluid dynamics, we focused on GPU computation and the lattice Boltzmann method (LBM). The GPU computation and LBM are compatible due to the characteristics of the GPU. As the optimisation of data access is essential for the performance of the GPU computation, we developed an adaptive meshing method, in which an airway model is covered by isotropic subdomains consisting of a uniform Cartesian mesh. We found that 4(3) size subdomains gave the best performance. The code was also tested on a small GPU cluster to confirm its performance and applicability, as the price and footprint are reasonable for medical applications.
GPU-based Integration with Application in Sensitivity Analysis
Atanassov, Emanouil; Ivanovska, Sofiya; Karaivanova, Aneta; Slavov, Dimitar
2010-05-01
The presented work is an important part of the grid application MCSAES (Monte Carlo Sensitivity Analysis for Environmental Studies) which aim is to develop an efficient Grid implementation of a Monte Carlo based approach for sensitivity studies in the domains of Environmental modelling and Environmental security. The goal is to study the damaging effects that can be caused by high pollution levels (especially effects on human health), when the main modeling tool is the Danish Eulerian Model (DEM). Generally speaking, sensitivity analysis (SA) is the study of how the variation in the output of a mathematical model can be apportioned to, qualitatively or quantitatively, different sources of variation in the input of a model. One of the important classes of methods for Sensitivity Analysis are Monte Carlo based, first proposed by Sobol, and then developed by Saltelli and his group. In MCSAES the general Saltelli procedure has been adapted for SA of the Danish Eulerian model. In our case we consider as factors the constants determining the speeds of the chemical reactions in the DEM and as output a certain aggregated measure of the pollution. Sensitivity simulations lead to huge computational tasks (systems with up to 4 × 109 equations at every time-step, and the number of time-steps can be more than a million) which motivates its grid implementation. MCSAES grid implementation scheme includes two main tasks: (i) Grid implementation of the DEM, (ii) Grid implementation of the Monte Carlo integration. In this work we present our new developments in the integration part of the application. We have developed an algorithm for GPU-based generation of scrambled quasirandom sequences which can be combined with the CPU-based computations related to the SA. Owen first proposed scrambling of Sobol sequence through permutation in a manner that improves the convergence rates. Scrambling is necessary not only for error analysis but for parallel implementations. Good scrambling is
CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.
Lei, Guoqing; Dou, Yong; Wan, Wen; Xia, Fei; Li, Rongchun; Ma, Meng; Zou, Dan
2012-01-01
Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.
Interior Point Methods on GPU with application to Model Predictive Control
Gade-Nielsen, Nicolai Fog
The goal of this thesis is to investigate the application of interior point methods to solve dynamical optimization problems, using a graphical processing unit (GPU) with a focus on problems arising in Model Predictice Control (MPC). Multi-core processors have been available for over ten years now...... equations of the Hessian matrix. The use of a GPU has been shown to be very efficient in the factorization of dense matrices, and several numeric libraries, which utilize the GPU, have become available during the course of this thesis. We have developed a direct interior point method, which utilizes the GPU...... of different optimization algorithms are available for solving optimization problems. Some of the most common method are the simplex method and interior point methods. We focus on interior point methods in this thesis, due to its polynomial complexity, and since the use of the simplex method with GPUs have...
Su, Lin; Du, Xining; Liu, Tianyu; Xu, X. George
2014-06-01
An electron-photon coupled Monte Carlo code ARCHER - Accelerated Radiation-transport Computations in Heterogeneous EnviRonments - is being developed at Rensselaer Polytechnic Institute as a software testbed for emerging heterogeneous high performance computers that utilize accelerators such as GPUs. This paper presents the preliminary code development and the testing involving radiation dose related problems. In particular, the paper discusses the electron transport simulations using the class-II condensed history method. The considered electron energy ranges from a few hundreds of keV to 30 MeV. For photon part, photoelectric effect, Compton scattering and pair production were modeled. Voxelized geometry was supported. A serial CPU code was first written in C++. The code was then transplanted to the GPU using the CUDA C 5.0 standards. The hardware involved a desktop PC with an Intel Xeon X5660 CPU and six NVIDIA Tesla™ M2090 GPUs. The code was tested for a case of 20 MeV electron beam incident perpendicularly on a water-aluminum-water phantom. The depth and later dose profiles were found to agree with results obtained from well tested MC codes. Using six GPU cards, 6x106 electron histories were simulated within 2 seconds. In comparison, the same case running the EGSnrc and MCNPX codes required 1645 seconds and 9213 seconds, respectively. On-going work continues to test the code for different medical applications such as radiotherapy and brachytherapy.
Geant4-based Monte Carlo simulations on GPU for medical applications.
Bert, Julien; Perez-Ponce, Hector; El Bitar, Ziad; Jan, Sébastien; Boursier, Yannick; Vintache, Damien; Bonissent, Alain; Morel, Christian; Brasse, David; Visvikis, Dimitris
2013-08-21
Monte Carlo simulation (MCS) plays a key role in medical applications, especially for emission tomography and radiotherapy. However MCS is also associated with long calculation times that prevent its use in routine clinical practice. Recently, graphics processing units (GPU) became in many domains a low cost alternative for the acquisition of high computational power. The objective of this work was to develop an efficient framework for the implementation of MCS on GPU architectures. Geant4 was chosen as the MCS engine given the large variety of physics processes available for targeting different medical imaging and radiotherapy applications. In addition, Geant4 is the MCS engine behind GATE which is actually the most popular medical applications' simulation platform. We propose the definition of a global strategy and associated structures for such a GPU based simulation implementation. Different photon and electron physics effects are resolved on the fly directly on GPU without any approximations with respect to Geant4. Validations have shown equivalence in the underlying photon and electron physics processes between the Geant4 and the GPU codes with a speedup factor of 80-90. More clinically realistic simulations in emission and transmission imaging led to acceleration factors of 400-800 respectively compared to corresponding GATE simulations.
The development of GPU-based parallel PRNG for Monte Carlo applications in CUDA Fortran
Hamed Kargaran
2016-04-01
Full Text Available The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number generation with good statistical properties on GPU. In this study, a GPU-based parallel pseudo random number generator (GPPRNG have been proposed to use in high performance computing systems. According to the type of GPU memory usage, GPU scheme is divided into two work modes including GLOBAL_MODE and SHARED_MODE. To generate parallel random numbers based on the independent sequence method, the combination of middle-square method and chaotic map along with the Xorshift PRNG have been employed. Implementation of our developed PPRNG on a single GPU showed a speedup of 150x and 470x (with respect to the speed of PRNG on a single CPU core for GLOBAL_MODE and SHARED_MODE, respectively. To evaluate the accuracy of our developed GPPRNG, its performance was compared to that of some other commercially available PPRNGs such as MATLAB, FORTRAN and Miller-Park algorithm through employing the specific standard tests. The results of this comparison showed that the developed GPPRNG in this study can be used as a fast and accurate tool for computational science applications.
The development of GPU-based parallel PRNG for Monte Carlo applications in CUDA Fortran
Kargaran, Hamed; Minuchehr, Abdolhamid; Zolfaghari, Ahmad
2016-04-01
The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number generation with good statistical properties on GPU. In this study, a GPU-based parallel pseudo random number generator (GPPRNG) have been proposed to use in high performance computing systems. According to the type of GPU memory usage, GPU scheme is divided into two work modes including GLOBAL_MODE and SHARED_MODE. To generate parallel random numbers based on the independent sequence method, the combination of middle-square method and chaotic map along with the Xorshift PRNG have been employed. Implementation of our developed PPRNG on a single GPU showed a speedup of 150x and 470x (with respect to the speed of PRNG on a single CPU core) for GLOBAL_MODE and SHARED_MODE, respectively. To evaluate the accuracy of our developed GPPRNG, its performance was compared to that of some other commercially available PPRNGs such as MATLAB, FORTRAN and Miller-Park algorithm through employing the specific standard tests. The results of this comparison showed that the developed GPPRNG in this study can be used as a fast and accurate tool for computational science applications.
Multi-GPU and multi-CPU accelerated FDTD scheme for vibroacoustic applications
Francés, J.; Otero, B.; Bleda, S.; Gallego, S.; Neipp, C.; Márquez, A.; Beléndez, A.
2015-06-01
with auto-vectorisation and also shared memory approach. In this scenario GPU computing is the best option since it provides a homogeneous behaviour. More specifically, the speedup of GPU computing achieves an upper limit of 12 for both one and two GPUs, whereas the performance reaches peak values of 80 GFlops and 146 GFlops for the performance for one GPU and two GPUs respectively. Finally, the method is applied to an earth crust profile in order to demonstrate the potential of our approach and the necessity of applying acceleration strategies in these type of applications.
Application of GPU processing for Brownian particle simulation
Cheng, Way Lee; Sheharyar, Ali; Sadr, Reza; Bouhali, Othmane
2015-01-01
Reports on the anomalous thermal-fluid properties of nanofluids (dilute suspension of nano-particles in a base fluid) have been the subject of attention for 15 years. The underlying physics that govern nanofluid behavior, however, is not fully understood and is a subject of much dispute. The interactions between the suspended particles and the base fluid have been cited as a major contributor to the improvement in heat transfer reported in the literature. Numerical simulations are instrumental in studying the behavior of nanofluids. However, such simulations can be computationally intensive due to the small dimensions and complexity of these problems. In this study, a simplified computational approach for isothermal nanofluid simulations was applied, and simulations were conducted using both conventional CPU and parallel GPU implementations. The GPU implementations significantly improved the computational performance, in terms of the simulation time, by a factor of 1000-2500. The results of this investigation show that, as the computational load increases, the simulation efficiency approaches a constant. At a very high computational load, the amount of improvement may even decrease due to limited system memory.
Cell-based Adaptive Mesh Refinement on the GPU with Applications to Exascale Supercomputing
Trujillo, Dennis; Robey, Robert; Davis, Neal; Nicholaeff, David
2011-10-01
We present an OpenCL implementation of a cell-based adaptive mesh refinement (AMR) scheme for the shallow water equations. The challenges associated with ensuring the locality of algorithm architecture to fully exploit the massive number of parallel threads on the GPU is discussed. This includes a proof of concept that a cell-based AMR code can be effectively implemented, even on a small scale, in the memory and threading model provided by OpenCL. Additionally, the program requires dynamic memory in order to properly implement the mesh; as this is not supported in the OpenCL 1.1 standard, a combination of CPU memory management and GPU computation effectively implements a dynamic memory allocation scheme. Load balancing is achieved through a new stencil-based implementation of a space-filling curve, eliminating the need for a complete recalculation of the indexing on the mesh. A cartesian grid hash table scheme to allow fast parallel neighbor accesses is also discussed. Finally, the relative speedup of the GPU-enabled AMR code is compared to the original serial version. We conclude that parallelization using the GPU provides significant speedup for typical numerical applications and is feasible for scientific applications in the next generation of supercomputing.
A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing
Ginés D. Guerrero
2014-01-01
Full Text Available Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO. This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.
GPU-accelerated elastic 3D image registration for intra-surgical applications.
Ruijters, Daniel; ter Haar Romeny, Bart M; Suetens, Paul
2011-08-01
Local motion within intra-patient biomedical images can be compensated by using elastic image registration. The application of B-spline based elastic registration during interventional treatment is seriously hampered by its considerable computation time. The graphics processing unit (GPU) can be used to accelerate the calculation of such elastic registrations by using its parallel processing power, and by employing the hardwired tri-linear interpolation capabilities in order to efficiently perform the cubic B-spline evaluation. In this article it is shown that the similarity measure and its derivatives also can be calculated on the GPU, using a two pass approach. On average a speedup factor 50 compared to a straight-forward CPU implementation was reached.
GpuCV : a GPU-accelerated framework for image processing and computer vision
ALLUSSE, Yannick; Horain, Patrick; Agarwal, Ankit; Saipriyadarshan, Cindula
2008-01-01
International audience; This paper presents briefly describes the state of the art of accelerating image processing with graphics hardware (GPU) and discusses some of its caveats. Then it describes GpuCV, an open source multi-platform library for GPU-accelerated image processing and Computer Vision operators and applications. It is meant for computer vision scientist not familiar with GPU technologies. GpuCV is designed to be compatible with the popular OpenCV library by offering GPU-accelera...
Pizette, Patrick; Govender, Nicolin; Wilke, Daniel N.; Abriak, Nor-Edine
2017-06-01
The use of the Discrete Element Method (DEM) for industrial civil engineering industrial applications is currently limited due to the computational demands when large numbers of particles are considered. The graphics processing unit (GPU) with its highly parallelized hardware architecture shows potential to enable solution of civil engineering problems using discrete granular approaches. We demonstrate in this study the pratical utility of a validated GPU-enabled DEM modeling environment to simulate industrial scale granular problems. As illustration, the flow discharge of storage silos using 8 and 17 million particles is considered. DEM simulations have been performed to investigate the influence of particle size (equivalent size for the 20/40-mesh gravel) and induced shear stress for two hopper shapes. The preliminary results indicate that the shape of the hopper significantly influences the discharge rates for the same material. Specifically, this work shows that GPU-enabled DEM modeling environments can model industrial scale problems on a single portable computer within a day for 30 seconds of process time.
Discrete shearlet transform on GPU with applications in anomaly detection and denoising
Gibert, Xavier; Patel, Vishal M.; Labate, Demetrio; Chellappa, Rama
2014-12-01
Shearlets have emerged in recent years as one of the most successful methods for the multiscale analysis of multidimensional signals. Unlike wavelets, shearlets form a pyramid of well-localized functions defined not only over a range of scales and locations, but also over a range of orientations and with highly anisotropic supports. As a result, shearlets are much more effective than traditional wavelets in handling the geometry of multidimensional data, and this was exploited in a wide range of applications from image and signal processing. However, despite their desirable properties, the wider applicability of shearlets is limited by the computational complexity of current software implementations. For example, denoising a single 512 × 512 image using a current implementation of the shearlet-based shrinkage algorithm can take between 10 s and 2 min, depending on the number of CPU cores, and much longer processing times are required for video denoising. On the other hand, due to the parallel nature of the shearlet transform, it is possible to use graphics processing units (GPU) to accelerate its implementation. In this paper, we present an open source stand-alone implementation of the 2D discrete shearlet transform using CUDA C++ as well as GPU-accelerated MATLAB implementations of the 2D and 3D shearlet transforms. We have instrumented the code so that we can analyze the running time of each kernel under different GPU hardware. In addition to denoising, we describe a novel application of shearlets for detecting anomalies in textured images. In this application, computation times can be reduced by a factor of 50 or more, compared to multicore CPU implementations.
Software Graphics Processing Unit (sGPU) for Deep Space Applications
McCabe, Mary; Salazar, George; Steele, Glen
2015-01-01
A graphics processing capability will be required for deep space missions and must include a range of applications, from safety-critical vehicle health status to telemedicine for crew health. However, preliminary radiation testing of commercial graphics processing cards suggest they cannot operate in the deep space radiation environment. Investigation into an Software Graphics Processing Unit (sGPU)comprised of commercial-equivalent radiation hardened/tolerant single board computers, field programmable gate arrays, and safety-critical display software shows promising results. Preliminary performance of approximately 30 frames per second (FPS) has been achieved. Use of multi-core processors may provide a significant increase in performance.
基于GPU的CUDA应用开发环境构架%CUDA Application Development Framework Structure based on GPU
邓力; 陈晓翔; 林嘉宇
2013-01-01
With its rapid development, the powerful computing power of the GPU ( graphics processing unit) makes it to be used for the non - graphics of the calculation increasingly from the first using only for the graphics of the calculation. In CPU - GPU heterogeneous system, CPU is responsible for the calculation of complex logic operation and affairs management which is not suitable for parallel processing of data, GPU is responsible for the large - scale data calculation of intensity calculation and simple logic branch which is suitable for parallel processing. The continuous improvement of CPU - GPU system makes the large - scale scientific computing to be an inevitable trend by means of speed up of GPU. Focusing on the application development of GPU, the paper introduces the structure of VS2008 + CUDA development platform in WINDOWS environment, and compares the scientific computing performance of GPU and CPU in this structure.%随着GPU(graphics processing unit,图像处理单元)的快速发展,其强大的计算能力使得GPU由最初仅用于加速图形计算,越来越多地应用到非图形领域的计算.在CPU-GPU体系中,CPU负责进行复杂的逻辑运算和事务管理等不适合并行处理的数据计算,GPU负责进行计算密集度高、逻辑分支简单的适合并行处理的大规模数据计算.CPU-GPU体系的不断完善,使得利用GPU来加速大规模科学计算成为了一种必然趋势.着眼GPU的应用开发,介绍在windows环境下CUDA+ VS2008开发平台的构架,并对该构架下GPU与CPU的科学计算性能进行比对.
GPU-accelerated phase extraction algorithm for interferograms: a real-time application
Zhu, Xiaoqiang; Wu, Yongqian; Liu, Fengwei
2016-11-01
Optical testing, having the merits of non-destruction and high sensitivity, provides a vital guideline for optical manufacturing. But the testing process is often computationally intensive and expensive, usually up to a few seconds, which is sufferable for dynamic testing. In this paper, a GPU-accelerated phase extraction algorithm is proposed, which is based on the advanced iterative algorithm. The accelerated algorithm can extract the right phase-distribution from thirteen 1024x1024 fringe patterns with arbitrary phase shifts in 233 milliseconds on average using NVIDIA Quadro 4000 graphic card, which achieved a 12.7x speedup ratio than the same algorithm executed on CPU and 6.6x speedup ratio than that on Matlab using DWANING W5801 workstation. The performance improvement can fulfill the demand of computational accuracy and real-time application.
Real-Time Nonlinear Finite Element Computations on GPU - Application to Neurosurgical Simulation.
Joldes, Grand Roman; Wittek, Adam; Miller, Karol
2010-12-15
Application of biomechanical modeling techniques in the area of medical image analysis and surgical simulation implies two conflicting requirements: accurate results and high solution speeds. Accurate results can be obtained only by using appropriate models and solution algorithms. In our previous papers we have presented algorithms and solution methods for performing accurate nonlinear finite element analysis of brain shift (which includes mixed mesh, different non-linear material models, finite deformations and brain-skull contacts) in less than a minute on a personal computer for models having up to 50.000 degrees of freedom. In this paper we present an implementation of our algorithms on a Graphics Processing Unit (GPU) using the new NVIDIA Compute Unified Device Architecture (CUDA) which leads to more than 20 times increase in the computation speed. This makes possible the use of meshes with more elements, which better represent the geometry, are easier to generate, and provide more accurate results.
Rueda, Antonio J.; Noguera, José M.; Luque, Adrián
2016-02-01
In recent years GPU computing has gained wide acceptance as a simple low-cost solution for speeding up computationally expensive processing in many scientific and engineering applications. However, in most cases accelerating a traditional CPU implementation for a GPU is a non-trivial task that requires a thorough refactorization of the code and specific optimizations that depend on the architecture of the device. OpenACC is a promising technology that aims at reducing the effort required to accelerate C/C++/Fortran code on an attached multicore device. Virtually with this technology the CPU code only has to be augmented with a few compiler directives to identify the areas to be accelerated and the way in which data has to be moved between the CPU and GPU. Its potential benefits are multiple: better code readability, less development time, lower risk of errors and less dependency on the underlying architecture and future evolution of the GPU technology. Our aim with this work is to evaluate the pros and cons of using OpenACC against native GPU implementations in computationally expensive hydrological applications, using the classic D8 algorithm of O'Callaghan and Mark for river network extraction as case-study. We implemented the flow accumulation step of this algorithm in CPU, using OpenACC and two different CUDA versions, comparing the length and complexity of the code and its performance with different datasets. We advance that although OpenACC can not match the performance of a CUDA optimized implementation (×3.5 slower in average), it provides a significant performance improvement against a CPU implementation (×2-6) with by far a simpler code and less implementation effort.
gpuPOM: a GPU-based Princeton Ocean Model
S. Xu
2014-11-01
Full Text Available Rapid advances in the performance of the graphics processing unit (GPU have made the GPU a compelling solution for a series of scientific applications. However, most existing GPU acceleration works for climate models are doing partial code porting for certain hot spots, and can only achieve limited speedup for the entire model. In this work, we take the mpiPOM (a parallel version of the Princeton Ocean Model as our starting point, design and implement a GPU-based Princeton Ocean Model. By carefully considering the architectural features of the state-of-the-art GPU devices, we rewrite the full mpiPOM model from the original Fortran version into a new Compute Unified Device Architecture C (CUDA-C version. We take several accelerating methods to further improve the performance of gpuPOM, including optimizing memory access in a single GPU, overlapping communication and boundary operations among multiple GPUs, and overlapping input/output (I/O between the hybrid Central Processing Unit (CPU and the GPU. Our experimental results indicate that the performance of the gpuPOM on a workstation containing 4 GPUs is comparable to a powerful cluster with 408 CPU cores and it reduces the energy consumption by 6.8 times.
Porting Large HPC Applications to GPU Clusters: The Codes GENE and VERTEX
Dannert, Tilman; Rampp, Markus
2013-01-01
We have developed GPU versions for two major high-performance-computing (HPC) applications originating from two different scientific domains. GENE is a plasma microturbulence code which is employed for simulations of nuclear fusion plasmas. VERTEX is a neutrino-radiation hydrodynamics code for "first principles"-simulations of core-collapse supernova explosions. The codes are considered state of the art in their respective scientific domains, both concerning their scientific scope and functionality as well as the achievable compute performance, in particular parallel scalability on all relevant HPC platforms. GENE and VERTEX were ported by us to HPC cluster architectures with two NVidia Kepler GPUs mounted in each node in addition to two Intel Xeon CPUs of the Sandy Bridge family. On such platforms we achieve up to twofold gains in the overall application performance in the sense of a reduction of the time to solution for a given setup with respect to a pure CPU cluster. The paper describes our basic porting ...
Tang, Yu-Hang; Karniadakis, George; Crunch Team
2014-03-01
We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to illustrate the practicality of our code in real-world applications. This work was supported by the new Department of Energy Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4). Simulations were carried out at the Oak Ridge Leadership Computing Facility through the INCITE program under project BIP017.
Application of Photon Transport Monte Carlo Module with GPU-based Parallel System
Park, Chang Je [Sejong University, Seoul (Korea, Republic of); Shon, Heejeong [Golden Eng. Co. LTD, Seoul (Korea, Republic of); Lee, Donghak [CoCo Link Inc., Seoul (Korea, Republic of)
2015-05-15
In general, it takes lots of computing time to get reliable results in Monte Carlo simulations especially in deep penetration problems with a thick shielding medium. To mitigate such a weakness of Monte Carlo methods, lots of variance reduction algorithms are proposed including geometry splitting and Russian roulette, weight windows, exponential transform, and forced collision, etc. Simultaneously, advanced computing hardware systems such as GPU(Graphics Processing Units)-based parallel machines are used to get a better performance of the Monte Carlo simulation. The GPU is much easier to access and to manage when comparing a CPU cluster system. It also becomes less expensive these days due to enhanced computer technology. There, lots of engineering areas adapt GPU-bases massive parallel computation technique. based photon transport Monte Carlo method. It provides almost 30 times speedup without any optimization and it is expected almost 200 times with fully supported GPU system. It is expected that GPU system with advanced parallelization algorithm will contribute successfully for development of the Monte Carlo module which requires quick and accurate simulations.
GPU Implementation of Bayesian Neural Network Construction for Data-Intensive Applications
Perry, Michelle; Prosper, Harrison B.; Meyer-Baese, Anke
2014-06-01
We describe a graphical processing unit (GPU) implementation of the Hybrid Markov Chain Monte Carlo (HMC) method for training Bayesian Neural Networks (BNN). Our implementation uses NVIDIA's parallel computing architecture, CUDA. We briefly review BNNs and the HMC method and we describe our implementations and give preliminary results.
Papaloizou, J C B
2004-01-01
We carry out a general study of the stability of astrophysical flows that appear steady in a uniformly rotating frame. Such a flow might correspond to a stellar pulsation mode or an accretion disk with a free global distortion giving it finite eccentricity. We consider perturbations arbitrarily localized in the neighbourhood of unperturbed fluid streamlines.When conditions do not vary around them, perturbations take the form of oscillatory inertial or gravity modes. However, when conditions do vary so that a circulating fluid element is subject to periodic variations, parametric instability may occur. For nearly circular streamlines, the dense spectra associated with inertial or gravity modes ensure that resonance conditions can always be satisfied when twice the period of circulation round a streamline falls within. We apply our formalism to a differentially rotating disk for which the streamlines are Keplerian ellipses, with free eccentricity up to 0.7, which do not precess in an inertial frame. We show tha...
SU-E-T-29: A Web Application for GPU-Based Monte Carlo IMRT/VMAT QA with Delivered Dose Verification
Folkerts, M [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States); University of California, San Diego, La Jolla, CA (United States); Graves, Y [University of California, San Diego, La Jolla, CA (United States); Tian, Z; Gu, X; Jia, X; Jiang, S [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States)
2014-06-01
Purpose: To enable an existing web application for GPU-based Monte Carlo (MC) 3D dosimetry quality assurance (QA) to compute “delivered dose” from linac logfile data. Methods: We added significant features to an IMRT/VMAT QA web application which is based on existing technologies (HTML5, Python, and Django). This tool interfaces with python, c-code libraries, and command line-based GPU applications to perform a MC-based IMRT/VMAT QA. The web app automates many complicated aspects of interfacing clinical DICOM and logfile data with cutting-edge GPU software to run a MC dose calculation. The resultant web app is powerful, easy to use, and is able to re-compute both plan dose (from DICOM data) and delivered dose (from logfile data). Both dynalog and trajectorylog file formats are supported. Users upload zipped DICOM RP, CT, and RD data and set the expected statistic uncertainty for the MC dose calculation. A 3D gamma index map, 3D dose distribution, gamma histogram, dosimetric statistics, and DVH curves are displayed to the user. Additional the user may upload the delivery logfile data from the linac to compute a 'delivered dose' calculation and corresponding gamma tests. A comprehensive PDF QA report summarizing the results can also be downloaded. Results: We successfully improved a web app for a GPU-based QA tool that consists of logfile parcing, fluence map generation, CT image processing, GPU based MC dose calculation, gamma index calculation, and DVH calculation. The result is an IMRT and VMAT QA tool that conducts an independent dose calculation for a given treatment plan and delivery log file. The system takes both DICOM data and logfile data to compute plan dose and delivered dose respectively. Conclusion: We sucessfully improved a GPU-based MC QA tool to allow for logfile dose calculation. The high efficiency and accessibility will greatly facilitate IMRT and VMAT QA.
Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction
Agulleiro, J.I.; Vazquez, F.; Garzon, E.M. [Supercomputing and Algorithms Group, Associated Unit CSIC-UAL, University of Almeria, 04120 Almeria (Spain); Fernandez, J.J., E-mail: JJ.Fernandez@csic.es [National Centre for Biotechnology, National Research Council (CNB-CSIC), Campus UAM, C/Darwin 3, Cantoblanco, 28049 Madrid (Spain)
2012-04-15
Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM. -- Highlights: Black-Right-Pointing-Pointer Hybrid computing allows full exploitation of the power (CPU+GPU) in a computer. Black-Right-Pointing-Pointer Proper orchestration of workload is managed by an on-demand strategy. Black-Right-Pointing-Pointer Total number of threads running in the system should be limited to the number of CPUs.
Mena, Andres; Ferrero, Jose M.; Rodriguez Matas, Jose F.
2015-11-01
Solving the electric activity of the heart possess a big challenge, not only because of the structural complexities inherent to the heart tissue, but also because of the complex electric behaviour of the cardiac cells. The multi-scale nature of the electrophysiology problem makes difficult its numerical solution, requiring temporal and spatial resolutions of 0.1 ms and 0.2 mm respectively for accurate simulations, leading to models with millions degrees of freedom that need to be solved for thousand time steps. Solution of this problem requires the use of algorithms with higher level of parallelism in multi-core platforms. In this regard the newer programmable graphic processing units (GPU) has become a valid alternative due to their tremendous computational horsepower. This paper presents results obtained with a novel electrophysiology simulation software entirely developed in Compute Unified Device Architecture (CUDA). The software implements fully explicit and semi-implicit solvers for the monodomain model, using operator splitting. Performance is compared with classical multi-core MPI based solvers operating on dedicated high-performance computer clusters. Results obtained with the GPU based solver show enormous potential for this technology with accelerations over 50 × for three-dimensional problems.
GPU computing and its application in biomedical research%CPU计算及其在生物医学研究中的应用
李江域; 赵东升; 王玉民
2011-01-01
High-performance computing is an important tool and method for modern biomedical research. The traditional central processing unit( CPU )-based computer is unable to satisfy all the demands in computing performance, efficiency and cost of biomedical research. In recent years, graphics processing unit( GPU ) computing has emerged to become a hot-spot in high-performance computing. The concept, programming method and feature of GPU computing is introduced in the is paper, then applications of and problems with GPU computing in biomedicine are summarized. Finally, the author gives advice on GPU computing application in our academy.%高性能计算是现代生物医学研究的重要工具和手段,传统的基于通用处理器(CPU)的计算机已很难满足生物医学研究对计算性能、效率和成本等多方面的综合性要求.近年来,图形处理器(GPU)计算技术异军突起,成为高性能计算领域的研究热点.本文介绍了GPU计算的基本概念、编程方法和特点,总结和讨论了GPU计算在生物医学中的应用现状和存在问题.最后,结合实际情况提出了利用GPU计算的一些研究工作设想.
Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction.
Agulleiro, J I; Vázquez, F; Garzón, E M; Fernández, J J
2012-04-01
Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM. Copyright © 2012 Elsevier B.V. All rights reserved.
Dugan, Nazim; Healy, John J.; Ryle, James P.; Hennelly, Bryan M.
2014-05-01
Digital holographic microscopy is suitable for the detection of microbial particles in a rapidly flowing fluid since in this technique the focusing can be carried out as post-processing of a single captured image. This image, known as a digital hologram, contains the full complex wave front information emanating from the object which forms an interference pattern with a known reference beam. Post-processing is computationally intense and it constitutes a bottleneck for real time inspection of fast moving scenes. In the current work, GPU computation is used to accelerate the post-processing of the holographic images captured by digital holographic microscopy. Efficiency and reliability of a pre-processing step in order to eliminate low information content holographic images is also investigated.
Wang, Y; Mazur, T; Green, O; Hu, Y; Wooten, H; Yang, D; Zhao, T; Mutic, S; Li, H [Washington University School of Medicine, St. Louis, MO (United States)
2015-06-15
Purpose: To build a fast, accurate and easily-deployable research platform for Monte-Carlo dose calculations. We port the dose calculation engine PENELOPE to C++, and accelerate calculations using GPU acceleration. Simulations of a Co-60 beam model provided by ViewRay demonstrate the capabilities of the platform. Methods: We built software that incorporates a beam model interface, CT-phantom model, GPU-accelerated PENELOPE engine, and GUI front-end. We rewrote the PENELOPE kernel in C++ (from Fortran) and accelerated the code on a GPU. We seamlessly integrated a Co-60 beam model (obtained from ViewRay) into our platform. Simulations of various field sizes and SSDs using a homogeneous water phantom generated PDDs, dose profiles, and output factors that were compared to experiment data. Results: With GPU acceleration using a dated graphics card (Nvidia Tesla C2050), a highly accurate simulation – including 100*100*100 grid, 3×3×3 mm3 voxels, <1% uncertainty, and 4.2×4.2 cm2 field size – runs 24 times faster (20 minutes versus 8 hours) than when parallelizing on 8 threads across a new CPU (Intel i7-4770). Simulated PDDs, profiles and output ratios for the commercial system agree well with experiment data measured using radiographic film or ionization chamber. Based on our analysis, this beam model is precise enough for general applications. Conclusions: Using a beam model for a Co-60 system provided by ViewRay, we evaluate a dose calculation platform that we developed. Comparison to measurements demonstrates the promise of our software for use as a research platform for dose calculations, with applications including quality assurance and treatment plan verification.
Memory-Scalable GPU Spatial Hierarchy Construction.
Qiming Hou; Xin Sun; Kun Zhou; Lauterbach, C; Manocha, D
2011-04-01
Recent GPU algorithms for constructing spatial hierarchies have achieved promising performance for moderately complex models by using the breadth-first search (BFS) construction order. While being able to exploit the massive parallelism on the GPU, the BFS order also consumes excessive GPU memory, which becomes a serious issue for interactive applications involving very complex models with more than a few million triangles. In this paper, we propose to use the partial breadth-first search (PBFS) construction order to control memory consumption while maximizing performance. We apply the PBFS order to two hierarchy construction algorithms. The first algorithm is for kd-trees that automatically balances between the level of parallelism and intermediate memory usage. With PBFS, peak memory consumption during construction can be efficiently controlled without costly CPU-GPU data transfer. We also develop memory allocation strategies to effectively limit memory fragmentation. The resulting algorithm scales well with GPU memory and constructs kd-trees of models with millions of triangles at interactive rates on GPUs with 1 GB memory. Compared with existing algorithms, our algorithm is an order of magnitude more scalable for a given GPU memory bound. The second algorithm is for out-of-core bounding volume hierarchy (BVH) construction for very large scenes based on the PBFS construction order. At each iteration, all constructed nodes are dumped to the CPU memory, and the GPU memory is freed for the next iteration's use. In this way, the algorithm is able to build trees that are too large to be stored in the GPU memory. Experiments show that our algorithm can construct BVHs for scenes with up to 20 M triangles, several times larger than previous GPU algorithms.
Putnam, William M.
2011-01-01
Earth system models like the Goddard Earth Observing System model (GEOS-5) have been pushing the limits of large clusters of multi-core microprocessors, producing breath-taking fidelity in resolving cloud systems at a global scale. GPU computing presents an opportunity for improving the efficiency of these leading edge models. A GPU implementation of GEOS-5 will facilitate the use of cloud-system resolving resolutions in data assimilation and weather prediction, at resolutions near 3.5 km, improving our ability to extract detailed information from high-resolution satellite observations and ultimately produce better weather and climate predictions
NMF-mGPU: non-negative matrix factorization on multi-GPU systems.
Mejía-Roa, Edgardo; Tabas-Madrid, Daniel; Setoain, Javier; García, Carlos; Tirado, Francisco; Pascual-Montano, Alberto
2015-02-13
In the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster. In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU. NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA's framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system's main memory to the GPU's memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup). Applications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the
Design and Implementation of GPU-Based Prim's Algorithm
Wei Wang
2011-07-01
Full Text Available Minimum spanning tree is a classical problem in graph theory that plays a key role in a broad domain of applications. This paper proposes a minimum spanning tree algorithm using Prim's approach on Nvidia GPU under CUDA architecture. By using new developed GPU-based Min-Reduction data parallel primitive in the key step of the algorithm, higher efficiency is achieved. Experimental results show that we obtain about 2 times speedup on Nvidia GTX260 GPU over the CPU implementation and 3 times speedup over non-primitives GPU implementation.
Yang, Po; Dong, Feng; Codreanu, Valeriu
2017-01-01
SME applications. This system designs and implements a directive programming model with new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator...
CULA: hybrid GPU accelerated linear algebra routines
Humphrey, John R.; Price, Daniel K.; Spagnoli, Kyle E.; Paolini, Aaron L.; Kelmelis, Eric J.
2010-04-01
The modern graphics processing unit (GPU) found in many standard personal computers is a highly parallel math processor capable of nearly 1 TFLOPS peak throughput at a cost similar to a high-end CPU and an excellent FLOPS/watt ratio. High-level linear algebra operations are computationally intense, often requiring O(N3) operations and would seem a natural fit for the processing power of the GPU. Our work is on CULA, a GPU accelerated implementation of linear algebra routines. We present results from factorizations such as LU decomposition, singular value decomposition and QR decomposition along with applications like system solution and least squares. The GPU execution model featured by NVIDIA GPUs based on CUDA demands very strong parallelism, requiring between hundreds and thousands of simultaneous operations to achieve high performance. Some constructs from linear algebra map extremely well to the GPU and others map poorly. CPUs, on the other hand, do well at smaller order parallelism and perform acceptably during low-parallelism code segments. Our work addresses this via hybrid a processing model, in which the CPU and GPU work simultaneously to produce results. In many cases, this is accomplished by allowing each platform to do the work it performs most naturally.
High Performance Multi-GPU SpMV for Multi-component PDE-Based Applications
Abdelfattah, Ahmad
2015-07-25
Leveraging optimization techniques (e.g., register blocking and double buffering) introduced in the context of KBLAS, a Level 2 BLAS high performance library on GPUs, the authors implement dense matrix-vector multiplications within a sparse-block structure. While these optimizations are important for high performance dense kernel executions, they are even more critical when dealing with sparse linear algebra operations. The most time-consuming phase of many multicomponent applications, such as models of reacting flows or petroleum reservoirs, is the solution at each implicit time step of large, sparse spatially structured or unstructured linear systems. The standard method is a preconditioned Krylov solver. The Sparse Matrix-Vector multiplication (SpMV) is, in turn, one of the most time-consuming operations in such solvers. Because there is no data reuse of the elements of the matrix within a single SpMV, kernel performance is limited by the speed at which data can be transferred from memory to registers, making the bus bandwidth the major bottleneck. On the other hand, in case of a multi-species model, the resulting Jacobian has a dense block structure. For contemporary petroleum reservoir simulations, the block size typically ranges from three to a few dozen among different models, and still larger blocks are relevant within adaptively model-refined regions of the domain, though generally the size of the blocks, related to the number of conserved species, is constant over large regions within a given model. This structure can be exploited beyond the convenience of a block compressed row data format, because it offers opportunities to hide the data motion with useful computations. The new SpMV kernel outperforms existing state-of-the-art implementations on single and multi-GPUs using matrices with dense block structure representative of porous media applications with both structured and unstructured multi-component grids.
Scalable Computation of Streamlines on Very Large Datasets
Pugmire, David; Childs, Hank; Garth, Christoph; Ahern, Sean; Weber, Gunther H.
2009-09-01
Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Streamlines, curves that are tangential to a vector field at each point, are a powerful visualization method in this context. Application of streamline-based visualization to very large vector field data represents a significant challenge due to the non-local and data-dependent nature of streamline computation, and requires careful balancing of computational demands placed on I/O, memory, communication, and processors. In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a novel hybrid algorithm for computing streamlines. Our algorithm is aimed at good scalability and performance across the widely varying computational characteristics of streamline-based problems. We perform performance and scalability studies of all three algorithms on a number of prototypical application problems and demonstrate that our hybrid scheme is able to perform well in different settings.
Colloquium: Large scale simulations on GPU clusters
Bernaschi, Massimo; Bisson, Mauro; Fatica, Massimiliano
2015-06-01
Graphics processing units (GPU) are currently used as a cost-effective platform for computer simulations and big-data processing. Large scale applications require that multiple GPUs work together but the efficiency obtained with cluster of GPUs is, at times, sub-optimal because the GPU features are not exploited at their best. We describe how it is possible to achieve an excellent efficiency for applications in statistical mechanics, particle dynamics and networks analysis by using suitable memory access patterns and mechanisms like CUDA streams, profiling tools, etc. Similar concepts and techniques may be applied also to other problems like the solution of Partial Differential Equations.
Li, Yongbao; Tian, Zhen; Song, Ting; Wu, Zhaoxia; Liu, Yaqiang; Jiang, Steve; Jia, Xun
2017-01-01
Monte Carlo (MC)-based spot dose calculation is highly desired for inverse treatment planning in proton therapy because of its accuracy. Recent studies on biological optimization have also indicated the use of MC methods to compute relevant quantities of interest, e.g. linear energy transfer. Although GPU-based MC engines have been developed to address inverse optimization problems, their efficiency still needs to be improved. Also, the use of a large number of GPUs in MC calculation is not favorable for clinical applications. The previously proposed adaptive particle sampling (APS) method can improve the efficiency of MC-based inverse optimization by using the computationally expensive MC simulation more effectively. This method is more efficient than the conventional approach that performs spot dose calculation and optimization in two sequential steps. In this paper, we propose a computational library to perform MC-based spot dose calculation on GPU with the APS scheme. The implemented APS method performs a non-uniform sampling of the particles from pencil beam spots during the optimization process, favoring those from the high intensity spots. The library also conducts two computationally intensive matrix-vector operations frequently used when solving an optimization problem. This library design allows a streamlined integration of the MC-based spot dose calculation into an existing proton therapy inverse planning process. We tested the developed library in a typical inverse optimization system with four patient cases. The library achieved the targeted functions by supporting inverse planning in various proton therapy schemes, e.g. single field uniform dose, 3D intensity modulated proton therapy, and distal edge tracking. The efficiency was 41.6 ± 15.3% higher than the use of a GPU-based MC package in a conventional calculation scheme. The total computation time ranged between 2 and 50 min on a single GPU card depending on the problem size.
Acquisition streamlining: A cultural change
Stewart, Jesse
1992-01-01
The topics are presented in viewgraph form and include the following: the defense systems management college, educational philosophy, the defense acquisition environment, streamlining initiatives, organizational streamlining types, defense law review, law review purpose, law review objectives, the Public Law Pilot Program, and cultural change.
GPU-based high-performance computing for radiation therapy.
Jia, Xun; Ziegenhein, Peter; Jiang, Steve B
2014-02-21
Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. The graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of study has been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this paper, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented.
Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm
Gang Mei
2014-01-01
Full Text Available We first present two GPU implementations of the standard Inverse Distance Weighting (IDW interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP. Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental results show that the tilted version has the speedups of 120x and 670x over the CPU version when the power parameter p is set to 2 and 3.0, respectively. In addition, compared to the naive GPU implementation, the tiled version is about two times faster. However, the CDP version is 4.8x∼6.0x slower than the naive GPU version, and therefore does not have any potential advantages in practical applications.
Evaluating the power of GPU acceleration for IDW interpolation algorithm.
Mei, Gang
2014-01-01
We first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental results show that the tilted version has the speedups of 120x and 670x over the CPU version when the power parameter p is set to 2 and 3.0, respectively. In addition, compared to the naive GPU implementation, the tiled version is about two times faster. However, the CDP version is 4.8x ∼ 6.0x slower than the naive GPU version, and therefore does not have any potential advantages in practical applications.
GPU-acceleration of parallel unconditionally stable group explicit finite difference method
Parand, K; Hossayni, Sayyed A
2013-01-01
Graphics Processing Units (GPUs) are high performance co-processors originally intended to improve the use and quality of computer graphics applications. Since researchers and practitioners realized the potential of using GPU for general purpose, their application has been extended to other fields out of computer graphics scope. The main objective of this paper is to evaluate the impact of using GPU in solution of the transient diffusion type equation by parallel and stable group explicit finite difference method. To accomplish that, GPU and CPU-based (multi-core) approaches were developed. Moreover, we proposed an optimal synchronization arrangement for its implementation pseudo-code. Also, the interrelation of GPU parallel programming and initializing the algorithm variables was discussed, using numerical experiences. The GPU-approach results are faster than a much expensive parallel 8-thread CPU-based approach results. The GPU, used in this paper, is an ordinary laptop GPU (GT 335M) and is accessible for e...
GPU-accelerated computation of electron transfer.
Höfinger, Siegfried; Acocella, Angela; Pop, Sergiu C; Narumi, Tetsu; Yasuoka, Kenji; Beu, Titus; Zerbetto, Francesco
2012-11-05
Electron transfer is a fundamental process that can be studied with the help of computer simulation. The underlying quantum mechanical description renders the problem a computationally intensive application. In this study, we probe the graphics processing unit (GPU) for suitability to this type of problem. Time-critical components are identified via profiling of an existing implementation and several different variants are tested involving the GPU at increasing levels of abstraction. A publicly available library supporting basic linear algebra operations on the GPU turns out to accelerate the computation approximately 50-fold with minor dependence on actual problem size. The performance gain does not compromise numerical accuracy and is of significant value for practical purposes. Copyright © 2012 Wiley Periodicals, Inc.
Monte Carlo integration on GPU
Kanzaki, J.
2010-01-01
We use a graphics processing unit (GPU) for fast computations of Monte Carlo integrations. Two widely used Monte Carlo integration programs, VEGAS and BASES, are parallelized on GPU. By using $W^{+}$ plus multi-gluon production processes at LHC, we test integrated cross sections and execution time for programs in FORTRAN and C on CPU and those on GPU. Integrated results agree with each other within statistical errors. Execution time of programs on GPU run about 50 times faster than those in C...
Regulatory Streamlining and Improvement
Mark A. Carl
2006-07-11
The Interstate Oil and Gas Compact Commission (IOGCC) engaged in numerous projects outlined under the scope of work discussed in the United States Department of Energy (DOE) grant number DE-FC26-04NT15456 awarded to the IOGCC. Numerous projects were completed that were extremely valuable to state oil and gas agencies as a result of work performed utilizing resources provided by the grant. There are numerous areas in which state agencies still need assistance. This additional assistance will need to be addressed under future scopes of work submitted annually to DOE's Project Officer for this grant. This report discusses the progress of the projects outlined under the grant scope of work for the 2005-2006 areas of interest, which are as follows: Area of Interest No. 1--Regulatory Streamlining and Improvement: This area of interest continues to support IOGCC's regulatory streamlining efforts that include the identification and elimination of unnecessary duplications of efforts between and among state and federal programs dealing with exploration and production on public lands. Area of Interest No. 2--Technology: This area of interest seeks to improve efficiency in states through the identification of technologies that can reduce costs. Area of Interest No. 3--Training and Education: This area of interest is vital to upgrading the skills of regulators and industry alike. Within the National Energy Policy, there are many appropriate training and education opportunities. Education was strongly endorsed by the President's National Energy Policy Development group. Acting through the governors offices, states are very effective conduits for the dissemination of energy education information. While the IOGCC favors the development of a comprehensive, long-term energy education plan, states are also supportive of immediate action on important concerns, such as energy prices, availability and conservation. Area of Interest No. 4--Resource Assessment and
Kazachenko, Sergey; Giovinazzo, Mark; Hall, Kyle Wm; Cann, Natalie M
2015-09-15
A custom code for molecular dynamics simulations has been designed to run on CUDA-enabled NVIDIA graphics processing units (GPUs). The double-precision code simulates multicomponent fluids, with intramolecular and intermolecular forces, coarse-grained and atomistic models, holonomic constraints, Nosé-Hoover thermostats, and the generation of distribution functions. Algorithms to compute Lennard-Jones and Gay-Berne interactions, and the electrostatic force using Ewald summations, are discussed. A neighbor list is introduced to improve scaling with respect to system size. Three test systems are examined: SPC/E water; an n-hexane/2-propanol mixture; and a liquid crystal mesogen, 2-(4-butyloxyphenyl)-5-octyloxypyrimidine. Code performance is analyzed for each system. With one GPU, a 33-119 fold increase in performance is achieved compared with the serial code while the use of two GPUs leads to a 69-287 fold improvement and three GPUs yield a 101-377 fold speedup.
Hydrodynamic Drag on Streamlined Projectiles and Cavities
Jetly, Aditya
2016-04-19
The air cavity formation resulting from the water-entry of solid objects has been the subject of extensive research due to its application in various fields such as biology, marine vehicles, sports and oil and gas industries. Recently we demonstrated that at certain conditions following the closing of the air cavity formed by the initial impact of a superhydrophobic sphere on a free water surface a stable streamlined shape air cavity can remain attached to the sphere. The formation of superhydrophobic sphere and attached air cavity reaches a steady state during the free fall. In this thesis we further explore this novel phenomenon to quantify the drag on streamlined shape cavities. The drag on the sphere-cavity formation is then compared with the drag on solid projectile which were designed to have self-similar shape to that of the cavity. The solid projectiles of adjustable weight were produced using 3D printing technique. In a set of experiments on the free fall of projectile we determined the variation of projectiles drag coefficient as a function of the projectiles length to diameter ratio and the projectiles specific weight, covering a range of intermediate Reynolds number, Re ~ 104 – 105 which are characteristic for our streamlined cavity experiments. Parallel free fall experiment with sphere attached streamlined air cavity and projectile of the same shape and effective weight clearly demonstrated the drag reduction effect due to the stress-free boundary condition at cavity liquid interface. The streamlined cavity experiments can be used as the upper bound estimate of the drag reduction by air layers naturally sustained on superhydrophobic surfaces in contact with water. In the final part of the thesis we design an experiment to test the drag reduction capacity of robust superhydrophobic coatings deposited on the surface of various model vessels.
GPU-accelerated voxelwise hepatic perfusion quantification.
Wang, H; Cao, Y
2012-09-07
Voxelwise quantification of hepatic perfusion parameters from dynamic contrast enhanced (DCE) imaging greatly contributes to assessment of liver function in response to radiation therapy. However, the efficiency of the estimation of hepatic perfusion parameters voxel-by-voxel in the whole liver using a dual-input single-compartment model requires substantial improvement for routine clinical applications. In this paper, we utilize the parallel computation power of a graphics processing unit (GPU) to accelerate the computation, while maintaining the same accuracy as the conventional method. Using compute unified device architecture-GPU, the hepatic perfusion computations over multiple voxels are run across the GPU blocks concurrently but independently. At each voxel, nonlinear least-squares fitting the time series of the liver DCE data to the compartmental model is distributed to multiple threads in a block, and the computations of different time points are performed simultaneously and synchronically. An efficient fast Fourier transform in a block is also developed for the convolution computation in the model. The GPU computations of the voxel-by-voxel hepatic perfusion images are compared with ones by the CPU using the simulated DCE data and the experimental DCE MR images from patients. The computation speed is improved by 30 times using a NVIDIA Tesla C2050 GPU compared to a 2.67 GHz Intel Xeon CPU processor. To obtain liver perfusion maps with 626 400 voxels in a patient's liver, it takes 0.9 min with the GPU-accelerated voxelwise computation, compared to 110 min with the CPU, while both methods result in perfusion parameters differences less than 10(-6). The method will be useful for generating liver perfusion images in clinical settings.
Architecting the Finite Element Method Pipeline for the GPU.
Fu, Zhisong; Lewis, T James; Kirby, Robert M; Whitaker, Ross T
2014-02-01
The finite element method (FEM) is a widely employed numerical technique for approximating the solution of partial differential equations (PDEs) in various science and engineering applications. Many of these applications benefit from fast execution of the FEM pipeline. One way to accelerate the FEM pipeline is by exploiting advances in modern computational hardware, such as the many-core streaming processors like the graphical processing unit (GPU). In this paper, we present the algorithms and data-structures necessary to move the entire FEM pipeline to the GPU. First we propose an efficient GPU-based algorithm to generate local element information and to assemble the global linear system associated with the FEM discretization of an elliptic PDE. To solve the corresponding linear system efficiently on the GPU, we implement a conjugate gradient method preconditioned with a geometry-informed algebraic multi-grid (AMG) method preconditioner. We propose a new fine-grained parallelism strategy, a corresponding multigrid cycling stage and efficient data mapping to the many-core architecture of GPU. Comparison of our on-GPU assembly versus a traditional serial implementation on the CPU achieves up to an 87 × speedup. Focusing on the linear system solver alone, we achieve a speedup of up to 51 × versus use of a comparable state-of-the-art serial CPU linear system solver. Furthermore, the method compares favorably with other GPU-based, sparse, linear solvers.
Performance evaluation of image processing algorithms on the GPU.
Castaño-Díez, Daniel; Moser, Dominik; Schoenegger, Andreas; Pruggnaller, Sabine; Frangakis, Achilleas S
2008-10-01
The graphics processing unit (GPU), which originally was used exclusively for visualization purposes, has evolved into an extremely powerful co-processor. In the meanwhile, through the development of elaborate interfaces, the GPU can be used to process data and deal with computationally intensive applications. The speed-up factors attained compared to the central processing unit (CPU) are dependent on the particular application, as the GPU architecture gives the best performance for algorithms that exhibit high data parallelism and high arithmetic intensity. Here, we evaluate the performance of the GPU on a number of common algorithms used for three-dimensional image processing. The algorithms were developed on a new software platform called "CUDA", which allows a direct translation from C code to the GPU. The implemented algorithms include spatial transformations, real-space and Fourier operations, as well as pattern recognition procedures, reconstruction algorithms and classification procedures. In our implementation, the direct porting of C code in the GPU achieves typical acceleration values in the order of 10-20 times compared to a state-of-the-art conventional processor, but they vary depending on the type of the algorithm. The gained speed-up comes with no additional costs, since the software runs on the GPU of the graphics card of common workstations.
龚曙光; 刘奇良; 卢海山; 周志勇; 张佳
2015-01-01
针对无网格 Galerkin 法计算耗时的问题，采用逐节点对法来组装刚度矩阵、共轭梯度法求解基于 CSR 格式存储的稀疏线性方程组，提出了一种利用罚函数法施加本质边界条件的 EFG 法 GPU 加速并行算法，给出了刚度矩阵和惩罚刚度矩阵的统一格式，以及 GPU 加速并行算法的流程图。编写了基于 CUDA 构架平台的 GPU 程序，且在 NVIDIA GeForce GTX 660显卡上通过数值算例对所提算法进行了性能测试与分析比较，探讨了影响加速比的因素。算例结果验证了所提算法的可行性，并在满足计算精度的前提下，其加速比最大可达17倍；同时线性方程组的求解对加速比起决定性影响。%In order to reduce the computing time of Element-Free Galerkin(EFG)method,a GPU accele-ration parallel algorithm of EFG method that essential boundary condition is imposed by penalty function method is proposed,in which stiffness matrix is assembled by node pair-wise approach,and sparse linear equations based on CSR format is solved by conjugate gradient methods.The unified format of stiffness matrix and penalty stiffness matrix was derived,and the flow chart of the parallel algorithm was provided.The GPU codes were programmed on CUDA,and algorithm testing was finished on the device of NVIDIA GeForce GTX 660 by numerical examples.The factors of affecting speedup ratio were discussed.The example results verified the feasibility of the proposed algorithm.The maximum speedup ratio was up to 17 times on the premise that the calculating accuracy is met,and to solve linear equations is the major factor in the speedup.
CrystalGPU: Transparent and Efficient Utilization of GPU Power
Gharaibeh, Abdullah; Al-Kiswany, Samer; Ripeanu, Matei
2010-01-01
General-purpose computing on graphics processing units (GPGPU) has recently gained considerable attention in various domains such as bioinformatics, databases and distributed computing. GPGPU is based on using the GPU as a co-processor accelerator to offload computationally-intensive tasks from the CPU. This study starts from the observation that a number of GPU features (such as overlapping communication and computation, short lived buffer reuse, and harnessing multi-GPU systems) can be abst...
Accelerating Dense Linear Algebra on the GPU
Sørensen, Hans Henrik Brandenborg
and matrix-vector operations on GPUs. Such operations form the backbone of level 1 and level 2 routines in the Basic Linear Algebra Subroutines (BLAS) library and are therefore of great importance in many scientific applications. The target hardware is the most recent NVIDIA Tesla 20-series (Fermi...... architecture). Most of the techniques I discuss for accelerating dense linear algebra are applicable to memory-bound GPU algorithms in general....
Almeida, Adino Americo Heimlich
2009-07-01
Graphics Processing Units (GPU) are high performance co-processors intended, originally, to improve the use and quality of computer graphics applications. Since researchers and practitioners realized the potential of using GPU for general purpose, their application has been extended to other fields out of computer graphics scope. The main objective of this work is to evaluate the impact of using GPU in two typical problems of Nuclear area. The neutron transport simulation using Monte Carlo method and solve heat equation in a bi-dimensional domain by finite differences method. To achieve this, we develop parallel algorithms for GPU and CPU in the two problems described above. The comparison showed that the GPU-based approach is faster than the CPU in a computer with two quad core processors, without precision loss. (author)
Masset, Frédéric
2015-09-01
GFARGO is a GPU version of FARGO. It is written in C and C for CUDA and runs only on NVIDIA’s graphics cards. Though it corresponds to the standard, isothermal version of FARGO, not all functionnalities of the CPU version have been translated to CUDA. The code is available in single and double precision versions, the latter compatible with FERMI architectures. GFARGO can run on a graphics card connected to the display, allowing the user to see in real time how the fields evolve.
2016-07-15
The Kokkos Clang compiler is a version of the Clang C++ compiler that has been modified to perform targeted code generation for Kokkos constructs in the goal of generating highly optimized code and to provide semantic (domain) awareness throughout the compilation toolchain of these constructs such as parallel for and parallel reduce. This approach is taken to explore the possibilities of exposing the developer’s intentions to the underlying compiler infrastructure (e.g. optimization and analysis passes within the middle stages of the compiler) instead of relying solely on the restricted capabilities of C++ template metaprogramming. To date our current activities have focused on correct GPU code generation and thus we have not yet focused on improving overall performance. The compiler is implemented by recognizing specific (syntactic) Kokkos constructs in order to bypass normal template expansion mechanisms and instead use the semantic knowledge of Kokkos to directly generate code in the compiler’s intermediate representation (IR); which is then translated into an NVIDIA-centric GPU program and supporting runtime calls. In addition, by capturing and maintaining the higher-level semantics of Kokkos directly within the lower levels of the compiler has the potential for significantly improving the ability of the compiler to communicate with the developer in the terms of their original programming model/semantics.
GPU Computing Gems Emerald Edition
Hwu, Wen-mei W
2011-01-01
".the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010 Graphics processing units (GPUs) can do much more than render graphics. Scientists and researchers increasingly look to GPUs to improve the efficiency and performance of computationally-intensive experiments across a range of disciplines. GPU Computing Gems: Emerald Edition brings their techniques to you, showcasing GPU-based solutions including: Black hole simulations with CUDA GPU-accelerated computation and interactive display of
GPU-accelerated compressive holography.
Endo, Yutaka; Shimobaba, Tomoyoshi; Kakue, Takashi; Ito, Tomoyoshi
2016-04-18
In this paper, we show fast signal reconstruction for compressive holography using a graphics processing unit (GPU). We implemented a fast iterative shrinkage-thresholding algorithm on a GPU to solve the ℓ1 and total variation (TV) regularized problems that are typically used in compressive holography. Since the algorithm is highly parallel, GPUs can compute it efficiently by data-parallel computing. For better performance, our implementation exploits the structure of the measurement matrix to compute the matrix multiplications. The results show that GPU-based implementation is about 20 times faster than CPU-based implementation.
Interactive Streamline Exploration and Manipulation Using Deformation
Tong, Xin; Chen, Chun-Ming; Shen, Han-Wei; Wong, Pak C.
2015-01-12
Occlusion presents a major challenge in visualizing three-dimensional flow fields with streamlines. Displaying too many streamlines at once makes it difficult to locate interesting regions, but displaying too few streamlines risks missing important features. A more ideal streamline exploration model is to allow the viewer to freely move across the field that has been populated with interesting streamlines and pull away the streamlines that cause occlusion so that the viewer can inspect the hidden ones in detail. In this paper, we present a streamline deformation algorithm that supports such user-driven interaction with three-dimensional flow fields. We define a view-dependent focus+context technique that moves the streamlines occluding the focus area using a novel displacement model. To preserve the context surrounding the user-chosen focus area, we propose two shape models to define the transition zone for the surrounding streamlines, and the displacement of the contextual streamlines is solved interactively with a goal of preserving their shapes as much as possible. Based on our deformation model, we design an interactive streamline exploration tool using a lens metaphor. Our system runs interactively so that users can move their focus and examine the flow field freely.
GPU-based video motion magnification
DomŻał, Mariusz; Jedrasiak, Karol; Sobel, Dawid; Ryt, Artur; Nawrat, Aleksander
2016-06-01
Video motion magnification (VMM) allows people see otherwise not visible subtle changes in surrounding world. VMM is also capable of hiding them with a modified version of the algorithm. It is possible to magnify motion related to breathing of patients in hospital to observe it or extinguish it and extract other information from stabilized image sequence for example blood flow. In both cases we would like to perform calculations in real time. Unfortunately, the VMM algorithm requires a great amount of computing power. In the article we suggest that VMM algorithm can be parallelized (each thread processes one pixel) and in order to prove that we implemented the algorithm on GPU using CUDA technology. CPU is used only to grab, write, display frame and schedule work for GPU. Each GPU kernel performs spatial decomposition, reconstruction and motion amplification. In this work we presented approach that achieves a significant speedup over existing methods and allow to VMM process video in real-time. This solution can be used as preprocessing for other algorithms in more complex systems or can find application wherever real time motion magnification would be useful. It is worth to mention that the implementation runs on most modern desktops and laptops compatible with CUDA technology.
Fast box-counting algorithm on GPU.
Jiménez, J; Ruiz de Miras, J
2012-12-01
The box-counting algorithm is one of the most widely used methods for calculating the fractal dimension (FD). The FD has many image analysis applications in the biomedical field, where it has been used extensively to characterize a wide range of medical signals. However, computing the FD for large images, especially in 3D, is a time consuming process. In this paper we present a fast parallel version of the box-counting algorithm, which has been coded in CUDA for execution on the Graphic Processing Unit (GPU). The optimized GPU implementation achieved an average speedup of 28 times (28×) compared to a mono-threaded CPU implementation, and an average speedup of 7 times (7×) compared to a multi-threaded CPU implementation. The performance of our improved box-counting algorithm has been tested with 3D models with different complexity, features and sizes. The validity and accuracy of the algorithm has been confirmed using models with well-known FD values. As a case study, a 3D FD analysis of several brain tissues has been performed using our GPU box-counting algorithm.
Research Progress of GPU Applications in Medical Ultrasound Imaging%图形处理器在医学超声成像中的应用研究进展
陈胤燃; 罗建文
2015-01-01
In recent years, some advanced medical ultrasound imaging methods have been proposed to obtain more information of biological tissues. However, they are computational y time-consuming and are known to be chal enging to implement in real time. Graphics Processing Unit (GPU) plays an important role in massive data processing because of high paral elism and powerful computational capacity. Therefore, there are more and more GPU applications in medical ultrasound imaging. In this paper, GPU applications in medical ultrasound imaging, including ultrafast imaging, elastography and blood flow imaging, are reviewed.%近年来提出的多种先进超声成像方法可以提供生物组织的更多信息，但极大的数据量和计算量限制了这些成像方法的实时实现。图形处理器（Graphics Processing Unit，GPU）凭借其高度的可并行性和强大的数值计算能力在大规模数据处理中发挥出了重要作用。因此，关于GPU在医学超声成像中的应用越来越多。本文综述了GPU在医学超声成像中的应用，包括超高速成像、弹性成像、血流成像等方面的应用研究进展。
GPU-Powered Coherent Beamforming
Magro, Alessio; Hickish, Jack
2014-01-01
GPU-based beamforming is a relatively unexplored area in radio astronomy, possibly due to the assumption that any such system will be severely limited by the PCIe bandwidth required to transfer data to the GPU. We have developed a CUDA-based GPU implementation of a coherent beamformer, specifically designed and optimised for deployment at the BEST-2 array which can generate an arbitrary number of synthesized beams for a wide range of parameters. It achieves $\\sim$1.3 TFLOPs on an NVIDIA Tesla K20, approximately 10x faster than an optimised, multithreaded CPU implementation. This kernel has been integrated into two real-time, GPU-based time-domain software pipelines deployed at the BEST-2 array in Medicina: a standalone beamforming pipeline and a transient detection pipeline. We present performance benchmarks for the beamforming kernel as well as the transient detection pipeline with beamforming capabilities as well as results of test observation.
GPU-based high performance Monte Carlo simulation in neutron transport
Heimlich, Adino; Mol, Antonio C.A.; Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil). Lab. de Inteligencia Artificial Aplicada], e-mail: cmnap@ien.gov.br
2009-07-01
Graphics Processing Units (GPU) are high performance co-processors intended, originally, to improve the use and quality of computer graphics applications. Since researchers and practitioners realized the potential of using GPU for general purpose, their application has been extended to other fields out of computer graphics scope. The main objective of this work is to evaluate the impact of using GPU in neutron transport simulation by Monte Carlo method. To accomplish that, GPU- and CPU-based (single and multicore) approaches were developed and applied to a simple, but time-consuming problem. Comparisons demonstrated that the GPU-based approach is about 15 times faster than a parallel 8-core CPU-based approach also developed in this work. (author)
Tanner, David E; Phillips, James C; Schulten, Klaus
2012-07-10
Molecular dynamics methodologies comprise a vital research tool for structural biology. Molecular dynamics has benefited from technological advances in computing, such as multi-core CPUs and graphics processing units (GPUs), but harnessing the full power of hybrid GPU/CPU computers remains difficult. The generalized Born/solvent-accessible surface area implicit solvent model (GB/SA) stands to benefit from hybrid GPU/CPU computers, employing the GPU for the GB calculation and the CPU for the SA calculation. Here, we explore the computational challenges facing GB/SA calculations on hybrid GPU/CPU computers and demonstrate how NAMD, a parallel molecular dynamics program, is able to efficiently utilize GPUs and CPUs simultaneously for fast GB/SA simulations. The hybrid computation principles demonstrated here are generally applicable to parallel applications employing hybrid GPU/CPU calculations.
High-level GPU computing with jacket for MATLAB and C/C++
Pryor, Gallagher; Lucey, Brett; Maddipatla, Sandeep; McClanahan, Chris; Melonakos, John; Venugopalakrishnan, Vishwanath; Patel, Krunal; Yalamanchili, Pavan; Malcolm, James
2011-06-01
We describe a software platform for the rapid development of general purpose GPU (GPGPU) computing applications within the MATLAB computing environment, C, and C++: Jacket. Jacket provides thousands of GPU-tuned function syntaxes within MATLAB, C, and C++, including linear algebra, convolutions, reductions, and FFTs as well as signal, image, statistics, and graphics libraries. Additionally, Jacket includes a compiler that translates MATLAB and C++ code to CUDA PTX assembly and OpenGL shaders on demand at runtime. A facility is also included to compile a domain specific version of the MATLAB language to CUDA assembly at build time. Jacket includes the first parallel GPU FOR-loop construction and the first profiler for comparative analysis of CPU and GPU execution times. Jacket provides full GPU compute capability on CUDA hardware and limited, image processing focused compute on OpenGL/ES (2.0 and up) devices for mobile and embedded applications.
2012-12-04
... COMMISSION 47 CFR Part 73 Policies To Promote Rural Radio Service and To Streamline Allotment and Assignment... policies to promote rural radio service and to streamline allotment and assignment procedures. This notice...: Control Number: 3060-0031. Title: Application for Consent to Assignment of Broadcast Station Construction...
Kantor, Jiří
2013-01-01
Tato práce popisuje tvorbu jednoduchého raytraceru pro OpenSceneGraph, který běží na grafické kartě. V práci jsou popsány věci, které bylo nutné provést v OpenSceneGraphu, aby bylo možno předávat data do GPU a také několik metod pro hledání průsečíků paprsku a trojúhelníku, což je klíčový algoritmus v raytracingu. This work describes creation of a simple raytracer for OpenSceneGraph, which performs its operations on the graphics card. Things, that needed to be done in OpenSceneGraph in ord...
Real-time Flame Rendering with GPU and CUDA
Wei Wei
2011-02-01
Full Text Available This paper proposes a method of flame simulation based on Lagrange process and chemical composition, which was non-grid and the problems associated with there grids were overcome. The turbulence movement of flame was described by Lagrange process and chemical composition was added into flame simulation which increased the authenticity of flame. For real-time applications, this paper simplified the EMST model. GPU-based particle system combined with OpenGL VBO and PBO unique technology was used to accelerate finally, the speed of vertex and pixel data interaction between CPU and GPU increased two orders of magnitude, frame rate of rendering increased by 30%, which achieved fast dynamic flame real-time simulation. For further real-time applications, this paper presented a strategy to implement flame simulation with CUDA on GPU, which achieved a speed up to 2.5 times the previous implementation.
Implementation of a Parallel Tree Method on a GPU
Nakasato, Naohito
2011-01-01
The kd-tree is a fundamental tool in computer science. Among other applications, the application of kd-tree search (by the tree method) to the fast evaluation of particle interactions and neighbor search is highly important, since the computational complexity of these problems is reduced from O(N^2) for a brute force method to O(N log N) for the tree method, where N is the number of particles. In this paper, we present a parallel implementation of the tree method running on a graphics processing unit (GPU). We present a detailed description of how we have implemented the tree method on a Cypress GPU. An optimization that we found important is localized particle ordering to effectively utilize cache memory. We present a number of test results and performance measurements. Our results show that the execution of the tree traversal in a force calculation on a GPU is practical and efficient.
Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin
2014-01-01
Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633
GPU-centric resolved-particle disperse two-phase flow simulation using the Physalis method
Sierakowski, Adam J.
2016-10-01
We present work on a new implementation of the Physalis method for resolved-particle disperse two-phase flow simulations. We discuss specifically our GPU-centric programming model that avoids all device-host data communication during the simulation. Summarizing the details underlying the implementation of the Physalis method, we illustrate the application of two GPU-centric parallelization paradigms and record insights on how to best leverage the GPU's prioritization of bandwidth over latency. We perform a comparison of the computational efficiency between the current GPU-centric implementation and a legacy serial-CPU-optimized code and conclude that the GPU hardware accounts for run time improvements up to a factor of 60 by carefully normalizing the run times of both codes.
Medical image processing on the GPU - past, present and future.
Eklund, Anders; Dufort, Paul; Forsberg, Daniel; LaConte, Stephen M
2013-12-01
Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.
Gpu Implementation of a Viscous Flow Solver on Unstructured Grids
Xu, Tianhao; Chen, Long
2016-06-01
Graphics processing units have gained popularities in scientific computing over past several years due to their outstanding parallel computing capability. Computational fluid dynamics applications involve large amounts of calculations, therefore a latest GPU card is preferable of which the peak computing performance and memory bandwidth are much better than a contemporary high-end CPU. We herein focus on the detailed implementation of our GPU targeting Reynolds-averaged Navier-Stokes equations solver based on finite-volume method. The solver employs a vertex-centered scheme on unstructured grids for the sake of being capable of handling complex topologies. Multiple optimizations are carried out to improve the memory accessing performance and kernel utilization. Both steady and unsteady flow simulation cases are carried out using explicit Runge-Kutta scheme. The solver with GPU acceleration in this paper is demonstrated to have competitive advantages over the CPU targeting one.
gPGA: GPU Accelerated Population Genetics Analyses.
Chunbao Zhou
Full Text Available The isolation with migration (IM model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC simulations of gene genealogies. But computational burden of IM program has placed limits on its application.With strong computational power, Graphics Processing Unit (GPU has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA, which we call gPGA.Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA.
How General-Purpose can a GPU be?
Philip Machanick
2015-12-01
Full Text Available The use of graphics processing units (GPUs in general-purpose computation (GPGPU is a growing field. GPU instruction sets, while implementing a graphics pipeline, draw from a range of single instruction multiple datastream (SIMD architectures characteristic of the heyday of supercomputers. Yet only one of these SIMD instruction sets has been of application on a wide enough range of problems to survive the era when the full range of supercomputer design variants was being explored: vector instructions. This paper proposes a reconceptualization of the GPU as a multicore design with minimal exotic modes of parallelism so as to make GPGPU truly general.
A generalized GPU-based connected component labeling algorithm
Komura, Yukihiro
2016-01-01
We propose a generalized GPU-based connected component labeling (CCL) algorithm that can be applied to both various lattices and to non-lattice environments in a uniform fashion. We extend our recent GPU-based CCL algorithm without the use of conventional iteration to the generalized method. As an application of this algorithm, we deal with the bond percolation problem. We investigate bond percolation on the honeycomb and triangle lattices to confirm the correctness of this algorithm. Moreover, we deal with bond percolation on the Bethe lattice as a substitute for a network structure, and demonstrate the performance of this algorithm on those lattices.
Numerical cosmology on the GPU with Enzo and Ramses
Gheller, Claudio; Vazza, Franco; Teyssier, Romain
2014-01-01
A number of scientific numerical codes can currently exploit GPUs with remarkable performance. In astrophysics, Enzo and Ramses are prime examples of such applications. The two codes have been ported to GPUs adopting different strategies and programming models, Enzo adopting CUDA and Ramses using OpenACC. We describe here the different solutions used for the GPU implementation of both cases. Performance benchmarks will be presented for Ramses. The results of the usage of the more mature GPU version of Enzo, adopted for a scientific project within the CHRONOS programme, will be summarised.
Numerical cosmology on the GPU with Enzo and Ramses
Gheller, C.; Wang, P.; Vazza, F.; Teyssier, R.
2015-09-01
A number of scientific numerical codes can currently exploit GPUs with remarkable performance. In astrophysics, Enzo and Ramses are prime examples of such applications. The two codes have been ported to GPUs adopting different strategies and programming models, Enzo adopting CUDA and Ramses using OpenACC. We describe here the different solutions used for the GPU implementation of both cases. Performance benchmarks will be presented for Ramses. The results of the usage of the more mature GPU version of Enzo, adopted for a scientific project within the CHRONOS programme, will be summarised.
A survey of GPU-based medical image computing techniques.
Shi, Lin; Liu, Wen; Zhang, Heye; Xie, Yongming; Wang, Defeng
2012-09-01
Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine.
GPU-Acceleration of Parallel Unconditionally Stable Group Explicit Finite Difference Method
Parand, K.; Zafarvahedian, Saeed; Hossayni, Sayyed A.
2013-01-01
Graphics Processing Units (GPUs) are high performance co-processors originally intended to improve the use and quality of computer graphics applications. Once, researchers and practitioners noticed the potential of using GPU for general purposes, GPUs applications have been extended from graphics applications to other fields. The main objective of this paper is to evaluate the impact of using GPU in solution of the transient diffusion type equation by parallel and stable group explicit finite...
Better Faster Noise with the GPU
Wyvill, Geoff; Frisvad, Jeppe Revall
Filtered noise [Perlin 1985] has, for twenty years, been a fundamental tool for creating functional texture and it has many other applications; for example, animating water waves or the motion of grass waving in the wind. Perlin noise suffers from a number of defects and there have been many atte...... attempts to create better or faster noise but Perlin’s ‘Gradient Noise’ has consistently proved to be the best compromise between speed and quality. Our objective was to create a better noise cheaply by use of the GPU....
Using GPU shaders for visualization, part 2.
Bailey, M
2011-01-01
GPU shaders aren't just for special effects. Previously, I looked at some uses for them in visualization. Here, the idea continues. Because visualization relies so much on high speed interaction, we use shaders for the same reason we use them in effects programming: appearance and performance. In the drive to understand large, complex data sets, no method should be overlooked. This article describes two additional visualization applications: line integral convolution (LIC) and terrain bump-mapping. I also comment on the recent (and rapid) changes to OpenGL and what these mean to educators.
Margot Gerritsen
2008-10-31
the redundant work generally done in the near-well regions. We improved the accuracy of the streamline simulator with a higher order mapping from pressure grid to streamlines that significantly reduces smoothing errors, and a Kriging algorithm is used to map from the streamlines to the background grid. The higher accuracy of the Kriging mapping means that it is not essential for grid blocks to be crossed by one or more streamlines. The higher accuracy comes at the price of increased computational costs, but allows coarser coverage and so does not generally increase the overall costs of the computations. To reduce errors associated with fixing the pressure field between pressure updates, we developed a higher order global time-stepping method that allows the use of larger global time steps. Third-order ENO schemes are suggested to propagate components along streamlines. Both in the two-phase and three-phase experiments these ENO schemes outperform other (higher order) upwind schemes. Application of the third order ENO scheme leads to overall computational savings because the computational grid used can be coarsened. Grid adaptivity along streamlines is implemented to allow sharp but efficient resolution of solution fronts at reduced computational costs when displacement fronts are sufficiently separated. A correction for Volume Change On Mixing (VCOM) is implemented that is very effective at handling this effect. Finally, a specialized gravity operator splitting method is proposed for use in compositional streamline methods that gives an effective correction of gravity segregation. A significant part of our effort went into the development of a parallelization strategy for streamline solvers on the next generation shared memory machines. We found in this work that the built-in dynamic scheduling strategies of OpenMP lead to parallel efficiencies that are comparable to optimal schedules obtained with customized explicit load balancing strategies as long as the ratio of
View-Dependent Streamline Deformation and Exploration
Tong, Xin; Edwards, John; Chen, Chun-Ming; Shen, Han-Wei; Johnson, Chris R.; Wong, Pak Chung
2016-07-01
Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual cluttering for visualizing 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.
GPU-BSM: a GPU-based tool to map bisulfite-treated reads.
Andrea Manconi
Full Text Available Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads.
GPU-BSM: a GPU-based tool to map bisulfite-treated reads.
Manconi, Andrea; Orro, Alessandro; Manca, Emanuele; Armano, Giuliano; Milanesi, Luciano
2014-01-01
Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads.
Randomized selection on the GPU
Monroe, Laura Marie [Los Alamos National Laboratory; Wendelberger, Joanne R [Los Alamos National Laboratory; Michalak, Sarah E [Los Alamos National Laboratory
2011-01-13
We implement here a fast and memory-sparing probabilistic top N selection algorithm on the GPU. To our knowledge, this is the first direct selection in the literature for the GPU. The algorithm proceeds via a probabilistic-guess-and-chcck process searching for the Nth element. It always gives a correct result and always terminates. The use of randomization reduces the amount of data that needs heavy processing, and so reduces the average time required for the algorithm. Probabilistic Las Vegas algorithms of this kind are a form of stochastic optimization and can be well suited to more general parallel processors with limited amounts of fast memory.
Distributed GPU Computing in GIScience
Jiang, Y.; Yang, C.; Huang, Q.; Li, J.; Sun, M.
2013-12-01
Geoscientists strived to discover potential principles and patterns hidden inside ever-growing Big Data for scientific discoveries. To better achieve this objective, more capable computing resources are required to process, analyze and visualize Big Data (Ferreira et al., 2003; Li et al., 2013). Current CPU-based computing techniques cannot promptly meet the computing challenges caused by increasing amount of datasets from different domains, such as social media, earth observation, environmental sensing (Li et al., 2013). Meanwhile CPU-based computing resources structured as cluster or supercomputer is costly. In the past several years with GPU-based technology matured in both the capability and performance, GPU-based computing has emerged as a new computing paradigm. Compare to traditional computing microprocessor, the modern GPU, as a compelling alternative microprocessor, has outstanding high parallel processing capability with cost-effectiveness and efficiency(Owens et al., 2008), although it is initially designed for graphical rendering in visualization pipe. This presentation reports a distributed GPU computing framework for integrating GPU-based computing within distributed environment. Within this framework, 1) for each single computer, computing resources of both GPU-based and CPU-based can be fully utilized to improve the performance of visualizing and processing Big Data; 2) within a network environment, a variety of computers can be used to build up a virtual super computer to support CPU-based and GPU-based computing in distributed computing environment; 3) GPUs, as a specific graphic targeted device, are used to greatly improve the rendering efficiency in distributed geo-visualization, especially for 3D/4D visualization. Key words: Geovisualization, GIScience, Spatiotemporal Studies Reference : 1. Ferreira de Oliveira, M. C., & Levkowitz, H. (2003). From visual data exploration to visual data mining: A survey. Visualization and Computer Graphics, IEEE
Visualizing whole-brain DTI tractography with GPU-based Tuboids and LoD management.
Petrovic, Vid; Fallon, James; Kuester, Falko
2007-01-01
Diffusion Tensor Imaging (DTI) of the human brain, coupled with tractography techniques, enable the extraction of large-collections of three-dimensional tract pathways per subject. These pathways and pathway bundles represent the connectivity between different brain regions and are critical for the understanding of brain related diseases. A flexible and efficient GPU-based rendering technique for DTI tractography data is presented that addresses common performance bottlenecks and image-quality issues, allowing interactive render rates to be achieved on commodity hardware. An occlusion query-based pathway LoD management system for streamlines/streamtubes/tuboids is introduced that optimizes input geometry, vertex processing, and fragment processing loads, and helps reduce overdraw. The tuboid, a fully-shaded streamtube impostor constructed entirely on the GPU from streamline vertices, is also introduced. Unlike full streamtubes and other impostor constructs, tuboids require little to no preprocessing or extra space over the original streamline data. The supported fragment processing levels of detail range from texture-based draft shading to full raycast normal computation, Phong shading, environment mapping, and curvature-correct text labeling. The presented text labeling technique for tuboids provides adaptive, aesthetically pleasing labels that appear attached to the surface of the tubes. Furthermore, an occlusion query aggregating and scheduling scheme for tuboids is described that reduces the query overhead. Results for a tractography dataset are presented, and demonstrate that LoD-managed tuboids offer benefits over traditional streamtubes both in performance and appearance.
Streamlining genomes: toward the generation of simplified and stabilized microbial systems
Leprince, A.; Passel, van M.W.J.; Martins Dos Santos, V.A.P.
2012-01-01
At the junction between systems and synthetic biology, genome streamlining provides a solid foundation both for increased understanding of cellular circuitry, and for the tailoring of microbial chassis towards innovative biotechnological applications. Iterative genomic deletions (targeted and random
GPU TECHNOLOGIES EMBODIED IN PARALLEL SOLVERS OF LINEAR ALGEBRAIC EQUATION SYSTEMS
Sidorov Alexander Vladimirovich
2012-10-01
Full Text Available The author reviews existing shareware solvers that are operated by graphical computer devices. The purpose of this review is to explore the opportunities and limitations of the above parallel solvers applicable for resolution of linear algebraic problems that arise at Research and Educational Centre of Computer Modeling at MSUCE, and Research and Engineering Centre STADYO. The author has explored new applications of the GPU in the PETSc suite and compared them with the results generated absent of the GPU. The research is performed within the CUSP library developed to resolve the problems of linear algebra through the application of GPU. The author has also reviewed the new MAGMA project which is analogous to LAPACK for the GPU.
GPU computing for 2-d spin systems: CUDA vs OpenGL
Anselmi, V; Di Renzo, F
2008-01-01
In recent years the more and more powerful GPU's available on the PC market have attracted attention as a cost effective solution for parallel (SIMD) computing. CUDA is a solid evidence of the attention that the major companies are devoting to the field. CUDA is a hardware and software architecture developed by Nvidia for computing on the GPU. It qualifies as a friendly alternative to the approach to GPU computing that has been pioneered in the OpenGL environment. We discuss the application of both the CUDA and the OpenGL approach to the simulation of 2-d spin systems (XY model).
Considerations for GPU SEE Testing
Wyrwas, Edward J.
2017-01-01
This presentation will discuss the considerations an engineer should take to perform Single Event Effects (SEE) testing on GPU devices. Notable topics will include setup complexity, architecture insight which permits cross platform normalization, acquiring a reasonable detail of information from the test suite, and a few lessons learned from preliminary testing.
Travel Software using GPU Hardware
Szalwinski, Chris M; Dimov, Veliko Atanasov; CERN. Geneva. ATS Department
2015-01-01
Travel is the main multi-particle tracking code being used at CERN for the beam dynamics calculations through hadron and ion linear accelerators. It uses two routines for the calculation of space charge forces, namely, rings of charges and point-to-point. This report presents the studies to improve the performance of Travel using GPU hardware. The studies showed that the performance of Travel with the point-to-point simulations of space-charge effects can be speeded up at least 72 times using current GPU hardware. Simple recompilation of the source code using an Intel compiler can improve performance at least 4 times without GPU support. The limited memory of the GPU is the bottleneck. Two algorithms were investigated on this point: repeated computation and tiling. The repeating computation algorithm is simpler and is the currently recommended solution. The tiling algorithm was more complicated and degraded performance. Both build and test instructions for the parallelized version of the software are inclu...
Commodity CPU-GPU System for Low-Cost , High-Performance Computing
Wang, S.; Zhang, S.; Weiss, R. M.; Barnett, G. A.; Yuen, D. A.
2009-12-01
We have put together a desktop computer system for under 2.5 K dollars from commodity components that consist of one quad-core CPU (Intel Core 2 Quad Q6600 Kentsfield 2.4GHz) and two high end GPUs (nVidia's GeForce GTX 295 and Tesla C1060). A 1200 watt power supply is required. On this commodity system, we have constructed an easy-to-use hybrid computing environment, in which Message Passing Interface (MPI) is used for managing the working loads, for transferring the data among different GPU devices, and for minimizing the need of CPU’s memory. The test runs using the MAGMA (Matrix Algebra on GPU and Multicore Architectures) library show that the speed ups for double precision calculations can be greater than 10 (GPU vs. CPU) and they are bigger (> 20) for single precision calculations. In addition we have enabled the combination of Matlab with CUDA for interactive visualization through MPI, i.e., two GPU devices are used for simulation and one GPU device is used for visualizing the computing results as the simulation goes. Our experience with this commodity system has shown that running multiple applications on one GPU device or running one application across multiple GPU devices can be done as conveniently as on CPUs. With NVIDIA CEO Jen-Hsun Huang's claim that over the next 6 years GPU processing power will increase by 570x compared to the 3x for CPUs, future low-cost commodity computers such as ours may be a remedy for the long wait queues of the world's supercomputers, especially for small- and mid-scale computation. Our goal here is to explore the limits and capabilities of this emerging technology and to get ourselves ready to run large-scale simulations on the next generation of computing environment, which we believe will hybridize CPU and GPU architectures.
GPU Computing to Improve Game Engine Performance
Abu Asaduzzaman
2014-07-01
Full Text Available Although the graphics processing unit (GPU was originally designed to accelerate the image creation for output to display, today’s general purpose GPU (GPGPU computing offers unprecedented performance by offloading computing-intensive portions of the application to the GPGPU, while running the remainder of the code on the central processing unit (CPU. The highly parallel structure of a many core GPGPU can process large blocks of data faster using multithreaded concurrent processing. A game engine has many “components” and multithreading can be used to implement their parallelism. However, effective implementation of multithreading in a multicore processor has challenges, such as data and task parallelism. In this paper, we investigate the impact of using a GPGPU with a CPU to design high-performance game engines. First, we implement a separable convolution filter (heavily used in image processing with the GPGPU. Then, we implement a multiobject interactive game console in an eight-core workstation using a multithreaded asynchronous model (MAM, a multithreaded synchronous model (MSM, and an MSM with data parallelism (MSMDP. According to the experimental results, speedup of about 61x and 5x is achieved due to GPGPU and MSMDP implementation, respectively. Therefore, GPGPU-assisted parallel computing has the potential to improve multithreaded game engine performance.
Zhixiang Zhu
Full Text Available Although genome-wide association studies (GWAS have identified a significant number of single-nucleotide polymorphisms (SNPs associated with many complex human traits, the susceptibility loci identified so far can explain only a small fraction of the genetic risk. Among other possible explanations, the lack of a comprehensive examination of gene-gene interaction (G×G is often considered a source of the missing heritability. Previously, we reported a model-free Generalized Multifactor Dimensionality Reduction (GMDR approach for detecting G×G in both dichotomous and quantitative phenotypes. However, the computational burden and less efficient implementation of the original programs make them impossible to use for GWAS. In this study, we developed a graphics processing unit (GPU-based GMDR program (named GWAS-GPU, which is able not only to analyze GWAS data but also to run much faster than the earlier version of the GMDR program. As a demonstration of the program, we used the GMDR-GPU software to analyze a publicly available GWAS dataset on type 2 diabetes (T2D from the Wellcome Trust Case Control Consortium. Through an exhaustive search of pair-wise interactions and a selected search of three- to five-way interactions conditioned on significant pair-wise results, we identified 24 core SNPs in six genes (FTO: rs9939973, rs9940128, rs9922047, rs1121980, rs9939609, rs9930506; TSPAN8: rs1495377; TCF7L2: rs4074720, rs7901695, rs4506565, rs4132670, rs10787472, rs11196205, rs10885409, rs11196208; L3MBTL3: rs10485400, rs4897366; CELF4: rs2852373, rs608489; RUNX1: rs445984, rs1040328, rs990074, rs2223046, rs2834970 that appear to be important for T2D. Of these core SNPs, 11 in FTO, TSPAN8, and TCF7L2 have been reported to be associated with T2D, obesity, or both, providing an independent replication of previously reported SNPs. Importantly, we identified three new susceptibility genes; i.e., L3MBTL3, CELF4, and RUNX1, for T2D, a finding that warrants
Papadopoulos, Agathoklis; Kostoglou, Kyriaki; Mitsis, Georgios D; Theocharides, Theocharis
2015-01-01
The use of a GPGPU programming paradigm (running CUDA-enabled algorithms on GPU cards) in biomedical engineering and biology-related applications have shown promising results. GPU acceleration can be used to speedup computation-intensive models, such as the mathematical modeling of biological systems, which often requires the use of nonlinear modeling approaches with a large number of free parameters. In this context, we developed a CUDA-enabled version of a model which implements a nonlinear identification approach that combines basis expansions and polynomial-type networks, termed Laguerre-Volterra networks and can be used in diverse biological applications. The proposed software implementation uses the GPGPU programming paradigm to take advantage of the inherent parallel characteristics of the aforementioned modeling approach to execute the calculations on the GPU card of the host computer system. The initial results of the GPU-based model presented in this work, show performance improvements over the original MATLAB model.
Streamlined Islands in Ares Valles
2002-01-01
(Released 10 June 2002) The Science Although liquid water is not stable on the surface of Mars today, there is substantial geologic evidence that large quantities of water once flowed across the surface in the distant past. Streamlined islands, shown here, are one piece of evidence for this ancient water. The tremendous force of moving water, possibly from a catastrophic flood, carved these teardrop-shaped islands within a much larger channel called Ares Valles. The orientation of the islands can be used as an indicator of the direction the water flowed. The islands have a blunt end that is usually associated with an obstacle, commonly an impact crater. The crater is resistant to erosion and creates a geologic barrier around which the water must flow. As the water flows past the obstacle, its erosive power is directed outward, leaving the area in the lee of the obstacle relatively uneroded. However, some scientists have also argued that the area in the lee of the obstacle might be a depositional zone, where material is dropped out of the water as it briefly slows. The ridges observed on the high-standing terrain in the leeward parts of the islands may be benches carved into the rock that mark the height of the water at various times during the flood, or they might be indicative of layering in the leeward rock. As the water makes its way downstream, the interference of the water flow by the obstacle is reduced, and the water that was diverted around the obstacle rejoins itself at the narrow end of the island. Therefore, the direction of the water flow is parallel to the orientation of the island, and the narrow end of the island points downstream. In addition to the streamlined islands, the channel floor exhibits fluting that is also suggestive of flowing water. The flutes (also known as longitudinal grooves) are also parallel to the direction of flow, indicating that the water flow was turbulent and probably quite fast, which is consistent with the hypothesized
GPU-accelerated denoising of 3D magnetic resonance images
Howison, Mark; Wes Bethel, E.
2014-05-29
The raw computational power of GPU accelerators enables fast denoising of 3D MR images using bilateral filtering, anisotropic diffusion, and non-local means. In practice, applying these filtering operations requires setting multiple parameters. This study was designed to provide better guidance to practitioners for choosing the most appropriate parameters by answering two questions: what parameters yield the best denoising results in practice? And what tuning is necessary to achieve optimal performance on a modern GPU? To answer the first question, we use two different metrics, mean squared error (MSE) and mean structural similarity (MSSIM), to compare denoising quality against a reference image. Surprisingly, the best improvement in structural similarity with the bilateral filter is achieved with a small stencil size that lies within the range of real-time execution on an NVIDIA Tesla M2050 GPU. Moreover, inappropriate choices for parameters, especially scaling parameters, can yield very poor denoising performance. To answer the second question, we perform an autotuning study to empirically determine optimal memory tiling on the GPU. The variation in these results suggests that such tuning is an essential step in achieving real-time performance. These results have important implications for the real-time application of denoising to MR images in clinical settings that require fast turn-around times.
Creating customer value by streamlining business processes.
Vantrappen, H
1992-02-01
Much of the strategic preoccupation of senior managers in the 1990s is focusing on the creation of customer value. Companies are seeking competitive advantage by streamlining the three processes through which they interact with their customers: product creation, order handling and service assurance. 'Micro-strategy' is a term which has been coined for the trade-offs and decisions on where and how to streamline these three processes. The article discusses micro-strategies applied by successful companies.
THE APPLICATION OF GPU IN OCEAN GENERAL CIRCULATION MODE POP%GPU在海洋环流模式POP中的应用
宋振亚; 刘海行; 雷晓燕; 赵伟
2010-01-01
在CUDA(Compute Unified Device Architecture)架构下将GPU(Graphic Processing Unit)计算首次应用到海洋环流模式POP(Parallel Ocean Program)中.测试结果表明:无论高分辨率还是低分辨率,GPU都能够提高海洋环流数值模式POP的计算速度,GPU加速比最低都在1.5倍以上,最高可以超过2.2倍;并且随着模式使用线程数目的增多,GPU的加速比在降低,但是GPU利用效率在增长.
Accelerated protein structure comparison using TM-score-GPU.
Hung, Ling-Hong; Samudrala, Ram
2012-08-15
Accurate comparisons of different protein structures play important roles in structural biology, structure prediction and functional annotation. The root-mean-square-deviation (RMSD) after optimal superposition is the predominant measure of similarity due to the ease and speed of computation. However, global RMSD is dependent on the length of the protein and can be dominated by divergent loops that can obscure local regions of similarity. A more sophisticated measure of structure similarity, Template Modeling (TM)-score, avoids these problems, and it is one of the measures used by the community-wide experiments of critical assessment of protein structure prediction to compare predicted models with experimental structures. TM-score calculations are, however, much slower than RMSD calculations. We have therefore implemented a very fast version of TM-score for Graphical Processing Units (TM-score-GPU), using a new and novel hybrid Kabsch/quaternion method for calculating the optimal superposition and RMSD that is designed for parallel applications. This acceleration in speed allows TM-score to be used efficiently in computationally intensive applications such as for clustering of protein models and genome-wide comparisons of structure. TM-score-GPU was applied to six sets of models from Nutritious Rice for the World for a total of 3 million comparisons. TM-score-GPU is 68 times faster on an ATI 5870 GPU, on average, than the original CPU single-threaded implementation on an AMD Phenom II 810 quad-core processor. The complete source, including the GPU code and the hybrid RMSD subroutine, can be downloaded and used without restriction at http://software.compbio.washington.edu/misc/downloads/tmscore/. The implementation is in C++/OpenCL.
Nishikant P Deshmukh
Full Text Available A system for real-time ultrasound (US elastography will advance interventions for the diagnosis and treatment of cancer by advancing methods such as thermal monitoring of tissue ablation. A multi-stream graphics processing unit (GPU based accelerated normalized cross-correlation (NCC elastography, with a maximum frame rate of 78 frames per second, is presented in this paper. A study of NCC window size is undertaken to determine the effect on frame rate and the quality of output elastography images. This paper also presents a novel system for Online Tracked Ultrasound Elastography (O-TRuE, which extends prior work on an offline method. By tracking the US probe with an electromagnetic (EM tracker, the system selects in-plane radio frequency (RF data frames for generating high quality elastograms. A novel method for evaluating the quality of an elastography output stream is presented, suggesting that O-TRuE generates more stable elastograms than generated by untracked, free-hand palpation. Since EM tracking cannot be used in all systems, an integration of real-time elastography and the da Vinci Surgical System is presented and evaluated for elastography stream quality based on our metric. The da Vinci surgical robot is outfitted with a laparoscopic US probe, and palpation motions are autonomously generated by customized software. It is found that a stable output stream can be achieved, which is affected by both the frequency and amplitude of palpation. The GPU framework is validated using data from in-vivo pig liver ablation; the generated elastography images identify the ablated region, outlined more clearly than in the corresponding B-mode US images.
Ghiorso, M. S.
2014-12-01
Computational thermodynamics (CT) represents a collection of numerical techniques that are used to calculate quantitative results from thermodynamic theory. In the Earth sciences, CT is most often applied to estimate the equilibrium properties of solutions, to calculate phase equilibria from models of the thermodynamic properties of materials, and to approximate irreversible reaction pathways by modeling these as a series of local equilibrium steps. The thermodynamic models that underlie CT calculations relate the energy of a phase to temperature, pressure and composition. These relationships are not intuitive and they are seldom well constrained by experimental data; often, intuition must be applied to generate a robust model that satisfies the expectations of use. As a consequence of this situation, the models and databases the support CT applications in geochemistry and petrology are tedious to maintain as new data and observations arise. What is required to make the process more streamlined and responsive is a computational framework that permits the rapid generation of observable outcomes from the underlying data/model collections, and importantly, the ability to update and re-parameterize the constitutive models through direct manipulation of those outcomes. CT procedures that take models/data to the experiential reference frame of phase equilibria involve function minimization, gradient evaluation, the calculation of implicit lines, curves and surfaces, contour extraction, and other related geometrical measures. All these procedures are the mainstay of image processing analysis. Since the commercial escalation of video game technology, open source image processing libraries have emerged (e.g., VTK) that permit real time manipulation and analysis of images. These tools find immediate application to CT calculations of phase equilibria by permitting rapid calculation and real time feedback between model outcome and the underlying model parameters.
GPU-based 3D lower tree wavelet video encoder
Galiano, Vicente; López-Granado, Otoniel; Malumbres, Manuel P.; Drummond, Leroy Anthony; Migallón, Hector
2013-12-01
The 3D-DWT is a mathematical tool of increasing importance in those applications that require an efficient processing of huge amounts of volumetric info. Other applications like professional video editing, video surveillance applications, multi-spectral satellite imaging, HQ video delivery, etc, would rather use 3D-DWT encoders to reconstruct a frame as fast as possible. In this article, we introduce a fast GPU-based encoder which uses 3D-DWT transform and lower trees. Also, we present an exhaustive analysis of the use of GPU memory. Our proposal shows good trade off between R/D, coding delay (as fast as MPEG-2 for High definition) and memory requirements (up to 6 times less memory than x264).
Parallelization of MODFLOW using a GPU library.
Ji, Xiaohui; Li, Dandan; Cheng, Tangpei; Wang, Xu-Sheng; Wang, Qun
2014-01-01
A new method based on a graphics processing unit (GPU) library is proposed in the paper to parallelize MODFLOW. Two programs, GetAb_CG and CG_GPU, have been developed to reorganize the equations in MODFLOW and solve them with the GPU library. Experimental tests using the NVIDIA Tesla C1060 show that a 1.6- to 10.6-fold speedup can be achieved for models with more than 10(5) cells. The efficiency can be further improved by using up-to-date GPU devices.
GPU PRO 3 Advanced rendering techniques
Engel, Wolfgang
2012-01-01
GPU Pro3, the third volume in the GPU Pro book series, offers practical tips and techniques for creating real-time graphics that are useful to beginners and seasoned game and graphics programmers alike. Section editors Wolfgang Engel, Christopher Oat, Carsten Dachsbacher, Wessam Bahnassi, and Sebastien St-Laurent have once again brought together a high-quality collection of cutting-edge techniques for advanced GPU programming. With contributions by more than 50 experts, GPU Pro3: Advanced Rendering Techniques covers battle-tested tips and tricks for creating interesting geometry, realistic sha
Cosmological Calculations on the GPU
Bard, Deborah; Allen, Mark T; Yepremyan, Hasmik; Kratochvil, Jan M
2012-01-01
Cosmological measurements require the calculation of nontrivial quantities over large datasets. The next generation of survey telescopes (such as DES, PanSTARRS, and LSST) will yield measurements of billions of galaxies. The scale of these datasets, and the nature of the calculations involved, make cosmological calculations ideal models for implementation on graphics processing units (GPUs). We consider two cosmological calculations, the two-point angular correlation function and the aperture mass statistic, and aim to improve the calculation time by constructing code for calculating them on the GPU. Using CUDA, we implement the two algorithms on the GPU and compare the calculation speeds to comparable code run on the CPU. We obtain a code speed-up of between 10 - 180x faster, compared to performing the same calculation on the CPU. The code has been made publicly available.
Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System
Nehemia, Rangga; Lim, Charles; Galinium, Maulahikmah; Rinaldi Widianto, Ahmad
2017-04-01
As Internet-related security threats continue to increase in terms of volume and sophistication, existing Intrusion Detection System is also being challenged to cope with the current Internet development. Multi Pattern String Matching algorithm accelerated with Graphical Processing Unit is being utilized to improve the packet scanning performance of the IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho Corasick accelerated with GPU to improve the performance of IDS. OpenCL library is used to allow the IDS to support various GPU, including popular GPU such as NVIDIA and AMD, used in our research. The experiment result shows that the application of Multi Pattern String Matching using GPU accelerated platform provides a speed up, by up to 141% in term of throughput compared to the previous research.
G Boroni
2017-03-01
Full Text Available Lattice Boltzmann Method (LBM has shown great potential in fluid simulations, but performance issues and difficulties to manage complex boundary conditions have hindered a wider application. The upcoming of Graphic Processing Units (GPU Computing offered a possible solution for the performance issue, and methods like the Immersed Boundary (IB algorithm proved to be a flexible solution to boundaries. Unfortunately, the implicit IB algorithm makes the LBM implementation in GPU a non-trivial task. This work presents a fully parallel GPU implementation of LBM in combination with IB. The fluid-boundary interaction is implemented via GPU kernels, using execution configurations and data structures specifically designed to accelerate each code execution. Simulations were validated against experimental and analytical data showing good agreement and improving the computational time. Substantial reductions of calculation rates were achieved, lowering down the required time to execute the same model in a CPU to about two magnitude orders.
Mehrenberger, M; Marradi, L; Crouseilles, N; Sonnendrucker, E; Afeyan, B
2013-01-01
This work concerns the numerical simulation of the Vlasov-Poisson set of equations using semi- Lagrangian methods on Graphical Processing Units (GPU). To accomplish this goal, modifications to traditional methods had to be implemented. First and foremost, a reformulation of semi-Lagrangian methods is performed, which enables us to rewrite the governing equations as a circulant matrix operating on the vector of unknowns. This product calculation can be performed efficiently using FFT routines. Second, to overcome the limitation of single precision inherent in GPU, a {\\delta}f type method is adopted which only needs refinement in specialized areas of phase space but not throughout. Thus, a GPU Vlasov-Poisson solver can indeed perform high precision simulations (since it uses very high order reconstruction methods and a large number of grid points in phase space). We show results for rather academic test cases on Landau damping and also for physically relevant phenomena such as the bump on tail instability and t...
GPU Accelerated Vector Median Filter
Aras, Rifat; Shen, Yuzhong
2011-01-01
Noise reduction is an important step for most image processing tasks. For three channel color images, a widely used technique is vector median filter in which color values of pixels are treated as 3-component vectors. Vector median filters are computationally expensive; for a window size of n x n, each of the n(sup 2) vectors has to be compared with other n(sup 2) - 1 vectors in distances. General purpose computation on graphics processing units (GPUs) is the paradigm of utilizing high-performance many-core GPU architectures for computation tasks that are normally handled by CPUs. In this work. NVIDIA's Compute Unified Device Architecture (CUDA) paradigm is used to accelerate vector median filtering. which has to the best of our knowledge never been done before. The performance of GPU accelerated vector median filter is compared to that of the CPU and MPI-based versions for different image and window sizes, Initial findings of the study showed 100x improvement of performance of vector median filter implementation on GPUs over CPU implementations and further speed-up is expected after more extensive optimizations of the GPU algorithm .
Berczik, P; Wang, L; Zhong, S; Veles, O; Zinchenko, I; Huang, S; Tsai, M; Kennedy, G; Li, S; Naso, L; Li, C
2013-01-01
We present direct astrophysical N-body simulations with up to a few million bodies using our parallel MPI/CUDA code on large GPU clusters in China, Ukraine and Germany, with different kinds of GPU hardware. These clusters are directly linked under the Chinese Academy of Sciences special GPU cluster program in the cooperation of ICCS (International Center for Computational Science). We reach about the half the peak Kepler K20 GPU performance for our phi-GPU code [2], in a real application scenario with individual hierarchically block time-steps with the high (4th, 6th and 8th) order Hermite integration schemes and a real core-halo density structure of the modeled stellar systems. The code and hardware are mainly used to simulate star clusters [23, 24] and galactic nuclei with supermassive black holes [20], in which correlations between distant particles cannot be neglected.
GPU-accelerated molecular dynamics simulation for study of liquid crystalline flows
Sunarso, Alfeus; Tsuji, Tomohiro; Chono, Shigeomi
2010-08-01
We have developed a GPU-based molecular dynamics simulation for the study of flows of fluids with anisotropic molecules such as liquid crystals. An application of the simulation to the study of macroscopic flow (backflow) generation by molecular reorientation in a nematic liquid crystal under the application of an electric field is presented. The computations of intermolecular force and torque are parallelized on the GPU using the cell-list method, and an efficient algorithm to update the cell lists was proposed. Some important issues in the implementation of computations that involve a large number of arithmetic operations and data on the GPU that has limited high-speed memory resources are addressed extensively. Despite the relatively low GPU occupancy in the calculation of intermolecular force and torque, the computation on a recent GPU is about 50 times faster than that on a single core of a recent CPU, thus simulations involving a large number of molecules using a personal computer are possible. The GPU-based simulation should allow an extensive investigation of the molecular-level mechanisms underlying various macroscopic flow phenomena in fluids with anisotropic molecules.
Impact assessment: Eroding benefits through streamlining?
Bond, Alan, E-mail: alan.bond@uea.ac.uk [School of Environmental Sciences, University of East Anglia (United Kingdom); School of Geo and Spatial Sciences, North-West University (South Africa); Pope, Jenny, E-mail: jenny@integral-sustainability.net [Integral Sustainability (Australia); Curtin University Sustainability Policy Institute (Australia); Morrison-Saunders, Angus, E-mail: A.Morrison-Saunders@murdoch.edu.au [School of Geo and Spatial Sciences, North-West University (South Africa); Environmental Science, Murdoch University (Australia); Retief, Francois, E-mail: francois.retief@nwu.ac.za [School of Geo and Spatial Sciences, North-West University (South Africa); Gunn, Jill A.E., E-mail: jill.gunn@usask.ca [Department of Geography and Planning and School of Environment and Sustainability, University of Saskatchewan (Canada)
2014-02-15
This paper argues that Governments have sought to streamline impact assessment in recent years (defined as the last five years) to counter concerns over the costs and potential for delays to economic development. We hypothesise that this has had some adverse consequences on the benefits that subsequently accrue from the assessments. This hypothesis is tested using a framework developed from arguments for the benefits brought by Environmental Impact Assessment made in 1982 in the face of the UK Government opposition to its implementation in a time of economic recession. The particular benefits investigated are ‘consistency and fairness’, ‘early warning’, ‘environment and development’, and ‘public involvement’. Canada, South Africa, the United Kingdom and Western Australia are the jurisdictions tested using this framework. The conclusions indicate that significant streamlining has been undertaken which has had direct adverse effects on some of the benefits that impact assessment should deliver, particularly in Canada and the UK. The research has not examined whether streamlining has had implications for the effectiveness of impact assessment, but the causal link between streamlining and benefits does sound warning bells that merit further investigation. -- Highlights: • Investigation of the extent to which government has streamlined IA. • Evaluation framework was developed based on benefits of impact assessment. • Canada, South Africa, the United Kingdom, and Western Australia were examined. • Trajectory in last five years is attrition of benefits of impact assessment.
GPU Based Software Correlators - Perspectives for VLBI2010
Hobiger, Thomas; Kimura, Moritaka; Takefuji, Kazuhiro; Oyama, Tomoaki; Koyama, Yasuhiro; Kondo, Tetsuro; Gotoh, Tadahiro; Amagai, Jun
2010-01-01
Caused by historical separation and driven by the requirements of the PC gaming industry, Graphics Processing Units (GPUs) have evolved to massive parallel processing systems which entered the area of non-graphic related applications. Although a single processing core on the GPU is much slower and provides less functionality than its counterpart on the CPU, the huge number of these small processing entities outperforms the classical processors when the application can be parallelized. Thus, in recent years various radio astronomical projects have started to make use of this technology either to realize the correlator on this platform or to establish the post-processing pipeline with GPUs. Therefore, the feasibility of GPUs as a choice for a VLBI correlator is being investigated, including pros and cons of this technology. Additionally, a GPU based software correlator will be reviewed with respect to energy consumption/GFlop/sec and cost/GFlop/sec.
Margot Gerritsen
2008-10-31
the redundant work generally done in the near-well regions. We improved the accuracy of the streamline simulator with a higher order mapping from pressure grid to streamlines that significantly reduces smoothing errors, and a Kriging algorithm is used to map from the streamlines to the background grid. The higher accuracy of the Kriging mapping means that it is not essential for grid blocks to be crossed by one or more streamlines. The higher accuracy comes at the price of increased computational costs, but allows coarser coverage and so does not generally increase the overall costs of the computations. To reduce errors associated with fixing the pressure field between pressure updates, we developed a higher order global time-stepping method that allows the use of larger global time steps. Third-order ENO schemes are suggested to propagate components along streamlines. Both in the two-phase and three-phase experiments these ENO schemes outperform other (higher order) upwind schemes. Application of the third order ENO scheme leads to overall computational savings because the computational grid used can be coarsened. Grid adaptivity along streamlines is implemented to allow sharp but efficient resolution of solution fronts at reduced computational costs when displacement fronts are sufficiently separated. A correction for Volume Change On Mixing (VCOM) is implemented that is very effective at handling this effect. Finally, a specialized gravity operator splitting method is proposed for use in compositional streamline methods that gives an effective correction of gravity segregation. A significant part of our effort went into the development of a parallelization strategy for streamline solvers on the next generation shared memory machines. We found in this work that the built-in dynamic scheduling strategies of OpenMP lead to parallel efficiencies that are comparable to optimal schedules obtained with customized explicit load balancing strategies as long as the ratio of
Systems Biology and Ecology of Streamlined Bacterioplankton
Giovannoni, S. J.
2014-12-01
The salient feature of streamlined cells is their small genome size, but "streamlining" refers more generally to selection that favors minimization of cell size and complexity. The essence of streamlining theory is that selection is most efficient in organisms that have large effective population sizes, and, in nutrient-limited systems, favors cell architecture that minimizes resources required for replication. Regardless of the cause of genome reduction, lost coding potential eventually dictates loss of function, raising the questions, what genome features are expendable, and how do cells become highly successful with a minimal genomic repertoire? One consequence of reductive evolution in streamlined organisms is atypical patterns of prototrophy, for example the recent discovery of a requirement for the thiamin precursor 4-amino-5-hydroxymethyl-2-methylpyrimidine in some plankton taxa. Examples such as this fit within the framework of the Black Queen Hypothesis, which describes genome reduction that results in reliance on community goods and increased community connectivity. Other examples of genome reduction include losses of regulatory functions, or replacement with simpler regulatory systems, and increased metabolic integration. In one such case, in the order Pelagibacterales, the PII system for regulating responses to N limitation has been replaced with a simpler system composed of fewer genes. Both the absence of common regulatory systems and atypical patterns of prototrophy have been linked to difficulty in culturing Pelagibacterales, lending credibility to the idea that streamlining might broadly explain the phenomenon of the uncultured microbial majority. The success of streamlined osmotrophic bacterioplankton suggests that they successfully compete for labile organic matter and capture a large share of this resource, but an alternative theory postulates they are not good resource competitors and instead prosper by avoiding predation. The answers to these
Zhang, Bo; Yang, Xiang; Yang, Fei; Yang, Xin; Qin, Chenghu; Han, Dong; Ma, Xibo; Liu, Kai; Tian, Jie
2010-09-13
In molecular imaging (MI), especially the optical molecular imaging, bioluminescence tomography (BLT) emerges as an effective imaging modality for small animal imaging. The finite element methods (FEMs), especially the adaptive finite element (AFE) framework, play an important role in BLT. The processing speed of the FEMs and the AFE framework still needs to be improved, although the multi-thread CPU technology and the multi CPU technology have already been applied. In this paper, we for the first time introduce a new kind of acceleration technology to accelerate the AFE framework for BLT, using the graphics processing unit (GPU). Besides the processing speed, the GPU technology can get a balance between the cost and performance. The CUBLAS and CULA are two main important and powerful libraries for programming on NVIDIA GPUs. With the help of CUBLAS and CULA, it is easy to code on NVIDIA GPU and there is no need to worry about the details about the hardware environment of a specific GPU. The numerical experiments are designed to show the necessity, effect and application of the proposed CUBLAS and CULA based GPU acceleration. From the results of the experiments, we can reach the conclusion that the proposed CUBLAS and CULA based GPU acceleration method can improve the processing speed of the AFE framework very much while getting a balance between cost and performance.
Real-time GPU surface curvature estimation on deforming meshes and volumetric data sets.
Griffin, Wesley; Wang, Yu; Berrios, David; Olano, Marc
2012-10-01
Surface curvature is used in a number of areas in computer graphics, including texture synthesis and shape representation, mesh simplification, surface modeling, and nonphotorealistic line drawing. Most real-time applications must estimate curvature on a triangular mesh. This estimation has been limited to CPU algorithms, forcing object geometry to reside in main memory. However, as more computational work is done directly on the GPU, it is increasingly common for object geometry to exist only in GPU memory. Examples include vertex skinned animations and isosurfaces from GPU-based surface reconstruction algorithms. For static models, curvature can be precomputed and CPU algorithms are a reasonable choice. For deforming models where the geometry only resides on the GPU, transferring the deformed mesh back to the CPU limits performance. We introduce a GPU algorithm for estimating curvature in real time on arbitrary triangular meshes. We demonstrate our algorithm with curvature-based NPR feature lines and a curvature-based approximation for an ambient occlusion. We show curvature computation on volumetric data sets with a GPU isosurface extraction algorithm and vertex-skinned animations. We present a graphics pipeline and CUDA implementation. Our curvature estimation is up to ~18x faster than a multithreaded CPU benchmark.
A new embedded solution of hyperspectral data processing platform: the embedded GPU computer
Zhang, Lei; Gao, Jiao Bo; Hu, Yu; Sun, Ke Feng; Wang, Ying Hui; Cheng, Juan; Sun, Dan Dan; Li, Yu
2016-10-01
During the research of hyper-spectral imaging spectrometer, how to process the huge amount of image data is a difficult problem for all researchers. The amount of image data is about the order of magnitude of several hundred megabytes per second. Traditional solution of the embedded hyper-spectral data processing platform such as DSP and FPGA has its own drawback. With the development of GPU, parallel computing on GPU is increasingly applied in large-scale data processing. In this paper, we propose a new embedded solution of hyper-spectral data processing platform which is based on the embedded GPU computer. We also give a detailed discussion of how to acquire and process hyper-spectral data in embedded GPU computer. We use C++ AMP technology to control GPU and schedule the parallel computing. Experimental results show that the speed of hyper-spectral data processing on embedded GPU computer is apparently faster than ordinary computer. Our research has significant meaning for the engineering application of hyper-spectral imaging spectrometer.
The experience of GPU calculations at Lunarc
Sjöström, Anders; Lindemann, Jonas; Church, Ross
2011-09-01
To meet the ever increasing demand for computational speed and use of ever larger datasets, multi GPU instal- lations look very tempting. Lunarc and the Theoretical Astrophysics group at Lund Observatory collaborate on a pilot project to evaluate and utilize multi-GPU architectures for scientific calculations. Starting with a small workshop in 2009, continued investigations eventually lead to the procurement of the GPU-resource Timaeus, which is a four-node eight-GPU cluster with two Nvidia m2050 GPU-cards per node. The resource is housed within the larger cluster Platon and share disk-, network- and system resources with that cluster. The inaugu- ration of Timaeus coincided with the meeting "Computational Physics with GPUs" in November 2010, hosted by the Theoretical Astrophysics group at Lund Observatory. The meeting comprised of a two-day workshop on GPU-computing and a two-day science meeting on using GPUs as a tool for computational physics research, with a particular focus on astrophysics and computational biology. Today Timaeus is used by research groups from Lund, Stockholm and Lule in fields ranging from Astrophysics to Molecular Chemistry. We are investigating the use of GPUs with commercial software packages and user supplied MPI-enabled codes. Looking ahead, Lunarc will be installing a new cluster during the summer of 2011 which will have a small number of GPU-enabled nodes that will enable us to continue working with the combination of parallel codes and GPU-computing. It is clear that the combination of GPUs/CPUs is becoming an important part of high performance computing and here we will describe what has been done at Lunarc regarding GPU-computations and how we will continue to investigate the new and coming multi-GPU servers and how they can be utilized in our environment.
Interactive brain shift compensation using GPU based programming
van der Steen, Sander; Noordmans, Herke Jan; Verdaasdonk, Rudolf
2009-02-01
Processing large images files or real-time video streams requires intense computational power. Driven by the gaming industry, the processing power of graphic process units (GPUs) has increased significantly. With the pixel shader model 4.0 the GPU can be used for image processing 10x faster than the CPU. Dedicated software was developed to deform 3D MR and CT image sets for real-time brain shift correction during navigated neurosurgery using landmarks or cortical surface traces defined by the navigation pointer. Feedback was given using orthogonal slices and an interactively raytraced 3D brain image. GPU based programming enables real-time processing of high definition image datasets and various applications can be developed in medicine, optics and image sciences.
Performance potential for simulating spin models on GPU
Weigel, Martin
2011-01-01
Graphics processing units (GPUs) are recently being used to an increasing degree for general computational purposes. This development is motivated by their theoretical peak performance, which significantly exceeds that of broadly available CPUs. For practical purposes, however, it is far from clear how much of this theoretical performance can be realized in actual scientific applications. As is discussed here for the case of studying classical spin models of statistical mechanics by Monte Carlo simulations, only an explicit tailoring of the involved algorithms to the specific architecture under consideration allows to harvest the computational power of GPU systems. A number of examples, ranging from Metropolis simulations of ferromagnetic Ising models, over continuous Heisenberg and disordered spin-glass systems to parallel-tempering simulations are discussed. Significant speed-ups by factors of up to 1000 compared to serial CPU code as well as previous GPU implementations are observed.
Performance potential for simulating spin models on GPU
Weigel, Martin
2012-04-01
Graphics processing units (GPUs) are recently being used to an increasing degree for general computational purposes. This development is motivated by their theoretical peak performance, which significantly exceeds that of broadly available CPUs. For practical purposes, however, it is far from clear how much of this theoretical performance can be realized in actual scientific applications. As is discussed here for the case of studying classical spin models of statistical mechanics by Monte Carlo simulations, only an explicit tailoring of the involved algorithms to the specific architecture under consideration allows to harvest the computational power of GPU systems. A number of examples, ranging from Metropolis simulations of ferromagnetic Ising models, over continuous Heisenberg and disordered spin-glass systems to parallel-tempering simulations are discussed. Significant speed-ups by factors of up to 1000 compared to serial CPU code as well as previous GPU implementations are observed.
A Method for Streamlining and Assessing Sound Velocity Profiles Based on Improved D-P Algorithm
Zhao, D.; WU, Z. Y.; Zhou, J.
2015-12-01
A multi-beam system transmits sound waves and receives the round-trip time of their reflection or scattering, and thus it is possible to determine the depth and coordinates of the detected targets using the sound velocity profile (SVP) based on Snell's Law. The SVP is determined by a device. Because of the high sampling rate of the modern device, the operational time of ray tracing and beam footprint reduction will increase, lowering the overall efficiency. To promote the timeliness of multi-beam surveys and data processing, redundant points in the original SVP must be screened out and at the same time, errors following the streamlining of the SVP must be evaluated and controlled. We presents a new streamlining and evaluation method based on the Maximum Offset of sound Velocity (MOV) algorithm. Based on measured SVP data, this method selects sound velocity data points by calculating the maximum distance to the sound-velocity-dimension based on an improved Douglas-Peucker Algorithm to streamline the SVP (Fig. 1). To evaluate whether the streamlined SVP meets the desired accuracy requirements, this method is divided into two parts: SVP streamlining, and an accuracy analysis of the multi-beam sounding data processing using the streamlined SVP. Therefore, the method is divided into two modules: the streamlining module and the evaluation module (Fig. 2). The streamlining module is used for streamlining the SVP. Its core is the MOV algorithm.To assess the accuracy of the streamlined SVP, we uses ray tracing and the percentage error analysis method to evaluate the accuracy of the sounding data both before and after streamlining the SVP (Fig. 3). By automatically optimizing the threshold, the reduction rate of sound velocity profile data can reach over 90% and the standard deviation percentage error of sounding data can be controlled to within 0.1% (Fig. 4). The optimized sound velocity profile data improved the operational efficiency of the multi-beam survey and data post
Parallel Implementation of Texture Based Image Retrieval on The GPU
Alireza Ahmadi Mohammadabadi
2013-07-01
Full Text Available Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In huge image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. Graphical Processors Units (GPU is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement texture based image retrieval system in parallel using Compute Unified Device Architecture (CUDA programming model to run on GPU. The main goal of this research work is to parallelize the process of texture based image retrieval through entropy, standard deviation, and local range, also whole process is much faster than normal. Our work uses extensive usage of highly multithreaded architecture of multi-cored GPU. We evaluated the retrieval of the proposed technique using Recall, Precision, and Average Precision measures. Experimental results showed that parallel implementation led to an average speed up of 140.046×over the serial implementation. The average Precision and the average Recall of presented method are 39.67% and 55.00% respectively.
77 FR 14700 - Streamlining Inherited Regulations
2012-03-13
... April 3, 2012. \\1\\ 76 FR 75825. The Bureau received a joint request from several industry and consumer... Consumer Law Center. For the reasons described in the joint request for an extension, the Bureau is...; ] BUREAU OF CONSUMER FINANCIAL PROTECTION 12 CFR Chapter X Streamlining Inherited Regulations...
Streamlining the Bankability Process using International Standards
Kurtz, Sarah [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Repins, Ingrid L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kelly, George [Sunset Technology, Mount Airy, MD; Ramu, Govind [SunPower, San Jose, California; Heinz, Matthias [TUV Rheinland, Cologne, Germany; Chen, Yingnan [CGC (China General Certification Center), Beijing; Wohlgemuth, John [PowerMark, Union Hall, VA; Lokanath, Sumanth [First Solar, Tempe, Arizona; Daniels, Eric [Suncycle USA, Frederick MD; Hsi, Edward [Swiss RE, Zurich, Switzerland; Yamamichi, Masaaki [RTS, Trumbull, CT
2017-09-27
NREL has supported the international efforts to create a streamlined process for documenting bankability and/or completion of each step of a PV project plan. IECRE was created for this purpose in 2014. This poster describes the goals, current status of this effort, and how individuals and companies can become involved.
Streamlining Exceptional Student Placement with PERT.
Radencich, Marguerite C.
1985-01-01
The article explains the usefulness of PERT (Program Evaluation and Review Technique) in streamlining exceptional student referral processes. With PERT, realistic time and personnel needs can be established, and the status of every referral can be known at all times. The initial step in PERT breaks the process into small manageable units and…
鲍春永; 赵啦啦; 刘万英; 杨康康
2016-01-01
Particle discrete element method is a kind of numerical simulation method widely used in the research of granular material mechanics behaviour.Computation efficiency is one of the main factors that restricts its development and application.In this paper,we build a hopper model by using Pro/E software,and use Stream DEM software to study the stimulations of discrete element method in regard to hopper’s particles filling process.We also compare the operation processes and results of CPU-based and GPU-based acceleration algorithms. Results show that the GPU-based computer graphics acceleration algorithm can dramatically improve the computation efficiency of the simulation process of particle discrete element method.When the number of particles to be filled reaches 130 000,its computational efficiency improves over 10 times than that of the CPU-based acceleration algorithm.%颗粒离散元法是一种广泛应用于研究颗粒物料力学行为的数值模拟方法，而计算效率是制约其发展和应用的主要因素之一。通过Pro／E软件建立了料斗模型，利用Stream DEM软件对料斗的颗粒充填过程进行离散元法模拟研究，并对基于CPU 和GPU加速算法的运算过程和结果进行对比。结果表明，基于GPU的计算机图形学加速算法可大幅提高颗粒离散元法模拟过程的运算效率。当填充颗粒数量达到13万时，其运算效率比基于CPU的运算效率提高了10倍以上。
GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.
Dorgham, Osama M; Laycock, Stephen D; Fisher, Mark H
2012-09-01
Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC
GASPRNG: GPU accelerated scalable parallel random number generator library
Gao, Shuang; Peterson, Gregory D.
2013-04-01
Graphics processors represent a promising technology for accelerating computational science applications. Many computational science applications require fast and scalable random number generation with good statistical properties, so they use the Scalable Parallel Random Number Generators library (SPRNG). We present the GPU Accelerated SPRNG library (GASPRNG) to accelerate SPRNG in GPU-based high performance computing systems. GASPRNG includes code for a host CPU and CUDA code for execution on NVIDIA graphics processing units (GPUs) along with a programming interface to support various usage models for pseudorandom numbers and computational science applications executing on the CPU, GPU, or both. This paper describes the implementation approach used to produce high performance and also describes how to use the programming interface. The programming interface allows a user to be able to use GASPRNG the same way as SPRNG on traditional serial or parallel computers as well as to develop tightly coupled programs executing primarily on the GPU. We also describe how to install GASPRNG and use it. To help illustrate linking with GASPRNG, various demonstration codes are included for the different usage models. GASPRNG on a single GPU shows up to 280x speedup over SPRNG on a single CPU core and is able to scale for larger systems in the same manner as SPRNG. Because GASPRNG generates identical streams of pseudorandom numbers as SPRNG, users can be confident about the quality of GASPRNG for scalable computational science applications. Catalogue identifier: AEOI_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOI_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: UTK license. No. of lines in distributed program, including test data, etc.: 167900 No. of bytes in distributed program, including test data, etc.: 1422058 Distribution format: tar.gz Programming language: C and CUDA. Computer: Any PC or
A streamlined ribosome profiling protocol for the characterization of microorganisms
Latif, Haythem; Szubin, Richard; Tan, Justin
2015-01-01
in the microbial research community. Here we present a streamlined ribosome profiling protocol with reduced barriers to entry for microbial characterization studies. Our approach provides simplified alternatives during harvest, lysis, and recovery of monosomes and also eliminates several time-consuming steps......Ribosome profiling is a powerful tool for characterizing in vivo protein translation at the genome scale, with multiple applications ranging from detailed molecular mechanisms to systems-level predictive modeling. Though highly effective, this intricate technique has yet to become widely used...
Fast Clustering of Radar Reflectivity Data Using GPU-CPU Pipeline Scheme%基于GPU-CPU流水线的雷达回波快速聚类
周伟; 施宁; 王健; 汪群山
2012-01-01
In our meteorological application,the clustering algorithm was adopted for analysis and processing of radar reflectivity data.While facing problems of large scale of dataset and high dimension of feature vector,the clustering algorithm is too time-consuming to satisfy the real-time constraint in our applications.This paper proposes a parallelized clustering algorithm using GPU-CPU pipeline scheme to solve this problem.In our method,we utilized the feature of asynchronous execution between GPU and CPU,and organized the process of clustering into pipeline-style,with which we can largely exploit the parallelism in algorithm.The experimental results show that our GPU-CPU pipeline based parallelized clustering algorithm outperform normally parallelized clustering algorithm using CUDA without GPU-CPU pipeline by 38%.Compared to the serial code on CPU,out approach can achieve a 47x performance improvement,which makes it satisfy the requirements of real-time applications.%提出了基于GPU-CPU流水线的雷达回波快速聚类方法.该方法利用GPU与CPU异步执行的特征,将聚类的各步骤组织成流水线,大大的挖掘了聚类全过程的的并行性.实验表明,引入这种GPU-CPU流水线机制后,该方法比一般策略的基于GPU的并行聚类算法性能有38%的提升,而相对于传统的CPU上的串行程序,获得了47x的加速比,满足了气象实时分析应用中的实时性要求.
Fast quantum Monte Carlo on a GPU
Lutsyshyn, Y
2013-01-01
We present a scheme for the parallelization of quantum Monte Carlo on graphical processing units, focusing on bosonic systems and variational Monte Carlo. We use asynchronous execution schemes with shared memory persistence, and obtain an excellent acceleration. Comparing with single core execution, GPU-accelerated code runs over x100 faster. The CUDA code is provided along with the package that is necessary to execute variational Monte Carlo for a system representing liquid helium-4. The program was benchmarked on several models of Nvidia GPU, including Fermi GTX560 and M2090, and the latest Kepler architecture K20 GPU. Kepler-specific optimization is discussed.
High-speed optical coherence tomography signal processing on GPU
Li Xiqi; Shi Guohua; Zhang Yudong, E-mail: lixiqi@yahoo.cn [Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209 (China)
2011-01-01
The signal processing speed of spectral domain optical coherence tomography (SD-OCT) has become a bottleneck in many medical applications. Recently, a time-domain interpolation method was proposed. This method not only gets a better signal-to noise ratio (SNR) but also gets a faster signal processing time for the SD-OCT than the widely used zero-padding interpolation method. Furthermore, the re-sampled data is obtained by convoluting the acquired data and the coefficients in time domain. Thus, a lot of interpolations can be performed concurrently. So, this interpolation method is suitable for parallel computing. An ultra-high optical coherence tomography signal processing can be realized by using graphics processing unit (GPU) with computer unified device architecture (CUDA). This paper will introduce the signal processing steps of SD-OCT on GPU. An experiment is performed to acquire a frame SD-OCT data (400A-linesx2048 pixel per A-line) and real-time processed the data on GPU. The results show that it can be finished in 6.208 milliseconds, which is 37 times faster than that on Central Processing Unit (CPU).
Implementation of GPU-accelerated back projection for EPR imaging.
Qiao, Zhiwei; Redler, Gage; Epel, Boris; Qian, Yuhua; Halpern, Howard
2015-01-01
Electron paramagnetic resonance (EPR) Imaging (EPRI) is a robust method for measuring in vivo oxygen concentration (pO2). For 3D pulse EPRI, a commonly used reconstruction algorithm is the filtered backprojection (FBP) algorithm, in which the backprojection process is computationally intensive and may be time consuming when implemented on a CPU. A multistage implementation of the backprojection can be used for acceleration, however it is not flexible (requires equal linear angle projection distribution) and may still be time consuming. In this work, single-stage backprojection is implemented on a GPU (Graphics Processing Units) having 1152 cores to accelerate the process. The GPU implementation results in acceleration by over a factor of 200 overall and by over a factor of 3500 if only the computing time is considered. Some important experiences regarding the implementation of GPU-accelerated backprojection for EPRI are summarized. The resulting accelerated image reconstruction is useful for real-time image reconstruction monitoring and other time sensitive applications.
48 CFR 52.207-2 - Notice of Streamlined Competition.
2010-10-01
... Competition. 52.207-2 Section 52.207-2 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION....207-2 Notice of Streamlined Competition. As prescribed in 7.305(b), insert the following provision: Notice of Streamlined Competition (MAY 2006) (a) This solicitation is part of a streamlined...
Kohno, R; Hotta, K; Nishioka, S; Matsubara, K; Tansho, R; Suzuki, T
2011-11-21
We implemented the simplified Monte Carlo (SMC) method on graphics processing unit (GPU) architecture under the computer-unified device architecture platform developed by NVIDIA. The GPU-based SMC was clinically applied for four patients with head and neck, lung, or prostate cancer. The results were compared to those obtained by a traditional CPU-based SMC with respect to the computation time and discrepancy. In the CPU- and GPU-based SMC calculations, the estimated mean statistical errors of the calculated doses in the planning target volume region were within 0.5% rms. The dose distributions calculated by the GPU- and CPU-based SMCs were similar, within statistical errors. The GPU-based SMC showed 12.30-16.00 times faster performance than the CPU-based SMC. The computation time per beam arrangement using the GPU-based SMC for the clinical cases ranged 9-67 s. The results demonstrate the successful application of the GPU-based SMC to a clinical proton treatment planning.
Software Tools Streamline Project Management
2009-01-01
Three innovative software inventions from Ames Research Center (NETMARK, Program Management Tool, and Query-Based Document Management) are finding their way into NASA missions as well as industry applications. The first, NETMARK, is a program that enables integrated searching of data stored in a variety of databases and documents, meaning that users no longer have to look in several places for related information. NETMARK allows users to search and query information across all of these sources in one step. This cross-cutting capability in information analysis has exponentially reduced the amount of time needed to mine data from days or weeks to mere seconds. NETMARK has been used widely throughout NASA, enabling this automatic integration of information across many documents and databases. NASA projects that use NETMARK include the internal reporting system and project performance dashboard, Erasmus, NASA s enterprise management tool, which enhances organizational collaboration and information sharing through document routing and review; the Integrated Financial Management Program; International Space Station Knowledge Management; Mishap and Anomaly Information Reporting System; and management of the Mars Exploration Rovers. Approximately $1 billion worth of NASA s projects are currently managed using Program Management Tool (PMT), which is based on NETMARK. PMT is a comprehensive, Web-enabled application tool used to assist program and project managers within NASA enterprises in monitoring, disseminating, and tracking the progress of program and project milestones and other relevant resources. The PMT consists of an integrated knowledge repository built upon advanced enterprise-wide database integration techniques and the latest Web-enabled technologies. The current system is in a pilot operational mode allowing users to automatically manage, track, define, update, and view customizable milestone objectives and goals. The third software invention, Query
Streamlining of Plant Patches in Streams
Sand-Jensen, Kaj; Pedersen, Morten Lauge
2008-01-01
area or root area was significantly lower in shallow water . Canopy shape and indices of streamlining did not change significantly with approach velocity (0.02-0.40 m s)1), either because canopy shape is not sensitive to approach velocity or summer velocities were too low to induce such changes. 3......1. Plants in shallow streams often grow in well-defined monospecific patches experiencing a predictable unidirectional flow, though of temporally variable velocity. During maximum patch development in summer we studied: (i) the shape and streamlining of 59 patches of Callitriche cophocarpa, (ii...... averaged 0.25. The canopy and root area of the patches were more elongate and slender in sites with shallow water, where currents accelerate alongside patches and restrict lateral expansion, compared to deeper sites where currents can pass above the canopy. Similarly, the frontal area relative to planform...
Plain Polynomial Arithmetic on GPU
Anisul Haque, Sardar; Moreno Maza, Marc
2012-10-01
As for serial code on CPUs, parallel code on GPUs for dense polynomial arithmetic relies on a combination of asymptotically fast and plain algorithms. Those are employed for data of large and small size, respectively. Parallelizing both types of algorithms is required in order to achieve peak performances. In this paper, we show that the plain dense polynomial multiplication can be efficiently parallelized on GPUs. Remarkably, it outperforms (highly optimized) FFT-based multiplication up to degree 212 while on CPU the same threshold is usually at 26. We also report on a GPU implementation of the Euclidean Algorithm which is both work-efficient and runs in linear time for input polynomials up to degree 218 thus showing the performance of the GCD algorithm based on systolic arrays.
An evaluation of GPU acceleration for sparse reconstruction
Braun, Thomas R.
2010-04-01
Image processing applications typically parallelize well. This gives a developer interested in data throughput several different implementation options, including multiprocessor machines, general purpose computation on the graphics processor, and custom gate-array designs. Herein, we will investigate these first two options for dictionary learning and sparse reconstruction, specifically focusing on the K-SVD algorithm for dictionary learning and the Batch Orthogonal Matching Pursuit for sparse reconstruction. These methods have been shown to provide state of the art results for image denoising, classification, and object recognition. We'll explore the GPU implementation and show that GPUs are not significantly better or worse than CPUs for this application.
Fast placement of evenly spaced streamlines on curvilinear grid surfaces
Mao, Xiaoyang; Higashida, Hidenori; Imamiya, Atsumi
2000-02-01
The success of using streamline technique for visualizing a vector field usually depends largely on the choosing of adequate seed points. This paper propose a new technique for automatically placing seed points to create evenly spaced streamlines on 3D parametric surfaces found in curvilinear grids. The new technique extends Jobard and Lefer's distance-based single pass approach for placing streamlines in the 2D computational space of the surface. Experimental result show that the new technique produces streamline images of competitive quality at much lower computational expense image-guided progressive refinement approach. A method for compensating the visual streamline density distortion caused by projection is also presented.
Streamlining the RI/FS process
Dumas, L.; Doss, R.C.
1998-07-01
In 1994, Pacific Gas and Electric Company (PG and E) contracted with CH2M HILL to manage remedial investigations and feasibility studies (RI/FS) at its former manufactured gas plant (MGP) sites in Chico, Willows, and Marysville, California. These three sites had similar histories, MGP-related contaminants, similar geologic settings, and geographically were close together. Recognizing the advantages that may be gained, both in time and money, by streamlining the RI/FS process, PG and E and CH2M HILL combined the sites into one project. From the start of the project, PG and E and CH2M HILL looked for an implemented changes to the RI/FS process to streamline the project. These changes included combining deliverables, linking field programs at the three sites, and negotiating bulk discounts on laboratory and other services by combining the work to be done at the three sites under one contract. CH2M HILL later proposed additional measures to streamline the project that were eventually adopted by both PG and E and the regulatory agencies. PG and E and CH2M HILL are currently working with the regulatory agencies to negotiate realistic measures to address contaminants in soil and groundwater, and are jointly preparing the FS with the regulatory agencies using a unique means of documentation.
Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems.
Teodoro, George; Kurc, Tahsin M; Pan, Tony; Cooper, Lee A D; Kong, Jun; Widener, Patrick; Saltz, Joel H
2012-05-01
The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches.
3D data denoising via Nonlocal Means filter by using parallel GPU strategies.
Cuomo, Salvatore; De Michele, Pasquale; Piccialli, Francesco
2014-01-01
Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.
GPU-Monte Carlo based fast IMRT plan optimization
Yongbao Li
2014-03-01
Full Text Available Purpose: Intensity-modulated radiation treatment (IMRT plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow.Methods: A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result.Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion: A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.--------------------------------Cite this article as: Li Y, Tian Z
GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method
Gong, Chunye; Liu, Jie; Chi, Lihua; Huang, Haowei; Fang, Jingyue; Gong, Zhenghu
2011-07-01
Graphics Processing Unit (GPU), originally developed for real-time, high-definition 3D graphics in computer games, now provides great faculty in solving scientific applications. The basis of particle transport simulation is the time-dependent, multi-group, inhomogeneous Boltzmann transport equation. The numerical solution to the Boltzmann equation involves the discrete ordinates ( Sn) method and the procedure of source iteration. In this paper, we present a GPU accelerated simulation of one energy group time-independent deterministic discrete ordinates particle transport in 3D Cartesian geometry (Sweep3D). The performance of the GPU simulations are reported with the simulations of vacuum boundary condition. The discussion of the relative advantages and disadvantages of the GPU implementation, the simulation on multi GPUs, the programming effort and code portability are also reported. The results show that the overall performance speedup of one NVIDIA Tesla M2050 GPU ranges from 2.56 compared with one Intel Xeon X5670 chip to 8.14 compared with one Intel Core Q6600 chip for no flux fixup. The simulation with flux fixup on one M2050 is 1.23 times faster than on one X5670.
Fast 3D elastic micro-seismic source location using new GPU features
Xue, Qingfeng; Wang, Yibo; Chang, Xu
2016-12-01
In this paper, we describe new GPU features and their applications in passive seismic - micro-seismic location. Locating micro-seismic events is quite important in seismic exploration, especially when searching for unconventional oil and gas resources. Different from the traditional ray-based methods, the wave equation method, such as the method we use in our paper, has a remarkable advantage in adapting to low signal-to-noise ratio conditions and does not need a person to select the data. However, because it has a conspicuous deficiency due to its computation cost, these methods are not widely used in industrial fields. To make the method useful, we implement imaging-like wave equation micro-seismic location in a 3D elastic media and use GPU to accelerate our algorithm. We also introduce some new GPU features into the implementation to solve the data transfer and GPU utilization problems. Numerical and field data experiments show that our method can achieve a more than 30% performance improvement in GPU implementation just by using these new features.
Eco-friendly streamlined process for sporopollenin exine capsule extraction
Mundargi, Raghavendra C.; Potroz, Michael G.; Park, Jae Hyeon; Seo, Jeongeun; Tan, Ee-Lin; Lee, Jae Ho; Cho, Nam-Joon
2016-01-01
Sporopollenin exine capsules (SECs) extracted from Lycopodium clavatum spores are an attractive biomaterial possessing a highly robust structure suitable for microencapsulation strategies. Despite several decades of research into SEC extraction methods, the protocols commonly used for L. clavatum still entail processing with both alkaline and acidolysis steps at temperatures up to 180 °C and lasting up to 7 days. Herein, we demonstrate a significantly streamlined processing regimen, which indicates that much lower temperatures and processing durations can be used without alkaline lysis. By employing CHN elemental analysis, scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), and dynamic image particle analysis (DIPA), the optimum conditions for L. clavatum SEC processing were determined to include 30 hours acidolysis at 70 °C without alkaline lysis. Extending these findings to proof-of-concept encapsulation studies, we further demonstrate that our SECs are able to achieve a loading of 0.170 ± 0.01 g BSA per 1 g SECs by vacuum-assisted loading. Taken together, our streamlined processing method and corresponding characterization of SECs provides important insights for the development of applications including drug delivery, cosmetics, personal care products, and foods.
Analysis of Jupiter's Oval BA: A Streamlined Approach
Sussman, Michael G.; Chanover, Nancy J.; Simon-Miller, Amy A.; Vasavada, Ashwin R.; Beebe, Reta F.
2010-01-01
We present a novel method of constructing streamlines to derive wind speeds within jovian vortices and demonstrate its application to Oval BA for 2001 pre-reddened Cassini flyby data, 2007 post-reddened New Horizons flyby data, and 1998 Galileo data of precursor Oval DE. Our method, while automated, attempts to combine the advantages of both automated and manual cloud tracking methods. The southern maximum wind speed of Oval BA does not show significant changes between these data sets to within our measurement uncertainty. The northern maximum dries appear to have increased in strength during this time interval, tvhich likely correlates with the oval's return to a symmetric shape. We demonstrate how the use of closed streamlines can provide measurements of vorticity averaged over the encircled area with no a priori assumptions concerning oval shape. We find increased averaged interior vorticity between pre- and post-reddened Oval BA, with the precursor Oval DE occupying a middle value of vorticity between these two.
Using a 3-dimensional laser anemometer to determine mean streamline patterns in a turbulent flow
Orloff, K. L.; Snyder, P. K.
1984-01-01
The determination of mean streamline patterns by moving the test point in the direction of the measured velocity is shown to produce cumulative errors that are unacceptable. A two-dimensional algorithm that minimizes these errors is presented and is analytically validated using simple potential flows. The algorithm is extended to three-dimensional flows and is again validated analytically. Finally, as an example of a typical application of the algorithm, mean streamlines are measured in a complex, turbulent flow with a three-dimensional laser anemometer.
Nakasato, N
2009-01-01
The kd-tree is a fundamental tool in computer science. Among others, an application of the kd-tree search (oct-tree method) to fast evaluation of particle interactions and neighbor search is highly important since computational complexity of these problems are reduced from O(N^2) with a brute force method to O(N log N) with the tree method where N is a number of particles. In this paper, we present a parallel implementation of the tree method running on a graphic processor unit (GPU). We successfully run a simulation of structure formation in the universe very efficiently. On our system, which costs roughly $900, the run with N ~ 2.87x10^6 particles took 5.79 hours and executed 1.2x10^13 force evaluations in total. We obtained the sustained computing speed of 21.8 Gflops and the cost per Gflops of 41.6/Gflops that is two and half times better than the previous record in 2006.
Landau gauge fixing on the lattice using GPU's
Cardoso, Nuno; Oliveira, Orlando; Bicudo, Pedro
2013-01-01
In this work, we consider the GPU implementation of the steepest descent method with Fourier acceleration for Laudau gauge fixing, using CUDA. The performance of the code in a Tesla C2070 GPU is compared with a parallel CPU implementation.
High performance technique for database applicationsusing a hybrid GPU/CPU platform
Zidan, Mohammed A.
2012-07-28
Many database applications, such as sequence comparing, sequence searching, and sequence matching, etc, process large database sequences. we introduce a novel and efficient technique to improve the performance of database applica- tions by using a Hybrid GPU/CPU platform. In particular, our technique solves the problem of the low efficiency result- ing from running short-length sequences in a database on a GPU. To verify our technique, we applied it to the widely used Smith-Waterman algorithm. The experimental results show that our Hybrid GPU/CPU technique improves the average performance by a factor of 2.2, and improves the peak performance by a factor of 2.8 when compared to earlier implementations. Copyright © 2011 by ASME.
GPU-based fast Monte Carlo simulation for radiotherapy dose calculation
Jia, Xun; Graves, Yan Jiang; Folkerts, Michael; Jiang, Steve B
2011-01-01
Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our recent progress towards the development a GPU-based MC dose calculation package, gDPM v2.0. It utilizes the parallel computation ability of a GPU to achieve high efficiency, while maintaining the same particle transport physics as in the original DPM code and hence the same level of simulation accuracy. In GPU computing, divergence of execution paths between threads can considerably reduce the efficiency. Since photons and electrons undergo different physics and hence attain different execution paths, we use a simulation scheme where photon transport and electron transport are separated to partially relieve the thread divergence issue. High performance random number generator and hardware linear interpolation are also utilized. We have also developed various components to hand...
Gallarno, George [Christian Brothers University; Rogers, James H [ORNL; Maxwell, Don E [ORNL
2015-01-01
The high computational capability of graphics processing units (GPUs) is enabling and driving the scientific discovery process at large-scale. The world s second fastest supercomputer for open science, Titan, has more than 18,000 GPUs that computational scientists use to perform scientific simu- lations and data analysis. Understanding of GPU reliability characteristics, however, is still in its nascent stage since GPUs have only recently been deployed at large-scale. This paper presents a detailed study of GPU errors and their impact on system operations and applications, describing experiences with the 18,688 GPUs on the Titan supercom- puter as well as lessons learned in the process of efficient operation of GPUs at scale. These experiences are helpful to HPC sites which already have large-scale GPU clusters or plan to deploy GPUs in the future.
GPU peer-to-peer techniques applied to a cluster interconnect
Ammendola, Roberto; Biagioni, Andrea; Bisson, Mauro; Fatica, Massimiliano; Frezza, Ottorino; Cicero, Francesca Lo; Lonardo, Alessandro; Mastrostefano, Enrico; Paolucci, Pier Stanislao; Rossetti, Davide; Simula, Francesco; Tosoratto, Laura; Vicini, Piero
2013-01-01
Modern GPUs support special protocols to exchange data directly across the PCI Express bus. While these protocols could be used to reduce GPU data transmission times, basically by avoiding staging to host memory, they require specific hardware features which are not available on current generation network adapters. In this paper we describe the architectural modifications required to implement peer-to-peer access to NVIDIA Fermi- and Kepler-class GPUs on an FPGA-based cluster interconnect. Besides, the current software implementation, which integrates this feature by minimally extending the RDMA programming model, is discussed, as well as some issues raised while employing it in a higher level API like MPI. Finally, the current limits of the technique are studied by analyzing the performance improvements on low-level benchmarks and on two GPU-accelerated applications, showing when and how they seem to benefit from the GPU peer-to-peer method.
2011-07-19
... Assignment Procedures AGENCY: Federal Communications Commission. ACTION: Final rules; announcement of... Matter of Policies to Promote Rural Radio Service and to Streamline Allotment and Assignment Procedures... assignments (AM) over the current facility. Applications that are submitted to change an existing radio...
Accelerate micromagnetic simulations with GPU programming in MATLAB
Zhu, Ru
2015-01-01
A finite-difference Micromagnetic simulation code written in MATLAB is presented with Graphics Processing Unit (GPU) acceleration. The high performance of Graphics Processing Unit (GPU) is demonstrated compared to a typical Central Processing Unit (CPU) based code. The speed-up of GPU to CPU is shown to be greater than 30 for problems with larger sizes on a mid-end GPU in single precision. The code is less than 200 lines and suitable for new algorithm developing.
Accelerate micromagnetic simulations with GPU programming in MATLAB
Zhu, Ru
2015-01-01
A finite-difference Micromagnetic simulation code written in MATLAB is presented with Graphics Processing Unit (GPU) acceleration. The high performance of Graphics Processing Unit (GPU) is demonstrated compared to a typical Central Processing Unit (CPU) based code. The speed-up of GPU to CPU is shown to be greater than 30 for problems with larger sizes on a mid-end GPU in single precision. The code is less than 200 lines and suitable for new algorithm developing.
A GPU code for analytic continuation through a sampling method
Nordström, Johan; Schött, Johan; Locht, Inka L. M.; Di Marco, Igor
We here present a code for performing analytic continuation of fermionic Green's functions and self-energies as well as bosonic susceptibilities on a graphics processing unit (GPU). The code is based on the sampling method introduced by Mishchenko et al. (2000), and is written for the widely used CUDA platform from NVidia. Detailed scaling tests are presented, for two different GPUs, in order to highlight the advantages of this code with respect to standard CPU computations. Finally, as an example of possible applications, we provide the analytic continuation of model Gaussian functions, as well as more realistic test cases from many-body physics.
Lightroom 5 streamlining your digital photography process
Sylvan, Rob
2014-01-01
Manage your images with Lightroom and this beautifully illustrated guide Image management can soak up huge amounts of a photographer's time, but help is on hand. This complete guides teaches you how to use Adobe Lightroom 5 to import, manage, edit, and showcase large quantities of images with impressive results. The authors, both professional photographers and Lightroom experts, walk you through step by step, demonstrating real-world techniques as well as a variety of practical tips, tricks, and shortcuts that save you time. Streamline image management tasks like a pro, and get back to doing
MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.
Katsigiannis, Stamos; Zacharia, Eleni; Maroulis, Dimitris
2016-03-03
cDNA microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images which often suffer from noise, artifacts, and uneven background. In this work, the MIGS-GPU (Microarray Image Gridding and Segmentation on GPU) software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the graphics processing unit (GPU) by means of the CUDA architecture in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a userfriendly interface that requires minimum input in order to run.
GPU acceleration of the stochastic grid bundling method for early-exercise options
A. Leitao Rodriguez (Álvaro); C.W. Oosterlee (Cornelis)
2015-01-01
htmlabstractIn this work, a parallel graphics processing units (GPU) version of the Monte Carlo stochastic grid bundling method (SGBM) for pricing multi-dimensional early-exercise options is presented. To extend the method’s applicability, the problem dimensions and the number of bundles will be inc
GPU acceleration of the stochastic grid bundling method for early-exercise options
Leitao Rodriguez, A.; Oosterlee, C.W.
2015-01-01
In this work, a parallel graphics processing units (GPU) version of the Monte Carlo stochastic grid bundling method (SGBM) for pricing multi-dimensional early-exercise options is presented. To extend the method’s applicability, the problem dimensions and the number of bundles will be increased drast
Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA Tesla GPU Cluster
Allada, Veerendra, Benjegerdes, Troy; Bode, Brett
2009-08-31
Commodity clusters augmented with application accelerators are evolving as competitive high performance computing systems. The Graphical Processing Unit (GPU) with a very high arithmetic density and performance per price ratio is a good platform for the scientific application acceleration. In addition to the interconnect bottlenecks among the cluster compute nodes, the cost of memory copies between the host and the GPU device have to be carefully amortized to improve the overall efficiency of the application. Scientific applications also rely on efficient implementation of the BAsic Linear Algebra Subroutines (BLAS), among which the General Matrix Multiply (GEMM) is considered as the workhorse subroutine. In this paper, they study the performance of the memory copies and GEMM subroutines that are critical to port the computational chemistry algorithms to the GPU clusters. To that end, a benchmark based on the NetPIPE framework is developed to evaluate the latency and bandwidth of the memory copies between the host and the GPU device. The performance of the single and double precision GEMM subroutines from the NVIDIA CUBLAS 2.0 library are studied. The results have been compared with that of the BLAS routines from the Intel Math Kernel Library (MKL) to understand the computational trade-offs. The test bed is a Intel Xeon cluster equipped with NVIDIA Tesla GPUs.
Streamlining environmental product declarations: a stage model
Lefebvre, Elisabeth; Lefebvre, Louis A.; Talbot, Stephane; Le Hen, Gael
2001-02-01
General public environmental awareness and education is increasing, therefore stimulating the demand for reliable, objective and comparable information about products' environmental performances. The recently published standard series ISO 14040 and ISO 14025 are normalizing the preparation of Environmental Product Declarations (EPDs) containing comprehensive information relevant to a product's environmental impact during its life cycle. So far, only a few environmentally leading manufacturing organizations have experimented the preparation of EPDs (mostly from Europe), demonstrating its great potential as a marketing weapon. However the preparation of EPDs is a complex process, requiring collection and analysis of massive amounts of information coming from disparate sources (suppliers, sub-contractors, etc.). In a foreseeable future, the streamlining of the EPD preparation process will require product manufacturers to adapt their information systems (ERP, MES, SCADA) in order to make them capable of gathering, and transmitting the appropriate environmental information. It also requires strong functional integration all along the product supply chain in order to ensure that all the information is made available in a standardized and timely manner. The goal of the present paper is two fold: first to propose a transitional model towards green supply chain management and EPD preparation; second to identify key technologies and methodologies allowing to streamline the EPD process and subsequently the transition toward sustainable product development
The Influence of Streamlined Music on Cognition and Mood
Mossbridge, Julia
2016-01-01
Recent advances in sound engineering have led to the development of so-called streamlined music designed to reduce exogenous attention and improve endogenous attention. Although anecdotal reports suggest that streamlined music does indeed improve focus on daily work tasks and may improve mood, the specific influences of streamlined music on cognition and mood have yet to be examined. In this paper, we report the results of a series of online experiments that examined the impact of one form of...
Arafat, Humayun [Department of Computer Science and Engineering, The Ohio State University, Columbus OH USA; Dinan, James [Mathematics and Computer Science Division, Argonne National Laboratory, Lemont IL USA; Krishnamoorthy, Sriram [Computer Science and Mathematics Division, Pacific Northwest National Laboratory, Richland WA USA; Balaji, Pavan [Mathematics and Computer Science Division, Argonne National Laboratory, Lemont IL USA; Sadayappan, P. [Department of Computer Science and Engineering, The Ohio State University, Columbus OH USA
2016-01-06
Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parallel programs executed on hybrid distributed-memory CPU-graphics processing unit (GPU) systems in a global-address space framework. We take into account the unique nature of the accelerator model employed by GPUs, the significant performance difference between GPU and CPU execution as a function of problem size, and the distinct CPU and GPU memory domains. We consider various alternatives in designing a distributed work stealing algorithm for CPU-GPU systems, while taking into account the impact of task distribution and data movement overheads. These strategies are evaluated using microbenchmarks that capture various execution configurations as well as the state-of-the-art CCSD(T) application module from the computational chemistry domain.
Radial basis function networks GPU-based implementation.
Brandstetter, Andreas; Artusi, Alessandro
2008-12-01
Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain. A possible way to accelerate the learning process of an NN is to implement it in hardware, but due to the high cost and the reduced flexibility of the original central processing unit (CPU) implementation, this solution is often not chosen. Recently, the power of the graphic processing unit (GPU), on the market, has increased and it has started to be used in many applications. In particular, a kind of NN named radial basis function network (RBFN) has been used extensively, proving its power. However, their limiting time performances reduce their application in many areas. In this brief paper, we describe a GPU implementation of the entire learning process of an RBFN showing the ability to reduce the computational cost by about two orders of magnitude with respect to its CPU implementation.
GPU-accelerated raster map reprojection
Petr Sloup
2016-07-01
Full Text Available Reprojecting raster maps from one projection to another is an essential part of many cartographic processes (map comparison, overlays, data presentation, ... and reducing the required computational time is desirable and often significantly decreases overall processing costs.The raster reprojection process operates per-pixel and is, therefore, a good candidate for GPU-based parallelization where the large number of processors can lead to a very high degree of parallelism.We have created an experimental implementation of the raster reprojection with GPU-based parallelization (using OpenCL API.During the evaluation, we compared the performance of our implementation to the optimized GDAL and showed that there is a class of problems where GPU-based parallelization can lead to more than sevenfold speedup.
ITS Cluster Finding Algorithm on GPU
Changaival, Boonyarit
2014-01-01
ITS cluster finding algorithm is one of the data reduction algorithms at ALICE. It needs to be processed fast due to a high amount of data readout from the detector. A variety of platforms were studied for the system design. My work is to design, implement and benchmark this algorithm on a GPU platform. GPU is one of many platform that promote parallel computing. A high-end GPU can contain over 2000 processing cores comparing to the commodity CPUs which have only four cores. The program is written in C and CUDA library. The throughput (Number of events per second) is used as a metric to measure the performance. With the latest implementation, the throughput was increased by a factor of 5.
Similarity-Guided Streamline Placement with Error Evaluation
Chen, Y; Cohen, J D; Krolik, J H
2007-08-15
Most streamline generation algorithms either provide a particular density of streamlines across the domain or explicitly detect features, such as critical points, and follow customized rules to emphasize those features. However, the former generally includes many redundant streamlines, and the latter requires Boolean decisions on which points are features (and may thus suffer from robustness problems for real-world data). We take a new approach to adaptive streamline placement for steady vector fields in 2D and 3D. We define a metric for local similarity among streamlines and use this metric to grow streamlines from a dense set of candidate seed points. The metric considers not only Euclidean distance, but also a simple statistical measure of shape and directional similarity. Without explicit feature detection, our method produces streamlines that naturally accentuate regions of geometric interest. In conjunction with this method, we also propose a quantitative error metric for evaluating a streamline representation based on how well it preserves the information from the original vector field. This error metric reconstructs a vector field from points on the streamline representation and computes a difference of the reconstruction from the original vector field.
GPU based framework for geospatial analyses
Cosmin Sandric, Ionut; Ionita, Cristian; Dardala, Marian; Furtuna, Titus
2017-04-01
Parallel processing on multiple CPU cores is already used at large scale in geocomputing, but parallel processing on graphics cards is just at the beginning. Being able to use an simple laptop with a dedicated graphics card for advanced and very fast geocomputation is an advantage that each scientist wants to have. The necessity to have high speed computation in geosciences has increased in the last 10 years, mostly due to the increase in the available datasets. These datasets are becoming more and more detailed and hence they require more space to store and more time to process. Distributed computation on multicore CPU's and GPU's plays an important role by processing one by one small parts from these big datasets. These way of computations allows to speed up the process, because instead of using just one process for each dataset, the user can use all the cores from a CPU or up to hundreds of cores from GPU The framework provide to the end user a standalone tools for morphometry analyses at multiscale level. An important part of the framework is dedicated to uncertainty propagation in geospatial analyses. The uncertainty may come from the data collection or may be induced by the model or may have an infinite sources. These uncertainties plays important roles when a spatial delineation of the phenomena is modelled. Uncertainty propagation is implemented inside the GPU framework using Monte Carlo simulations. The GPU framework with the standalone tools proved to be a reliable tool for modelling complex natural phenomena The framework is based on NVidia Cuda technology and is written in C++ programming language. The code source will be available on github at https://github.com/sandricionut/GeoRsGPU Acknowledgement: GPU framework for geospatial analysis, Young Researchers Grant (ICUB-University of Bucharest) 2016, director Ionut Sandric
GPU Pro 4 advanced rendering techniques
Engel, Wolfgang
2013-01-01
GPU Pro4: Advanced Rendering Techniques presents ready-to-use ideas and procedures that can help solve many of your day-to-day graphics programming challenges. Focusing on interactive media and games, the book covers up-to-date methods producing real-time graphics. Section editors Wolfgang Engel, Christopher Oat, Carsten Dachsbacher, Michal Valient, Wessam Bahnassi, and Sebastien St-Laurent have once again assembled a high-quality collection of cutting-edge techniques for advanced graphics processing unit (GPU) programming. Divided into six sections, the book begins with discussions on the abi
GPU Pro 5 advanced rendering techniques
Engel, Wolfgang
2014-01-01
In GPU Pro5: Advanced Rendering Techniques, section editors Wolfgang Engel, Christopher Oat, Carsten Dachsbacher, Michal Valient, Wessam Bahnassi, and Marius Bjorge have once again assembled a high-quality collection of cutting-edge techniques for advanced graphics processing unit (GPU) programming. Divided into six sections, the book covers rendering, lighting, effects in image space, mobile devices, 3D engine design, and compute. It explores rasterization of liquids, ray tracing of art assets that would otherwise be used in a rasterized engine, physically based area lights, volumetric light
Triggering events with GPU at ATLAS
Kama, Sami; The ATLAS collaboration
2015-01-01
The growing complexity of events produced in LHC collisions demands more and more computing power both for the on line selection and for the offline reconstruction of events. In recent years, the explosive performance growth of massively parallel processors like Graphical Processing Units both in computing power and in low energy consumption, make GPU extremely attractive for using them in a complex high energy experiment like ATLAS. Together with the optimization of reconstruction algorithms exploiting this new massively parallel paradigm, a small scale prototype of the full ATLAS High Level Trigger exploiting GPU has been implemented. We discuss the integration procedure of this prototype, the achieved performance and the prospects for the future.
Bédorf, Jeroen; Zwart, Simon Portegies
2012-01-01
We present a gravitational hierarchical N-body code that is designed to run efficiently on Graphics Processing Units (GPUs). All parts of the algorithm are executed on the GPU which eliminates the need for data transfer between the Central Processing Unit (CPU) and the GPU. Our tests indicate that the gravitational tree-code outperforms tuned CPU code for all parts of the algorithm and show an overall performance improvement of more than a factor 20, resulting in a processing rate of more than 2.8 million particles per second.
MRISIMUL: a GPU-based parallel approach to MRI simulations.
Xanthis, Christos G; Venetis, Ioannis E; Chalkias, A V; Aletras, Anthony H
2014-03-01
A new step-by-step comprehensive MR physics simulator (MRISIMUL) of the Bloch equations is presented. The aim was to develop a magnetic resonance imaging (MRI) simulator that makes no assumptions with respect to the underlying pulse sequence and also allows for complex large-scale analysis on a single computer without requiring simplifications of the MRI model. We hypothesized that such a simulation platform could be developed with parallel acceleration of the executable core within the graphic processing unit (GPU) environment. MRISIMUL integrates realistic aspects of the MRI experiment from signal generation to image formation and solves the entire complex problem for densely spaced isochromats and for a densely spaced time axis. The simulation platform was developed in MATLAB whereas the computationally demanding core services were developed in CUDA-C. The MRISIMUL simulator imaged three different computer models: a user-defined phantom, a human brain model and a human heart model. The high computational power of GPU-based simulations was compared against other computer configurations. A speedup of about 228 times was achieved when compared to serially executed C-code on the CPU whereas a speedup between 31 to 115 times was achieved when compared to the OpenMP parallel executed C-code on the CPU, depending on the number of threads used in multithreading (2-8 threads). The high performance of MRISIMUL allows its application in large-scale analysis and can bring the computational power of a supercomputer or a large computer cluster to a single GPU personal computer.
Molecular dynamics simulations through GPU video games technologies
Loukatou, Styliani; Papageorgiou, Louis; Fakourelis, Paraskevas; Filntisi, Arianna; Polychronidou, Eleftheria; Bassis, Ioannis; Megalooikonomou, Vasileios; Makałowski, Wojciech; Vlachakis, Dimitrios; Kossida, Sophia
2016-01-01
Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD). MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU). A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations. PMID:27525251
True 4D Image Denoising on the GPU
Eklund, Anders; Andersson, Mats; Knutsson, Hans
2011-01-01
The use of image denoising techniques is an important part of many medical imaging applications. One common application is to improve the image quality of low-dose (noisy) computed tomography (CT) data. While 3D image denoising previously has been applied to several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considers several volumes at the same time. The problem with 4D image denoising, compared to 2D and 3D denoising, is that the computational complexity increases exponentially. In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implement it on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 × 512 × 445 × 20. The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFT-based filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutes for FFT-based filtering. The short processing time increases the clinical value of true 4D image denoising significantly. PMID:21977020
王玉; 王宏; 黄海龙
2012-01-01
The application of GPGPU model of NVIDIA in dose calculation based on CUDA program technique was studied.It was the first time that GPGPU method was used in commercial 3D radiation therapy planning system based on point kernel convolution/superposition model.The old dose calculation model was modified in order to make it be used in device for the parallel computing.At the same time the MFC DLL and export class technique was used to avoid mass code migrating work.The most efficient number of parallel threads was determined by analyzing the result data.The results showed that the dose calculation speed is improved remarkably and the single beam computing time is less than one second using GPU speedup method,which improved the competition ability of the product.%基于CUDA编程技术,研究了如何将NVIDIA的GPGPU模型应用于剂量计算,并首次将该技术应用于基于点核卷积/迭加模型的三维放射治疗计划系统商业化产品.本工作对原有剂量计算模型做了改进,使其可以在device端进行并行处理.在程序架构设计中使用MFC导出类及动态库技术,避免了大量代码移植工作.对结果数据进行了比较与分析,确定了基于特定显卡效率最高的thread数目.结果表明：基于实际患者计划数据执行结果的评估,采用GPU技术加速,大大提高了系统剂量计算速度,使射野剂量计算速度在1 s以内,大大增强了产品市场竞争力.
Quantum mechanical streamlines. I - Square potential barrier
Hirschfelder, J. O.; Christoph, A. C.; Palke, W. E.
1974-01-01
Exact numerical calculations are made for scattering of quantum mechanical particles hitting a square two-dimensional potential barrier (an exact analog of the Goos-Haenchen optical experiments). Quantum mechanical streamlines are plotted and found to be smooth and continuous, to have continuous first derivatives even through the classical forbidden region, and to form quantized vortices around each of the nodal points. A comparison is made between the present numerical calculations and the stationary wave approximation, and good agreement is found between both the Goos-Haenchen shifts and the reflection coefficients. The time-independent Schroedinger equation for real wavefunctions is reduced to solving a nonlinear first-order partial differential equation, leading to a generalization of the Prager-Hirschfelder perturbation scheme. Implications of the hydrodynamical formulation of quantum mechanics are discussed, and cases are cited where quantum and classical mechanical motions are identical.
A streamlined failure mode and effects analysis
Ford, Eric C., E-mail: eford@uw.edu; Smith, Koren; Terezakis, Stephanie; Croog, Victoria; Gollamudi, Smitha; Gage, Irene; Keck, Jordie; DeWeese, Theodore; Sibley, Greg [Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287 (United States)
2014-06-15
Purpose: Explore the feasibility and impact of a streamlined failure mode and effects analysis (FMEA) using a structured process that is designed to minimize staff effort. Methods: FMEA for the external beam process was conducted at an affiliate radiation oncology center that treats approximately 60 patients per day. A structured FMEA process was developed which included clearly defined roles and goals for each phase. A core group of seven people was identified and a facilitator was chosen to lead the effort. Failure modes were identified and scored according to the FMEA formalism. A risk priority number,RPN, was calculated and used to rank failure modes. Failure modes with RPN > 150 received safety improvement interventions. Staff effort was carefully tracked throughout the project. Results: Fifty-two failure modes were identified, 22 collected during meetings, and 30 from take-home worksheets. The four top-ranked failure modes were: delay in film check, missing pacemaker protocol/consent, critical structures not contoured, and pregnant patient simulated without the team's knowledge of the pregnancy. These four failure modes hadRPN > 150 and received safety interventions. The FMEA was completed in one month in four 1-h meetings. A total of 55 staff hours were required and, additionally, 20 h by the facilitator. Conclusions: Streamlined FMEA provides a means of accomplishing a relatively large-scale analysis with modest effort. One potential value of FMEA is that it potentially provides a means of measuring the impact of quality improvement efforts through a reduction in risk scores. Future study of this possibility is needed.
Parallel fuzzy connected image segmentation on GPU
Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.
2011-01-01
Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037
High performance GPU processing for inversion using uniform grid searches
Venetis, Ioannis E.; Saltogianni, Vasso; Stiros, Stathis; Gallopoulos, Efstratios
2017-04-01
both platforms, and execution time as a function of the grid dimension for each problem was recorded. Results indicate an average speedup in calculations by a factor of 100 on the GPU platform; for example problems with 1012 grid-points require less than two hours instead of several days on conventional desktop computers. Such a speedup encourages the application of TOPINV on high performance platforms, as a GPU, in cases where nearly real time decisions are necessary, for example finite fault modeling to identify possible tsunami sources.
Streamline curvature and bed resistance in shallow water flow
De Vriend, H.J.
1979-01-01
The relationship between streamline curvature and bed resistance in shallow water flow with little side constraint, as derived in 1970 by H.J. Schoemaker, is reconsidered. Schoemaker concluded that the bed resistance causes the curvature of a free streamline to grow exponentially with the distance a
Parallel Implementation of Color Based Image Retrieval Using CUDA on the GPU
Hadis Heidari
2013-12-01
Full Text Available Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In huge image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. Graphical Processors Units (GPU is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement color based image retrieval system in parallel using Compute Unified Device Architecture (CUDA programming model to run on GPU. The main goal of this research work is to parallelize the process of color based image retrieval through color moments; also whole process is much faster than normal. Our work uses extensive usage of highly multithreaded architecture of multi-cored GPU. An efficient use of shared memory is needed to optimize parallel reduction in CUDA. We evaluated the retrieval of the proposed technique using Recall, Precision, and Average Precision measures. Experimental results showed that parallel implementation led to an average speed up of 6.305×over the serial implementation when running on a NVIDIA GPU GeForce 610M. The average Precision and the average Recall of presented method are 53.84% and 55.00% respectively.
Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
Yi-Shan Lin
2017-01-01
Full Text Available Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU or the graphic processing unit (GPU were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA. In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.
Graphics Processing Unit (GPU) Acceleration of the Goddard Earth Observing System Atmospheric Model
Putnam, Williama
2011-01-01
The Goddard Earth Observing System 5 (GEOS-5) is the atmospheric model used by the Global Modeling and Assimilation Office (GMAO) for a variety of applications, from long-term climate prediction at relatively coarse resolution, to data assimilation and numerical weather prediction, to very high-resolution cloud-resolving simulations. GEOS-5 is being ported to a graphics processing unit (GPU) cluster at the NASA Center for Climate Simulation (NCCS). By utilizing GPU co-processor technology, we expect to increase the throughput of GEOS-5 by at least an order of magnitude, and accelerate the process of scientific exploration across all scales of global modeling, including: The large-scale, high-end application of non-hydrostatic, global, cloud-resolving modeling at 10- to I-kilometer (km) global resolutions Intermediate-resolution seasonal climate and weather prediction at 50- to 25-km on small clusters of GPUs Long-range, coarse-resolution climate modeling, enabled on a small box of GPUs for the individual researcher After being ported to the GPU cluster, the primary physics components and the dynamical core of GEOS-5 have demonstrated a potential speedup of 15-40 times over conventional processor cores. Performance improvements of this magnitude reduce the required scalability of 1-km, global, cloud-resolving models from an unfathomable 6 million cores to an attainable 200,000 GPU-enabled cores.
permGPU: Using graphics processing units in RNA microarray association studies
George Stephen L
2010-06-01
Full Text Available Abstract Background Many analyses of microarray association studies involve permutation, bootstrap resampling and cross-validation, that are ideally formulated as embarrassingly parallel computing problems. Given that these analyses are computationally intensive, scalable approaches that can take advantage of multi-core processor systems need to be developed. Results We have developed a CUDA based implementation, permGPU, that employs graphics processing units in microarray association studies. We illustrate the performance and applicability of permGPU within the context of permutation resampling for a number of test statistics. An extensive simulation study demonstrates a dramatic increase in performance when using permGPU on an NVIDIA GTX 280 card compared to an optimized C/C++ solution running on a conventional Linux server. Conclusions permGPU is available as an open-source stand-alone application and as an extension package for the R statistical environment. It provides a dramatic increase in performance for permutation resampling analysis in the context of microarray association studies. The current version offers six test statistics for carrying out permutation resampling analyses for binary, quantitative and censored time-to-event traits.
Parallel generation of architecture on the GPU
Steinberger, Markus
2014-05-01
In this paper, we present a novel approach for the parallel evaluation of procedural shape grammars on the graphics processing unit (GPU). Unlike previous approaches that are either limited in the kind of shapes they allow, the amount of parallelism they can take advantage of, or both, our method supports state of the art procedural modeling including stochasticity and context-sensitivity. To increase parallelism, we explicitly express independence in the grammar, reduce inter-rule dependencies required for context-sensitive evaluation, and introduce intra-rule parallelism. Our rule scheduling scheme avoids unnecessary back and forth between CPU and GPU and reduces round trips to slow global memory by dynamically grouping rules in on-chip shared memory. Our GPU shape grammar implementation is multiple orders of magnitude faster than the standard in CPU-based rule evaluation, while offering equal expressive power. In comparison to the state of the art in GPU shape grammar derivation, our approach is nearly 50 times faster, while adding support for geometric context-sensitivity. © 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
Graph coarsening and clustering on the GPU
Fagginger Auer, B.O.; Bisseling, R.H.
2013-01-01
Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph co
Fully 3D GPU PET reconstruction
Herraiz, J.L., E-mail: joaquin@nuclear.fis.ucm.es [Grupo de Fisica Nuclear, Departmento Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Espana, S. [Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States); Cal-Gonzalez, J. [Grupo de Fisica Nuclear, Departmento Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain); Vaquero, J.J. [Departmento de Bioingenieria e Ingenieria Espacial, Universidad Carlos III, Madrid (Spain); Desco, M. [Departmento de Bioingenieria e Ingenieria Espacial, Universidad Carlos III, Madrid (Spain); Unidad de Medicina y Cirugia Experimental, Hospital General Universitario Gregorio Maranon, Madrid (Spain); Udias, J.M. [Grupo de Fisica Nuclear, Departmento Fisica Atomica, Molecular y Nuclear, Universidad Complutense de Madrid (Spain)
2011-08-21
Fully 3D iterative tomographic image reconstruction is computationally very demanding. Graphics Processing Unit (GPU) has been proposed for many years as potential accelerators in complex scientific problems, but it has not been used until the recent advances in the programmability of GPUs that the best available reconstruction codes have started to be implemented to be run on GPUs. This work presents a GPU-based fully 3D PET iterative reconstruction software. This new code may reconstruct sinogram data from several commercially available PET scanners. The most important and time-consuming parts of the code, the forward and backward projection operations, are based on an accurate model of the scanner obtained with the Monte Carlo code PeneloPET and they have been massively parallelized on the GPU. For the PET scanners considered, the GPU-based code is more than 70 times faster than a similar code running on a single core of a fast CPU, obtaining in both cases the same images. The code has been designed to be easily adapted to reconstruct sinograms from any other PET scanner, including scanner prototypes.
GPU Accelerated Surgical Simulators for Complex Morhpology
Mosegaard, Jesper; Sørensen, Thomas Sangild
2005-01-01
a springmass system in order to simulate a complex organ such as the heart. Computations are accelerated by taking advantage of modern graphics processing units (GPUs). Two GPU implementations are presented. They vary in their generality of spring connections and in the speedup factor they achieve...
Graph coarsening and clustering on the GPU
Fagginger Auer, B.O.; Bisseling, R.H.
2013-01-01
Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph co
Synthetic Aperture Beamformation using the GPU
Hansen, Jens Munk; Schaa, Dana; Jensen, Jørgen Arendt
2011-01-01
A synthetic aperture ultrasound beamformer is implemented for a GPU using the OpenCL framework. The implementation supports beamformation of either RF signals or complex baseband signals. Transmit and receive apodization can be either parametric or dynamic using a fixed F-number, a reference, and...... workstation with 2 quad-core Xeon-processors....
Using GPU shaders for visualization, part 3.
Bailey, Mike
2013-01-01
GPU shaders aren't just for glossy special effects. Parts 1 and 2 of this discussion looked at using them for point clouds, cutting planes, line integral convolution, and terrain bump-mapping. Part 3 covers compute shaders and shader storage buffer objects-two features announced as part of OpenGL 4.3.
Graph coarsening and clustering on the GPU
Fagginger Auer, B.O.|info:eu-repo/dai/nl/326659072; Bisseling, R.H.|info:eu-repo/dai/nl/304828068
2013-01-01
Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph
Streamlined analysis of lactose-free dairy products.
Morlock, Gertrud E; Morlock, Lauritz P; Lemo, Carot
2014-01-10
Functional food for lactose-intolerant consumers and its global prevalence has created a large market for commercially available lactose-free food products. The simplest approach for detection and quantitation of lactose in lactose-free dairy products was developed. A one-step sample preparation was employed and the resulting 10% sample solution was directly subjected to the chromatographic system. LODs down to 0.04 mg/L were obtained for dairy products by application volumes up to 250 μL on a rectangular start zone, which is the lowest LOD reported in matrix so far. The highly matrix-robust, streamlined approach was demonstrated for a broad range of dairy products, even with high fat and protein contents. The mean recovery rate for 11 types of dairy products spiked at the strictest lactose content discussed (0.01%) was 90.5±10.5% (n=11). The mean repeatability for 11 dairy products spiked at the 0.01% level was 1.3±1.0% (n=11). It is the simplest approach with regard to sample preparation at low running costs (0.3 Euro or 0.4 USD/analysis) and fast analysis time (3 min/analysis). It enabled an efficient product screening, and at the same time, the quantitation of lactose in relevant samples. This streamlined analysis is highly attractive to the field of food safety and quality control of lactose-free dairy products, for which a limit value for lactose is expected soon in the EU. This methodological concept can be transferred to other challenging fields.
Streamlined bioreactor-based production of human cartilage tissues.
Tonnarelli, B; Santoro, R; Adelaide Asnaghi, M; Wendt, D
2016-05-27
Engineered tissue grafts have been manufactured using methods based predominantly on traditional labour-intensive manual benchtop techniques. These methods impart significant regulatory and economic challenges, hindering the successful translation of engineered tissue products to the clinic. Alternatively, bioreactor-based production systems have the potential to overcome such limitations. In this work, we present an innovative manufacturing approach to engineer cartilage tissue within a single bioreactor system, starting from freshly isolated human primary chondrocytes, through the generation of cartilaginous tissue grafts. The limited number of primary chondrocytes that can be isolated from a small clinically-sized cartilage biopsy could be seeded and extensively expanded directly within a 3D scaffold in our perfusion bioreactor (5.4 ± 0.9 doublings in 2 weeks), bypassing conventional 2D expansion in flasks. Chondrocytes expanded in 3D scaffolds better maintained a chondrogenic phenotype than chondrocytes expanded on plastic flasks (collagen type II mRNA, 18-fold; Sox-9, 11-fold). After this "3D expansion" phase, bioreactor culture conditions were changed to subsequently support chondrogenic differentiation for two weeks. Engineered tissues based on 3D-expanded chondrocytes were more cartilaginous than tissues generated from chondrocytes previously expanded in flasks. We then demonstrated that this streamlined bioreactor-based process could be adapted to effectively generate up-scaled cartilage grafts in a size with clinical relevance (50 mm diameter). Streamlined and robust tissue engineering processes, as the one described here, may be key for the future manufacturing of grafts for clinical applications, as they facilitate the establishment of compact and closed bioreactor-based production systems, with minimal automation requirements, lower operating costs, and increased compliance to regulatory guidelines.
Heterogeneous Highly Parallel Implementation of Matrix Exponentiation Using GPU
Raja, Chittampally Vasanth; Raghavendra, Prakash S; 10.5121/ijdps.2012.3209
2012-01-01
The vision of super computer at every desk can be realized by powerful and highly parallel CPUs or GPUs or APUs. Graphics processors once specialized for the graphics applications only, are now used for the highly computational intensive general purpose applications. Very expensive GFLOPs and TFLOP performance has become very cheap with the GPGPUs. Current work focuses mainly on the highly parallel implementation of Matrix Exponentiation. Matrix Exponentiation is widely used in many areas of scientific community ranging from highly critical flight, CAD simulations to financial, statistical applications. Proposed solution for Matrix Exponentiation uses OpenCL for exploiting the hyper parallelism offered by the many core GPGPUs. It employs many general GPU optimizations and architectural specific optimizations. This experimentation covers the optimizations targeted specific to the Scientific Graphics cards (Tesla-C2050). Heterogeneous Highly Parallel Matrix Exponentiation method has been tested for matrices of ...
Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas.
Labra, Nicole; Guevara, Pamela; Duclap, Delphine; Houenou, Josselin; Poupon, Cyril; Mangin, Jean-François; Figueroa, Miguel
2017-01-01
This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.
Accelerated GPU based SPECT Monte Carlo simulations
Garcia, Marie-Paule; Bert, Julien; Benoit, Didier; Bardiès, Manuel; Visvikis, Dimitris
2016-06-01
Monte Carlo (MC) modelling is widely used in the field of single photon emission computed tomography (SPECT) as it is a reliable technique to simulate very high quality scans. This technique provides very accurate modelling of the radiation transport and particle interactions in a heterogeneous medium. Various MC codes exist for nuclear medicine imaging simulations. Recently, new strategies exploiting the computing capabilities of graphical processing units (GPU) have been proposed. This work aims at evaluating the accuracy of such GPU implementation strategies in comparison to standard MC codes in the context of SPECT imaging. GATE was considered the reference MC toolkit and used to evaluate the performance of newly developed GPU Geant4-based Monte Carlo simulation (GGEMS) modules for SPECT imaging. Radioisotopes with different photon energies were used with these various CPU and GPU Geant4-based MC codes in order to assess the best strategy for each configuration. Three different isotopes were considered: 99m Tc, 111In and 131I, using a low energy high resolution (LEHR) collimator, a medium energy general purpose (MEGP) collimator and a high energy general purpose (HEGP) collimator respectively. Point source, uniform source, cylindrical phantom and anthropomorphic phantom acquisitions were simulated using a model of the GE infinia II 3/8" gamma camera. Both simulation platforms yielded a similar system sensitivity and image statistical quality for the various combinations. The overall acceleration factor between GATE and GGEMS platform derived from the same cylindrical phantom acquisition was between 18 and 27 for the different radioisotopes. Besides, a full MC simulation using an anthropomorphic phantom showed the full potential of the GGEMS platform, with a resulting acceleration factor up to 71. The good agreement with reference codes and the acceleration factors obtained support the use of GPU implementation strategies for improving computational efficiency
Accelerated GPU based SPECT Monte Carlo simulations.
Garcia, Marie-Paule; Bert, Julien; Benoit, Didier; Bardiès, Manuel; Visvikis, Dimitris
2016-06-07
Monte Carlo (MC) modelling is widely used in the field of single photon emission computed tomography (SPECT) as it is a reliable technique to simulate very high quality scans. This technique provides very accurate modelling of the radiation transport and particle interactions in a heterogeneous medium. Various MC codes exist for nuclear medicine imaging simulations. Recently, new strategies exploiting the computing capabilities of graphical processing units (GPU) have been proposed. This work aims at evaluating the accuracy of such GPU implementation strategies in comparison to standard MC codes in the context of SPECT imaging. GATE was considered the reference MC toolkit and used to evaluate the performance of newly developed GPU Geant4-based Monte Carlo simulation (GGEMS) modules for SPECT imaging. Radioisotopes with different photon energies were used with these various CPU and GPU Geant4-based MC codes in order to assess the best strategy for each configuration. Three different isotopes were considered: (99m) Tc, (111)In and (131)I, using a low energy high resolution (LEHR) collimator, a medium energy general purpose (MEGP) collimator and a high energy general purpose (HEGP) collimator respectively. Point source, uniform source, cylindrical phantom and anthropomorphic phantom acquisitions were simulated using a model of the GE infinia II 3/8" gamma camera. Both simulation platforms yielded a similar system sensitivity and image statistical quality for the various combinations. The overall acceleration factor between GATE and GGEMS platform derived from the same cylindrical phantom acquisition was between 18 and 27 for the different radioisotopes. Besides, a full MC simulation using an anthropomorphic phantom showed the full potential of the GGEMS platform, with a resulting acceleration factor up to 71. The good agreement with reference codes and the acceleration factors obtained support the use of GPU implementation strategies for improving computational
CPU and GPU (Cuda Template Matching Comparison
Evaldas Borcovas
2014-05-01
Full Text Available Image processing, computer vision or other complicated opticalinformation processing algorithms require large resources. It isoften desired to execute algorithms in real time. It is hard tofulfill such requirements with single CPU processor. NVidiaproposed CUDA technology enables programmer to use theGPU resources in the computer. Current research was madewith Intel Pentium Dual-Core T4500 2.3 GHz processor with4 GB RAM DDR3 (CPU I, NVidia GeForce GT320M CUDAcompliable graphics card (GPU I and Intel Core I5-2500K3.3 GHz processor with 4 GB RAM DDR3 (CPU II, NVidiaGeForce GTX 560 CUDA compatible graphic card (GPU II.Additional libraries as OpenCV 2.1 and OpenCV 2.4.0 CUDAcompliable were used for the testing. Main test were made withstandard function MatchTemplate from the OpenCV libraries.The algorithm uses a main image and a template. An influenceof these factors was tested. Main image and template have beenresized and the algorithm computing time and performancein Gtpix/s have been measured. According to the informationobtained from the research GPU computing using the hardwarementioned earlier is till 24 times faster when it is processing abig amount of information. When the images are small the performanceof CPU and GPU are not significantly different. Thechoice of the template size makes influence on calculating withCPU. Difference in the computing time between the GPUs canbe explained by the number of cores which they have.
Porting AMG2013 to Heterogeneous CPU+GPU Nodes
Samfass, Philipp [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-01-26
LLNL's future advanced technology system SIERRA will feature heterogeneous compute nodes that consist of IBM PowerV9 CPUs and NVIDIA Volta GPUs. Conceptually, the motivation for such an architecture is quite straightforward: While GPUs are optimized for throughput on massively parallel workloads, CPUs strive to minimize latency for rather sequential operations. Yet, making optimal use of heterogeneous architectures raises new challenges for the development of scalable parallel software, e.g., with respect to work distribution. Porting LLNL's parallel numerical libraries to upcoming heterogeneous CPU+GPU architectures is therefore a critical factor for ensuring LLNL's future success in ful lling its national mission. One of these libraries, called HYPRE, provides parallel solvers and precondi- tioners for large, sparse linear systems of equations. In the context of this intern- ship project, I consider AMG2013 which is a proxy application for major parts of HYPRE that implements a benchmark for setting up and solving di erent systems of linear equations. In the following, I describe in detail how I ported multiple parts of AMG2013 to the GPU (Section 2) and present results for di erent experiments that demonstrate a successful parallel implementation on the heterogeneous ma- chines surface and ray (Section 3). In Section 4, I give guidelines on how my code should be used. Finally, I conclude and give an outlook for future work (Section 5).
Cloud GPU-based simulations for SQUAREMR
Kantasis, George; Xanthis, Christos G.; Haris, Kostas; Heiberg, Einar; Aletras, Anthony H.
2017-01-01
Quantitative Magnetic Resonance Imaging (MRI) is a research tool, used more and more in clinical practice, as it provides objective information with respect to the tissues being imaged. Pixel-wise T1 quantification (T1 mapping) of the myocardium is one such application with diagnostic significance. A number of mapping sequences have been developed for myocardial T1 mapping with a wide range in terms of measurement accuracy and precision. Furthermore, measurement results obtained with these pulse sequences are affected by errors introduced by the particular acquisition parameters used. SQUAREMR is a new method which has the potential of improving the accuracy of these mapping sequences through the use of massively parallel simulations on Graphical Processing Units (GPUs) by taking into account different acquisition parameter sets. This method has been shown to be effective in myocardial T1 mapping; however, execution times may exceed 30 min which is prohibitively long for clinical applications. The purpose of this study was to accelerate the construction of SQUAREMR's multi-parametric database to more clinically acceptable levels. The aim of this study was to develop a cloud-based cluster in order to distribute the computational load to several GPU-enabled nodes and accelerate SQUAREMR. This would accommodate high demands for computational resources without the need for major upfront equipment investment. Moreover, the parameter space explored by the simulations was optimized in order to reduce the computational load without compromising the T1 estimates compared to a non-optimized parameter space approach. A cloud-based cluster with 16 nodes resulted in a speedup of up to 13.5 times compared to a single-node execution. Finally, the optimized parameter set approach allowed for an execution time of 28 s using the 16-node cluster, without compromising the T1 estimates by more than 10 ms. The developed cloud-based cluster and optimization of the parameter set reduced
Govender, Nicolin
2015-09-01
Full Text Available consideration due to the architectural differences between CPU and GPU platforms. This paper describes the DEM algorithms and heuristics that are optimized for the parallel NVIDIA Kepler GPU architecture in detail. This includes a GPU optimized collision...
Valdez-Balderas, Daniel; Rogers, Benedict D; Crespo, Alejandro J C
2012-01-01
Starting from the single graphics processing unit (GPU) version of the Smoothed Particle Hydrodynamics (SPH) code DualSPHysics, a multi-GPU SPH program is developed for free-surface flows. The approach is based on a spatial decomposition technique, whereby different portions (sub-domains) of the physical system under study are assigned to different GPUs. Communication between devices is achieved with the use of Message Passing Interface (MPI) application programming interface (API) routines. The use of the sorting algorithm radix sort for inter-GPU particle migration and sub-domain halo building (which enables interaction between SPH particles of different subdomains) is described in detail. With the resulting scheme it is possible, on the one hand, to carry out simulations that could also be performed on a single GPU, but they can now be performed even faster than on one of these devices alone. On the other hand, accelerated simulations can be performed with up to 32 million particles on the current architec...
Accelerating Satellite Image Based Large-Scale Settlement Detection with GPU
Patlolla, Dilip Reddy [ORNL; Cheriyadat, Anil M [ORNL; Weaver, Jeanette E [ORNL; Bright, Eddie A [ORNL
2012-01-01
Computer vision algorithms for image analysis are often computationally demanding. Application of such algorithms on large image databases\\---- such as the high-resolution satellite imagery covering the entire land surface, can easily saturate the computational capabilities of conventional CPUs. There is a great demand for vision algorithms running on high performance computing (HPC) architecture capable of processing petascale image data. We exploit the parallel processing capability of GPUs to present a GPU-friendly algorithm for robust and efficient detection of settlements from large-scale high-resolution satellite imagery. Feature descriptor generation is an expensive, but a key step in automated scene analysis. To address this challenge, we present GPU implementations for three different feature descriptors\\-- multiscale Historgram of Oriented Gradients (HOG), Gray Level Co-Occurrence Matrix (GLCM) Contrast and local pixel intensity statistics. We perform extensive experimental evaluations of our implementation using diverse and large image datasets. Our GPU implementation of the feature descriptor algorithms results in speedups of 220 times compared to the CPU version. We present an highly efficient settlement detection system running on a multiGPU architecture capable of extracting human settlement regions from a city-scale sub-meter spatial resolution aerial imagery spanning roughly 1200 sq. kilometers in just 56 seconds with detection accuracy close to 90\\%. This remarkable speedup gained by our vision algorithm maintaining high detection accuracy clearly demonstrates that such computational advancements clearly hold the solution for petascale image analysis challenges.
Accelerating image reconstruction in dual-head PET system by GPU and symmetry properties.
Cheng-Ying Chou
Full Text Available Positron emission tomography (PET is an important imaging modality in both clinical usage and research studies. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads, which produce more than 224 million lines of response and thus request dramatic computational demands. In this work, we employed a state-of-the-art graphics processing unit (GPU, NVIDIA Tesla C2070, to yield an efficient reconstruction process. Our approaches ingeniously integrate the distinguished features of the symmetry properties of the imaging system and GPU architectures, including block/warp/thread assignments and effective memory usage, to accelerate the computations for ordered subset expectation maximization (OSEM image reconstruction. The OSEM reconstruction algorithms were implemented employing both CPU-based and GPU-based codes, and their computational performance was quantitatively analyzed and compared. The results showed that the GPU-accelerated scheme can drastically reduce the reconstruction time and thus can largely expand the applicability of the dual-head PET system.
Chi, Yujie; Tian, Zhen; Jia, Xun
2016-08-01
Monte Carlo (MC) particle transport simulation on a graphics-processing unit (GPU) platform has been extensively studied recently due to the efficiency advantage achieved via massive parallelization. Almost all of the existing GPU-based MC packages were developed for voxelized geometry. This limited application scope of these packages. The purpose of this paper is to develop a module to model parametric geometry and integrate it in GPU-based MC simulations. In our module, each continuous region was defined by its bounding surfaces that were parameterized by quadratic functions. Particle navigation functions in this geometry were developed. The module was incorporated to two previously developed GPU-based MC packages and was tested in two example problems: (1) low energy photon transport simulation in a brachytherapy case with a shielded cylinder applicator and (2) MeV coupled photon/electron transport simulation in a phantom containing several inserts of different shapes. In both cases, the calculated dose distributions agreed well with those calculated in the corresponding voxelized geometry. The averaged dose differences were 1.03% and 0.29%, respectively. We also used the developed package to perform simulations of a Varian VS 2000 brachytherapy source and generated a phase-space file. The computation time under the parameterized geometry depended on the memory location storing the geometry data. When the data was stored in GPU's shared memory, the highest computational speed was achieved. Incorporation of parameterized geometry yielded a computation time that was ~3 times of that in the corresponding voxelized geometry. We also developed a strategy to use an auxiliary index array to reduce frequency of geometry calculations and hence improve efficiency. With this strategy, the computational time ranged in 1.75-2.03 times of the voxelized geometry for coupled photon/electron transport depending on the voxel dimension of the auxiliary index array, and in 0
A Versatile and Efficient GPU Data Structure for Spatial Indexing
Schneider, Jens
2016-08-10
In this paper we present a novel GPU-based data structure for spatial indexing. Based on Fenwick trees—a special type of binary indexed trees—our data structure allows construction in linear time. Updates and prefixes can be computed in logarithmic time, whereas point queries require only constant time on average. Unlike competing data structures such as summed-area tables and spatial hashing, our data structure requires a constant amount of bits for each data element, and it offers unconstrained point queries. This property makes our data structure ideally suited for applications requiring unconstrained indexing of large data, such as block-storage of large and block-sparse volumes. Finally, we provide asymptotic bounds on both run-time and memory requirements, and we show applications for which our new data structure is useful.
Streamlining HIV Testing for HIV Preexposure Prophylaxis
Leigler, Teri; Kallas, Esper; Schechter, Mauro; Sharma, Usha; Glidden, David; Grant, Robert M.
2014-01-01
HIV-testing algorithms for preexposure prophylaxis (PrEP) should be optimized to minimize the risk of drug resistance, the time off PrEP required to evaluate false-positive screening results, and costs and to expedite the start of therapy for those confirmed to be infected. HIV rapid tests (RTs) for anti-HIV antibodies provide results in less than 1 h and can be conducted by nonlicensed staff at the point of care. In many regions, Western blot (WB) testing is required to confirm reactive RT results. WB testing, however, causes delays in diagnosis and adds expense. The iPrEx study evaluated the safety and efficacy of daily oral emtricitabine-tenofovir disoproxil fumarate among HIV-seronegative men and transgender women who have sex with men: HIV infection was assessed with two RTs plus WB confirmation, followed by HIV-1 plasma viral load testing. During the iPrEx study, there were 51,260 HIV status evaluations among 2,499 volunteers using RTs: 142 (0.28%) had concordant positive results (100% were eventually confirmed) and 19 (0.04%) had discordant results among 14 participants; 11 were eventually determined to be HIV infected. A streamlined approach using only one RT to screen and a second RT to confirm (without WB) would have had nearly the same accuracy. Discrepant RT results are best evaluated with nucleic acid testing, which would also increase sensitivity. PMID:25378570
Streamlined expressed protein ligation using split inteins.
Vila-Perelló, Miquel; Liu, Zhihua; Shah, Neel H; Willis, John A; Idoyaga, Juliana; Muir, Tom W
2013-01-09
Chemically modified proteins are invaluable tools for studying the molecular details of biological processes, and they also hold great potential as new therapeutic agents. Several methods have been developed for the site-specific modification of proteins, one of the most widely used being expressed protein ligation (EPL) in which a recombinant α-thioester is ligated to an N-terminal Cys-containing peptide. Despite the widespread use of EPL, the generation and isolation of the required recombinant protein α-thioesters remain challenging. We describe here a new method for the preparation and purification of recombinant protein α-thioesters using engineered versions of naturally split DnaE inteins. This family of autoprocessing enzymes is closely related to the inteins currently used for protein α-thioester generation, but they feature faster kinetics and are split into two inactive polypeptides that need to associate to become active. Taking advantage of the strong affinity between the two split intein fragments, we devised a streamlined procedure for the purification and generation of protein α-thioesters from cell lysates and applied this strategy for the semisynthesis of a variety of proteins including an acetylated histone and a site-specifically modified monoclonal antibody.
GPU accelerated chemical similarity calculation for compound library comparison.
Ma, Chao; Wang, Lirong; Xie, Xiang-Qun
2011-07-25
Chemical similarity calculation plays an important role in compound library design, virtual screening, and "lead" optimization. In this manuscript, we present a novel GPU-accelerated algorithm for all-vs-all Tanimoto matrix calculation and nearest neighbor search. By taking advantage of multicore GPU architecture and CUDA parallel programming technology, the algorithm is up to 39 times superior to the existing commercial software that runs on CPUs. Because of the utilization of intrinsic GPU instructions, this approach is nearly 10 times faster than existing GPU-accelerated sparse vector algorithm, when Unity fingerprints are used for Tanimoto calculation. The GPU program that implements this new method takes about 20 min to complete the calculation of Tanimoto coefficients between 32 M PubChem compounds and 10K Active Probes compounds, i.e., 324G Tanimoto coefficients, on a 128-CUDA-core GPU.
Simulating and Visualizing Real-Time Crowds on GPU Clusters
Benjamín Hernández; Hugo Pérez; Isaac Rudomin; Sergio Ruiz; Oriam de Gyves; Leonel Toledo
2014-01-01
We present a set of algorithms for simulating and visualizing real-time crowds in GPU (Graphics Processing Units) clusters. First we present crowd simulation and rendering techniques that take advantage of single GPU machines. Then, using as an example a wandering crowd behavior simulation algorithm, we explain how this kind of algorithms can be extended for their use in GPU cluster environments. We also present a visualization architecture that renders the simulation results using detailed 3...
Haptic Feedback for the GPU-based Surgical Simulator
Sørensen, Thomas Sangild; Mosegaard, Jesper
2006-01-01
The GPU has proven to be a powerful processor to compute spring-mass based surgical simulations. It has not previously been shown however, how to effectively implement haptic interaction with a simulation running entirely on the GPU. This paper describes a method to calculate haptic feedback...... with limited performance cost. It allows easy balancing of the GPU workload between calculations of simulation, visualisation, and the haptic feedback....
Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm
Gang Mei
2014-01-01
We first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental resul...
Implementing Ultrasound Beamforming on the GPU using CUDA
Grønvold, Lars
2008-01-01
This thesis discusses the implementation of ultrasound beamforming on the GPU using CUDA. Fractional delay filters and the need for it when implementing beamforming is discussed. An introduction to CUDA programming is given as well as a study of the workings of the NVIDIA Tesla GPU(or G80). A number of suggestions for implementing beamforming on a GPU is presented as well as an actual implementation and an evaluation of it's performance.
Large Scale Simulations of the Euler Equations on GPU Clusters
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.
Adaptive Remote Sensing Texture Compression on GPU
Xiao-Xia Lu
2010-11-01
Full Text Available Considering the properties of remote sensing texture such as strong randomness and weak local correlation, a novel adaptive compression method based on vector quantizer is presented and implemented on GPU. Utilizing the property of Human Visual System (HVS, a new similarity measurement function is designed instead of using Euclid distance. Correlated threshold between blocks can be obtained adaptively according to the property of different images without artificial auxiliary. Furthermore, a self-adaptive threshold adjustment during the compression is designed to improve the reconstruct quality. Experiments show that the method can handle various resolution images adaptively. It can achieve satisfied compression rate and reconstruct quality at the same time. Index is coded to further increase the compression rate. The coding way is designed to guarantee accessing the index randomly too. Furthermore, the compression and decompression process is speed up with the usage of GPU, on account of their parallelism.
Implementation of Membrane Algorithms on GPU
Xingyi Zhang
2014-01-01
Full Text Available Membrane algorithms are a new class of parallel algorithms, which attempt to incorporate some components of membrane computing models for designing efficient optimization algorithms, such as the structure of the models and the way of communication between cells. Although the importance of the parallelism of such algorithms has been well recognized, membrane algorithms were usually implemented on the serial computing device central processing unit (CPU, which makes the algorithms unable to work in an efficient way. In this work, we consider the implementation of membrane algorithms on the parallel computing device graphics processing unit (GPU. In such implementation, all cells of membrane algorithms can work simultaneously. Experimental results on two classical intractable problems, the point set matching problem and TSP, show that the GPU implementation of membrane algorithms is much more efficient than CPU implementation in terms of runtime, especially for solving problems with a high complexity.
Solving global optimization problems on GPU cluster
Barkalov, Konstantin; Gergel, Victor; Lebedev, Ilya
2016-06-01
The paper contains the results of investigation of a parallel global optimization algorithm combined with a dimension reduction scheme. This allows solving multidimensional problems by means of reducing to data-independent subproblems with smaller dimension solved in parallel. The new element implemented in the research consists in using several graphic accelerators at different computing nodes. The paper also includes results of solving problems of well-known multiextremal test class GKLS on Lobachevsky supercomputer using tens of thousands of GPU cores.
Providing Source Code Level Portability Between CPU and GPU with MapCG
Chun-Tao Hong; De-Hao Chen; Yu-Bei Chen; Wen-Guang Chen; Wei-Min Zheng; Hai-Bo Lin
2012-01-01
Graphics processing units (GPU) have taken an important role in the general purpose computing market in recent years.At present,the common approach to programming GPU units is to write GPU specific code with low level GPU APIs such as CUDA.Although this approach can achieve good performance,it creates serious portability issues as programmers are required to write a specific version of the code for each potential target architecture.This results in high development and maintenance costs.We believe it is desirable to have a programming model which provides source code portability between CPUs and GPUs,as well as different GPUs.This would allow programmers to write one version of the code,which can be compiled and executed on either CPUs or GPUs efficiently without modification.In this paper,we propose MapCG,a MapReduce framework to provide source code level portability between CPUs and GPUs.In contrast to other approaches such as OpenCL,our framework,based on MapReduce,provides a high level programming model and makes programming much easier.We describe the design of MapCG,including the MapReduce-style high-level programming framework and the runtime system on the CPU and GPU.A prototype of the MapCG runtime,supporting multi-core CPUs and NVIDIA GPUs,was implemented. Our experimental results show that this implementation can execute the same source code efficiently on multi-core CPU platforms and GPUs,achieving an average speedup of 1.6～2.5x over previous implementations of MapReduce on eight commonly used applications.
GPU-based fast Monte Carlo simulation for radiotherapy dose calculation.
Jia, Xun; Gu, Xuejun; Graves, Yan Jiang; Folkerts, Michael; Jiang, Steve B
2011-11-21
Monte Carlo (MC) simulation is commonly considered to be the most accurate dose calculation method in radiotherapy. However, its efficiency still requires improvement for many routine clinical applications. In this paper, we present our recent progress toward the development of a graphics processing unit (GPU)-based MC dose calculation package, gDPM v2.0. It utilizes the parallel computation ability of a GPU to achieve high efficiency, while maintaining the same particle transport physics as in the original dose planning method (DPM) code and hence the same level of simulation accuracy. In GPU computing, divergence of execution paths between threads can considerably reduce the efficiency. Since photons and electrons undergo different physics and hence attain different execution paths, we use a simulation scheme where photon transport and electron transport are separated to partially relieve the thread divergence issue. A high-performance random number generator and a hardware linear interpolation are also utilized. We have also developed various components to handle the fluence map and linac geometry, so that gDPM can be used to compute dose distributions for realistic IMRT or VMAT treatment plans. Our gDPM package is tested for its accuracy and efficiency in both phantoms and realistic patient cases. In all cases, the average relative uncertainties are less than 1%. A statistical t-test is performed and the dose difference between the CPU and the GPU results is not found to be statistically significant in over 96% of the high dose region and over 97% of the entire region. Speed-up factors of 69.1 ∼ 87.2 have been observed using an NVIDIA Tesla C2050 GPU card against a 2.27 GHz Intel Xeon CPU processor. For realistic IMRT and VMAT plans, MC dose calculation can be completed with less than 1% standard deviation in 36.1 ∼ 39.6 s using gDPM.
High energy electromagnetic particle transportation on the GPU
Canal, P.; Elvira, D.; Jun, S. Y.; Kowalkowski, J.; Paterno, M.; Apostolakis, J.
2014-06-01
We present massively parallel high energy electromagnetic particle transportation through a finely segmented detector on a Graphics Processing Unit (GPU). Simulating events of energetic particle decay in a general-purpose high energy physics (HEP) detector requires intensive computing resources, due to the complexity of the geometry as well as physics processes applied to particles copiously produced by primary collisions and secondary interactions. The recent advent of hardware architectures of many-core or accelerated processors provides the variety of concurrent programming models applicable not only for the high performance parallel computing, but also for the conventional computing intensive application such as the HEP detector simulation. The components of our prototype are a transportation process under a non-uniform magnetic field, geometry navigation with a set of solid shapes and materials, electromagnetic physics processes for electrons and photons, and an interface to a framework that dispatches bundles of tracks in a highly vectorized manner optimizing for spatial locality and throughput. Core algorithms and methods are excerpted from the Geant4 toolkit, and are modified and optimized for the GPU application. Program kernels written in C/C++ are designed to be compatible with CUDA and OpenCL and with the aim to be generic enough for easy porting to future programming models and hardware architectures. To improve throughput by overlapping data transfers with kernel execution, multiple CUDA streams are used. Issues with floating point accuracy, random numbers generation, data structure, kernel divergences and register spills are also considered. Performance evaluation for the relative speedup compared to the corresponding sequential execution on CPU is presented as well.
High energy electromagnetic particle transportation on the GPU
Canal, P. [Fermilab; Elvira, D. [Fermilab; Jun, S. Y. [Fermilab; Kowalkowski, J. [Fermilab; Paterno, M. [Fermilab; Apostolakis, J. [CERN
2014-01-01
We present massively parallel high energy electromagnetic particle transportation through a finely segmented detector on a Graphics Processing Unit (GPU). Simulating events of energetic particle decay in a general-purpose high energy physics (HEP) detector requires intensive computing resources, due to the complexity of the geometry as well as physics processes applied to particles copiously produced by primary collisions and secondary interactions. The recent advent of hardware architectures of many-core or accelerated processors provides the variety of concurrent programming models applicable not only for the high performance parallel computing, but also for the conventional computing intensive application such as the HEP detector simulation. The components of our prototype are a transportation process under a non-uniform magnetic field, geometry navigation with a set of solid shapes and materials, electromagnetic physics processes for electrons and photons, and an interface to a framework that dispatches bundles of tracks in a highly vectorized manner optimizing for spatial locality and throughput. Core algorithms and methods are excerpted from the Geant4 toolkit, and are modified and optimized for the GPU application. Program kernels written in C/C++ are designed to be compatible with CUDA and OpenCL and with the aim to be generic enough for easy porting to future programming models and hardware architectures. To improve throughput by overlapping data transfers with kernel execution, multiple CUDA streams are used. Issues with floating point accuracy, random numbers generation, data structure, kernel divergences and register spills are also considered. Performance evaluation for the relative speedup compared to the corresponding sequential execution on CPU is presented as well.
Zhang, Lei; Gao, Jiao Bo; Hu, Yu; Wang, Ying Hui; Sun, Ke Feng; Cheng, Juan; Sun, Dan Dan; Li, Yu
2017-02-01
During the research of hyper-spectral imaging spectrometer, how to process the huge amount of image data is a difficult problem for all researchers. The amount of image data is about the order of magnitude of several hundred megabytes per second. The only way to solve this problem is parallel computing technology. With the development of multi-core CPU and GPU parallel computing on multi-core CPU or GPU is increasingly applied in large-scale data processing. In this paper, we propose a new parallel computing solution of hyper-spectral data processing which is based on the multi-CPU and multi-GPU heterogeneous computing platform. We use OpenMP technology to control multi-core CPU, we also use CUDA to schedule the parallel computing on multi-GPU. Experimental results show that the speed of hyper-spectral data processing on the multi-CPU and multi-GPU heterogeneous computing platform is apparently faster than the traditional serial algorithm which is run on single core CPU. Our research has significant meaning for the engineering application of the windowing Fourier transform imaging spectrometer.
Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS.
Davis, Nicholas A; Pandey, Ahwan; McKinney, B A
2011-01-15
Bioinformatics researchers have a variety of programming languages and architectures at their disposal, and recent advances in graphics processing unit (GPU) computing have added a promising new option. However, many performance comparisons inflate the actual advantages of GPU technology. In this study, we carry out a realistic performance evaluation of SNPrank, a network centrality algorithm that ranks single nucleotide polymorhisms (SNPs) based on their importance in the context of a phenotype-specific interaction network. Our goal is to identify the best computational engine for the SNPrank web application and to provide a variety of well-tested implementations of SNPrank for Bioinformaticists to integrate into their research. Using SNP data from the Wellcome Trust Case Control Consortium genome-wide association study of Bipolar Disorder, we compare multiple SNPrank implementations, including Python, Matlab and Java as well as CPU versus GPU implementations. When compared with naïve, single-threaded CPU implementations, the GPU yields a large improvement in the execution time. However, with comparable effort, multi-threaded CPU implementations negate the apparent advantage of GPU implementations. The SNPrank code is open source and available at http://insilico.utulsa.edu/snprank.
Proposal - Streamline forest data analysis on R4 refuges
US Fish and Wildlife Service, Department of the Interior — Proposal to obtain software to streamline analysis of forested habitat inventories that use parameters from Desired Forest Condition. This would include data upload...
ALICE HLT high speed tracking on GPU
Gorbunov, Sergey; Aamodt, Kenneth; Alt, Torsten; Appelshauser, Harald; Arend, Andreas; Bach, Matthias; Becker, Bruce; Bottger, Stefan; Breitner, Timo; Busching, Henner; Chattopadhyay, Sukalyan; Cleymans, Jean; Cicalo, Corrado; Das, Indranil; Djuvsland, Oystein; Engel, Heiko; Erdal, Hege Austrheim; Fearick, Roger; Haaland, Oystein Senneset; Hille, Per Thomas; Kalcher, Sebastian; Kanaki, Kalliopi; Kebschull, Udo Wolfgang; Kisel, Ivan; Kretz, Matthias; Lara, Camillo; Lindal, Sven; Lindenstruth, Volker; Masoodi, Arshad Ahmad; Ovrebekk, Gaute; Panse, Ralf; Peschek, Jorg; Ploskon, Mateusz; Pocheptsov, Timur; Ram, Dinesh; Rascanu, Theodor; Richter, Matthias; Rohrich, Dieter; Ronchetti, Federico; Skaali, Bernhard; Smorholm, Olav; Stokkevag, Camilla; Steinbeck, Timm Morten; Szostak, Artur; Thader, Jochen; Tveter, Trine; Ullaland, Kjetil; Vilakazi, Zeblon; Weis, Robert; Yin, Zhong-Bao; Zelnicek, Pierre
2011-01-01
The on-line event reconstruction in ALICE is performed by the High Level Trigger, which should process up to 2000 events per second in proton-proton collisions and up to 300 central events per second in heavy-ion collisions, corresponding to an inp ut data stream of 30 GB/s. In order to fulfill the time requirements, a fast on-line tracker has been developed. The algorithm combines a Cellular Automaton method being used for a fast pattern recognition and the Kalman Filter method for fitting of found trajectories and for the final track selection. The tracker was adapted to run on Graphics Processing Units (GPU) using the NVIDIA Compute Unified Device Architecture (CUDA) framework. The implementation of the algorithm had to be adjusted at many points to allow for an efficient usage of the graphics cards. In particular, achieving a good overall workload for many processor cores, efficient transfer to and from the GPU, as well as optimized utilization of the different memories the GPU offers turned out to be cri...
Su, Lin; Du, Xining; Liu, Tianyu; Ji, Wei; Xu, X. George, E-mail: xug2@rpi.edu [Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, New York 12180 (United States); Yang, Youming; Bednarz, Bryan [Medical Physics, University of Wisconsin, Madison, Wisconsin 53706 (United States); Sterpin, Edmond [Molecular Imaging, Radiotherapy and Oncology, Université catholique de Louvain, Brussels, Belgium 1348 (Belgium)
2014-07-15
Purpose: Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHER{sub RT} is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head and neck. Methods: To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHER{sub RT}. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHER{sub RT} and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. Results: For the water phantom, the depth dose curve and dose profiles from ARCHER{sub RT} agree well with DOSXYZnrc. For clinical cases, results from ARCHER{sub RT} are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head and neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to
利用GPU进行通用数值计算的研究%Research on General-Purpose Computation Using GPU
徐品; 蓝善祯; 刘兰兰
2009-01-01
近年来,图形处理器(GPU)的发展日益成熟,应用范围不在局限于计算机图形学本身,已逐步扩展到通用数值计算领域.本文介绍了最新GPU用于通用计算的原理和方法,并在图像处理和科学计算方面对GPU和CPU算法进行了计算速度的对比研究,实验结果表明GPU在通用计算领域相对于CPU具有明显优势.%Recently, the development of Graphics Processing Unit (GPU) has become more and more sophisticated. The scope of application of GPU has been expanded to general purpose com-putations, except from those applications in graphics itself. In this paper, a detail introduction is given to the principles and methods of general purpose computation by GPU, and a comparative study of the calculation speed of GPU and CPU algorithm in image processing and scientific com-puting is made, and the experimental results show that GPU has an obvious advantage in general purpose computing compared with CPU.
Singular value decomposition for collaborative filtering on a GPU
Kato, Kimikazu; Hosino, Tikara
2010-06-01
A collaborative filtering predicts customers' unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an effective algorithm to compute SVD toward a solution of an open competition called "Netflix Prize". The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be efficient by an experiment.
MATCHED FILTER COMPUTATION ON FPGA, CELL, AND GPU
BAKER, ZACHARY K. [Los Alamos National Laboratory; GOKHALE, MAYA B. [Los Alamos National Laboratory; TRIPP, JUSTIN L. [Los Alamos National Laboratory
2007-01-08
The matched filter is an important kernel in the processing of hyperspectral data. The filter enables researchers to sift useful data from instruments that span large frequency bands. In this work, they evaluate the performance of a matched filter algorithm implementation on accelerated co-processor (XD1000), the IBM Cell microprocessor, and the NVIDIA GeForce 6900 GTX GPU graphics card. They provide extensive discussion of the challenges and opportunities afforded by each platform. In particular, they explore the problems of partitioning the filter most efficiently between the host CPU and the co-processor. Using their results, they derive several performance metrics that provide the optimal solution for a variety of application situations.
Random number generators for massively parallel simulations on GPU
Manssen, Markus; Hartmann, Alexander K
2012-01-01
High-performance streams of (pseudo) random numbers are crucial for the efficient implementation for countless stochastic algorithms, most importantly, Monte Carlo simulations and molecular dynamics simulations with stochastic thermostats. A number of implementations of random number generators has been discussed for GPU platforms before and some generators are even included in the CUDA supporting libraries. Nevertheless, not all of these generators are well suited for highly parallel applications where each thread requires its own generator instance. For this specific situation encountered, for instance, in simulations of lattice models, most of the high-quality generators with large states such as Mersenne twister cannot be used efficiently without substantial changes. We provide a broad review of existing CUDA variants of random-number generators and present the CUDA implementation of a new massively parallel high-quality, high-performance generator with a small memory load overhead.
Performance analysis of GPU-accelerated filter-based source finding for HI spectral line image data
Westerlund, Stefan; Harris, Christopher
2015-03-01
Searching for sources of electromagnetic emission in spectral-line radio astronomy interferometric data is a computationally intensive process. Parallel programming techniques and High Performance Computing hardware may be used to improve the computational performance of a source finding program. However, it is desirable to further reduce the processing time of source finding in order to decrease the computational resources required for the task. GPU acceleration is a method that may achieve significant increases in performance for some source finding algorithms, particularly for filtering image data. This work considers the application of GPU acceleration to the task of source finding and the techniques used to achieve the best performance, such as memory management. We also examine the changes in performance, where the algorithms that were GPU accelerated achieved a speedup of around 3.2 times the 12 core per node CPU-only performance, while the program as a whole experienced a speedup of 2.0 times.
Performance Analysis of GPU-Accelerated Filter-Based Source Finding for HI Spectral Line Image Data
Westerlund, Stefan
2015-01-01
Searching for sources of electromagnetic emission in spectral-line radio astronomy interferometric data is a computationally intensive process. Parallel programming techniques and High Performance Computing hardware may be used to improve the computational performance of a source finding program. However, it is desirable to further reduce the processing time of source finding in order to decrease the computational resources required for the task. GPU acceleration is a method that may achieve significant increases in performance for some source finding algorithms, particularly for filtering image data. This work considers the application of GPU acceleration to the task of source finding and the techniques used to achieve the best performance, such as memory management. We also examine the changes in performance, where the algorithms that were GPU accelerated achieved a speedup of around 3.2 times the 12 core per node CPU-only performance, while the program as a whole experienced a speedup of 2.0 times.
Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines
Rodrigues, Antonio Wendell De Oliveira; Dekeyser, Jean-Luc; Menach, Yvonnick Le
2011-01-01
The electrical and electronic engineering has used parallel programming to solve its large scale complex problems for performance reasons. However, as parallel programming requires a non-trivial distribution of tasks and data, developers find it hard to implement their applications effectively. Thus, in order to reduce design complexity, we propose an approach to generate code for hybrid architectures (e.g. CPU + GPU) using OpenCL, an open standard for parallel programming of heterogeneous systems. This approach is based on Model Driven Engineering (MDE) and the MARTE profile, standard proposed by Object Management Group (OMG). The aim is to provide resources to non-specialists in parallel programming to implement their applications. Moreover, thanks to model reuse capacity, we can add/change functionalities or the target architecture. Consequently, this approach helps industries to achieve their time-to-market constraints and confirms by experimental tests, performance improvements using multi-GPU environmen...
Heterogeneous Gpu&Cpu Cluster For High Performance Computing In Cryptography
Michał Marks
2012-01-01
Full Text Available This paper addresses issues associated with distributed computing systems andthe application of mixed GPU&CPU technology to data encryption and decryptionalgorithms. We describe a heterogenous cluster HGCC formed by twotypes of nodes: Intel processor with NVIDIA graphics processing unit and AMDprocessor with AMD graphics processing unit (formerly ATI, and a novel softwareframework that hides the heterogeneity of our cluster and provides toolsfor solving complex scientific and engineering problems. Finally, we present theresults of numerical experiments. The considered case study is concerned withparallel implementations of selected cryptanalysis algorithms. The main goal ofthe paper is to show the wide applicability of the GPU&CPU technology tolarge scale computation and data processing.
A block-wise approximate parallel implementation for ART algorithm on CUDA-enabled GPU.
Fan, Zhongyin; Xie, Yaoqin
2015-01-01
Computed tomography (CT) has been widely used to acquire volumetric anatomical information in the diagnosis and treatment of illnesses in many clinics. However, the ART algorithm for reconstruction from under-sampled and noisy projection is still time-consuming. It is the goal of our work to improve a block-wise approximate parallel implementation for the ART algorithm on CUDA-enabled GPU to make the ART algorithm applicable to the clinical environment. The resulting method has several compelling features: (1) the rays are allotted into blocks, making the rays in the same block parallel; (2) GPU implementation caters to the actual industrial and medical application demand. We test the algorithm on a digital shepp-logan phantom, and the results indicate that our method is more efficient than the existing CPU implementation. The high computation efficiency achieved in our algorithm makes it possible for clinicians to obtain real-time 3D images.
Streamlined Modeling for Characterizing Spacecraft Anomalous Behavior
Klem, B.; Swann, D.
2011-09-01
Anomalous behavior of on-orbit spacecraft can often be detected using passive, remote sensors which measure electro-optical signatures that vary in time and spectral content. Analysts responsible for assessing spacecraft operational status and detecting detrimental anomalies using non-resolved imaging sensors are often presented with various sensing and identification issues. Modeling and measuring spacecraft self emission and reflected radiant intensity when the radiation patterns exhibit a time varying reflective glint superimposed on an underlying diffuse signal contribute to assessment of spacecraft behavior in two ways: (1) providing information on body component orientation and attitude; and, (2) detecting changes in surface material properties due to the space environment. Simple convex and cube-shaped spacecraft, designed to operate without protruding solar panel appendages, may require an enhanced level of preflight characterization to support interpretation of the various physical effects observed during on-orbit monitoring. This paper describes selected portions of the signature database generated using streamlined signature modeling and simulations of basic geometry shapes apparent to non-imaging sensors. With this database, summarization of key observable features for such shapes as spheres, cylinders, flat plates, cones, and cubes in specific spectral bands that include the visible, mid wave, and long wave infrared provide the analyst with input to the decision process algorithms contained in the overall sensing and identification architectures. The models typically utilize baseline materials such as Kapton, paints, aluminum surface end plates, and radiators, along with solar cell representations covering the cylindrical and side portions of the spacecraft. Multiple space and ground-based sensors are assumed to be located at key locations to describe the comprehensive multi-viewing aspect scenarios that can result in significant specular reflection
GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform
Ronglin Jiang
2014-01-01
Full Text Available This paper introduces a (finite difference time domain FDTD code written in Fortran and CUDA for realistic electromagnetic calculations with parallelization methods of Message Passing Interface (MPI and Open Multiprocessing (OpenMP. Since both Central Processing Unit (CPU and Graphics Processing Unit (GPU resources are utilized, a faster execution speed can be reached compared to a traditional pure GPU code. In our experiments, 64 NVIDIA TESLA K20m GPUs and 64 INTEL XEON E5-2670 CPUs are used to carry out the pure CPU, pure GPU, and CPU + GPU tests. Relative to the pure CPU calculations for the same problems, the speedup ratio achieved by CPU + GPU calculations is around 14. Compared to the pure GPU calculations for the same problems, the CPU + GPU calculations have 7.6%–13.2% performance improvement. Because of the small memory size of GPUs, the FDTD problem size is usually very small. However, this code can enlarge the maximum problem size by 25% without reducing the performance of traditional pure GPU code. Finally, using this code, a microstrip antenna array with 16×18 elements is calculated and the radiation patterns are compared with the ones of MoM. Results show that there is a well agreement between them.
GPU in Physics Computation: Case Geant4 Navigation
Seiskari, Otto; Niemi, Tapio
2012-01-01
General purpose computing on graphic processing units (GPU) is a potential method of speeding up scientific computation with low cost and high energy efficiency. We experimented with the particle physics simulation toolkit Geant4 used at CERN to benchmark its geometry navigation functionality on a GPU. The goal was to find out whether Geant4 physics simulations could benefit from GPU acceleration and how difficult it is to modify Geant4 code to run in a GPU. We ported selected parts of Geant4 code to C99 & CUDA and implemented a simple gamma physics simulation utilizing this code to measure efficiency. The performance of the program was tested by running it on two different platforms: NVIDIA GeForce 470 GTX GPU and a 12-core AMD CPU system. Our conclusion was that GPUs can be a competitive alternate for multi-core computers but porting existing software in an efficient way is challenging.
GPU accelerated dynamic functional connectivity analysis for functional MRI data.
Akgün, Devrim; Sakoğlu, Ünal; Esquivel, Johnny; Adinoff, Bryon; Mete, Mutlu
2015-07-01
Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses.
GPU-based ultrafast IMRT plan optimization
Men, Chunhua; Gu, Xuejun; Choi, Dongju; Majumdar, Amitava; Zheng, Ziyi; Mueller, Klaus; Jiang, Steve B.
2009-11-01
The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California, San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity-modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20-40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific nine-field prostate IMRT case with 5 × 5 mm2 beamlet size and 2.5 × 2.5 × 2.5 mm3 voxel size, our GPU implementation takes only 2.8 s to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.
GPU-accelerated adjoint algorithmic differentiation
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2016-03-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the ;tape;. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
Animating streamlines with repeated asymmetric patterns for steady flow visualization
Yeh, Chih-Kuo; Liu, Zhanping; Lee, Tong-Yee
2012-01-01
Animation provides intuitive cueing for revealing essential spatial-temporal features of data in scientific visualization. This paper explores the design of Repeated Asymmetric Patterns (RAPs) in animating evenly-spaced color-mapped streamlines for dense accurate visualization of complex steady flows. We present a smooth cyclic variable-speed RAP animation model that performs velocity (magnitude) integral luminance transition on streamlines. This model is extended with inter-streamline synchronization in luminance varying along the tangential direction to emulate orthogonal advancing waves from a geometry-based flow representation, and then with evenly-spaced hue differing in the orthogonal direction to construct tangential flow streaks. To weave these two mutually dual sets of patterns, we propose an energy-decreasing strategy that adopts an iterative yet efficient procedure for determining the luminance phase and hue of each streamline in HSL color space. We also employ adaptive luminance interleaving in the direction perpendicular to the flow to increase the contrast between streamlines.
CFD Computations on Multi-GPU Configurations.
Menon, Sandeep; Perot, Blair
2007-11-01
Programmable graphics processors have shown favorable potential for use in practical CFD simulations -- often delivering a speed-up factor between 3 to 5 times over conventional CPUs. In recent times, most PCs are supplied with the option of installing multiple GPUs on a single motherboard, thereby providing the option of a parallel GPU configuration in a shared-memory paradigm. We demonstrate our implementation of an unstructured CFD solver using a set up which is configured to run two GPUs in parallel, and discuss its performance details.
An Efficient GPU General Sparse Matrix-Matrix Multiplication for Irregular Data
Liu, Weifeng; Vinter, Brian
2014-01-01
matrices. Recent work on GPU SpGEMM has demonstrated rather good both time and space complexity, but works best for fairly regular matrices. In this work we present a GPU SpGEMM algorithm that particularly focuses on the above three problems. Memory pre-allocation for the result matrix is organized......General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method, breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM algorithm has to handle extra...... irregularity from three aspects: (1) the number of the nonzero entries in the result sparse matrix is unknown in advance, (2) very expensive parallel insert operations at random positions in the result sparse matrix dominate the execution time, and (3) load balancing must account for sparse data in both input...
Real-time, fast radio transient searches with GPU de-dispersion
Magro, A.; Karastergiou, A.; Salvini, S.; Mort, B.; Dulwich, F.; Zarb Adami, K.
2011-11-01
The identification and subsequent discovery of fast radio transients using blind-search surveys require a large amount of processing power, in worst cases scaling as ?. For this reason, survey data are generally processed off-line, using high-performance computing architectures or hardware-based designs. In recent years, graphics processing units (GPUs) have been extensively used for numerical analysis and scientific simulations, especially after the introduction of new high-level application programming interfaces. Here, we show how GPUs can be used for fast transient discovery in real time. We present a solution to the problem of de-dispersion, providing performance comparisons with a typical computing machine and traditional pulsar processing software. We describe the architecture of a real-time, GPU-based transient search machine. In terms of performance, our GPU solution provides a speed-up factor of between 50 and 200, depending on the parameters of the search.
Real-time, fast radio transient searches with GPU de-dispersion
Magro, Alessio; Salvini, Stefano; Mort, Benjamin; Dulwich, Fred; Adami, Kristian Zarb
2011-01-01
The identification, and subsequent discovery, of fast radio transients through blind-search surveys requires a large amount of processing power, in worst cases scaling as $\\mathcal{O}(N^3)$. For this reason, survey data are generally processed offline, using high-performance computing architectures or hardware-based designs. In recent years, graphics processing units have been extensively used for numerical analysis and scientific simulations, especially after the introduction of new high-level application programming interfaces. Here we show how GPUs can be used for fast transient discovery in real-time. We present a solution to the problem of de-dispersion, providing performance comparisons with a typical computing machine and traditional pulsar processing software. We describe the architecture of a real-time, GPU-based transient search machine. In terms of performance, our GPU solution provides a speed-up factor of between 50 and 200, depending on the parameters of the search.
Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach
Quinn, John C
2011-01-01
The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a Graphics Processing Unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 200 times faster than an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.
A Real-Time, GPU-Based, Non-Imaging Back-End for Radio Telescopes
Magro, Alessio
2014-01-01
Since the discovery of RRATs, interest in single pulse radio searches has increased dramatically. Due to the large data volumes generated by these searches, especially in planned surveys for future radio telescopes, such searches have to be conducted in real-time. This has led to the development of a multitude of search techniques and real-time pipeline prototypes. In this work we investigated the applicability of GPUs. We have designed and implemented a scalable, flexibile, GPU-based, transient search pipeline composed of several processing stages, including RFI mitigation, dedispersion, event detection and classification, as well as data quantisation and persistence. These stages are encapsulated as a standalone framework. The optimised GPU implementation of direct dedispersion achieves a speedup of more than an order of magnitude when compared to an optimised CPU implementation. We use a density-based clustering algorithm, coupled with a candidate selection mechanism to group detections caused by the same ...
GPU-based acceleration of an automatic white matter segmentation algorithm using CUDA.
Labra, Nicole; Figueroa, Miguel; Guevara, Pamela; Duclap, Delphine; Hoeunou, Josselin; Poupon, Cyril; Mangin, Jean-Francois
2013-01-01
This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics Processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the memory hierarchy available on the GPU, we obtain a speedup in execution time of 33.6 with respect to an optimized sequential version of the algorithm written in C, and of 240 with respect to the original Python/C++ implementation. The execution time is reduced from more than two hours to only 35 seconds for a subject dataset of 800,000 fibers, thus enabling applications that use interactive segmentation and visualization of small to medium-sized tractography datasets.
Single-Pass GPU-Raycasting for Structured Adaptive Mesh Refinement Data
Kaehler, Ralf
2012-01-01
Structured Adaptive Mesh Refinement (SAMR) is a popular numerical technique to study processes with high spatial and temporal dynamic range. It reduces computational requirements by adapting the lattice on which the underlying differential equations are solved to most efficiently represent the solution. Particularly in astrophysics and cosmology such simulations now can capture spatial scales ten orders of magnitude apart and more. The irregular locations and extensions of the refined regions in the SAMR scheme and the fact that different resolution levels partially overlap, poses a challenge for GPU-based direct volume rendering methods. kD-trees have proven to be advantageous to subdivide the data domain into non-overlapping blocks of equally sized cells, optimal for the texture units of current graphics hardware, but previous GPU-supported raycasting approaches for SAMR data using this data structure required a separate rendering pass for each node, preventing the application of many advanced lighting sche...
Zhmurov, A; Dima, R I; Kholodov, Y; Barsegov, V
2010-11-01
Theoretical exploration of fundamental biological processes involving the forced unraveling of multimeric proteins, the sliding motion in protein fibers and the mechanical deformation of biomolecular assemblies under physiological force loads is challenging even for distributed computing systems. Using a C(α)-based coarse-grained self organized polymer (SOP) model, we implemented the Langevin simulations of proteins on graphics processing units (SOP-GPU program). We assessed the computational performance of an end-to-end application of the program, where all the steps of the algorithm are running on a GPU, by profiling the simulation time and memory usage for a number of test systems. The ∼90-fold computational speedup on a GPU, compared with an optimized central processing unit program, enabled us to follow the dynamics in the centisecond timescale, and to obtain the force-extension profiles using experimental pulling speeds (v(f) = 1-10 μm/s) employed in atomic force microscopy and in optical tweezers-based dynamic force spectroscopy. We found that the mechanical molecular response critically depends on the conditions of force application and that the kinetics and pathways for unfolding change drastically even upon a modest 10-fold increase in v(f). This implies that, to resolve accurately the free energy landscape and to relate the results of single-molecule experiments in vitro and in silico, molecular simulations should be carried out under the experimentally relevant force loads. This can be accomplished in reasonable wall-clock time for biomolecules of size as large as 10(5) residues using the SOP-GPU package.
Chi, Yujie; Tian, Zhen; Jia, Xun
2016-08-01
Monte Carlo (MC) particle transport simulation on a graphics-processing unit (GPU) platform has been extensively studied recently due to the efficiency advantage achieved via massive parallelization. Almost all of the existing GPU-based MC packages were developed for voxelized geometry. This limited application scope of these packages. The purpose of this paper is to develop a module to model parametric geometry and integrate it in GPU-based MC simulations. In our module, each continuous region was defined by its bounding surfaces that were parameterized by quadratic functions. Particle navigation functions in this geometry were developed. The module was incorporated to two previously developed GPU-based MC packages and was tested in two example problems: (1) low energy photon transport simulation in a brachytherapy case with a shielded cylinder applicator and (2) MeV coupled photon/electron transport simulation in a phantom containing several inserts of different shapes. In both cases, the calculated dose distributions agreed well with those calculated in the corresponding voxelized geometry. The averaged dose differences were 1.03% and 0.29%, respectively. We also used the developed package to perform simulations of a Varian VS 2000 brachytherapy source and generated a phase-space file. The computation time under the parameterized geometry depended on the memory location storing the geometry data. When the data was stored in GPU’s shared memory, the highest computational speed was achieved. Incorporation of parameterized geometry yielded a computation time that was ~3 times of that in the corresponding voxelized geometry. We also developed a strategy to use an auxiliary index array to reduce frequency of geometry calculations and hence improve efficiency. With this strategy, the computational time ranged in 1.75-2.03 times of the voxelized geometry for coupled photon/electron transport depending on the voxel dimension of the auxiliary index array, and in 0
Efficient parallelisation techniques for applications running on GPUs using the CUDA framework
Ottesen, Alexander
2009-01-01
Modern graphic processing units (GPU) are powerful parallel processing multi-core devices that are found in most computers today. The increase in processing resources of the GPU, coupled with improvements and flexibility of the programming frameworks, has increased the interest in general purpose programming on theGPU(GPGPU). In this thesis, we investigate how the GPU architecture and its processing capabilities can be utilised in general purpose applications using the NVIDIA c...
Streamline topologies near a fixed wall using normal forms
Hartnack, Johan
1999-01-01
Streamline patterns and their bifurcations in two-dimensional incompressible viscous flow in the vicinity of a fixed wall have been investigated from a topological point of view by Bakker [11]. Bakker's work is revisited in a more general setting allowing a curvature of the fixed wall and a time...... dependence of the streamlines. The velocity field is expanded at a point on the wall, and the expansion coefficients are considered as bifurcation parameters. A series of nonlinear coordinate changes results in a much simplified system of differential equations for the streamlines (a normal form......) encapsulating all the features of the original system. From this, a complete description of bifurcations up to codimension three close to a simple linear degeneracy is obtained. Further, the case of a non-simple degeneracy is considered. Finally the effect of the Navier-Stokes equations on the local topology...
Vision and air flow combine to streamline flying honeybees.
Taylor, Gavin J; Luu, Tien; Ball, David; Srinivasan, Mandyam V
2013-01-01
Insects face the challenge of integrating multi-sensory information to control their flight. Here we study a 'streamlining' response in honeybees, whereby honeybees raise their abdomen to reduce drag. We find that this response, which was recently reported to be mediated by optic flow, is also strongly modulated by the presence of air flow simulating a head wind. The Johnston's organs in the antennae were found to play a role in the measurement of the air speed that is used to control the streamlining response. The response to a combination of visual motion and wind is complex and can be explained by a model that incorporates a non-linear combination of the two stimuli. The use of visual and mechanosensory cues increases the strength of the streamlining response when the stimuli are present concurrently. We propose this multisensory integration will make the response more robust to transient disturbances in either modality.
Streamlining Cooperative Office Education Student Selection
Herwick, Mary Jo; Regennitter, John F.
1977-01-01
When applicants for the cooperative office education program exceed space and funds, the coordinator must have a sound basis for selecting students. This article presents both fixed and variable selection criteria to enable the coordinator to evaluate applicants with minimum subjectivity. (MF)
Efficient implementation of MrBayes on multi-GPU.
Bao, Jie; Xia, Hongju; Zhou, Jianfu; Liu, Xiaoguang; Wang, Gang
2013-06-01
MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)(3)), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)(3) Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new "node-by-node" task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.
Accelerating the XGBoost algorithm using GPU computing
Rory Mitchell
2017-07-01
Full Text Available We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3× and 6× using a Titan X compared to a 4 core i7 CPU, and 1.2× using a Titan X compared to 2× Xeon CPUs (24 cores. We show that it is possible to process the Higgs dataset (10 million instances, 28 features entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks.
Image stylization with enhanced structure on GPU
LI Ping; SUN HanQiu; SHENG Bin; SHEN JianBing
2012-01-01
This paper presents a graphics processing unit (GPU) based stylization approach that preserves the fine structure between the original and the stylized images using gradient optimization.Existing abstraction and painterly stylization methods focused on contrast manipulation only,and thus the detailed salient structures of the input images are always destroyed when performing the current stylization techniques because of limitations like unavoidable salience information loss caused by contrast abstraction.We propose an image structure map to naturally model the fine structure existing in the original images.Gradient-based structure tangent generation and tangent-guided image morphology are used to construct the structure map. The image structure map,unlike an edge map,not only systematically models the boundary information within the imagery but also accentuates the underlying inner structure detail for further stylization.We facilitate the final stylization via parallel bilateral grid and structure-aware stylizing optimization on a GPU-CUDA platform in real time.In multiple experiments,the proposed method consistently demonstrates efficient and high quality image stylization performance.
Parallel hyperspectral compressive sensing method on GPU
Bernabé, Sergio; Martín, Gabriel; Nascimento, José M. P.
2015-10-01
Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.
Streamline topologies near a fixed wall using normal forms
Hartnack, Johan
1998-01-01
Streamline patterns and their bifurcations in two-dimensional incompressible flow in the vicinity of a fixed wall has been investigated from a topological point of view by Bakker [Bifurcations in Flow Patterns. Kluwer Academic Publishers, 1991]. Bakkers work is revisited in a more general setting...... allowing curvature of the fixed wall and a time dependence of the streamlines. The velocity field is expanded at a point on the wall, and the expansion coefficients are considered as bifurcation parameters. A series of non-linear coordinate changes results in a much simplified system of differential...... of the Navier-Stokes equations on the local topology is considered....
Bayesian Lasso and multinomial logistic regression on GPU.
Češnovar, Rok; Štrumbelj, Erik
2017-01-01
We describe an efficient Bayesian parallel GPU implementation of two classic statistical models-the Lasso and multinomial logistic regression. We focus on parallelizing the key components: matrix multiplication, matrix inversion, and sampling from the full conditionals. Our GPU implementations of Bayesian Lasso and multinomial logistic regression achieve 100-fold speedups on mid-level and high-end GPUs. Substantial speedups of 25 fold can also be achieved on older and lower end GPUs. Samplers are implemented in OpenCL and can be used on any type of GPU and other types of computational units, thereby being convenient and advantageous in practice compared to related work.
Work-Efficient Parallel Skyline Computation for the GPU
Bøgh, Kenneth Sejdenfaden; Chester, Sean; Assent, Ira
2015-01-01
offers the potential for parallelizing skyline computation across thousands of cores. However, attempts to port skyline algorithms to the GPU have prioritized throughput and failed to outperform sequential algorithms. In this paper, we introduce a new skyline algorithm, designed for the GPU, that uses...... a global, static partitioning scheme. With the partitioning, we can permit controlled branching to exploit transitive relationships and avoid most point-to-point comparisons. The result is a non-traditional GPU algorithm, SkyAlign, that prioritizes work-effciency and respectable throughput, rather than...
GPU real-time processing in NA62 trigger system
Ammendola, R.; Biagioni, A.; Chiozzi, S.; Cretaro, P.; Di Lorenzo, S.; Fantechi, R.; Fiorini, M.; Frezza, O.; Lamanna, G.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Piandani, R.; Piccini, M.; Pontisso, L.; Rossetti, D.; Simula, F.; Sozzi, M.; Vicini, P.
2017-01-01
A commercial Graphics Processing Unit (GPU) is used to build a fast Level 0 (L0) trigger system tested parasitically with the TDAQ (Trigger and Data Acquisition systems) of the NA62 experiment at CERN. In particular, the parallel computing power of the GPU is exploited to perform real-time fitting in the Ring Imaging CHerenkov (RICH) detector. Direct GPU communication using a FPGA-based board has been used to reduce the data transmission latency. The performance of the system for multi-ring reconstrunction obtained during the NA62 physics run will be presented.
GPU Linear algebra extensions for GNU/Octave
Bosi, L. B.; Mariotti, M.; Santocchia, A.
2012-06-01
Octave is one of the most widely used open source tools for numerical analysis and liner algebra. Our project aims to improve Octave by introducing support for GPU computing in order to speed up some linear algebra operations. The core of our work is a C library that executes some BLAS operations concerning vector- vector, vector matrix and matrix-matrix functions on the GPU. OpenCL functions are used to program GPU kernels, which are bound within the GNU/octave framework. We report the project implementation design and some preliminary results about performance.
GPU-based Ray Tracing of Dynamic Scenes
Christopher Lux
2010-08-01
Full Text Available Interactive ray tracing of non-trivial scenes is just becoming feasible on single graphics processing units (GPU. Recent work in this area focuses on building effective acceleration structures, which work well under the constraints of current GPUs. Most approaches are targeted at static scenes and only allow navigation in the virtual scene. So far support for dynamic scenes has not been considered for GPU implementations. We have developed a GPU-based ray tracing system for dynamic scenes consisting of a set of individual objects. Each object may independently move around, but its geometry and topology are static.
GPU accelerated flow solver for direct numerical simulation of turbulent flows
Salvadore, Francesco; Bernardini, Matteo; Botti, Michela
2013-02-01
Graphical processing units (GPUs), characterized by significant computing performance, are nowadays very appealing for the solution of computationally demanding tasks in a wide variety of scientific applications. However, to run on GPUs, existing codes need to be ported and optimized, a procedure which is not yet standardized and may require non trivial efforts, even to high-performance computing specialists. In the present paper we accurately describe the porting to CUDA (Compute Unified Device Architecture) of a finite-difference compressible Navier-Stokes solver, suitable for direct numerical simulation (DNS) of turbulent flows. Porting and validation processes are illustrated in detail, with emphasis on computational strategies and techniques that can be applied to overcome typical bottlenecks arising from the porting of common computational fluid dynamics solvers. We demonstrate that a careful optimization work is crucial to get the highest performance from GPU accelerators. The results show that the overall speedup of one NVIDIA Tesla S2070 GPU is approximately 22 compared with one AMD Opteron 2352 Barcelona chip and 11 compared with one Intel Xeon X5650 Westmere core. The potential of GPU devices in the simulation of unsteady three-dimensional turbulent flows is proved by performing a DNS of a spatially evolving compressible mixing layer.
GPU accelerated flow solver for direct numerical simulation of turbulent flows
Salvadore, Francesco [CASPUR – via dei Tizii 6/b, 00185 Rome (Italy); Bernardini, Matteo, E-mail: matteo.bernardini@uniroma1.it [Department of Mechanical and Aerospace Engineering, University of Rome ‘La Sapienza’ – via Eudossiana 18, 00184 Rome (Italy); Botti, Michela [CASPUR – via dei Tizii 6/b, 00185 Rome (Italy)
2013-02-15
Graphical processing units (GPUs), characterized by significant computing performance, are nowadays very appealing for the solution of computationally demanding tasks in a wide variety of scientific applications. However, to run on GPUs, existing codes need to be ported and optimized, a procedure which is not yet standardized and may require non trivial efforts, even to high-performance computing specialists. In the present paper we accurately describe the porting to CUDA (Compute Unified Device Architecture) of a finite-difference compressible Navier–Stokes solver, suitable for direct numerical simulation (DNS) of turbulent flows. Porting and validation processes are illustrated in detail, with emphasis on computational strategies and techniques that can be applied to overcome typical bottlenecks arising from the porting of common computational fluid dynamics solvers. We demonstrate that a careful optimization work is crucial to get the highest performance from GPU accelerators. The results show that the overall speedup of one NVIDIA Tesla S2070 GPU is approximately 22 compared with one AMD Opteron 2352 Barcelona chip and 11 compared with one Intel Xeon X5650 Westmere core. The potential of GPU devices in the simulation of unsteady three-dimensional turbulent flows is proved by performing a DNS of a spatially evolving compressible mixing layer.
GPU-BASED IMAGE SEGMENTATION USING LEVEL SET METHOD WITH SCALING APPROACH
Zafer Guler
2013-11-01
Full Text Available In recent years, with the development of graphics processors, graphics cards have been widely used to perform general-purpose calculations. Especially with release of CUDA C programming languages in 2007, most of the researchers have been used CUDA C programming language for the processes which needs high performance computing. In this paper, a scaling approach for image segmentation using level sets is carried out by the GPU programming techniques. Approach to level sets is mainly based on the solution of partial differential equations. The proposed method does not require the solution of partial differential equation. Scaling approach, which uses basic geometric transformations, is used. Thus, the required computational cost reduces. The use of the CUDA programming on the GPU has taken advantage of classic programming as spending time and performance. Thereby results are obtained faster. The use of the GPU has provided to enable real-time processing. The developed application in this study is used to find tumor on MRI brain images.
Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods
David S. Smith
2012-01-01
Full Text Available Compressive sensing (CS has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.
Yu, Fengchao; Liu, Huafeng; Hu, Zhenghui; Shi, Pengcheng
2012-04-01
As a consequence of the random nature of photon emissions and detections, the data collected by a positron emission tomography (PET) imaging system can be shown to be Poisson distributed. Meanwhile, there have been considerable efforts within the tracer kinetic modeling communities aimed at establishing the relationship between the PET data and physiological parameters that affect the uptake and metabolism of the tracer. Both statistical and physiological models are important to PET reconstruction. The majority of previous efforts are based on simplified, nonphysical mathematical expression, such as Poisson modeling of the measured data, which is, on the whole, completed without consideration of the underlying physiology. In this paper, we proposed a graphics processing unit (GPU)-accelerated reconstruction strategy that can take both statistical model and physiological model into consideration with the aid of state-space evolution equations. The proposed strategy formulates the organ activity distribution through tracer kinetics models and the photon-counting measurements through observation equations, thus making it possible to unify these two constraints into a general framework. In order to accelerate reconstruction, GPU-based parallel computing is introduced. Experiments of Zubal-thorax-phantom data, Monte Carlo simulated phantom data, and real phantom data show the power of the method. Furthermore, thanks to the computing power of the GPU, the reconstruction time is practical for clinical application.
GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume
Xing Zhao
2009-01-01
Full Text Available Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.
GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing
Nicholas Hinitt
2010-01-01
Full Text Available The next generation Graphics Processing Units (GPUs are being considered for non-graphics applications. Millimeter wave (60 Ghz wireless networks that are capable of multi-gigabit per second (Gbps transfer rates require a significant baseband throughput. In this work, we consider the baseband of WirelessHD, a 60 GHz communications system, which can provide a data rate of up to 3.8 Gbps over a short range wireless link. Thus, we explore the feasibility of achieving gigabit baseband throughput using the GPUs. One of the most computationally intensive functions commonly used in baseband communications, the Fast Fourier Transform (FFT algorithm, is implemented on an NVIDIA GPU using their general-purpose computing platform called the Compute Unified Device Architecture (CUDA. The paper, first, investigates the implementation of an FFT algorithm using the GPU hardware and exploiting the computational capability available. It then outlines the limitations discovered and the methods used to overcome these challenges. Finally a new algorithm to compute FFT is proposed, which reduces interprocessor communication. It is further optimized by improving memory access, enabling the processing rate to exceed 4 Gbps, achieving a processing time of a 512-point FFT in less than 200 ns using a two-GPU solution.
Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation.
Mei, Gang; Tian, Hong
2016-01-01
This paper focuses on evaluating the impact of different data layouts on the computational efficiency of GPU-accelerated Inverse Distance Weighting (IDW) interpolation algorithm. First we redesign and improve our previous GPU implementation that was performed by exploiting the feature of CUDA dynamic parallelism (CDP). Then we implement three versions of GPU implementations, i.e., the naive version, the tiled version, and the improved CDP version, based upon five data layouts, including the Structure of Arrays (SoA), the Array of Structures (AoS), the Array of aligned Structures (AoaS), the Structure of Arrays of aligned Structures (SoAoS), and the Hybrid layout. We also carry out several groups of experimental tests to evaluate the impact. Experimental results show that: the layouts AoS and AoaS achieve better performance than the layout SoA for both the naive version and tiled version, while the layout SoA is the best choice for the improved CDP version. We also observe that: for the two combined data layouts (the SoAoS and the Hybrid), there are no notable performance gains when compared to other three basic layouts. We recommend that: in practical applications, the layout AoaS is the best choice since the tiled version is the fastest one among three versions. The source code of all implementations are publicly available.
An Efficient Parallel Algorithm for Graph Isomorphism on GPU using CUDA
Min-Young Son
2015-10-01
Full Text Available Modern Graphics Processing Units (GPUs have high computation power and low cost. Recently, many applications in various fields have been computed powerfully on the GPU using CUDA. In this paper, we propose an efficient parallel algorithm for graph isomorphism which runs on the GPU using CUDA for matching large graphs. Parallelization of a sequential graph isomorphism algorithm is one of the hardest problems because it includes inherently sequential characteristics. Our approach divides the given graphs into smaller blocks using a divide-and-conquer, and then maps the blocks to parallel processing units on the GPU. The smaller blocks are solved in individual processing units, and then the results are combined using hierarchical procedures. In the experiment, we used random graphs from vertices of small size to up to tens of thousands of vertices in order to solve efficiently graph isomorphism for large graphs. The experimental results show that the proposed approach brings a considerable improvement in performance and efficiency comparing to the CPU-based results. Our result also shows high performance, especially on large graphs.
Redundancy computation analysis and implementation of phase diversity based on GPU
Zhang, Quan; Bao, Hua; Rao, Changhui; Peng, Zhenming
2015-10-01
Phase diversity method is not only used as an image restoration technique, but also as a wavefront sensor. However, its computations have been perceived as being too burdensome to achieve its real-time applications on a desktop computer platform. In this paper, the implementation of the phase diversity algorithm based on graphic processing unit (GPU) is presented. The redundancy computations for the pupil function, point spread function, and optical transfer function are analyzed. Two kinds of implementation methods based on GPU are compared: one is the general method which is accomplished by GPU library CUFFT without precision loss (method-1) and the other one performed by our own custom FFT with little damage of precision considering the redundant calculations (method-2). The results show the cost and gradient functions can be speeded up by method-2 in contrast with the method-1 and the overhead of global memory access by kernel fusion can be reduced. For the image of 256 × 256 with the sampling factor of 3, the results reveal that method-2 achieves speedup of 1.83× compared with method-1 when the central 128 × 128 pixels of the point spread function are used.
A GPU Implementation of Local Search Operators for Symmetric Travelling Salesman Problem
Juraj Fosin
2013-06-01
Full Text Available The Travelling Salesman Problem (TSP is one of the most studied combinatorial optimization problem which is significant in many practical applications in transportation problems. The TSP problem is NP-hard problem and requires large computation power to be solved by the exact algorithms. In the past few years, fast development of general-purpose Graphics Processing Units (GPUs has brought huge improvement in decreasing the applications’ execution time. In this paper, we implement 2-opt and 3-opt local search operators for solving the TSP on the GPU using CUDA. The novelty presented in this paper is a new parallel iterated local search approach with 2-opt and 3-opt operators for symmetric TSP, optimized for the execution on GPUs. With our implementation large TSP problems (up to 85,900 cities can be solved using the GPU. We will show that our GPU implementation can be up to 20x faster without losing quality for all TSPlib problems as well as for our CRO TSP problem.
Wong, Un-Hong; Aoki, Takayuki; Wong, Hon-Cheng
2014-07-01
Modern graphics processing units (GPUs) have been widely utilized in magnetohydrodynamic (MHD) simulations in recent years. Due to the limited memory of a single GPU, distributed multi-GPU systems are needed to be explored for large-scale MHD simulations. However, the data transfer between GPUs bottlenecks the efficiency of the simulations on such systems. In this paper we propose a novel GPU Direct-MPI hybrid approach to address this problem for overall performance enhancement. Our approach consists of two strategies: (1) We exploit GPU Direct 2.0 to speedup the data transfers between multiple GPUs in a single node and reduce the total number of message passing interface (MPI) communications; (2) We design Compute Unified Device Architecture (CUDA) kernels instead of using memory copy to speedup the fragmented data exchange in the three-dimensional (3D) decomposition. 3D decomposition is usually not preferable for distributed multi-GPU systems due to its low efficiency of the fragmented data exchange. Our approach has made a breakthrough to make 3D decomposition available on distributed multi-GPU systems. As a result, it can reduce the memory usage and computation time of each partition of the computational domain. Experiment results show twice the FLOPS comparing to common 2D decomposition MPI-only implementation method. The proposed approach has been developed in an efficient implementation for MHD simulations on distributed multi-GPU systems, called MGPU-MHD code. The code realizes the GPU parallelization of a total variation diminishing (TVD) algorithm for solving the multidimensional ideal MHD equations, extending our work from single GPU computation (Wong et al., 2011) to multiple GPUs. Numerical tests and performance measurements are conducted on the TSUBAME 2.0 supercomputer at the Tokyo Institute of Technology. Our code achieves 2 TFLOPS in double precision for the problem with 12003 grid points using 216 GPUs.
Performance of Сellular Automata-based Stream Ciphers in GPU Implementation
P. G. Klyucharev
2016-01-01
Full Text Available Earlier the author had developed methods to build high-performance generalized cellular automata-based symmetric ciphers, which allow obtaining the encryption algorithms that show extremely high performance in hardware implementation. However, their implementation based on the conventional microprocessors lacks high performance. The mere fact is quite common - it shows a scope of applications for these ciphers. Nevertheless, the use of graphic processors enables achieving an appropriate performance for a software implementation.The article is extension of a series of the articles, which study various aspects to construct and implement cryptographic algorithms based on the generalized cellular automata. The article is aimed at studying the capabilities to implement the GPU-based cryptographic algorithms under consideration.Representing a key generator, the implemented encryption algorithm comprises 2k generalized cellular automata. The cellular automata graphs are Ramanujan’s ones. The cells of produced k gamma streams alternate, thereby allowing the GPU capabilities to be better used.To implement was used OpenCL, as the most universal and platform-independent API. The software written in C ++ was designed so that the user could set various parameters, including the encryption key, the graph structure, the local communication function, various constants, etc. To test were used a variety of graphics processors (NVIDIA GTX 650; NVIDIA GTX 770; AMD R9 280X.Depending on operating conditions, and GPU used, a performance range is from 0.47 to 6.61 Gb / s, which is comparable to the performance of the countertypes.Thus, the article has demonstrated that using the GPU makes it is possible to provide efficient software implementation of stream ciphers based on the generalized cellular automata.This work was supported by the RFBR, the project №16-07-00542.
Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU.
Wei-Jen Wang
Full Text Available This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP in MATLAB by using external function calls to a graphics processing unit (GPU. DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.
Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU.
Wang, Wei-Jen; Hsieh, I-Fan; Chen, Chun-Chuan
2013-01-01
This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis.
Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU
Wang, Wei-Jen; Hsieh, I-Fan; Chen, Chun-Chuan
2013-01-01
This study aims to improve the performance of Dynamic Causal Modelling for Event Related Potentials (DCM for ERP) in MATLAB by using external function calls to a graphics processing unit (GPU). DCM for ERP is an advanced method for studying neuronal effective connectivity. DCM utilizes an iterative procedure, the expectation maximization (EM) algorithm, to find the optimal parameters given a set of observations and the underlying probability model. As the EM algorithm is computationally demanding and the analysis faces possible combinatorial explosion of models to be tested, we propose a parallel computing scheme using the GPU to achieve a fast estimation of DCM for ERP. The computation of DCM for ERP is dynamically partitioned and distributed to threads for parallel processing, according to the DCM model complexity and the hardware constraints. The performance efficiency of this hardware-dependent thread arrangement strategy was evaluated using the synthetic data. The experimental data were used to validate the accuracy of the proposed computing scheme and quantify the time saving in practice. The simulation results show that the proposed scheme can accelerate the computation by a factor of 155 for the parallel part. For experimental data, the speedup factor is about 7 per model on average, depending on the model complexity and the data. This GPU-based implementation of DCM for ERP gives qualitatively the same results as the original MATLAB implementation does at the group level analysis. In conclusion, we believe that the proposed GPU-based implementation is very useful for users as a fast screen tool to select the most likely model and may provide implementation guidance for possible future clinical applications such as online diagnosis. PMID:23840507
GPU-S2S:面向GPU的源到源翻译转化%GPU-S2S: a source to source compiler for GPU
李丹; 曹海军; 董小社; 张保
2012-01-01
To address the problem of poor software portability and programmability of a graphic processing unit ( GPU ) , and to facilitate the development of parallel programs on GPU, this study proposed a novel directive based compiler guided approach, and then the GPU-S2S, a prototypic tool for automatic source-to-source translation, was implemented through combining automatic mapping with static compilation configuration, which is capable of translating a C sequential program with directives into a compute unified device architecture (CUDA) program. The experimental results show that CUDA codes generated by the GPU-S2S can achieve comparable performance to that of CUDA benchmarks provided by NVIDIA CUDA SDK, and have significant performance improvements compared to its original C sequential codes.%针对图形处理器(GPU)架构下的软件可移植性、可编程性差的问题,为了便于在GPU上开发并行程序,通过自动映射与静态编译相结合,提出了一种新的基于制导语句控制的编译优化方法,实现了一个源到源的自动转化工具GPU-S2S,它能够将插入了制导语句的串行C程序转化为统一计算架构(CUDA)程序.实验结果表明,经GPU-S2S转化生成的代码和英伟达(NVIDIA)提供的基准测试代码具有相当的性能；与原串行程序在CPU上执行相比,转换后的并行程序在GPU上能够获取显著的性能提升.
Computer program calculates velocities and streamlines in turbomachines
Katsanis, T.
1968-01-01
Computer program calculates the velocity distribution and streamlines over widely separated blades of turbomachines. It gives the solutions of a two dimensional, subsonic, compressible nonviscous flow problem for a rotating or stationary circular cascade of blades on a blade-to-blade surface of revolution.
75 FR 4031 - Streamlining Hard-Copy Postage Statement Processing
2010-01-26
... From the Federal Register Online via the Government Publishing Office POSTAL SERVICE 39 CFR Part 111 Streamlining Hard-Copy Postage Statement Processing AGENCY: Postal Service\\TM\\. ACTION: Proposed rule. SUMMARY: The Postal Service\\TM\\ is proposing to revise Mailing Standards of the United States...
On stagnation points and streamline topology in vortex flows
Aref, Hassan; Brøns, Morten
1998-01-01
of the stagnation point in a flow produced by three vortices with sum of strengths zero is found. Using topological arguments the distinct streamline patterns for flow about three vortices are also determined. Partial results are given for two special sets of vortex strengths on the changes between these patterns...
On periodic water waves with Coriolis effects and isobaric streamlines
Matioc, Anca-Voichita
2012-01-01
In this paper we prove that solutions of the f-plane approximation for equatorial geophysical deep water waves, which have the property that the pressure is constant along the streamlines and do not possess stagnation points,are Gerstner-type waves. Furthermore, for waves traveling over a flat bed, we prove that there are only laminar flow solutions with these properties.
Streamline topology: Patterns in fluid flows and their bifurcations
Brøns, Morten
2007-01-01
Using dynamical systems theory, we consider structures such as vortices and separation in the streamline patterns of fluid flows. Bifurcation of patterns under variation of external parameters is studied using simplifying normal form transformations. Flows away from boundaries, flows close to fixed...
STREAMLINING THE POWERS AND DUTIES OF A RECEIVER ...
Mofasony
would contain the essential terms of the contract. ... shares of the company, ows the assets of the company, a proposition rightly ... Streamlining the Powers and Duties of a Receiver/ Manager and Liquidator in the Organization of ... Oman P., Corporate Governance and National Development, OCED Research Papers, No.
GPU-Accelerated Stony-Brook University 5-class Microphysics Scheme in WRF
Mielikainen, J.; Huang, B.; Huang, A.
2011-12-01
The Weather Research and Forecasting (WRF) model is a next-generation mesoscale numerical weather prediction system. Microphysics plays an important role in weather and climate prediction. Several bulk water microphysics schemes are available within the WRF, with different numbers of simulated hydrometeor classes and methods for estimating their size fall speeds, distributions and densities. Stony-Brook University scheme (SBU-YLIN) is a 5-class scheme with riming intensity predicted to account for mixed-phase processes. In the past few years, co-processing on Graphics Processing Units (GPUs) has been a disruptive technology in High Performance Computing (HPC). GPUs use the ever increasing transistor count for adding more processor cores. Therefore, GPUs are well suited for massively data parallel processing with high floating point arithmetic intensity. Thus, it is imperative to update legacy scientific applications to take advantage of this unprecedented increase in computing power. CUDA is an extension to the C programming language offering programming GPU's directly. It is designed so that its constructs allow for natural expression of data-level parallelism. A CUDA program is organized into two parts: a serial program running on the CPU and a CUDA kernel running on the GPU. The CUDA code consists of three computational phases: transmission of data into the global memory of the GPU, execution of the CUDA kernel, and transmission of results from the GPU into the memory of CPU. CUDA takes a bottom-up point of view of parallelism is which thread is an atomic unit of parallelism. Individual threads are part of groups called warps, within which every thread executes exactly the same sequence of instructions. To test SBU-YLIN, we used a CONtinental United States (CONUS) benchmark data set for 12 km resolution domain for October 24, 2001. A WRF domain is a geographic region of interest discretized into a 2-dimensional grid parallel to the ground. Each grid point has
Fast calculation of HELAS amplitudes using graphics processing unit (GPU)
Hagiwara, K; Okamura, N; Rainwater, D L; Stelzer, T
2009-01-01
We use the graphics processing unit (GPU) for fast calculations of helicity amplitudes of physics processes. As our first attempt, we compute $u\\overline{u}\\to n\\gamma$ ($n=2$ to 8) processes in $pp$ collisions at $\\sqrt{s} = 14$TeV by transferring the MadGraph generated HELAS amplitudes (FORTRAN) into newly developed HEGET ({\\bf H}ELAS {\\bf E}valuation with {\\bf G}PU {\\bf E}nhanced {\\bf T}echnology) codes written in CUDA, a C-platform developed by NVIDIA for general purpose computing on the GPU. Compared with the usual CPU programs, we obtain 40-150 times better performance on the GPU.
Local alignment tool based on Hadoop framework and GPU architecture.
Hung, Che-Lun; Hua, Guan-Jie
2014-01-01
With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.
Local Alignment Tool Based on Hadoop Framework and GPU Architecture
Che-Lun Hung
2014-01-01
Full Text Available With the rapid growth of next generation sequencing technologies, such as Slex, more and more data have been discovered and published. To analyze such huge data the computational performance is an important issue. Recently, many tools, such as SOAP, have been implemented on Hadoop and GPU parallel computing architectures. BLASTP is an important tool, implemented on GPU architectures, for biologists to compare protein sequences. To deal with the big biology data, it is hard to rely on single GPU. Therefore, we implement a distributed BLASTP by combining Hadoop and multi-GPUs. The experimental results present that the proposed method can improve the performance of BLASTP on single GPU, and also it can achieve high availability and fault tolerance.
Importance of Explicit Vectorization for CPU and GPU Software Performance
Dickson, Neil G; Hamze, Firas
2010-01-01
Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (GPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and GPU implementations of a particular computationally intensive Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the equivalent, explicit memory coalescing, on the GPU are found to be critical to achieving good performance of this algorithm in both environments. The fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU version, in addition to speedup from multi-threading. This is 2x faster than the fully-optimized GPU version.
Connectivity-Based Segmentation for GPU-Accelerated Mesh Decompression
Jie-Yi Zhao; Min Tang; Ruo-Feng Tong
2012-01-01
We present a novel algorithm to partition large 3D meshes for GPU-accelerated decompression.Our formulation focuses on minimizing the replicated vertices between patches,and balancing the numbers of faces of patches for efficient parallel computing.First we generate a topology model of the original mesh and remove vertex positions.Then we assign the centers of patches using geodesic farthest point sampling and cluster the faces according to the geodesic distance to the centers.After the segmentation we swap boundary faces to fix jagged boundaries and store the boundary vertices for whole-mesh preservation.The decompression of each patch runs on a thread of GPU,and we evaluate its performance on various large benchmarks.In practice,the GPU-based decompression algorithm runs more than 48x faster on NVIDIA GeForce GTX 580 GPU compared with that on the CPU using single core.
GPU Accelerated Semiclassical Initial Value Representation Molecular Dynamics
Tamascelli, Dario; Conte, Riccardo; Ceotto, Michele
2013-01-01
This paper presents a graphics processing units (GPUs) implementation of the semiclassical initial value representation (SC-IVR) propagator for vibrational molecular spectroscopy calculations. The time-averaging formulation of the SC-IVR for power spectrum calculations is employed. Details about the CUDA implementation of the semiclassical code are provided. 4 molecules with an increasing number of atoms are considered and the GPU-calculated vibrational frequencies perfectly match the benchmark values. The computational time scaling of two GPUs (C2075 and K20) versus two CPUs (intel core i5 and Intel Xeon E5-2687W) shows that the CPU code scales linearly, whereas the GPU CUDA code roughly constantly for most of the trajectory range considered. Critical issues related to the GPU implementation are discussed. The resulting reduction in computational time and power consumption is significant and semiclassical GPU calculations are shown to be environment friendly.
3D- VISUALIZATION BY RAYTRACING IMAGE SYNTHESIS ON GPU
Al-Oraiqat Anas M.
2016-06-01
Full Text Available This paper presents a realization of the approach to spatial 3D stereo of visualization of 3D images with use parallel Graphics processing unit (GPU. The experiments of realization of synthesis of images of a 3D stage by a method of trace of beams on GPU with Compute Unified Device Architecture (CUDA have shown that 60 % of the time is spent for the decision of a computing problem approximately, the major part of time (40 % is spent for transfer of data between the central processing unit and GPU for calculations and the organization process of visualization. The study of the influence of increase in the size of the GPU network at the speed of calculations showed importance of the correct task of structure of formation of the parallel computer network and general mechanism of parallelization.
GPU accelerated numerical simulations of viscoelastic phase separation model.
Yang, Keda; Su, Jiaye; Guo, Hongxia
2012-07-05
We introduce a complete implementation of viscoelastic model for numerical simulations of the phase separation kinetics in dynamic asymmetry systems such as polymer blends and polymer solutions on a graphics processing unit (GPU) by CUDA language and discuss algorithms and optimizations in details. From studies of a polymer solution, we show that the GPU-based implementation can predict correctly the accepted results and provide about 190 times speedup over a single central processing unit (CPU). Further accuracy analysis demonstrates that both the single and the double precision calculations on the GPU are sufficient to produce high-quality results in numerical simulations of viscoelastic model. Therefore, the GPU-based viscoelastic model is very promising for studying many phase separation processes of experimental and theoretical interests that often take place on the large length and time scales and are not easily addressed by a conventional implementation running on a single CPU.
Acceleration of a QM/MM-QMC simulation using GPU.
Uejima, Yutaka; Terashima, Tomoharu; Maezono, Ryo
2011-07-30
We accelerated an ab initio molecular QMC calculation by using GPGPU. Only the bottle-neck part of the calculation is replaced by CUDA subroutine and performed on GPU. The performance on a (single core CPU + GPU) is compared with that on a (single core CPU with double precision), getting 23.6 (11.0) times faster calculations in single (double) precision treatments on GPU. The energy deviation caused by the single precision treatment was found to be within the accuracy required in the calculation, ∼10(-5) hartree. The accelerated computational nodes mounting GPU are combined to form a hybrid MPI cluster on which we confirmed the performance linearly scales to the number of nodes.
Leang, Sarom S; Rendell, Alistair P; Gordon, Mark S
2014-03-11
Increasingly, modern computer systems comprise a multicore general-purpose processor augmented with a number of special purpose devices or accelerators connected via an external interface such as a PCI bus. The NVIDIA Kepler Graphical Processing Unit (GPU) and the Intel Phi are two examples of such accelerators. Accelerators offer peak performances that can be well above those of the host processor. How to exploit this heterogeneous environment for legacy application codes is not, however, straightforward. This paper considers how matrix operations in typical quantum chemical calculations can be migrated to the GPU and Phi systems. Double precision general matrix multiply operations are endemic in electronic structure calculations, especially methods that include electron correlation, such as density functional theory, second order perturbation theory, and coupled cluster theory. The use of approaches that automatically determine whether to use the host or an accelerator, based on problem size, is explored, with computations that are occurring on the accelerator and/or the host. For data-transfers over PCI-e, the GPU provides the best overall performance for data sizes up to 4096 MB with consistent upload and download rates between 5-5.6 GB/s and 5.4-6.3 GB/s, respectively. The GPU outperforms the Phi for both square and nonsquare matrix multiplications.
The Coming Role of GPU in Computational Geodynamics (Invited)
Yuen, D. A.; Knepley, M. G.; Erlebacher, G.; Wright, G. B.
2009-12-01
With the proliferation of GPU ( graphics accelerator board) the computing landscape has changed enormously in the last 3 years. The new additional capabilities of the GPU , such as larger shared memories and load-store operations , allow it to be considered as a viable stand-alone computational and visualization engine. Today the massive threading and computing capability of GPU can deliver at least an order of magnitude performance over the multi-core CPU architecture. The cost of a GPU system is also considerably cheaper than a CPU cluster by more than an order of magnitude.The introduction of CUDA and ancillary software aids, such as Jackets, have allowed the rapid translation of many venerable codes into software usable on GPU. We will discuss our experience acquired over the past year in attacking five different computational problems in the geosciences, using the GPU. They include (1.) 3-D seismic wave propagation with the spectral-element method (2.)2-D shallow water equation as applied to tsunami wave propagation, using finite-differences (3.) 3-D mantle convection with constant viscosity using a 4th order compact finite-difference operator (4.) non-linear heat-diffusion equation in 2-D using a collocation method based on radial basis functions over an elliptical area . Grid points are divided so as to lie on a centroidal Voronoi mesh . Derivatives are calculated at each grid point using a point-dependent stencil computed from the nearest neighbors .(5.) Stokes flow with variable viscosity by means of a pre-conditioner calculated on the GPU based on the vortex method using Green’s functions, along with the radial basis functions and the fast multi-pole method. The Krylov method is used on the CPU for the final iterative step .We will discuss the relative speed-ups of the GPU over the CPU in each of these cases. We will point out the need to go to more computationally intensive mode with multiple GPUs, which calls for key CPUs to control the message
LDPC Decoding on GPU for Mobile Device
Yiqin Lu
2016-01-01
Full Text Available A flexible software LDPC decoder that exploits data parallelism for simultaneous multicode words decoding on the mobile device is proposed in this paper, supported by multithreading on OpenCL based graphics processing units. By dividing the check matrix into several parts to make full use of both the local memory and private memory on GPU and properly modify the code capacity each time, our implementation on a mobile phone shows throughputs above 100 Mbps and delay is less than 1.6 millisecond in decoding, which make high-speed communication like video calling possible. To realize efficient software LDPC decoding on the mobile device, the LDPC decoding feature on communication baseband chip should be replaced to save the cost and make it easier to upgrade decoder to be compatible with a variety of channel access schemes.
GPU-accelerated micromagnetic simulations using cloud computing
Jermain, C. L.; Rowlands, G. E.; Buhrman, R. A.; Ralph, D. C.
2016-03-01
Highly parallel graphics processing units (GPUs) can improve the speed of micromagnetic simulations significantly as compared to conventional computing using central processing units (CPUs). We present a strategy for performing GPU-accelerated micromagnetic simulations by utilizing cost-effective GPU access offered by cloud computing services with an open-source Python-based program for running the MuMax3 micromagnetics code remotely. We analyze the scaling and cost benefits of using cloud computing for micromagnetics.
GPU-accelerated micromagnetic simulations using cloud computing
Jermain, C L; Buhrman, R A; Ralph, D C
2015-01-01
Highly-parallel graphics processing units (GPUs) can improve the speed of micromagnetic simulations significantly as compared to conventional computing using central processing units (CPUs). We present a strategy for performing GPU-accelerated micromagnetic simulations by utilizing cost-effective GPU access offered by cloud computing services with an open-source Python-based program for running the MuMax3 micromagnetics code remotely. We analyze the scaling and cost benefits of using cloud computing for micromagnetics.
GPU-based large-scale visualization
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
Real-Time Incompressible Fluid Simulation on the GPU
Xiao Nie
2015-01-01
Full Text Available We present a parallel framework for simulating incompressible fluids with predictive-corrective incompressible smoothed particle hydrodynamics (PCISPH on the GPU in real time. To this end, we propose an efficient GPU streaming pipeline to map the entire computational task onto the GPU, fully exploiting the massive computational power of state-of-the-art GPUs. In PCISPH-based simulations, neighbor search is the major performance obstacle because this process is performed several times at each time step. To eliminate this bottleneck, an efficient parallel sorting method for this time-consuming step is introduced. Moreover, we discuss several optimization techniques including using fast on-chip shared memory to avoid global memory bandwidth limitations and thus further improve performance on modern GPU hardware. With our framework, the realism of real-time fluid simulation is significantly improved since our method enforces incompressibility constraint which is typically ignored due to efficiency reason in previous GPU-based SPH methods. The performance results illustrate that our approach can efficiently simulate realistic incompressible fluid in real time and results in a speed-up factor of up to 23 on a high-end NVIDIA GPU in comparison to single-threaded CPU-based implementation.
2010-11-30
... COMMISSION 47 CFR Part 73 Policies To Promote Rural Radio Service and To Streamline Allotment and Assignment... and to Streamline Allotment and Assignment Procedures, MB Docket No. 09-52, FCC 09-30, 24 FCC Rcd 5239... to Promote Rural Radio Service and to Streamline Allotment and Assignment Procedures (the ``Order...
A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC)
Tian, Zhen; Shi, Feng; Folkerts, Michael; Qin, Nan; Jiang, Steve B.; Jia, Xun
2015-09-01
Monte Carlo (MC) simulation has been recognized as the most accurate dose calculation method for radiotherapy. However, the extremely long computation time impedes its clinical application. Recently, a lot of effort has been made to realize fast MC dose calculation on graphic processing units (GPUs). However, most of the GPU-based MC dose engines have been developed under NVidia’s CUDA environment. This limits the code portability to other platforms, hindering the introduction of GPU-based MC simulations to clinical practice. The objective of this paper is to develop a GPU OpenCL based cross-platform MC dose engine named goMC with coupled photon-electron simulation for external photon and electron radiotherapy in the MeV energy range. Compared to our previously developed GPU-based MC code named gDPM (Jia et al 2012 Phys. Med. Biol. 57 7783-97), goMC has two major differences. First, it was developed under the OpenCL environment for high code portability and hence could be run not only on different GPU cards but also on CPU platforms. Second, we adopted the electron transport model used in EGSnrc MC package and PENELOPE’s random hinge method in our new dose engine, instead of the dose planning method employed in gDPM. Dose distributions were calculated for a 15 MeV electron beam and a 6 MV photon beam in a homogenous water phantom, a water-bone-lung-water slab phantom and a half-slab phantom. Satisfactory agreement between the two MC dose engines goMC and gDPM was observed in all cases. The average dose differences in the regions that received a dose higher than 10% of the maximum dose were 0.48-0.53% for the electron beam cases and 0.15-0.17% for the photon beam cases. In terms of efficiency, goMC was ~4-16% slower than gDPM when running on the same NVidia TITAN card for all the cases we tested, due to both the different electron transport models and the different development environments. The code portability of our new dose engine goMC was validated by
A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC).
Tian, Zhen; Shi, Feng; Folkerts, Michael; Qin, Nan; Jiang, Steve B; Jia, Xun
2015-10-07
Monte Carlo (MC) simulation has been recognized as the most accurate dose calculation method for radiotherapy. However, the extremely long computation time impedes its clinical application. Recently, a lot of effort has been made to realize fast MC dose calculation on graphic processing units (GPUs). However, most of the GPU-based MC dose engines have been developed under NVidia's CUDA environment. This limits the code portability to other platforms, hindering the introduction of GPU-based MC simulations to clinical practice. The objective of this paper is to develop a GPU OpenCL based cross-platform MC dose engine named goMC with coupled photon-electron simulation for external photon and electron radiotherapy in the MeV energy range. Compared to our previously developed GPU-based MC code named gDPM (Jia et al 2012 Phys. Med. Biol. 57 7783-97), goMC has two major differences. First, it was developed under the OpenCL environment for high code portability and hence could be run not only on different GPU cards but also on CPU platforms. Second, we adopted the electron transport model used in EGSnrc MC package and PENELOPE's random hinge method in our new dose engine, instead of the dose planning method employed in gDPM. Dose distributions were calculated for a 15 MeV electron beam and a 6 MV photon beam in a homogenous water phantom, a water-bone-lung-water slab phantom and a half-slab phantom. Satisfactory agreement between the two MC dose engines goMC and gDPM was observed in all cases. The average dose differences in the regions that received a dose higher than 10% of the maximum dose were 0.48-0.53% for the electron beam cases and 0.15-0.17% for the photon beam cases. In terms of efficiency, goMC was ~4-16% slower than gDPM when running on the same NVidia TITAN card for all the cases we tested, due to both the different electron transport models and the different development environments. The code portability of our new dose engine goMC was validated by
Streamlining the path from physics to medicine
2013-01-01
We all know them: the well-established cases of knowledge transfer from physics to medicine in which CERN has played an important role. From technology for PET scanners to dedicated accelerator designs for cancer therapy, we have contributed a lot over the years. But until recently, the pathway has been a little ad hoc, depending largely on enthusiastic individuals. That’s about to change. CERN’s commitment to formalising the transfer of knowledge to the field of medicine has been growing over recent years. Notable successes are the establishment of a new conference series, ICTR-PHE, that brings together medical practitioners and members of the physics community, and the establishment of cancer therapy centres like CNAO in Italy and MedAustron in Austria, built on CERN accelerator technology. These are important, but there’s still more that we can and should do. To this end, we’ve created a new Office for CERN Medical Applications, whose first head w...
Workload Analysis for Typical GPU Programs Using CUPTI Interface%基于 CUPTI 接口的典型 GPU 程序负载特征分析
郑祯; 翟季冬; 李焱; 陈文光
2016-01-01
GPU‐based high performance computers have become an important trend in the area of high performance computing .However ,developing efficient parallel programs on current GPU devices is very complex because of the complex memory hierarchy and thread hierarchy . To address this problem ,we summarize five kinds of key metrics that reflect the performance of programs according to the hardware and software architecture .Then we design and implement a performance analysis tool based on underlying CUPTI interfaces provided by NVIDIA , which can collect key metrics automatically without modifying the source code .The tool can analyze the performance behaviors of GPU programs effectively with very little impact on the execution of programs .Finally ,we analyze 17 programs in Rodinia benchmark , which is a famous benchmark for GPU programs , and a real application using our tool .By analyzing the value of key metrics ,we find the performance bottlenecks of each program and map the bottlenecks back to source code .These analysis results can be used to guide the optimization of CUDA programs and GPU architecture .Result shows that most bottlenecks come from inefficient memory access ,and include unreasonable global memory and shared memory access pattern ,and low concurrency for these programs . We summarize the common reasons for typical performance bottlenecks and give some high‐level suggestions for developing efficient GPU programs .%基于图形处理器（graphics processing unit ，GPU）加速设备的高性能计算机已经成为目前高性能计算领域的一个重要发展趋势。然而，在当前的 GPU 设备上开发高效的并行程序仍然是一件非常复杂的事情。针对这一问题，1）总结了影响 GPU 程序性能的5类关键性能指标；2）采用 NVIDIA 公司提供的 CUPTI 底层接口，设计并实现了一套 GPU 程序性能分析工具集，该工具集可以有效地分析 GPU程序的性能行为；3）
Streamlining geospatial metadata in the Semantic Web
Fugazza, Cristiano; Pepe, Monica; Oggioni, Alessandro; Tagliolato, Paolo; Carrara, Paola
2016-04-01
In the geospatial realm, data annotation and discovery rely on a number of ad-hoc formats and protocols. These have been created to enable domain-specific use cases generalized search is not feasible for. Metadata are at the heart of the discovery process and nevertheless they are often neglected or encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Linked Open Data (LOD) movement did not induce so far a consistent, interlinked baseline in the geospatial domain. In a nutshell, datasets, scientific literature related to them, and ultimately the researchers behind these products are only loosely connected; the corresponding metadata intelligible only to humans, duplicated on different systems, seldom consistently. Instead, our workflow for metadata management envisages i) editing via customizable web- based forms, ii) encoding of records in any XML application profile, iii) translation into RDF (involving the semantic lift of metadata records), and finally iv) storage of the metadata as RDF and back-translation into the original XML format with added semantics-aware features. Phase iii) hinges on relating resource metadata to RDF data structures that represent keywords from code lists and controlled vocabularies, toponyms, researchers, institutes, and virtually any description one can retrieve (or directly publish) in the LOD Cloud. In the context of a distributed Spatial Data Infrastructure (SDI) built on free and open-source software, we detail phases iii) and iv) of our workflow for the semantics-aware management of geospatial metadata.
Streamlining Throughput with the Implementation of a CT Coordinator.
Johnson, Kathleen; Johnson, Charles E; Porter, Linda; Bryant, Karen
2016-01-01
Imaging departments today are challenged with streamlining processes to keep up with advancements in healthcare, the increasing complexity of imaging studies and procedures, and bundling of charges for services rendered. Ordering providers are often required to get insurance pre-authorizations for imaging orders, and what is pre-authorized must be the study/procedure performed or reimbursement is not guaranteed. Insurance companies have inhibited radiologists from providing optimal service by placing restrictions on changing orders per radiologist protocol to best meet the individual needs of each patient. Many healthcare systems that are using a central scheduling model are losing money due to scans and procedures being inappropriately ordered and pre-authorized. Implementing a computed tomography (CT) coordinator can streamline throughput of imaging services in radiology departments. The CT improvement project described here used a Lean methodology Plan-Do-Check-Act (PDCA) approach to increase the effectiveness of an organization's ability to maximize process efficiency and revenue.
Streamline topologies and their bifurcations for mixed convective peristaltic flow
Z. Asghar
2015-09-01
Full Text Available In this work our focus is on streamlines patterns and their bifurcations for mixed convective peristaltic flow of Newtonian fluid with heat transfer. The flow is considered in a two dimensional symmetric channel and the governing equations are simplified under widely taken assumptions of large wavelength and low Reynolds number in a wave frame of reference. In order to study the streamlines patterns, a system of nonlinear autonomous differential equations are established and dynamical systems approach is used to discuss the local bifurcations and their topological changes. We have discussed all types of bifurcations and their topological changes are presented graphically. We found that the vortices contract along the vertical direction whereas they expand along horizontal direction. A global bifurcations diagram is used to summarize the bifurcations. The trapping and backward flow regions are mainly affected by increasing Grashof number and constant heat source parameter in such a way that trapping region increases whereas backward flow region shrinks.
Implementation science in the real world: a streamlined model.
Knapp, Herschel; Anaya, Henry D
2012-01-01
The process of quality improvement may involve enhancing or revising existing practices or the introduction of a novel element. Principles of Implementation Science provide key theories to guide these processes, however, such theories tend to be highly technical in nature and do not provide pragmatic nor streamlined approaches to real-world implementation. This paper presents a concisely comprehensive six step theory-based Implementation Science model that we have successfully used to launch more than two-dozen self-sustaining implementations. In addition, we provide an abbreviated case study in which we used our streamlined theoretical model to successfully guide the development and implementation of an HIV testing/linkage to care campaign in homeless shelter settings in Los Angeles County.
Energy of Unmanned Aerial Vehicle (UAV Windmill (theory, streamlined airflow
L. I. Grechikhin
2010-01-01
Full Text Available A windmill theory as an open power system is resolved in the paper. The paper presents a mechanism of a frontal resistance and a thrust load of the operating windmill which is based on occurrence of an active environmental component and formulates the conditions under which any minimum resistance and maximum thrust load are realized. An algorithm and software for calculation of windmill streamlining pattern are developed in the paper. The calculation results are given the paper.
Zephyr: an internet-based process to streamline engineering
Alford, F A; Cavitt, R E; Jordan, C W; Mauvais, M J; Niven, W A; Taylor, J M; Taylor, S S; Vickers, D L; Warren, F E; Weaver, R L
1998-07-01
Lawrence Livermore National Laboratory (LLNL) is implementing an Internet-based process pilot called 'Zephyr' to streamline engineering and commerce using the internet. Major benefits have accrued by using Zephyr in facilitating industrial collaboration, speeding the engineering development cycle, reducing procurement time, and lowering overall costs. Programs at LLNL are potentializing the efficiencies introduced since implementing Zephyr. Zephyr"s pilot functionality is undergoing full integration with Business Systems, Finance, and Vendors to support major programs at the Laboratory.
Zephyr: A secure Internet process to streamline engineering
Jordan, C.W.; Niven, W.A.; Cavitt, R.E. [and others
1998-05-12
Lawrence Livermore National Laboratory (LLNL) is implementing an Internet-based process pilot called `Zephyr` to streamline engineering and commerce using the Internet. Major benefits have accrued by using Zephyr in facilitating industrial collaboration, speeding the engineering development cycle, reducing procurement time, and lowering overall costs. Programs at LLNL are potentializing the efficiencies introduced since implementing Zephyr. Zephyr`s pilot functionality is undergoing full integration with Business Systems, Finance, and Vendors to support major programs at the Laboratory.
Streamlining CubeSat Solar Panel Fabrication Processes
Sandberg, Ariel; Smith, Timothy
2016-01-01
A critical facet of CubeSat fabrication is solar panel characterization and assembly. Though capable of producing flight quality solar subsystems, traditional methods of solar panel fabrication contain intrinsic inefficiencies and inconsistencies that compromise the subsystem’s overall reliability. Taking Michigan Exploration Laboratory’s (MXL) heritage solar panel procedures as a case study, this investigation sought to streamline the solar panel fabrication process to increase its yield, co...
Parallel Implementation of Similarity Measures on GPU Architecture using CUDA
Kuldeep Yadav
2012-02-01
Full Text Available Image processing and pattern recognition algorithms take more time for execution on a single core processor. Graphics Processing Unit (GPU is more popular now-a-days due to their speed, programmability,low cost and more inbuilt execution cores in it. Most of the researchers started work to use GPUs as a processing unit with a single core computer system to speedup execution of algorithms and in the field of Content based medical image retrieval (CBMIR, Euclidean distance and Mahalanobis plays an important role in retrieval of images. Distance formula is important because it plays an important role in matching the images. In this research work, we parallelized Euclidean distance algorithm on CUDA. CPU with Intel® Dual-CoreE5500 @ 2.80GHz and 2.0 GB of main memory which run on Windows XP (SP2. The next step was to convert this code in GPU format i.e. to run this program on GPU NVIDIA GeForce series 9500GT model having 1023MB of video memory of DDR2 type and bus width of 64bit. The graphic driver we used is of 270.81 series of NVIDIA. In this paper both the CPU and GPU version of algorithm is being implemented on the MATLABR2010. The CPU version of the algorithm is being analyzed in simple MATLAB but the GPU version is being implemented with the help of intermediate software Jacket-win-1.3.0. For using Jacket, we have to make some changes in our source code so to make the CPU and GPU to work simultaneously and thus reducing the overall computational acceleration . Our work employs extensive usage of highly multithreaded architecture of multicored GPU. An efficient use of shared memory is required to optimize parallel reduction in Compute Unified Device Architecture (CUDA, Graphic Processing Units (GPUs are emerging as powerful parallel systems at a cheap cost of a few thousand rupees.
cellGPU: Massively parallel simulations of dynamic vertex models
Sussman, Daniel M.
2017-10-01
Vertex models represent confluent tissue by polygonal or polyhedral tilings of space, with the individual cells interacting via force laws that depend on both the geometry of the cells and the topology of the tessellation. This dependence on the connectivity of the cellular network introduces several complications to performing molecular-dynamics-like simulations of vertex models, and in particular makes parallelizing the simulations difficult. cellGPU addresses this difficulty and lays the foundation for massively parallelized, GPU-based simulations of these models. This article discusses its implementation for a pair of two-dimensional models, and compares the typical performance that can be expected between running cellGPU entirely on the CPU versus its performance when running on a range of commercial and server-grade graphics cards. By implementing the calculation of topological changes and forces on cells in a highly parallelizable fashion, cellGPU enables researchers to simulate time- and length-scales previously inaccessible via existing single-threaded CPU implementations. Program Files doi:http://dx.doi.org/10.17632/6j2cj29t3r.1 Licensing provisions: MIT Programming language: CUDA/C++ Nature of problem: Simulations of off-lattice "vertex models" of cells, in which the interaction forces depend on both the geometry and the topology of the cellular aggregate. Solution method: Highly parallelized GPU-accelerated dynamical simulations in which the force calculations and the topological features can be handled on either the CPU or GPU. Additional comments: The code is hosted at https://gitlab.com/dmsussman/cellGPU, with documentation additionally maintained at http://dmsussman.gitlab.io/cellGPUdocumentation
National Aeronautics and Space Administration — The objective of this project was to use GPU enabled computing to accelerate the analyses of heat transfer and thermal effects. Graphical processing unit (GPU)...
基于GPU FPGA芯片原型的VxWorks下驱动软件开发%Development of Driver Software for GPU Based on FPGA in VxWorks
马城城; 田泽; 黎小玉
2013-01-01
为满足日益复杂的应用需求、减轻CPU日益繁重的图形处理任务,促使图形处理器GPU产生、应用和不断发展.驱动软件作为GPU的重要组成部分,与GPU硬件的契合程度直接影响整个图形系统性能的发挥,出于各种原因高端GPU配套的图形驱动软件对外不公开或价格昂贵,对图形应用系统的开发带来不便.文中基于自研GPU芯片FPGA原型图形系统,讲述了VxWorks下GPU驱动软件的设计与实现,该驱动软件为用户提供3D处理和2D处理接口.其中3D处理实现完整的OpenGL1.3基本库及GLU、GLUT辅助库;2D处理使用VxWorks操作系统的WindML组件实现.较好实现了图形处理软件与硬件的配合,对自主GPU芯片应用开发意义重大.%In order to meet the complicated application demand and reduce the increasingly graphic task on CPU,the Graphic Process Unit (GPU) has developed continually.The driver is an important part of GPU that affects the performance of whole system by cooperating with GPU hardware.It's difficult to create graphic applications on GPU because the driver is not opened for many reasons.It introduces the design and implementation of self-design GPU driver based on VxWorks.The driver offers 3D operation and 2D operation.The 3D operation achieves OpenGL1.3 kernel library,GLU library and GLUT library.2D operation is realized by WindML in VxWorks.The driver does well in the cooperation between graphic hardware and graphic software.It provides a useful reference for application on GPU chip.
Streamline segment scaling behavior in a turbulent wavy channel flow
Rubbert, A.; Hennig, F.; Klaas, M.; Pitsch, H.; Schröder, W.; Peters, N.
2017-02-01
A turbulent flow in a wavy channel was investigated by tomographic particle-image velocimetry measurements and direct numerical simulations. To analyze the turbulent structures and their scaling behavior in a flow undergoing favorable and adverse pressure gradients, the streamline segmentation method proposed by Wang (J Fluid Mech 648:183-203, 2010) was employed. This method yields joint statistical information about velocity fluctuations and length scale distributions of non-overlapping structures within the flow. In particular, the joint statistical properties are notably influenced by the pressure distribution. Previous findings from flat channel flows and synthetic turbulence simulations concerning the normalized segment length distribution could be reproduced and therefore appear to be largely universal. However, the mean streamline segment length of accelerating and decelerating segments varies within one wavelength typically elongating segments of the type which corresponds to the local mean flow. Furthermore, the local pressure gradient was found to significantly impact local joint streamline segmentation statistics as a main influence on their inherent asymmetry.
Dividing Streamline Formation Channel Confluences by Physical Modeling
Minarni Nur Trilita
2010-02-01
Full Text Available Confluence channels are often found in open channel network system and is the most important element. The incoming flow from the branch channel to the main cause various forms and cause vortex flow. Phenomenon can cause erosion of the side wall of the channel, the bed channel scour and sedimentation in the downstream confluence channel. To control these problems needed research into the current width of the branch channel. The incoming flow from the branch channel to the main channel flow bounded by a line distributors (dividing streamline. In this paper, the wide dividing streamline observed in the laboratory using a physical model of two open channels, a square that formed an angle of 30º. Observations were made with a variety of flow coming from each channel. The results obtained in the laboratory observation that the width of dividing streamline flow is influenced by the discharge ratio between the channel branch with the main channel. While the results of a comparison with previous studies showing that the observation in the laboratory is smaller than the results of previous research.
Development of a GPU Compatible Version of the Fast Radiation Code RRTMG
Iacono, M. J.; Mlawer, E. J.; Berthiaume, D.; Cady-Pereira, K. E.; Suarez, M.; Oreopoulos, L.; Lee, D.
2012-12-01
through GPU technology. This large number of independent cases will allow us to take full advantage of the computational power of the latest GPUs, ensuring that all thread cores in the GPU remain active, a key criterion for obtaining significant speedup. The CUDA (Compute Unified Device Architecture) Fortran compiler developed by PGI and Nvidia will allow us to construct this parallel implementation on the GPU while remaining in the Fortran language. This implementation will scale very well across various CUDA-supported GPUs such as the recently released Fermi Nvidia cards. We will present the computational speed improvements of the GPU-compatible code relative to the standard CPU-based RRTMG with respect to a very large and diverse suite of atmospheric profiles. This suite will also be utilized to demonstrate the minimal impact of the code restructuring on the accuracy of radiation calculations. The GPU-compatible version of RRTMG will be directly applicable to future versions of GEOS-5, but it is also likely to provide significant associated benefits for other GCMs that employ RRTMG.
A study on the GPU based parallel computation of a projection image
Lee, Hyunjeong; Han, Miseon; Kim, Jeongtae
2017-05-01
Fast computation of projection images is crucial in many applications such as medical image reconstruction and light field image processing. To do that, parallelization of the computation and efficient implementation of the computation using a parallel processor such as GPGPU (General-Purpose computing on Graphics Processing Units) is essential. In this research, we investigate methods for parallel computation of projection images and efficient implementation of the methods using CUDA (Compute Unified Device Architecture). We also study how to efficiently use the memory of GPU for the parallel processing.
Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU
Yong Xia
2015-01-01
Full Text Available Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation and the other is the diffusion term of the monodomain model (partial differential equation. Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations.
High Performance GPU-Based Fourier Volume Rendering.
Abdellah, Marwan; Eldeib, Ayman; Sharawi, Amr
2015-01-01
Fourier volume rendering (FVR) is a significant visualization technique that has been used widely in digital radiography. As a result of its (N (2)logN) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are (N (3)) computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a 3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs. Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis. The introduction of the compute unified device architecture (CUDA) technology enables embarrassingly-parallel algorithms to run efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the FVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to a single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering pipeline entirely on recent GPU architectures.
High Performance GPU-Based Fourier Volume Rendering
Marwan Abdellah
2015-01-01
Full Text Available Fourier volume rendering (FVR is a significant visualization technique that has been used widely in digital radiography. As a result of its O(N2logN time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are O(N3 computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a 3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs. Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU on a per-dollar-basis. The introduction of the compute unified device architecture (CUDA technology enables embarrassingly-parallel algorithms to run efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the FVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to a single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering pipeline entirely on recent GPU architectures.
GPU Lossless Hyperspectral Data Compression System
Aranki, Nazeeh I.; Keymeulen, Didier; Kiely, Aaron B.; Klimesh, Matthew A.
2014-01-01
Hyperspectral imaging systems onboard aircraft or spacecraft can acquire large amounts of data, putting a strain on limited downlink and storage resources. Onboard data compression can mitigate this problem but may require a system capable of a high throughput. In order to achieve a high throughput with a software compressor, a graphics processing unit (GPU) implementation of a compressor was developed targeting the current state-of-the-art GPUs from NVIDIA(R). The implementation is based on the fast lossless (FL) compression algorithm reported in "Fast Lossless Compression of Multispectral-Image Data" (NPO- 42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26, which operates on hyperspectral data and achieves excellent compression performance while having low complexity. The FL compressor uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. The new Consultative Committee for Space Data Systems (CCSDS) Standard for Lossless Multispectral & Hyperspectral image compression (CCSDS 123) is based on the FL compressor. The software makes use of the highly-parallel processing capability of GPUs to achieve a throughput at least six times higher than that of a software implementation running on a single-core CPU. This implementation provides a practical real-time solution for compression of data from airborne hyperspectral instruments.
Su, Xiaoquan; Wang, Xuetao; Jing, Gongchao; Ning, Kang
2014-04-01
The number of microbial community samples is increasing with exponential speed. Data-mining among microbial community samples could facilitate the discovery of valuable biological information that is still hidden in the massive data. However, current methods for the comparison among microbial communities are limited by their ability to process large amount of samples each with complex community structure. We have developed an optimized GPU-based software, GPU-Meta-Storms, to efficiently measure the quantitative phylogenetic similarity among massive amount of microbial community samples. Our results have shown that GPU-Meta-Storms would be able to compute the pair-wise similarity scores for 10 240 samples within 20 min, which gained a speed-up of >17 000 times compared with single-core CPU, and >2600 times compared with 16-core CPU. Therefore, the high-performance of GPU-Meta-Storms could facilitate in-depth data mining among massive microbial community samples, and make the real-time analysis and monitoring of temporal or conditional changes for microbial communities possible. GPU-Meta-Storms is implemented by CUDA (Compute Unified Device Architecture) and C++. Source code is available at http://www.computationalbioenergy.org/meta-storms.html.
BlazeDEM3D-GPU A Large Scale DEM simulation code for GPUs
Govender, Nicolin; Wilke, Daniel; Pizette, Patrick; Khinast, Johannes
2017-06-01
Accurately predicting the dynamics of particulate materials is of importance to numerous scientific and industrial areas with applications ranging across particle scales from powder flow to ore crushing. Computational discrete element simulations is a viable option to aid in the understanding of particulate dynamics and design of devices such as mixers, silos and ball mills, as laboratory scale tests comes at a significant cost. However, the computational time required to simulate an industrial scale simulation which consists of tens of millions of particles can take months to complete on large CPU clusters, making the Discrete Element Method (DEM) unfeasible for industrial applications. Simulations are therefore typically restricted to tens of thousands of particles with highly detailed particle shapes or a few million of particles with often oversimplified particle shapes. However, a number of applications require accurate representation of the particle shape to capture the macroscopic behaviour of the particulate system. In this paper we give an overview of the recent extensions to the open source GPU based DEM code, BlazeDEM3D-GPU, that can simulate millions of polyhedra and tens of millions of spheres on a desktop computer with a single or multiple GPUs.
Research on GPU Acceleration for Monte Carlo Criticality Calculation
Xu, Qi; Yu, Ganglin; Wang, Kan
2014-06-01
The Monte Carlo neutron transport method can be naturally parallelized by multi-core architectures due to the dependency between particles during the simulation. The GPU+CPU heterogeneous parallel mode has become an increasingly popular way of parallelism in the field of scientific supercomputing. Thus, this work focuses on the GPU acceleration method for the Monte Carlo criticality simulation, as well as the computational efficiency that GPUs can bring. The "neutron transport step" is introduced to increase the GPU thread occupancy. In order to test the sensitivity of the MC code's complexity, a 1D one-group code and a 3D multi-group general purpose code are respectively transplanted to GPUs, and the acceleration effects are compared. The result of numerical experiments shows considerable acceleration effect of the "neutron transport step" strategy. However, the performance comparison between the 1D code and the 3D code indicates the poor scalability of MC codes on GPUs.
Identifying attributes of GPU programs for difficulty evaluation
Dale Tristram
2014-08-01
Full Text Available General-purpose computation on graphics processing units (GPGPU has great potential to accelerate many scientific models and algorithms. However, some problems are considerably more difficult to accelerate than others, and it may be challenging for those new to GPGPU to ascertain the difficulty of accelerating a particular problem. Through what was learned in the acceleration of three problems, problem attributes have been identified that can assist in the evaluation of the difficulty of accelerating a problem on a GPU. The identified attributes are a problem's available parallelism, inherent parallelism, synchronisation requirements, and data transfer requirements. We envisage that with further development, these attributes could form the foundation of a difficulty classification system that could be used to determine whether GPU acceleration is practical for a candidate GPU acceleration problem, aid in identifying appropriate techniques and optimisations, and outline the required GPGPU knowledge.
Numerical simulation of lava flow using a GPU SPH model
Eugenio Rustico
2011-12-01
Full Text Available A smoothed particle hydrodynamics (SPH method for lava-flow modeling was implemented on a graphical processing unit (GPU using the compute unified device architecture (CUDA developed by NVIDIA. This resulted in speed-ups of up to two orders of magnitude. The three-dimensional model can simulate lava flow on a real topography with free-surface, non-Newtonian fluids, and with phase change. The entire SPH code has three main components, neighbor list construction, force computation, and integration of the equation of motion, and it is computed on the GPU, fully exploiting the computational power. The simulation speed achieved is one to two orders of magnitude faster than the equivalent central processing unit (CPU code. This GPU implementation of SPH allows high resolution SPH modeling in hours and days, rather than in weeks and months, on inexpensive and readily available hardware.
A High Performance Image Authentication Algorithm on GPU with CUDA
Caiwei Lin
2011-03-01
Full Text Available There has been large amounts of research on image authentication method. Many of the schemes perform well in verification results; however, most of them are time-consuming in traditional serial manners. And improving the efficiency of authentication process has become one of the challenges in image authentication field today. In the future, it’s a trend that authentication system with the properties of high performance, real-time, flexible and ease for development. In this paper, we present a CUDA-based implementation of an image authentication algorithm with NVIDIA’s Tesla C1060 GPU devices. Comparing with the original implementation on CPU, our CUDA-based implementation works 20x-50x faster with single GPU device. And experiment shows that, by using two GPUs, the performance gains can be further improved around 1.2 times in contras to single GPU.
A Novel Architecture of Multi-GPU Computing Card
Sen Guo
2013-08-01
Full Text Available The data transmission between GPUS in the existing multi_GPU computing card is often through PCIE which is in relative low speed, so the PCIE has become bottleneck of Overall performance. A novel architecture of multi_GPU computing card have been proposed in this paper: A multi-channel memory which have multiple interfaces is added, including one common interface shared by different GPUs, which is connected with a FPGA arbitration circuit and several other interfaces connected with dedicated GPUs frame buffer independently, and this multi-channel memory is called "global shared memory". The result of a simulation of accelerating computer tomography algebraic reconstruction on multi-GPU demonstrates effectiveness of this approach.
Direct numerical simulation of turbulence using GPU accelerated supercomputers
Khajeh-Saeed, Ali; Blair Perot, J.
2013-02-01
Direct numerical simulations of turbulence are optimized for up to 192 graphics processors. The results from two large GPU clusters are compared to the performance of corresponding CPU clusters. A number of important algorithm changes are necessary to access the full computational power of graphics processors and these adaptations are discussed. It is shown that the handling of subdomain communication becomes even more critical when using GPU based supercomputers. The potential for overlap of MPI communication with GPU computation is analyzed and then optimized. Detailed timings reveal that the internal calculations are now so efficient that the operations related to MPI communication are the primary scaling bottleneck at all but the very largest problem sizes that can fit on the hardware. This work gives a glimpse of the CFD performance issues will dominate many hardware platform in the near future.
A GPU-Computing Approach to Solar Stokes Profile Inversion
Harker, Brian J
2012-01-01
We present a new computational approach to the inversion of solar photospheric Stokes polarization profiles, under the Milne-Eddington model, for vector magnetography. Our code, named GENESIS (GENEtic Stokes Inversion Strategy), employs multi-threaded parallel-processing techniques to harness the computing power of graphics processing units GPUs, along with algorithms designed to exploit the inherent parallelism of the Stokes inversion problem. Using a genetic algorithm (GA) engineered specifically for use with a GPU, we produce full-disc maps of the photospheric vector magnetic field from polarized spectral line observations recorded by the Synoptic Optical Long-term Investigations of the Sun (SOLIS) Vector Spectromagnetograph (VSM) instrument. We show the advantages of pairing a population-parallel genetic algorithm with data-parallel GPU-computing techniques, and present an overview of the Stokes inversion problem, including a description of our adaptation to the GPU-computing paradigm. Full-disc vector ma...
Accelerated 3D Monte Carlo light dosimetry using a graphics processing unit (GPU) cluster
Lo, William Chun Yip; Lilge, Lothar
2010-11-01
This paper presents a basic computational framework for real-time, 3-D light dosimetry on graphics processing unit (GPU) clusters. The GPU-based approach offers a direct solution to overcome the long computation time preventing Monte Carlo simulations from being used in complex optimization problems such as treatment planning, particularly if simulated annealing is employed as the optimization algorithm. The current multi- GPU implementation is validated using a commercial light modelling software (ASAP from Breault Research Organization). It also supports the latest Fermi GPU architecture and features an interactive 3-D visualization interface. The software is available for download at http://code.google.com/p/gpu3d.
Reduced energy consumption by using streamlined gating systems
Seren Skov-Hansen; Niels Skat Tiedje
2008-01-01
In foundries a lot of effort is done to minimize energy consumption in the production to reduce costs and hence increase the competitiveness. At the same time the foundries must live up to the increased demands for high quality castings.Traditional gating systems are known for a straight tapered down runner, a well base and 90° bends in the runner system. Previous work has shown that the traditional way of designing gating systems creates high inconsistency in flow patterns during filling. In the streamlined gating systems there are no sharp changes in direction and a large effort is done to confine and control the flow of the molten metal during mould filling. The main objective in the work presented here is to use the principles of the streamlined gating systems to reduce the weight of the gating system relative to the traditional layouts. By reducing the weight of gating system and thereby improving yield, the amount of molten iron needed is also reduced, hence reducing the energy consumption for melting.Experiments in real production lines have proven that it is possible to achieve a reduction in the poured weight by using the streamlined gating systems. In a layout for casting of three valve housings in a vertically parted mould the weight of the gating system was reduced by 1.1 kg changing from the traditional layouts to the streamlined gating systems. This weight reduction corresponds in this case to a 20% weight reduction for the gating system. Using streamlined gating systems with fan gates to give a beneficial heat distribution in the castings may be an efficient tool to eliminate the need for heat treatment. In the experiments the change in gating system from the traditional layout to the streamlined layout removed the need for heat treatment. This obviously means a huge energy saving in the foundry. The energy consumption for heat treatment of iron has been found to be 0.489 kWh/kg. The valve housing in the experiments weighs 3 kg so when the need for
GPU-based four-dimensional general-relativistic ray tracing
Kuchelmeister, Daniel; Müller, Thomas; Ament, Marco; Wunner, Günter; Weiskopf, Daniel
2012-10-01
This paper presents a new general-relativistic ray tracer that enables image synthesis on an interactive basis by exploiting the performance of graphics processing units (GPUs). The application is capable of visualizing the distortion of the stellar background as well as trajectories of moving astronomical objects orbiting a compact mass. Its source code includes metric definitions for the Schwarzschild and Kerr spacetimes that can be easily extended to other metric definitions, relying on its object-oriented design. The basic functionality features a scene description interface based on the scripting language Lua, real-time image output, and the ability to edit almost every parameter at runtime. The ray tracing code itself is implemented for parallel execution on the GPU using NVidia's Compute Unified Device Architecture (CUDA), which leads to performance improvement of an order of magnitude compared to a single CPU and makes the application competitive with small CPU cluster architectures. Program summary Program title: GpuRay4D Catalog identifier: AEMV_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 73649 No. of bytes in distributed program, including test data, etc.: 1334251 Distribution format: tar.gz Programming language: C++, CUDA. Computer: Linux platforms with a NVidia CUDA enabled GPU (Compute Capability 1.3 or higher), C++ compiler, NVCC (The CUDA Compiler Driver). Operating system: Linux. RAM: 2 GB Classification: 1.5. External routines: OpenGL Utility Toolkit development files, NVidia CUDA Toolkit 3.2, Lua5.2 Nature of problem: Ray tracing in four-dimensional Lorentzian spacetimes. Solution method: Numerical integration of light rays, GPU-based parallel programming using CUDA, 3D
Multi-GPU three dimensional Stokes solver for simulating glacier flow
Licul, Aleksandar; Herman, Frédéric; Podladchikov, Yuri; Räss, Ludovic; Omlin, Samuel
2016-04-01
Here we present how we have recently developed a three-dimensional Stokes solver on the GPUs and apply it to a glacier flow. We numerically solve the Stokes momentum balance equations together with the incompressibility equation, while also taking into account strong nonlinearities for ice rheology. We have developed a fully three-dimensional numerical MATLAB application based on an iterative finite difference scheme with preconditioning of residuals. Differential equations are discretized on a regular staggered grid. We have ported it to C-CUDA to run it on GPU's in parallel, using MPI. We demonstrate the accuracy and efficiency of our developed model by manufactured analytical solution test for three-dimensional Stokes ice sheet models (Leng et al.,2013) and by comparison with other well-established ice sheet models on diagnostic ISMIP-HOM benchmark experiments (Pattyn et al., 2008). The results show that our developed model is capable to accurately and efficiently solve Stokes system of equations in a variety of different test scenarios, while preserving good parallel efficiency on up to 80 GPU's. For example, in 3D test scenarios with 250000 grid points our solver converges in around 3 minutes for single precision computations and around 10 minutes for double precision computations. We have also optimized the developed code to efficiently run on our newly acquired state-of-the-art GPU cluster octopus. This allows us to solve our problem on more than 20 million grid points, by just increasing the number of GPU used, while keeping the computation time the same. In future work we will apply our solver to real world applications and implement the free surface evolution capabilities. REFERENCES Leng,W.,Ju,L.,Gunzburger,M. & Price,S., 2013. Manufactured solutions and the verification of three-dimensional stokes ice-sheet models. Cryosphere 7,19-29. Pattyn, F., Perichon, L., Aschwanden, A., Breuer, B., de Smedt, B., Gagliardini, O., Gudmundsson,G.H., Hindmarsh, R
GPU accelerated spectral finite elements on all-hex meshes
Remacle, J.-F.; Gandham, R.; Warburton, T.
2016-11-01
This paper presents a spectral element finite element scheme that efficiently solves elliptic problems on unstructured hexahedral meshes. The discrete equations are solved using a matrix-free preconditioned conjugate gradient algorithm. An additive Schwartz two-scale preconditioner is employed that allows h-independence convergence. An extensible multi-threading programming API is used as a common kernel language that allows runtime selection of different computing devices (GPU and CPU) and different threading interfaces (CUDA, OpenCL and OpenMP). Performance tests demonstrate that problems with over 50 million degrees of freedom can be solved in a few seconds on an off-the-shelf GPU.
STEM image simulation with hybrid CPU/GPU programming.
Yao, Y; Ge, B H; Shen, X; Wang, Y G; Yu, R C
2016-07-01
STEM image simulation is achieved via hybrid CPU/GPU programming under parallel algorithm architecture to speed up calculation on a personal computer (PC). To utilize the calculation power of a PC fully, the simulation is performed using the GPU core and multi-CPU cores at the same time to significantly improve efficiency. GaSb and an artificial GaSb/InAs interface with atom diffusion have been used to verify the computation. Copyright © 2016 Elsevier B.V. All rights reserved.
Streamlining digital signal processing a tricks of the trade guidebook
2012-01-01
Streamlining Digital Signal Processing, Second Edition, presents recent advances in DSP that simplify or increase the computational speed of common signal processing operations and provides practical, real-world tips and tricks not covered in conventional DSP textbooks. It offers new implementations of digital filter design, spectrum analysis, signal generation, high-speed function approximation, and various other DSP functions. It provides:Great tips, tricks of the trade, secrets, practical shortcuts, and clever engineering solutions from seasoned signal processing professionalsAn assortment.
Streamlined library programming how to improve services and cut costs
Porter-Reynolds, Daisy
2014-01-01
In their roles as community centers, public libraries offer many innovative and appealing programs; but under current budget cuts, library resources are stretched thin. With slashed budgets and limited staff hours, what can libraries do to best serve their publics? This how-to guide provides strategies for streamlining library programming in public libraries while simultaneously maintaining-or even improving-quality delivery. The wide variety of principles and techniques described can be applied on a selective basis to libraries of all sizes. Based upon the author's own extensive experience as
Using the GPU based Model Tsunami-HySEA for the Italian CTSP
Gonzalez Vida, J. M., Sr.; Castro, M. J.; Macias, J.; de la Asuncion, M.; Molinari, I.; Melini, D.; Romano, F.; Tonini, R.; Lorito, S.; Piatanesi, A.
2015-12-01
The Istituto Nazionale di Geofisica e Vulcanologia of Italy (INGV) in collaboration with the EDANYA Group (University of Málaga) are proposing a FTRT (Faster Than Real Time) tsunami simulation approach that is being implemented in the NEAMTWS Italian CTSP, namely the Centro Allerta Tsunami (CAT), which is in pre-operational stage starting from 1 October 2014, in the 24/7 seismic monitoring room at INGV. We here present the different versions and capabilities of the NTHMP benchmarked HySEA model, developed by EDANYA Group. HySEA implements a multi-GPU version that can compute in several minutes the maximum amplitude and arrival time of the main tsunami wave in about 17,000 selected locations along the Mediterranean. At the same time, HySEA is implemented in nested meshes with different resolution and multi-GPU environment, which allows much faster than real time (below few minutes) inundation simulations. The performances of the code allows as well the preparation of a huge number of different pre-calculated scenarios that are being used for PTHA and for warning applications. Acknowledgements. This research has been partially supported by the Junta de Andalucía research project TESELA (P11-RNM7069), the Spanish Government Research project DAIFLUID (MTM2012-38383-C02-01). Also these results have received funding from the Italian Flagship Project RITMARE, from the INGV-DPC agreement, All.B2, and from EU FP7 project ASTARTE, Assessment, Strategy and Risk Reduction for Tsunamis in Europe grant n° 603839 (Project ASTARTE). The multi-GPU computationswere performed at the Laboratory of Numerical Methods (University of Malaga).
Bridging FPGA and GPU technologies for AO real-time control
Perret, Denis; Lainé, Maxime; Bernard, Julien; Gratadour, Damien; Sevin, Arnaud
2016-07-01
Our team has developed a common environment for high performance simulations and real-time control of AO systems based on the use of Graphics Processors Units in the context of the COMPASS project. Such a solution, based on the ability of the real time core in the simulation to provide adequate computing performance, limits the cost of developing AO RTC systems and makes them more scalable. A code developed and validated in the context of the simulation may be injected directly into the system and tested on sky. Furthermore, the use of relatively low cost components also offers significant advantages for the system hardware platform. However, the use of GPUs in an AO loop comes with drawbacks: the traditional way of offloading computation from CPU to GPUs - involving multiple copies and unacceptable overhead in kernel launching - is not well suited in a real time context. This last application requires the implementation of a solution enabling direct memory access (DMA) to the GPU memory from a third party device, bypassing the operating system. This allows this device to communicate directly with the real-time core of the simulation feeding it with the WFS camera pixel stream. We show that DMA between a custom FPGA-based frame-grabber and a computation unit (GPU, FPGA, or Coprocessor such as Xeon-phi) across PCIe allows us to get latencies compatible with what will be needed on ELTs. As a fine-grained synchronization mechanism is not yet made available by GPU vendors, we propose the use of memory polling to avoid interrupts handling and involvement of a CPU. Network and Vision protocols are handled by the FPGA-based Network Interface Card (NIC). We present the results we obtained on a complete AO loop using camera and deformable mirror simulators.
GPU-based fast Monte Carlo dose calculation for proton therapy.
Jia, Xun; Schümann, Jan; Paganetti, Harald; Jiang, Steve B
2012-12-07
Accurate radiation dose calculation is essential for successful proton radiotherapy. Monte Carlo (MC) simulation is considered to be the most accurate method. However, the long computation time limits it from routine clinical applications. Recently, graphics processing units (GPUs) have been widely used to accelerate computationally intensive tasks in radiotherapy. We have developed a fast MC dose calculation package, gPMC, for proton dose calculation on a GPU. In gPMC, proton transport is modeled by the class II condensed history simulation scheme with a continuous slowing down approximation. Ionization, elastic and inelastic proton nucleus interactions are considered. Energy straggling and multiple scattering are modeled. Secondary electrons are not transported and their energies are locally deposited. After an inelastic nuclear interaction event, a variety of products are generated using an empirical model. Among them, charged nuclear fragments are terminated with energy locally deposited. Secondary protons are stored in a stack and transported after finishing transport of the primary protons, while secondary neutral particles are neglected. gPMC is implemented on the GPU under the CUDA platform. We have validated gPMC using the TOPAS/Geant4 MC code as the gold standard. For various cases including homogeneous and inhomogeneous phantoms as well as a patient case, good agreements between gPMC and TOPAS/Geant4 are observed. The gamma passing rate for the 2%/2 mm criterion is over 98.7% in the region with dose greater than 10% maximum dose in all cases, excluding low-density air regions. With gPMC it takes only 6-22 s to simulate 10 million source protons to achieve ∼1% relative statistical uncertainty, depending on the phantoms and energy. This is an extremely high efficiency compared to the computational time of tens of CPU hours for TOPAS/Geant4. Our fast GPU-based code can thus facilitate the routine use of MC dose calculation in proton therapy.
GPU并行计算编程技术介绍%Introduction to GPU Parallel Programming Technology
王泽寰; 王鹏
2013-01-01
近年来 GPU 通用计算蓬勃发展。程序开发者和 GPU 通用计算应用程序的数量增长很快。针对不同的应用程序的要求和程序开发者不同的使用习惯，围绕着CUDA架构的GPU，NVIDIA及其合作伙伴共同开发了很多种不同的编程技术。本文详细介绍了它们的特点和适用对象。希望可以帮助广大开发人员针对自己的编程习惯和程序要求选择最为合适的编程技术。%Recently, GPGPU is developing rapidly. We can see a speedy growth in the number of programmers and the GPGPU applications. Tailoring to the demands of different applications and the various programming habits of the programmers, NVIDIA has co-developed different technologies used on GPU of CUDA architecture with its partners. This article is written for the purpose of making an elaborated introduction to them, their respective characteristics, and their target users. It is also written in the hope that programmers and developers can choose their most suitable technologies.
Bustamam, Alhadi; Burrage, Kevin; Hamilton, Nicholas A
2012-01-01
Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However,with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data.
Edge-preserving image denoising via group coordinate descent on the GPU.
McGaffin, Madison Gray; Fessler, Jeffrey A
2015-04-01
Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel 1D pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates in-place and only store the noisy data, denoised image, and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation. Both algorithms use the majorize-minimize framework to solve the 1D pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time.
A GPU-Based Gibbs Sampler for a Unidimensional IRT Model.
Sheng, Yanyan; Welling, William S; Zhu, Michelle M
2014-01-01
Item response theory (IRT) is a popular approach used for addressing large-scale statistical problems in psychometrics as well as in other fields. The fully Bayesian approach for estimating IRT models is usually memory and computationally expensive due to the large number of iterations. This limits the use of the procedure in many applications. In an effort to overcome such restraint, previous studies focused on utilizing the message passing interface (MPI) in a distributed memory-based Linux cluster to achieve certain speedups. However, given the high data dependencies in a single Markov chain for IRT models, the communication overhead rapidly grows as the number of cluster nodes increases. This makes it difficult to further improve the performance under such a parallel framework. This study aims to tackle the problem using massive core-based graphic processing units (GPU), which is practical, cost-effective, and convenient in actual applications. The performance comparisons among serial CPU, MPI, and compute unified device architecture (CUDA) programs demonstrate that the CUDA GPU approach has many advantages over the CPU-based approach and therefore is preferred.
Fast GPU based adaptive filtering of 4D echocardiography.
Broxvall, Mathias; Emilsson, Kent; Thunberg, Per
2012-06-01
Time resolved three-dimensional (3D) echocardiography generates four-dimensional (3D+time) data sets that bring new possibilities in clinical practice. Image quality of four-dimensional (4D) echocardiography is however regarded as poorer compared to conventional echocardiography where time-resolved 2D imaging is used. Advanced image processing filtering methods can be used to achieve image improvements but to the cost of heavy data processing. The recent development of graphics processing unit (GPUs) enables highly parallel general purpose computations, that considerably reduces the computational time of advanced image filtering methods. In this study multidimensional adaptive filtering of 4D echocardiography was performed using GPUs. Filtering was done using multiple kernels implemented in OpenCL (open computing language) working on multiple subsets of the data. Our results show a substantial speed increase of up to 74 times, resulting in a total filtering time less than 30 s on a common desktop. This implies that advanced adaptive image processing can be accomplished in conjunction with a clinical examination. Since the presented GPU processor method scales linearly with the number of processing elements, we expect it to continue scaling with the expected future increases in number of processing elements. This should be contrasted with the increases in data set sizes in the near future following the further improvements in ultrasound probes and measuring devices. It is concluded that GPUs facilitate the use of demanding adaptive image filtering techniques that in turn enhance 4D echocardiographic data sets. The presented general methodology of implementing parallelism using GPUs is also applicable for other medical modalities that generate multidimensional data.
Stratified Flow Past a Hill: Dividing Streamline Concept Revisited
Leo, Laura S.; Thompson, Michael Y.; Di Sabatino, Silvana; Fernando, Harindra J. S.
2016-06-01
The Sheppard formula (Q J R Meteorol Soc 82:528-529, 1956) for the dividing streamline height H_s assumes a uniform velocity U_∞ and a constant buoyancy frequency N for the approach flow towards a mountain of height h, and takes the form H_s/h=( {1-F} ) , where F=U_{∞}/Nh. We extend this solution to a logarithmic approach-velocity profile with constant N. An analytical solution is obtained for H_s/h in terms of Lambert-W functions, which also suggests alternative scaling for H_s/h. A `modified' logarithmic velocity profile is proposed for stably stratified atmospheric boundary-layer flows. A field experiment designed to observe H_s is described, which utilized instrumentation from the spring field campaign of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program. Multiple releases of smoke at F≈ 0.3-0.4 support the new formulation, notwithstanding the limited success of experiments due to logistical constraints. No dividing streamline is discerned for F≈ 10, since, if present, it is too close to the foothill. Flow separation and vortex shedding is observed in this case. The proposed modified logarithmic profile is in reasonable agreement with experimental observations.
Assessment of RANS to predict flows with large streamline curvature
Yin, J. L.; Wang, D. Z.; Cheng, H.; Gu, W. G.
2013-12-01
In order to provide a guideline for choosing turbulence models in computation of complex flows with large streamline curvature, this paper presents a comprehensive comparison investigation of different RANS models widely used in engineering to check each model's sensibility on the streamline curvature. First, different models including standard k-ε, Realizable k-ε, Renormalization-group (RNG) k-ε model, Shear-stress transport k-ω model and non-linear eddy-viscosity model v2-f model are tested to simulated the flow in a 2D U-bend which has the standard bench mark available. The comparisons in terms of non-dimensional velocity and turbulent kinetic energy show that large differences exist among the results calculated by various models. To further validate the capability to predict flows with secondary flows, the involved models are tested in a 3D 90° bend flow. Also, the velocities are compared. As a summary, the advantages and disadvantages of each model are analysed and guidelines for choice of turbulence model are presented.
GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking
Friborg, Rune Møllegaard; Hauberg, Søren; Erleben, Kenny
For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster...
GPU accelerated likelihoods for stereo-based articulated tracking
Friborg, Rune Møllegaard; Hauberg, Søren; Erleben, Kenny
2010-01-01
For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster...
Optimizing a mobile robot control system using GPU acceleration
Tuck, Nat; McGuinness, Michael; Martin, Fred
2012-01-01
This paper describes our attempt to optimize a robot control program for the Intelligent Ground Vehicle Competition (IGVC) by running computationally intensive portions of the system on a commodity graphics processing unit (GPU). The IGVC Autonomous Challenge requires a control program that performs a number of different computationally intensive tasks ranging from computer vision to path planning. For the 2011 competition our Robot Operating System (ROS) based control system would not run comfortably on the multicore CPU on our custom robot platform. The process of profiling the ROS control program and selecting appropriate modules for porting to run on a GPU is described. A GPU-targeting compiler, Bacon, is used to speed up development and help optimize the ported modules. The impact of the ported modules on overall performance is discussed. We conclude that GPU optimization can free a significant amount of CPU resources with minimal effort for expensive user-written code, but that replacing heavily-optimized library functions is more difficult, and a much less efficient use of time.
High-Performance Matrix-Vector Multiplication on the GPU
Sørensen, Hans Henrik Brandenborg
2012-01-01
In this paper, we develop a high-performance GPU kernel for one of the most popular dense linear algebra operations, the matrix-vector multiplication. The target hardware is the most recent Nvidia Tesla 20-series (Fermi architecture), which is designed from the ground up for scientific computing...
GPU Acceleration of Graph Matching, Clustering, and Partitioning
Fagginger Auer, B.O.|info:eu-repo/dai/nl/326659072
2013-01-01
We consider sequential algorithms for hypergraph partitioning and GPU (i.e., fine-grained shared-memory parallel) algorithms for graph partitioning and clustering. Our investigation into sequential hypergraph partitioning is concerned with the efficient construction of high-quality matchings for hyp
GPU-Boosted Camera-Only Indoor Localization
Özkil, Ali Gürcan; Fan, Zhun; Kristensen, Jens Klæstrup
relies on local image features detection, description and matching; by parallelizing these computationally intensive tasks on the graphical processing unit (GPU), it is possible to do online localization using a Topometric Appearance Map. The method is developed as an integral part of a mobile service...
GPU based contouring method on grid DEM data
Tan, Liheng; Wan, Gang; Li, Feng; Chen, Xiaohui; Du, Wenlong
2017-08-01
This paper presents a novel method to generate contour lines from grid DEM data based on the programmable GPU pipeline. The previous contouring approaches often use CPU to construct a finite element mesh from the raw DEM data, and then extract contour segments from the elements. They also need a tracing or sorting strategy to generate the final continuous contours. These approaches can be heavily CPU-costing and time-consuming. Meanwhile the generated contours would be unsmooth if the raw data is sparsely distributed. Unlike the CPU approaches, we employ the GPU's vertex shader to generate a triangular mesh with arbitrary user-defined density, in which the height of each vertex is calculated through a third-order Cardinal spline function. Then in the same frame, segments are extracted from the triangles by the geometry shader, and translated to the CPU-side with an internal order in the GPU's transform feedback stage. Finally we propose a ;Grid Sorting; algorithm to achieve the continuous contour lines by travelling the segments only once. Our method makes use of multiple stages of GPU pipeline for computation, which can generate smooth contour lines, and is significantly faster than the previous CPU approaches. The algorithm can be easily implemented with OpenGL 3.3 API or higher on consumer-level PCs.
Computing 2D constrained delaunay triangulation using the GPU.
Qi, Meng; Cao, Thanh-Tung; Tan, Tiow-Seng
2013-05-01
We propose the first graphics processing unit (GPU) solution to compute the 2D constrained Delaunay triangulation (CDT) of a planar straight line graph (PSLG) consisting of points and edges. There are many existing CPU algorithms to solve the CDT problem in computational geometry, yet there has been no prior approach to solve this problem efficiently using the parallel computing power of the GPU. For the special case of the CDT problem where the PSLG consists of just points, which is simply the normal Delaunay triangulation (DT) problem, a hybrid approach using the GPU together with the CPU to partially speed up the computation has already been presented in the literature. Our work, on the other hand, accelerates the entire computation on the GPU. Our implementation using the CUDA programming model on NVIDIA GPUs is numerically robust, and runs up to an order of magnitude faster than the best sequential implementations on the CPU. This result is reflected in our experiment with both randomly generated PSLGs and real-world GIS data having millions of points and edges.
GPU Acceleration of Graph Matching, Clustering, and Partitioning
Fagginger Auer, B.O.
2013-01-01
We consider sequential algorithms for hypergraph partitioning and GPU (i.e., fine-grained shared-memory parallel) algorithms for graph partitioning and clustering. Our investigation into sequential hypergraph partitioning is concerned with the efficient construction of high-quality matchings for hyp
Study on increasing calculation precision and convergence speed of streamline strip element method
彭艳; 刘宏民
2004-01-01
The calculation precision and convergence speed of streamline strip element method are increased by using the method whose initial value of the exit lateral displacement is determined with strip element variation method, and the accurate tension lateral distribution model is adopted based on the original third power spline function streamline strip element method. The basic theory of the strip element method is developed. The calculated results by the improved streamline strip element method and the original streamline strip element method are compared with the measured results, showing that the calculated results of the improved method are in good agreement with the measured results.
赵嵩; 徐彦; 曹海旺; 杨恒
2015-01-01
提出了一种多特征融合粒子滤波跟踪算法，并利用 GPU (Graphic Processing Unit)技术对算法进行了并行优化。针对单一特征描述目标模型的缺陷，此算法采用了具有互补性的灰度与梯度直方图特征建立目标模型，从而提高粒子滤波算法跟踪的稳定性和精度。同时，针对粒子滤波计算量大的缺点，此算法对粒子滤波进行了基于GPU 的并行优化设计和实现，从而提升跟踪算法的计算速度。可以满足算法的实时性应用。%A parallel particle filter object tracking algorithm is given out,which is based on multiple feature fusion with the help of GPU (Graphic Processing Unit)technology.Due to the limitation of the model representation based on single visual feature,two complementary visual features,which are gray histogram and gradient histogram,are used in the algorithm to improve the tracking stability and accuracy.Moreover,to handle the large amount computation cost of the particle filter,a GPU parallel optimized scheme is designed to improve the algorithm speed. and can meet the real-time application requirement.
A Modeling Approach based on UML/MARTE for GPU Architecture
Rodrigues, Antonio Wendell De Oliveira; Dekeyser, Jean-Luc
2011-01-01
Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many efforts have been done to describe abstractions of applications in relation to their target architectures. Thus, when developers need to associate applications and GPUs, for example, they find difficulty and prefer using API for these architectures. This paper presents a metamodel extension for MARTE profile and a model for GPU architectures. The main goal is to specify the task and data allocation in the memory hierarchy of these architectures. The results show that this approach will help to generate code for GPUs based on model transformations using Model Driven Engineering (MDE).
Gao, Jun; Zhu, Ruixin; Liu, Qi; Cao, Zhiwei
2011-01-01
Classical Petri net has been applied into biological analysis, especially as a qualitative model for biochemical pathways analysis, but lack of the ability for quantitative kinetic simulations. In our study, we presented an integra- tion work of the qualitative model--Petri nets with the quantitative approach-ordinary differential equations （ODEs） for the modeling and analysis of metabolic networks. As an application of our study, the computational modeling of arachidonic acid （AA） biochemical network was provided. A Petri net was set up to present the constraint-based dynamic simulations on AA metabolic network combined with the validated ODEs model. Furthermore, Graphics Processing Unit （GPU） was adopted to accelerate the calculation of kinetic parameters unavailable from experi- ments. Our results have indicated that the proposed simulation method was practicable and useful with GPU accel- eration, and provides new clues for the large-scale qualitative and quantitative models of biochemical networks.
Complex fluid flow modeling with SPH on GPU
Bilotta, Giuseppe; Hérault, Alexis; Del Negro, Ciro; Russo, Giovanni; Vicari, Annamaria
2010-05-01
We describe an implementation of the Smoothed Particle Hydrodynamics (SPH) method for the simulation of complex fluid flows. The algorithm is entirely executed on Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) developed by NVIDIA and fully exploiting their computational power. An increase of one to two orders of magnitude in simulation speed over equivalent CPU code is achieved. A complete modeling of the flow of a complex fluid such as lava is challenging from the modelistic, numerical and computational points of view. The natural topography irregularities, the dynamic free boundaries and phenomena such as solidification, presence of floating solid bodies or other obstacles and their eventual fragmentation make the problem difficult to solve using traditional numerical methods (finite volumes, finite elements): the need to refine the discretization grid in correspondence of high gradients, when possible, is computationally expensive and with an often inadequate control of the error; for real-world applications, moreover, the information needed by the grid refinement may not be available (e.g. because the Digital Elevation Models are too coarse); boundary tracking is also problematic with Eulerian discretizations, more so with complex fluids due to the presence of internal boundaries given by fluid inhomogeneity and presence of solidification fronts. An alternative approach is offered by mesh-free particle methods, that solve most of the problems connected to the dynamics of complex fluids in a natural way. Particle methods discretize the fluid using nodes which are not forced on a given topological structure: boundary treatment is therefore implicit and automatic; the movement freedom of the particles also permits the treatment of deformations without incurring in any significant penalty; finally, the accuracy is easily controlled by the insertion of new particles where needed. Our team has developed a new model based on the
GHOSTM: a GPU-accelerated homology search tool for metagenomics.
Shuji Suzuki
Full Text Available BACKGROUND: A large number of sensitive homology searches are required for mapping DNA sequence fragments to known protein sequences in public and private databases during metagenomic analysis. BLAST is currently used for this purpose, but its calculation speed is insufficient, especially for analyzing the large quantities of sequence data obtained from a next-generation sequencer. However, faster search tools, such as BLAT, do not have sufficient search sensitivity for metagenomic analysis. Thus, a sensitive and efficient homology search tool is in high demand for this type of analysis. METHODOLOGY/PRINCIPAL FINDINGS: We developed a new, highly efficient homology search algorithm suitable for graphics processing unit (GPU calculations that was implemented as a GPU system that we called GHOSTM. The system first searches for candidate alignment positions for a sequence from the database using pre-calculated indexes and then calculates local alignments around the candidate positions before calculating alignment scores. We implemented both of these processes on GPUs. The system achieved calculation speeds that were 130 and 407 times faster than BLAST with 1 GPU and 4 GPUs, respectively. The system also showed higher search sensitivity and had a calculation speed that was 4 and 15 times faster than BLAT with 1 GPU and 4 GPUs. CONCLUSIONS: We developed a GPU-optimized algorithm to perform sensitive sequence homology searches and implemented the system as GHOSTM. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We developed GHOSTM, which is a cost-efficient tool, and offer this tool as a potential solution to this problem.
Geological Visualization System with GPU-Based Interpolation
Huang, L.; Chen, K.; Lai, Y.; Chang, P.; Song, S.
2011-12-01
There has been a large number of research using parallel-processing GPU to accelerate the computation. In Near Surface Geology efficient interpolations are critical for proper interpretation of measured data. Additionally, an appropriate interpolation method for generating proper results depends on the factors such as the dense of the measured locations and the estimation model. Therefore, fast interpolation process is needed to efficiently find a proper interpolation algorithm for a set of collected data. However, a general CPU framework has to process each computation in a sequential manner and is not efficient enough to handle a large number of interpolation generally needed in Near Surface Geology. When carefully observing the interpolation processing, the computation for each grid point is independent from all other computation. Therefore, the GPU parallel framework should be an efficient technology to accelerate the interpolation process which is critical in Near Surface Geology. Thus in this paper we design a geological visualization system whose core includes a set of interpolation algorithms including Nearest Neighbor, Inverse Distance and Kriging. All these interpolation algorithms are implemented using both the CPU framework and GPU framework. The comparison between CPU and GPU implementation in the aspect of precision and processing speed shows that parallel computation can accelerate the interpolation process and also demonstrates the possibility of using GPU-equipped personal computer to replace the expensive workstation. Immediate update at the measurement site is the dream of geologists. In the future the parallel and remote computation ability of cloud will be explored to make the mobile computation on the measurement site possible.
Comparison of a 3-D GPU-Assisted Maxwell Code and Ray Tracing for Reflectometry on ITER
Gady, Sarah; Kubota, Shigeyuki; Johnson, Irena
2015-11-01
Electromagnetic wave propagation and scattering in magnetized plasmas are important diagnostics for high temperature plasmas. 1-D and 2-D full-wave codes are standard tools for measurements of the electron density profile and fluctuations; however, ray tracing results have shown that beam propagation in tokamak plasmas is inherently a 3-D problem. The GPU-Assisted Maxwell Code utilizes the FDTD (Finite-Difference Time-Domain) method for solving the Maxwell equations with the cold plasma approximation in a 3-D geometry. Parallel processing with GPGPU (General-Purpose computing on Graphics Processing Units) is used to accelerate the computation. Previously, we reported on initial comparisons of the code results to 1-D numerical and analytical solutions, where the size of the computational grid was limited by the on-board memory of the GPU. In the current study, this limitation is overcome by using domain decomposition and an additional GPU. As a practical application, this code is used to study the current design of the ITER Low Field Side Reflectometer (LSFR) for the Equatorial Port Plug 11 (EPP11). A detailed examination of Gaussian beam propagation in the ITER edge plasma will be presented, as well as comparisons with ray tracing. This work was made possible by funding from the Department of Energy for the Summer Undergraduate Laboratory Internship (SULI) program. This work is supported by the US DOE Contract No.DE-AC02-09CH11466 and DE-FG02-99-ER54527.
Francés, J.; Bleda, S.; Neipp, C.; Márquez, A.; Pascual, I.; Beléndez, A.
2013-03-01
The finite-difference time-domain method (FDTD) allows electromagnetic field distribution analysis as a function of time and space. The method is applied to analyze holographic volume gratings (HVGs) for the near-field distribution at optical wavelengths. Usually, this application requires the simulation of wide areas, which implies more memory and time processing. In this work, we propose a specific implementation of the FDTD method including several add-ons for a precise simulation of optical diffractive elements. Values in the near-field region are computed considering the illumination of the grating by means of a plane wave for different angles of incidence and including absorbing boundaries as well. We compare the results obtained by FDTD with those obtained using a matrix method (MM) applied to diffraction gratings. In addition, we have developed two optimized versions of the algorithm, for both CPU and GPU, in order to analyze the improvement of using the new NVIDIA Fermi GPU architecture versus highly tuned multi-core CPU as a function of the size simulation. In particular, the optimized CPU implementation takes advantage of the arithmetic and data transfer streaming SIMD (single instruction multiple data) extensions (SSE) included explicitly in the code and also of multi-threading by means of OpenMP directives. A good agreement between the results obtained using both FDTD and MM methods is obtained, thus validating our methodology. Moreover, the performance of the GPU is compared to the SSE+OpenMP CPU implementation, and it is quantitatively determined that a highly optimized CPU program can be competitive for a wider range of simulation sizes, whereas GPU computing becomes more powerful for large-scale simulations.
Cheng, Chun-Pei; Lan, Kuo-Lun; Liu, Wen-Chun; Chang, Ting-Tsung; Tseng, Vincent S
2016-12-01
Hepatitis B viral (HBV) infection is strongly associated with an increased risk of liver diseases like cirrhosis or hepatocellular carcinoma (HCC). Many lines of evidence suggest that deletions occurring in HBV genomic DNA are highly associated with the activity of HBV via the interplay between aberrant viral proteins release and human immune system. Deletions finding on the HBV whole genome sequences is thus a very important issue though there exist underlying the challenges in mining such big and complex biological data. Although some next generation sequencing (NGS) tools are recently designed for identifying structural variations such as insertions or deletions, their validity is generally committed to human sequences study. This design may not be suitable for viruses due to different species. We propose a graphics processing unit (GPU)-based data mining method called DeF-GPU to efficiently and precisely identify HBV deletions from large NGS data, which generally contain millions of reads. To fit the single instruction multiple data instructions, sequencing reads are referred to as multiple data and the deletion finding procedure is referred to as a single instruction. We use Compute Unified Device Architecture (CUDA) to parallelize the procedures, and further validate DeF-GPU on 5 synthetic and 1 real datasets. Our results suggest that DeF-GPU outperforms the existing commonly-used method Pindel and is able to exactly identify the deletions of our ground truth in few seconds. The source code and other related materials are available at https://sourceforge.net/projects/defgpu/.
High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System.
Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram
2014-01-01
We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second.
Feasibility of GPU-assisted iterative image reconstruction for mobile C-arm CT
Pan, Yongsheng; Whitaker, Ross; Cheryauka, Arvi; Ferguson, Dave
2009-02-01
Computed tomography (CT) has been extensively studied and widely used for a variety of medical applications. The reconstruction of 3D images from a projection series is an important aspect of the modality. Reconstruction by filtered backprojection (FBP) is used by most manufacturers because of speed, ease of implementation, and relatively few parameters. Iterative reconstruction methods have a significant potential to provide superior performance with incomplete or noisy data, or with less than ideal geometries, such as cone-beam systems. However, iterative methods have a high computational cost, and regularization is usually required to reduce the effects of noise. The simultaneous algebraic reconstruction technique (SART) is studied in this paper, where the Feldkamp method (FDK) for filtered back projection is used as an initialization for iterative SART. Additionally, graphics hardware is utilized to increase the speed of SART implementation. Nvidia processors and compute unified device architecture (CUDA) form the platform for GPU computation. Total variation (TV) minimization is applied for the regularization of SART results. Preliminary results of SART on 3-D Shepp-Logan phantom using using TV regularization and GPU computation are presented in this paper. Potential improvements of the proposed framework are also discussed.
GPU Multi-Scale Particle Tracking and Multi-Fluid Simulations of the Radiation Belts
Ziemba, T.; Carscadden, J.; O'Donnell, D.; Winglee, R.; Harnett, E.; Cash, M.
2007-12-01
The properties of the radiation belts can vary dramatically under the influence of magnetic storms and storm-time substorms. The task of understanding and predicting radiation belt properties is made difficult because their properties determined by global processes as well as small-scale wave-particle interactions. A full solution to the problem will require major innovations in technique and computer hardware. The proposed work will demonstrates liked particle tracking codes with new multi-scale/multi-fluid global simulations that provide the first means to include small-scale processes within the global magnetospheric context. A large hurdle to the problem is having sufficient computer hardware that is able to handle the dissipate temporal and spatial scale sizes. A major innovation of the work is that the codes are designed to run of graphics processing units (GPUs). GPUs are intrinsically highly parallelized systems that provide more than an order of magnitude computing speed over a CPU based systems, for little more cost than a high end-workstation. Recent advancements in GPU technologies allow for full IEEE float specifications with performance up to several hundred GFLOPs per GPU and new software architectures have recently become available to ease the transition from graphics based to scientific applications. This allows for a cheap alternative to standard supercomputing methods and should increase the time to discovery. A demonstration of the code pushing more than 500,000 particles faster than real time is presented, and used to provide new insight into radiation belt dynamics.
Real-time ray tracing of implicit surfaces on the GPU.
Singh, Jag Mohan; Narayanan, P J
2010-01-01
Compact representation of geometry using a suitable procedural or mathematical model and a ray-tracing mode of rendering fit the programmable graphics processor units (GPUs) well. Several such representations including parametric and subdivision surfaces have been explored in recent research. The important and widely applicable category of the general implicit surface has received less attention. In this paper, we present a ray-tracing procedure to render general implicit surfaces efficiently on the GPU. Though only the fourth or lower order surfaces can be rendered using analytical roots, our adaptive marching points algorithm can ray trace arbitrary implicit surfaces without multiple roots, by sampling the ray at selected points till a root is found. Adapting the sampling step size based on a proximity measure and a horizon measure delivers high speed. The sign test can handle any surface without multiple roots. The Taylor test that uses ideas from interval analysis can ray trace many surfaces with complex roots. Overall, a simple algorithm that fits the SIMD architecture of the GPU results in high performance. We demonstrate the ray tracing of algebraic surfaces up to order 50 and nonalgebraic surfaces including a Blinn's blobby with 75 spheres at better than interactive frame rates.
Lonardo, Alessandro; Ammendola, Roberto; Biagioni, Andrea; Cotta Ramusino, Angelo; Fiorini, Massimiliano; Frezza, Ottorino; Lamanna, Gianluca; Lo Cicero, Francesca; Martinelli, Michele; Neri, Ilaria; Paolucci, Pier Stanislao; Pastorelli, Elena; Pontisso, Luca; Rossetti, Davide; Simeone, Francesco; Simula, Francesco; Sozzi, Marco; Tosoratto, Laura; Vicini, Piero
2015-01-01
The capability of processing high bandwidth data streams in real-time is a computational requirement common to many High Energy Physics experiments. Keeping the latency of the data transport tasks under control is essential in order to meet this requirement. We present NaNet, a FPGA-based PCIe Network Interface Card design featuring Remote Direct Memory Access towards CPU and GPU memories plus a transport protocol offload module characterized by cycle-accurate upper-bound handling. The combination of these two features allows to relieve almost entirely the OS and the application from data tranfer management, minimizing the unavoidable jitter effects associated to OS process scheduling. The design currently supports one GbE (1000Base-T) and three custom 34 Gbps APElink I/O channels, but four-channels 10GbE (10Base-R) and 2.5 Gbps deterministic latency KM3link versions are being implemented. Two use cases of NaNet will be discussed: the GPU-based low level trigger for the RICH detector in the NA62 experiment an...
GPU-accelerated computing for Lagrangian coherent structures of multi-body gravitational regimes
Lin, Mingpei; Xu, Ming; Fu, Xiaoyu
2017-04-01
Based on a well-established theoretical foundation, Lagrangian Coherent Structures (LCSs) have elicited widespread research on the intrinsic structures of dynamical systems in many fields, including the field of astrodynamics. Although the application of LCSs in dynamical problems seems straightforward theoretically, its associated computational cost is prohibitive. We propose a block decomposition algorithm developed on Compute Unified Device Architecture (CUDA) platform for the computation of the LCSs of multi-body gravitational regimes. In order to take advantage of GPU's outstanding computing properties, such as Shared Memory, Constant Memory, and Zero-Copy, the algorithm utilizes a block decomposition strategy to facilitate computation of finite-time Lyapunov exponent (FTLE) fields of arbitrary size and timespan. Simulation results demonstrate that this GPU-based algorithm can satisfy double-precision accuracy requirements and greatly decrease the time needed to calculate final results, increasing speed by approximately 13 times. Additionally, this algorithm can be generalized to various large-scale computing problems, such as particle filters, constellation design, and Monte-Carlo simulation.
Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method
Hao Jiang
2017-07-01
Full Text Available The use of unmanned aerial vehicles (UAV can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application.
GPU Implementation of Real-Time Biologically Inspired Face Detection using CUDA
Elham Askary
2013-07-01
Full Text Available In this paper massively parallel real-time face detection based on a visual attention and cortex-like mechanism of cognitive vision system is presented. As a first step, we use saliency map model to select salient face regions and HMAX C1 model to extract features from salient input image and then apply mixture of expert neural network to classify multi-view faces from nonface images. The saliency map model is a complex concept for bottom-up attention selection that includes many processes to find face regions in a visual science. Parallel real-time implementation on Graphics Processing Unit (GPU provides a solution for this kind of computationally intensive image processing. By implementing saliency map and HMAX C1 model on a multi-GPU platform using CUDA programming with memory bandwidth, we achieve good performance compared to recent CPU. Running on NVIDIA Geforce 8800 (GTX graphics card at resolution 640×480 detection rate of 97% is achieved. In addition, we evaluate our results using a height speed camera with other parallel methods on face detection application.
JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure
Labschutz, Matthias
2015-08-12
Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.
Topology of streamlines and vorticity contours for two - dimensional flows
Andersen, Morten
Considering a coordinate-free formulation of helical symmetry rather than more traditional definitions based on coordinates, we discuss basic properties of helical vector fields and compare results from the literature. For inviscid flow where a velocity field is generated by a sum of helical vortex...... generated by a helical vortex filament in an ideal fluid. The classical expression for the stream function obtained by Hardin (Phys. Fluids 25, 1982) contains an infinite sum of modified Bessel functions. Using the approach by Okulov (Russ. J. Eng. Thermophys. 5, 1995) we obtain a closed-form approximation...... by a point vortex above a wall in inviscid fluid. There is no reason to a priori expect equivalent results of the three vortex definitions. However, the study is mainly motivated by the findings of Kudela & Malecha (Fluid Dyn. Res. 41, 2009) who find good agreement between the vorticity and streamlines...
Streamline upwind finite element method for conjugate heat transfer problems
Niphon Wansophark; Atipong Malatip; Pramote Dechaumphai; Yunming Chen
2005-01-01
This paper presents a combined finite element method for solving conjugate heat transfer problems where heat conduction in a solid is coupled with heat convection in viscous fluid flow. The streamline upwind finite element method is used for the analysis of thermal viscous flow in the fluid region, whereas the analysis of heat conduction in solid region is performed by the Galerkin method. The method uses the three-node triangular element with equal-order interpolation functions for all the variables of the velocity components,the pressure and the temperature. The main advantage of the proposed method is to consistently couple heat transfer along the fluid-solid interface. Three test cases, i.e. conjugate Couette flow problem in parallel plate channel, counter-flow in heat exchanger, and conjugate natural convection in a square cavity with a conducting wall, are selected to evaluate the efficiency of the present method.
A Streamlined Strategy for Biohydrogen Production with an Alkaliphilic Bacterium
Elias, Dwayne A [ORNL; Wall, Judy D. [University of Missouri; Mormile, Dr. Melanie R. [Missouri University of Science and Technology; Begemann, Matthew B [University of Wisconsin, Madison
2012-01-01
Biofuels are anticipated to enable a shift from fossil fuels for renewable transportation and manufacturing fuels, with biohydrogen considered attractive since it could offer the largest reduction of global carbon budgets. Currently, biohydrogen production remains inefficient and heavily fossil fuel-dependent. However, bacteria using alkali-treated biomass could streamline biofuel production while reducing costs and fossil fuel needs. An alkaliphilic bacterium, Halanaerobium strain sapolanicus, is described that is capable of biohydrogen production at levels rivaling neutrophilic strains, but at pH 11 and hypersaline conditions. H. sapolanicus ferments a variety of 5- and 6- carbon sugars derived from hemicellulose and cellulose including cellobiose, and forms the end products hydrogen and acetate. Further, it can also produce biohydrogen from switchgrass and straw pretreated at temperatures far lower than any previously reported and in solutions compatible with growth. Hence, this bacterium can potentially increase the efficiency and efficacy of biohydrogen production from renewable biomass resources.
The Cassini Solstice Mission: Streamlining Operations by Sequencing with PIEs
Vandermey, Nancy; Alonge, Eleanor K.; Magee, Kari; Heventhal, William
2014-01-01
The Cassini Solstice Mission (CSM) is the second extended mission phase of the highly successful Cassini/Huygens mission to Saturn. Conducted at a much-reduced funding level, operations for the CSM have been streamlined and simplified significantly. Integration of the science timeline, which involves allocating observation time in a balanced manner to each of the five different science disciplines (with representatives from the twelve different science instruments), has long been a labor-intensive endeavor. Lessons learned from the prime mission (2004-2008) and first extended mission (Equinox mission, 2008-2010) were utilized to design a new process involving PIEs (Pre-Integrated Events) to ensure the highest priority observations for each discipline could be accomplished despite reduced work force and overall simplification of processes. Discipline-level PIE lists were managed by the Science Planning team and graphically mapped to aid timeline deconfliction meetings prior to assigning discrete segments of time to the various disciplines. Periapse segments are generally discipline-focused, with the exception of a handful of PIEs. In addition to all PIEs being documented in a spreadsheet, allocated out-of-discipline PIEs were entered into the Cassini Information Management System (CIMS) well in advance of timeline integration. The disciplines were then free to work the rest of the timeline internally, without the need for frequent interaction, debate, and negotiation with representatives from other disciplines. As a result, the number of integration meetings has been cut back extensively, freeing up workforce. The sequence implementation process was streamlined as well, combining two previous processes (and teams) into one. The new Sequence Implementation Process (SIP) schedules 22 weeks to build each 10-week-long sequence, and only 3 sequence processes overlap. This differs significantly from prime mission during which 5-week-long sequences were built in 24 weeks
The Cassini Solstice Mission: Streamlining Operations by Sequencing with PIEs
Vandermey, Nancy; Alonge, Eleanor K.; Magee, Kari; Heventhal, William
2014-01-01
The Cassini Solstice Mission (CSM) is the second extended mission phase of the highly successful Cassini/Huygens mission to Saturn. Conducted at a much-reduced funding level, operations for the CSM have been streamlined and simplified significantly. Integration of the science timeline, which involves allocating observation time in a balanced manner to each of the five different science disciplines (with representatives from the twelve different science instruments), has long been a labor-intensive endeavor. Lessons learned from the prime mission (2004-2008) and first extended mission (Equinox mission, 2008-2010) were utilized to design a new process involving PIEs (Pre-Integrated Events) to ensure the highest priority observations for each discipline could be accomplished despite reduced work force and overall simplification of processes. Discipline-level PIE lists were managed by the Science Planning team and graphically mapped to aid timeline deconfliction meetings prior to assigning discrete segments of time to the various disciplines. Periapse segments are generally discipline-focused, with the exception of a handful of PIEs. In addition to all PIEs being documented in a spreadsheet, allocated out-of-discipline PIEs were entered into the Cassini Information Management System (CIMS) well in advance of timeline integration. The disciplines were then free to work the rest of the timeline internally, without the need for frequent interaction, debate, and negotiation with representatives from other disciplines. As a result, the number of integration meetings has been cut back extensively, freeing up workforce. The sequence implementation process was streamlined as well, combining two previous processes (and teams) into one. The new Sequence Implementation Process (SIP) schedules 22 weeks to build each 10-week-long sequence, and only 3 sequence processes overlap. This differs significantly from prime mission during which 5-week-long sequences were built in 24 weeks
Simulation of tsunamis generated by landslides using adaptive mesh refinement on GPU
de la Asunción, M.; Castro, M. J.
2017-09-01
Adaptive mesh refinement (AMR) is a widely used technique to accelerate computationally intensive simulations, which consists of dynamically increasing the spatial resolution of the areas of interest of the domain as the simulation advances. During the last years there have appeared many publications that tackle the implementation of AMR-based applications in GPUs in order to take advantage of their massively parallel architecture. In this paper we present the first AMR-based application implemented on GPU for the simulation of tsunamis generated by landslides by using a two-layer shallow water system. We also propose a new strategy for the interpolation and projection of the values of the fine cells in the AMR algorithm based on the fluctuations of the state values instead of the usual approach of considering the current state values. Numerical experiments on artificial and realistic problems show the validity and efficiency of the solver.
Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters
Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.
2014-01-01
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.
Astrophysical data mining with GPU. A case study: genetic classification of globular clusters
Cavuoti, Stefano; Brescia, Massimo; Paolillo, Maurizio; Pescape', Antonio; Longo, Giuseppe; Ventre, Giorgio
2013-01-01
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource (http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200x in the training phase with respect to the CPU based version.
2012-05-31
... From the Federal Register Online via the Government Publishing Office FEDERAL COMMUNICATIONS COMMISSION 47 CFR Part 73 Policies To Promote Rural Radio Service and To Streamline Allotment and Assignment... policies to promote rural radio service and to streamline allotment and assignment procedures. This notice...
A Vocabulary Approach to Partial Streamline Matching and Exploratory Flow Visualization.
Tao, Jun; Wang, Chaoli; Shene, Ching-Kuang; Shaw, Raymond A
2016-05-01
Measuring the similarity of integral curves is fundamental to many important flow data analysis and visualization tasks such as feature detection, pattern querying, streamline clustering, and hierarchical exploration. In this paper, we introduce FlowString, a novel vocabulary approach that extracts shape invariant features from streamlines and utilizes a string-based method for exploratory streamline analysis and visualization. Our solution first resamples streamlines by considering their local feature scales. We then classify resampled points along streamlines based on the shape similarity around their local neighborhoods. We encode each streamline into a string of well-selected shape characters, from which we construct meaningful words for querying and retrieval. A unique feature of our approach is that it captures intrinsic streamline similarity that is invariant under translation, rotation and scaling. We design an intuitive interface and user interactions to support flexible querying, allowing exact and approximate searches for partial streamline matching. Users can perform queries at either the character level or the word level, and define their own characters or words conveniently for customized search. We demonstrate the effectiveness of FlowString with several flow field data sets of different sizes and characteristics. We also extend FlowString to handle multiple data sets and perform an empirical expert evaluation to confirm the usefulness of this approach.
High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms.
Teodoro, George; Pan, Tony; Kurc, Tahsin M; Kong, Jun; Cooper, Lee A D; Podhorszki, Norbert; Klasky, Scott; Saltz, Joel H
2013-05-01
Analysis of large pathology image datasets offers significant opportunities for the investigation of disease morphology, but the resource requirements of analysis pipelines limit the scale of such studies. Motivated by a brain cancer study, we propose and evaluate a parallel image analysis application pipeline for high throughput computation of large datasets of high resolution pathology tissue images on distributed CPU-GPU platforms. To achieve efficient execution on these hybrid systems, we have built runtime support that allows us to express the cancer image analysis application as a hierarchical data processing pipeline. The application is implemented as a coarse-grain pipeline of stages, where each stage may be further partitioned into another pipeline of fine-grain operations. The fine-grain operations are efficiently managed and scheduled for computation on CPUs and GPUs using performance aware scheduling techniques along with several optimizations, including architecture aware process placement, data locality conscious task assignment, data prefetching, and asynchronous data copy. These optimizations are employed to maximize the utilization of the aggregate computing power of CPUs and GPUs and minimize data copy overheads. Our experimental evaluation shows that the cooperative use of CPUs and GPUs achieves significant improvements on top of GPU-only versions (up to 1.6×) and that the execution of the application as a set of fine-grain operations provides more opportunities for runtime optimizations and attains better performance than coarser-grain, monolithic implementations used in other works. An implementation of the cancer image analysis pipeline using the runtime support was able to process an image dataset consisting of 36,848 4Kx4K-pixel image tiles (about 1.8TB uncompressed) in less than 4 minutes (150 tiles/second) on 100 nodes of a state-of-the-art hybrid cluster system.
Donatas Krušna
2013-03-01
Full Text Available OpenCL, a modern parallel heterogeneous system programming language, enables problems to be partitioned and executed on modern CPU and GPU hardware, this increases performance of such applications considerably. Since GPU's are optimized for floating point and vector operations and specialize in them, they outperform general purpose CPU's in this field greatly. This language greatly simplifies the creation of applications for such heterogeneous system since it's cross-platform, vendor independent and is embeddable , hence letting it be used in any other general purpose programming language via libraries. There is more and more tools being developed that are aimed at low level programmers and scientists or engineers alike, that are developing applications or libraries for CPU’s and GPU’s of today as well as other heterogeneous platforms. The tendency today is to increase the number of cores or CPU‘s in hopes of increasing performance, however the increasing difficulty of parallelizing applications for such systems and the even increasing overhead of communication and synchronization are limiting the potential performance. This means that there is a point at which increasing cores or CPU‘s will no longer increase applications performance, and even can diminish performance. Even though parallel programming and GPU‘s with stream computing capabilities have decreased the need for communication and synchronization (since only the final result needs to be committed to memory, however this still is a weak link in developing such applications.
CONVOLUTION-BASED DETECTION MODELS ACCELERATION BASED ON GPU%基于 GPU 的卷积检测模型加速
刘琦; 黄咨; 陈璐艳; 胡福乔
2016-01-01
In recent years,convolution-based detection models (CDM),such as the deformable part-based models (DPM)and the convolutional neural networks (CNN),have achieved tremendous success in computer vision field.These models allow for large-scale machine learning training to achieve higher robustness and recognition performance.However,the huge computational cost of convolution operation in training and evaluation processes also restricts their further application in many practical scenes.In this paper,we accelerate both the algorithm and hardware of convolution-based detection models with mathematical theory and parallelisation technique.In the aspect of algorithm,we reduce the computation complexity by converting the convolution operation in space domain to the point multiplication operation in frequency domain.While in the aspect of hardware,the use of graphical process unit (GPU)parallelisation technique can reduce the computational time further.Results of experiment on public dataset Pascal VOC demonstrate that compared with multi-core CPU,the proposed algorithm can realise speeding up the convolution process by 2.13 to 4.31 times on single commodity GPU.%近年来，形变部件模型和卷积神经网络等卷积检测模型在计算机视觉领域取得了极大的成功。这类模型能够进行大规模的机器学习训练，实现较高的鲁棒性和识别性能。然而训练和评估过程中卷积运算巨大的计算开销，也限制了其在诸多实际场景中进一步的应用。利用数学理论和并行技术对卷积检测模型进行算法和硬件的双重加速。在算法层面，通过将空间域中的卷积运算转换为频率域中的点乘运算来降低计算复杂度；而在硬件层面，利用 GPU 并行技术可以进一步减少计算时间。在 PASCAL VOC数据集上的实验结果表明，相对于多核 CPU，该算法能够实现在单个商用 GPU 上加速卷积过程2．13～4．31倍。
Combating the Reliability Challenge of GPU Register File at Low Supply Voltage
Tan, Jingweijia; Song, Shuaiwen; Yan, Kaige; Fu, Xin; Marquez, Andres; Kerbyson, Darren J.
2016-09-11
Supply voltage reduction is an effective approach to significantly reduce GPU energy consumption. As the largest on-chip storage structure, the GPU register file becomes the reliability hotspot that prevents further supply voltage reduction below the safe limit (Vmin) due to process variation effects. This work addresses the reliability challenge of the GPU register file at low supply voltages, which is an essential first step for aggressive supply voltage reduction of the entire GPU chip. We propose GR-Guard, an architectural solution that leverages long register dead time to enable reliable operations from unreliable register file at low voltages.
Nagaoka, Tomoaki; Watanabe, Soichi
2012-01-01
Electromagnetic simulation with anatomically realistic computational human model using the finite-difference time domain (FDTD) method has recently been performed in a number of fields in biomedical engineering. To improve the method's calculation speed and realize large-scale computing with the computational human model, we adapt three-dimensional FDTD code to a multi-GPU cluster environment with Compute Unified Device Architecture and Message Passing Interface. Our multi-GPU cluster system consists of three nodes. The seven GPU boards (NVIDIA Tesla C2070) are mounted on each node. We examined the performance of the FDTD calculation on multi-GPU cluster environment. We confirmed that the FDTD calculation on the multi-GPU clusters is faster than that on a multi-GPU (a single workstation), and we also found that the GPU cluster system calculate faster than a vector supercomputer. In addition, our GPU cluster system allowed us to perform the large-scale FDTD calculation because were able to use GPU memory of over 100 GB.
GPU phase-field lattice Boltzmann simulations of growth and motion of a binary alloy dendrite
Takaki, T.; Rojas, R.; Ohno, M.; Shimokawabe, T.; Aoki, T.
2015-06-01
A GPU code has been developed for a phase-field lattice Boltzmann (PFLB) method, which can simulate the dendritic growth with motion of solids in a dilute binary alloy melt. The GPU accelerated PFLB method has been implemented using CUDA C. The equiaxed dendritic growth in a shear flow and settling condition have been simulated by the developed GPU code. It has been confirmed that the PFLB simulations were efficiently accelerated by introducing the GPU computation. The characteristic dendrite morphologies which depend on the melt flow and the motion of the dendrite could also be confirmed by the simulations.
GPU-Based Tracking Algorithms for the ATLAS High-Level Trigger
Emeliyanov, D; The ATLAS collaboration
2012-01-01
GPU-accelerated event processing is one of the possible options for the ATLAS High-Level Trigger (HLT) upgrade for higher LHC luminosity. This poster presents data preparation and track finding algorithms specifically designed to run on a GPU using a “client-server” solution for hybrid CPU/GPU event processing and integration of the GPU algorithms into existing ATLAS HLT software. The resulting speed-up of event processing times obtained with high-luminosity simulated data is presented and discussed.
GPU-ACCELERATED FEM SOLVER FOR THREE DIMENSIONAL ELECTROMAGNETIC ANALYSIS
Tian Jin; Gong Li; Shi Xiaowei; Le Xu
2011-01-01
A new Graphics Processing Unit (GPU) parallelization strategy is proposed to accelerate sparse finite element computation for three dimensional electromagnetic analysis.The parallelization strategy is employed based on a new compression format called sliced ELL Four (sliced ELL-F).The sliced ELL-F format-based parallelization strategy is designed for hastening many addition,dot product,and Sparse Matrix Vector Product (SMVP) operations in the Conjugate Gradient Norm (CGN) calculation of finite element equations.The new implementation of SMVP on GPUs is evaluated.The proposed strategy executed on a GPU can efficiently solve sparse finite element equations,especially when the equations are huge sparse (size of most rows in a coefficient matrix is less than 8).Numerical results show the sliced ELL-F format-based parallelization strategy can reach significant speedups compared to Compressed Sparse Row (CSR) format.
GPU and APU computations of Finite Time Lyapunov Exponent fields
Conti, Christian; Rossinelli, Diego; Koumoutsakos, Petros
2012-03-01
We present GPU and APU accelerated computations of Finite-Time Lyapunov Exponent (FTLE) fields. The calculation of FTLEs is a computationally intensive process, as in order to obtain the sharp ridges associated with the Lagrangian Coherent Structures an extensive resampling of the flow field is required. The computational performance of this resampling is limited by the memory bandwidth of the underlying computer architecture. The present technique harnesses data-parallel execution of many-core architectures and relies on fast and accurate evaluations of moment conserving functions for the mesh to particle interpolations. We demonstrate how the computation of FTLEs can be efficiently performed on a GPU and on an APU through OpenCL and we report over one order of magnitude improvements over multi-threaded executions in FTLE computations of bluff body flows.
A GPU-Based Wide-Band Radio Spectrometer
Chennamangalam, Jayanth; Jones, Glenn; Chen, Hong; Ford, John; Kepley, Amanda; Lorimer, D R; Nie, Jun; Prestage, Richard; Roshi, D Anish; Wagner, Mark; Werthimer, Dan
2014-01-01
The Graphics Processing Unit (GPU) has become an integral part of astronomical instrumentation, enabling high-performance online data reduction and accelerated online signal processing. In this paper, we describe a wide-band reconfigurable spectrometer built using an off-the-shelf GPU card. This spectrometer, when configured as a polyphase filter bank (PFB), supports a dual-polarization bandwidth of up to 1.1 GHz (or a single-polarization bandwidth of up to 2.2 GHz) on the latest generation of GPUs. On the other hand, when configured as a direct FFT, the spectrometer supports a dual-polarization bandwidth of up to 1.4 GHz (or a single-polarization bandwidth of up to 2.8 GHz).
Implementation and Optimization of Image Processing Algorithms on Embedded GPU
Singhal, Nitin; Yoo, Jin Woo; Choi, Ho Yeol; Park, In Kyu
In this paper, we analyze the key factors underlying the implementation, evaluation, and optimization of image processing and computer vision algorithms on embedded GPU using OpenGL ES 2.0 shader model. First, we present the characteristics of the embedded GPU and its inherent advantage when compared to embedded CPU. Additionally, we propose techniques to achieve increased performance with optimized shader design. To show the effectiveness of the proposed techniques, we employ cartoon-style non-photorealistic rendering (NPR), speeded-up robust feature (SURF) detection, and stereo matching as our example algorithms. Performance is evaluated in terms of the execution time and speed-up achieved in comparison with the implementation on embedded CPU.
Betatron tune measurement with the LHC damper using a GPU
Dubouchet, Frédéric; Höfle, Wolfgang
This thesis studies a possible futur implementation of a betatron tune measure- ment in the Large Hadron Collider (LHC) at European organization for nuclear research (CERN) using a General Purpose Graphic Processing Unit (GPGPU) to analyse data acquired with the LHC transverse transverse damper (ADT). The present hardware and future possible implementations using ADT acquisi- tions and Graphic Processing Unit (GPU) computing are described. The ADT data have to be processed to extract the betatron tune. To compute the tune, the signal is transformed from the time domain to the frequency domain using Fast Fourier Transform (FFT) on GPUs. We show that it is possible to achieve one order of magnitude faster FFTs on a Fermi generation GPU than what can be done using a i7 generation Central Processing Unit (CPU). This makes online per bunch FFT computation and betatron tune measurement possible.
M. Alvanos
2017-10-01
Full Text Available This paper presents an application of GPU accelerators in Earth system modeling. We focus on atmospheric chemical kinetics, one of the most computationally intensive tasks in climate–chemistry model simulations. We developed a software package that automatically generates CUDA kernels to numerically integrate atmospheric chemical kinetics in the global climate model ECHAM/MESSy Atmospheric Chemistry (EMAC, used to study climate change and air quality scenarios. A source-to-source compiler outputs a CUDA-compatible kernel by parsing the FORTRAN code generated by the Kinetic PreProcessor (KPP general analysis tool. All Rosenbrock methods that are available in the KPP numerical library are supported.Performance evaluation, using Fermi and Pascal CUDA-enabled GPU accelerators, shows achieved speed-ups of 4. 5 × and 20. 4 × , respectively, of the kernel execution time. A node-to-node real-world production performance comparison shows a 1. 75 × speed-up over the non-accelerated application using the KPP three-stage Rosenbrock solver. We provide a detailed description of the code optimizations used to improve the performance including memory optimizations, control code simplification, and reduction of idle time. The accuracy and correctness of the accelerated implementation are evaluated by comparing to the CPU-only code of the application. The median relative difference is found to be less than 0.000000001 % when comparing the output of the accelerated kernel the CPU-only code.The approach followed, including the computational workload division, and the developed GPU solver code can potentially be used as the basis for hardware acceleration of numerous geoscientific models that rely on KPP for atmospheric chemical kinetics applications.
Avaliação de desempenho e consumo energético para configurações de Wavefront pools de uma GPU AMD
Ariel Gustavo Zuquello
2016-07-01
Full Text Available O uso de sistemas heterogêneos CPU-GPU para atender à crescente demanda por aplicações com grande paralelismo de dados resulta na necessidade de estudar e avaliar tais arquiteturas para melhorá-las continuamente. Neste artigo foram feitas simulações da execução de uma suíte de benchmark em uma GPU AMD ATI RadeonTM HD 7970, de modo a avaliar o impacto sobre o desempenho e o consumo energético quando alterado o número de Wavefront Pools presentes em cada compute unit da GPU, que é 4 por padrão. O resultado mais significante evidencia um aumento de velocidade de cerca de 5,7% para a configuração com duas Wavefront Pools em conjunto com um aumento no consumo de energia de cerca de 5,1%. Todavia, as outras configurações avaliadas também representam opções para diferentes tipos de necessidades, conforme a categoria de demanda computacional.Palavras-chave: Sistemas heterogêneos. Simulações. Desempenho.Performance evaluation and energy consumption for settings of Wavefront pools of a GPU AMDAbstractThe use of CPU-GPU heterogeneous systems to meet the growing demand for applications with large data parallelism results in the need to study and evaluate these architectures in order to improve them continuously. In this paper we made simulations of running a benchmark suite on an AMD GPU ATI RadeonTM HD 7970 in order to assess the impact on performance and power consumption when tuning the number of Wavefront Pools present in each GPU compute unit, which is 4 by default. The most significant result shows a speedup of about 5.7% for configuration with two Wavefront Pools in conjunction with an increase of about 5.1% in the energy consumption. However, the other evaluated configuration also represent options for different kinds of needs, according to the computational demand.Keyworks: Heterogeneous systems. Simulation. Performance.
Research on GPU Parallel Algorithm of Heat Conduction Based on CUDA%基于CUDA的热传导GPU并行算法研究
孟小华; 黄丛珊; 朱丽莎
2014-01-01
在热传导算法中，使用传统的CPU串行算法或MPI并行算法处理大批量粒子时，存在执行效率低、处理时间长的问题。而图形处理单元(GPU)具有大数据量并行运算的优势，为此，在统一计算设备架构(CUDA)并行编程环境下，采用CPU和GPU协同合作的模式，提出并实现一个基于CUDA的热传导GPU并行算法。根据GPU硬件配置设定Block和Grid的大小，将粒子划分为若干个block，粒子输入到GPU显卡中并行计算，每一个线程执行一个粒子计算，并将结果传回CPU主存，由CPU计算出每个粒子的平均热流。实验结果表明，与CPU串行算法在时间效率方面进行对比，该算法在粒子数到达16000时，加速比提高近900倍，并且加速比随着粒子数的增加而加速提高。%For real applications processing large volume of particles in one-dimensional heat conduction problem, the response time of CPU serial algorithm and MPI parallel algorithm is too long. Considering Graphic Processing Unit(GPU) offers powerful parallel processing capabilities, it implements a GPU parallel heat conduction algorithm on Compute Unified Device Architecture(CUDA) parallel programming environment using CPU and GPU collaborative mode. The algorithm sets the block and grid size based on GPU hardware configuration. Particles are divided into a plurality of blocks, the particle is into the GPU graphics for parallel computing, and one thread performs a calculation of a particle. It retrieves the processed data to CPU main memory and calculates the average heat flow of each particle. Experimental results show that, compared with CPU serial algorithm, GPU parallel algorithm has a great advantage in time efficiency, the speedup is close to 900, and speedup can improve as the particle number size increases.
Numerical simulation of lava flow using a GPU SPH model
Eugenio Rustico; Annamaria Vicari; Giuseppe Bilotta; Alexis Hérault; Ciro Del Negro
2011-01-01
A smoothed particle hydrodynamics (SPH) method for lava-flow modeling was implemented on a graphical processing unit (GPU) using the compute unified device architecture (CUDA) developed by NVIDIA. This resulted in speed-ups of up to two orders of magnitude. The three-dimensional model can simulate lava flow on a real topography with free-surface, non- Newtonian fluids, and with phase change. The entire SPH code has three main components, neighbor list construction, force computation, an...
A GPU Accelerated Spring Mass System for Surgical Simulation
Mosegaard, Jesper; Sørensen, Thomas Sangild
2005-01-01
There is a growing demand for surgical simulators to dofast and precise calculations of tissue deformation to simulateincreasingly complex morphology in real-time. Unfortunately, evenfast spring-mass based systems have slow convergence rates for largemodels. This paper presents a method to accele...... to accelerate computation of aspring-mass system in order to simulate a complex organ such as theheart. This acceleration is achieved by taking advantage of moderngraphics processing units (GPU)....
FIESTA 4: optimized Feynman integral calculations with GPU support
Smirnov, Alexander V
2015-01-01
This paper presents a new major release of the program FIESTA (Feynman Integral Evaluation by a Sector decomposiTion Approach). The new release is mainly aimed at optimal performance at large scales when one is increasing the number of sampling points in order to reduce the uncertainty estimates. The release now supports graphical processor units (GPU) for the numerical integration, methods to optimize cluster-usage, as well as other speed, memory, and stability improvements.
Stream programming framework for global ilumination techniques using a GPU
Marino, Federico J.; Abbate, Horacio Antonio
2007-01-01
Los procesadores de streams están comenzando a ser una alternativa accesible para implementar técnicas de rendering asistidas por hardware que habitualmente estaban relegadas al uso offline. Nosotros elaboramos un marco de trabajo para procesamiento de streams basado en los conceptos del modelo de Stream Programming, seleccionamos el algoritmo de Photon Mapping y una GPU (Graphics Processing Unit) Nvidia para una implementación de un caso de prueba. Definimos un conjunto de clases en C++ p...
Basket Option Pricing Using GP-GPU Hardware Acceleration
Douglas, Craig C.
2010-08-01
We introduce a basket option pricing problem arisen in financial mathematics. We discretized the problem based on the alternating direction implicit (ADI) method and parallel cyclic reduction is applied to solve the set of tridiagonal matrices generated by the ADI method. To reduce the computational time of the problem, a general purpose graphics processing units (GP-GPU) environment is considered. Numerical results confirm the convergence and efficiency of the proposed method. © 2010 IEEE.
Proceedings of the GPU computing in high-energy physics conference 2014 GPUHEP2014
Bonati, Claudio; D' Elia, Massimo; Lamanna, Gianluca; Sozzi, Marco (eds.)
2015-06-15
The International Conference on GPUs in High-Energy Physics was held from September 10 to 12, 2014 at the University of Pisa, Italy. It represented a larger scale follow-up to a set of workshops which indicated the rising interest of the HEP community, experimentalists and theorists alike, towards the use of inexpensive and massively parallel computing devices, for very diverse purposes. The conference was organized in plenary sessions of invited and contributed talks, and poster presentations on the following topics: - GPUs in triggering applications - Low-level trigger systems based on GPUs - Use of GPUs in high-level trigger systems - GPUs in tracking and vertexing - Challenges for triggers in future HEP experiments - Reconstruction and Monte Carlo software on GPUs - Software frameworks and tools for GPU code integration - Hard real-time use of GPUs - Lattice QCD simulation - GPUs in phenomenology - GPUs for medical imaging purposes - GPUs in neutron and photon science - Massively parallel computations in HEP - Code parallelization. ''GPU computing in High-Energy Physics'' attracted 78 registrants to Pisa. The 38 oral presentations included talks on specific topics in experimental and theoretical applications of GPUs, as well as review talks on applications and technology. 5 posters were also presented, and were introduced by a short plenary oral illustration. A company exhibition was hosted on site. The conference consisted of 12 plenary sessions, together with a social program which included a banquet and guided excursions around Pisa. It was overall an enjoyable experience, offering an opportunity to share ideas and opinions, and getting updated on other participants' work in this emerging field, as well as being a valuable introduction for newcomers interested to learn more about the use of GPUs as accelerators for scientific progress on the elementary constituents of matter and energy.
Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU
Arefan D
2015-06-01
Full Text Available Digital Breast Tomosynthesis (DBT is a technology that creates three dimensional (3D images of breast tissue. Tomosynthesis mammography detects lesions that are not detectable with other imaging systems. If image reconstruction time is in the order of seconds, we can use Tomosynthesis systems to perform Tomosynthesis-guided Interventional procedures. This research has been designed to study ultra-fast image reconstruction technique for Tomosynthesis Mammography systems using Graphics Processing Unit (GPU. At first, projections of Tomosynthesis mammography have been simulated. In order to produce Tomosynthesis projections, it has been designed a 3D breast phantom from empirical data. It is based on MRI data in its natural form. Then, projections have been created from 3D breast phantom. The image reconstruction algorithm based on FBP was programmed with C++ language in two methods using central processing unit (CPU card and the Graphics Processing Unit (GPU. It calculated the time of image reconstruction in two kinds of programming (using CPU and GPU.
Bin recycling strategy for improving the histogram precision on GPU
Cárdenas-Montes, Miguel; Rodríguez-Vázquez, Juan José; Vega-Rodríguez, Miguel A.
2016-07-01
Histogram is an easily comprehensible way to present data and analyses. In the current scientific context with access to large volumes of data, the processing time for building histogram has dramatically increased. For this reason, parallel construction is necessary to alleviate the impact of the processing time in the analysis activities. In this scenario, GPU computing is becoming widely used for reducing until affordable levels the processing time of histogram construction. Associated to the increment of the processing time, the implementations are stressed on the bin-count accuracy. Accuracy aspects due to the particularities of the implementations are not usually taken into consideration when building histogram with very large data sets. In this work, a bin recycling strategy to create an accuracy-aware implementation for building histogram on GPU is presented. In order to evaluate the approach, this strategy was applied to the computation of the three-point angular correlation function, which is a relevant function in Cosmology for the study of the Large Scale Structure of Universe. As a consequence of the study a high-accuracy implementation for histogram construction on GPU is proposed.
Isaev, S. A.; Baranov, P. A.; Sudakov, A. G.; Popov, I. A.
2016-08-01
A modification of the popular model of shear stress transport aimed at calculating the separation flow of an incompressible viscous liquid is justified. The modification eliminates the nonphysical pumping of the vortex viscosity in the cores of large-scale vortices. It has been verified with regard to the influence of the streamline curvature on the vortex viscosity by introducing a reciprocal linear function of the turbulent Richardson number with the Isaev-Kharchenko-Usachov constant equal to 0.02.Verification is based on solving the test problem an axisymmetric steady flow about a disk-cylinder tandem with an optimally configured nose, which has an ultralow profile drag for a Reynolds number of 5 × 105. It has been shown that the Menter combined boundary conditions are valid if y + y of the wall does not exceed two.
Streamlining the Design-to-Build Transition with Build-Optimization Software Tools.
Oberortner, Ernst; Cheng, Jan-Fang; Hillson, Nathan J; Deutsch, Samuel
2017-03-17
Scaling-up capabilities for the design, build, and test of synthetic biology constructs holds great promise for the development of new applications in fuels, chemical production, or cellular-behavior engineering. Construct design is an essential component in this process; however, not every designed DNA sequence can be readily manufactured, even using state-of-the-art DNA synthesis methods. Current biological computer-aided design and manufacture tools (bioCAD/CAM) do not adequately consider the limitations of DNA synthesis technologies when generating their outputs. Designed sequences that violate DNA synthesis constraints may require substantial sequence redesign or lead to price-premiums and temporal delays, which adversely impact the efficiency of the DNA manufacturing process. We have developed a suite of build-optimization software tools (BOOST) to streamline the design-build transition in synthetic biology engineering workflows. BOOST incorporates knowledge of DNA synthesis success determinants into the design process to output ready-to-build sequences, preempting the need for sequence redesign. The BOOST web application is available at https://boost.jgi.doe.gov and its Application Program Interfaces (API) enable integration into automated, customized DNA design processes. The herein presented results highlight the effectiveness of BOOST in reducing DNA synthesis costs and timelines.
A Fast GPU-accelerated Mixed-precision Strategy for Fully NonlinearWater Wave Computations
Glimberg, Stefan Lemvig; Engsig-Karup, Allan Peter; Madsen, Morten G.
2011-01-01
We present performance results of a mixed-precision strategy developed to improve a recently developed massively parallel GPU-accelerated tool for fast and scalable simulation of unsteady fully nonlinear free surface water waves over uneven depths (Engsig-Karup et.al. 2011). The underlying wave...... model is based on a potential flow formulation, which requires efficient solution of a Laplace problem at large-scales. We report recent results on a new mixed-precision strategy for efficient iterative high-order accurate and scalable solution of the Laplace problem using a multigrid......-preconditioned defect correction method. The improved strategy improves the performance by exploiting architectural features of modern GPUs for mixed precision computations and is tested in a recently developed generic library for fast prototyping of PDE solvers. The new wave tool is applicable to solve and analyze...
Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines
Rodrigues, Antonio Wendell De Oliveira; Menach, Yvonnick Le; Dekeyser, Jean-Luc
2010-01-01
Nowadays, several industrial applications are being ported to parallel architectures. In fact, these platforms allow acquire more performance for system modelling and simulation. In the electric machines area, there are many problems which need speed-up on their solution. This paper examines the parallelism of sparse matrix solver on the graphics processors. More specifically, we implement the conjugate gradient technique with input matrix stored in CSR, and Symmetric CSR and CSC formats. This method is one of the most efficient iterative methods available for solving the finite-element basis functions of Maxwell's equations. The GPU (Graphics Processing Unit), which is used for its implementation, provides mechanisms to parallel the algorithm. Thus, it increases significantly the computation speed in relation to serial code on CPU based systems.
GPU-accelerated few-view CT reconstruction using the OSC and TV techniques
Matenine, Dmitri [Montreal Univ., QC (Canada). Dept. de Physique; Hissoiny, Sami [Ecole Polytechnique de Montreal, QC (Canada). Dept. de Genie Informatique et Genie Logiciel; Despres, Philippe [Centre Hospitalier Univ. de Quebec, QC (Canada). Dept. de Radio-Oncologie
2011-07-01
The present work proposes a promising iterative reconstruction technique designed specifically for X-ray transmission computed tomography (CT). The main objective is to reduce diagnostic radiation dose through the reduction of the number of CT projections, while preserving image quality. The second objective is to provide a fast implementation compatible with clinical activities. The proposed tomographic reconstruction technique is a combination of the Ordered Subsets Convex (OSC) algorithm and the Total Variation minimization (TV) regularization technique. The results in terms of image quality and computational speed are discussed. Using this technique, it was possible to obtain reconstructed slices of relatively good quality with as few as 100 projections, leading to potential dose reduction factors of up to an order of magnitude depending on the application. The algorithm was implemented on a Graphical Processing Unit (GPU) and yielded reconstruction times of approximately 185 ms per slice. (orig.)
Non-parametric co-clustering of large scale sparse bipartite networks on the GPU
Hansen, Toke Jansen; Mørup, Morten; Hansen, Lars Kai
2011-01-01
Co-clustering is a problem of both theoretical and practical importance, e.g., market basket analysis and collaborative filtering, and in web scale text processing. We state the co-clustering problem in terms of non-parametric generative models which can address the issue of estimating the number...... 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......-life large scale collaborative filtering data and web scale text corpora, demonstrating that latent mesoscale structures extracted by the co-clustering problem as formulated by the Infinite Relational Model (IRM) are consistent across consecutive runs with different initializations and also relevant...
Parallelizing the QUDA Library for Multi-GPU Calculations in Lattice Quantum Chromodynamics
Ronald Babich, Michael Clark, Balint Joo
2010-11-01
Graphics Processing Units (GPUs) are having a transformational effect on numerical lattice quantum chromodynamics (LQCD) calculations of importance in nuclear and particle physics. The QUDA library provides a package of mixed precision sparse matrix linear solvers for LQCD applications, supporting single GPUs based on NVIDIA's Compute Unified Device Architecture (CUDA). This library, interfaced to the QDP++/Chroma framework for LQCD calculations, is currently in production use on the "9g" cluster at the Jefferson Laboratory, enabling unprecedented price/performance for a range of problems in LQCD. Nevertheless, memory constraints on current GPU devices limit the problem sizes that can be tackled. In this contribution we describe the parallelization of the QUDA library onto multiple GPUs using MPI, including strategies for the overlapping of communication and computation. We report on both weak and strong scaling for up to 32 GPUs interconnected by InfiniBand, on which we sustain in excess of 4 Tflops.
GPU-Accelerated PIC/MCC Simulation of Laser-Plasma Interaction Using BUMBLEBEE
Jin, Xiaolin; Huang, Tao; Chen, Wenlong; Wu, Huidong; Tang, Maowen; Li, Bin
2015-11-01
The research of laser-plasma interaction in its wide applications relies on the use of advanced numerical simulation tools to achieve high performance operation while reducing computational time and cost. BUMBLEBEE has been developed to be a fast simulation tool used in the research of laser-plasma interactions. BUMBLEBEE uses a 1D3V electromagnetic PIC/MCC algorithm that is accelerated by using high performance Graphics Processing Unit (GPU) hardware. BUMBLEBEE includes a friendly user-interface module and four physics simulators. The user-interface provides a powerful solid-modeling front end and graphical and computational post processing functionality. The solver of BUMBLEBEE has four modules for now, which are used to simulate the field ionization, electron collisional ionization, binary coulomb collision and laser-plasma interaction processes. The ionization characteristics of laser-neutral interaction and the generation of high-energy electrons have been analyzed by using BUMBLEBEE for validation.
Optimized analysis of isotropic high-nuclearity spin clusters with GPU acceleration
Lamas Daviña, A.; Ramos, E.; Roman, J. E.
2016-12-01
The numerical simulation of molecular clusters formed by a finite number of exchange-coupled paramagnetic centers is very relevant for many applications, modeling systems between molecules and extended solids. In the context of realistic scenarios, many centers need to be considered, and thus the required computational effort grows very fast. In a previous work (Ramos et al., 2010), a set of parallel programs were presented with standard message-passing parallelization (MPI) for both anisotropic and isotropic systems. In this work, we have further developed the code for isotropic models. On one hand, the computational cost has been significantly reduced by avoiding some of the matrix diagonalizations, corresponding to blocks with negligible contribution for the particular configuration. On the other hand, we have extended the parallelization in order to exploit available graphics processing units (GPUs). The new MPI-GPU paradigm reduces the computational time by at least one additional order of magnitude and enables the resolution of larger problems.
Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU
Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis
2016-06-01
Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.
Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems.
Wang, Kaibo; Huai, Yin; Lee, Rubao; Wang, Fusheng; Zhang, Xiaodong; Saltz, Joel H
2012-07-01
As an important application of spatial databases in pathology imaging analysis, cross-comparing the spatial boundaries of a huge amount of segmented micro-anatomic objects demands extremely data- and compute-intensive operations, requiring high throughput at an affordable cost. However, the performance of spatial database systems has not been satisfactory since their implementations of spatial operations cannot fully utilize the power of modern parallel hardware. In this paper, we provide a customized software solution that exploits GPUs and multi-core CPUs to accelerate spatial cross-comparison in a cost-effective way. Our solution consists of an efficient GPU algorithm and a pipelined system framework with task migration support. Extensive experiments with real-world data sets demonstrate the effectiveness of our solution, which improves the performance of spatial cross-comparison by over 18 times compared with a parallelized spatial database approach.
Mixed precision numerical weather prediction on hybrid GPU-CPU supercomputers
Lapillonne, Xavier; Osuna, Carlos; Spoerri, Pascal; Osterried, Katherine; Charpilloz, Christophe; Fuhrer, Oliver
2017-04-01
A new version of the climate and weather model COSMO that runs faster on traditional high performance computing systems with CPUs as well as on heterogeneous architectures using graphics processing units (GPUs) has been developed. The model was in addition adapted to be able to run in "single precision" mode. After discussing the key changes introduced in this new model version and the tools used in the porting approach, we present 3 applications, namely the MeteoSwiss operational weather prediction system, COSMO-LEPS and the CALMO project, which already take advantage of the performance improvement, up to a factor 4, by running on GPU system and using the single precision mode. We discuss how the code changes open new perspectives for scientific research and can enable researchers to get access to a new class of supercomputers.
GPU-accelerated Block Matching Algorithm for Deformable Registration of Lung CT Images.
Li, Min; Xiang, Zhikang; Xiao, Liang; Castillo, Edward; Castillo, Richard; Guerrero, Thomas
2015-12-01
Deformable registration (DR) is a key technology in the medical field. However, many of the existing DR methods are time-consuming and the registration accuracy needs to be improved, which prevents their clinical applications. In this study, we propose a parallel block matching algorithm for lung CT image registration, in which the sum of squared difference metric is modified as the cost function and the moving least squares approach is used to generate the full displacement field. The algorithm is implemented on Graphic Processing Unit (GPU) with the Compute Unified Device Architecture (CUDA). Results show that the proposed parallel block matching method achieves a fast runtime while maintaining an average registration error (standard deviation) of 1.08 (0.69) mm.
Unexpectedly Streamlined Mitochondrial Genome of the Euglenozoan Euglena gracilis.
Dobáková, Eva; Flegontov, Pavel; Skalický, Tomáš; Lukeš, Julius
2015-11-20
In this study, we describe the mitochondrial genome of the excavate flagellate Euglena gracilis. Its gene complement is reduced as compared with the well-studied sister groups Diplonemea and Kinetoplastea. We have identified seven protein-coding genes: Three subunits of respiratory complex I (nad1, nad4, and nad5), one subunit of complex III (cob), and three subunits of complex IV (cox1, cox2, and a highly divergent cox3). Moreover, fragments of ribosomal RNA genes have also been identified. Genes encoding subunits of complex V, ribosomal proteins and tRNAs were missing, and are likely located in the nuclear genome. Although mitochondrial genomes of diplonemids and kinetoplastids possess the most complex RNA processing machineries known, including trans-splicing and editing of the uridine insertion/deletion type, respectively, our transcriptomic data suggest their total absence in E. gracilis. This finding supports a scenario in which the complex mitochondrial processing machineries of both sister groups evolved relatively late in evolution from a streamlined genome and transcriptome of their common predecessor.
The Single Crew Module Concept a Streamlined Way to Explore
Chambliss, Joe
2012-01-01
Many concepts have been proposed for exploring space. In early 2010 presidential direction called for reconsidering the approach to address changes in exploration destinations, use of new technologies and development of new capabilities to support exploration of space. Considering the proposed new technology and capabilities that NASA was directed to pursue, the single crew module (SCM) concept for a more streamlined approach to the infrastructure and conduct of exploration missions was developed. The SCM concept combines many of the new promising technologies with a central concept of mission architectures that uses a single habitat module for all phases of an exploration mission. Integrating mission elements near Earth and fully fueling them prior to departure of the vicinity of Earth provides the capability of using the single habitat both in transit to an exploration destination and while exploring the destination. The concept employs the capability to return the habitat and interplanetary propulsion system to Earth vicinity so that those elements can be reused on subsequent exploration missions. This paper describes the SCM concept, and the advantages it provides to accomplish exploration objectives.
Eckert, C. H. J.; Zenker, E.; Bussmann, M.; Albach, D.
2016-10-01
We present an adaptive Monte Carlo algorithm for computing the amplified spontaneous emission (ASE) flux in laser gain media pumped by pulsed lasers. With the design of high power lasers in mind, which require large size gain media, we have developed the open source code HASEonGPU that is capable of utilizing multiple graphic processing units (GPUs). With HASEonGPU, time to solution is reduced to minutes on a medium size GPU cluster of 64 NVIDIA Tesla K20m GPUs and excellent speedup is achieved when scaling to multiple GPUs. Comparison of simulation results to measurements of ASE in Y b 3 + : Y AG ceramics show perfect agreement.
STUDY FOR STREAMLINE OF ARBITRARY SHAPED HOMOGENEOUS RESERVOIRS WITH IMPERMEABLE BARRIERS
YIN Hong-jun; FU Chun-quan; HE Ying-fu
2006-01-01
The steady-state flow mathematical model of arbitrary shaped homogeneous reservoirs with impermeable barrier is constructed in this paper. By using Boundary Element Method (BEM), the mathematical model is solved. And a streamline generating technique is presented. The figures of streamlines are plotted and analyzed considering the effect of complex boundary and impermeable barriers. Through analyzing, it indicates that the size, shape and orientation of impermeable barriers have various degree of influence on the streamlines. So, if there are impermeable barriers in reservoir according to the geological materials, the influence of impermeable barriers must be considered when adjusting flood pattern and injection strategy.
Stream-lined Gating Systems with Improved Yield - Dimensioning and Experimental Validation
Tiedje, Niels Skat; Skov-Hansen, Søren Peter
The paper describes how a stream-lined gating system where the melt is confined and controlled during filling can be designed. Commercial numerical modelling software has been used to compare the stream-lined design with a traditional gating system. These results are confirmed by experiments where...... the two types of lay-outs are cast in production. It is shown that flow in the stream-lined lay-out is well controlled and that the quality of the castings is as at least equal to that of castings produced with a traditional lay-out. Further, the yield is improved by 4 % relative to a traditional lay-out....
Resolution of the Vlasov-Maxwell system by PIC discontinuous Galerkin method on GPU with OpenCL
Crestetto Anaïs
2013-01-01
Full Text Available We present an implementation of a Vlasov-Maxwell solver for multicore processors. The Vlasov equation describes the evolution of charged particles in an electromagnetic field, solution of the Maxwell equations. The Vlasov equation is solved by a Particle-In-Cell method (PIC, while the Maxwell system is computed by a Discontinuous Galerkin method. We use the OpenCL framework, which allows our code to run on multicore processors or recent Graphic Processing Units (GPU. We present several numerical applications to two-dimensional test cases.
Christian F. Janßen
2015-07-01
Full Text Available This contribution is dedicated to demonstrating the high potential and manifold applications of state-of-the-art computational fluid dynamics (CFD tools for free-surface flows in civil and environmental engineering. All simulations were performed with the academic research code ELBE (efficient lattice boltzmann environment, http://www.tuhh.de/elbe. The ELBE code follows the supercomputing-on-the-desktop paradigm and is especially designed for local supercomputing, without tedious accesses to supercomputers. ELBE uses graphics processing units (GPU to accelerate the computations and can be used in a single GPU-equipped workstation of, e.g., a design engineer. The code has been successfully validated in very different fields, mostly related to naval architecture and mechanical engineering. In this contribution, we give an overview of past and present applications with practical relevance for civil engineers. The presented applications are grouped into three major categories: (i tsunami simulations, considering wave propagation, wave runup, inundation and debris flows; (ii dam break simulations; and (iii numerical wave tanks for the calculation of hydrodynamic loads on fixed and moving bodies. This broad range of applications in combination with accurate numerical results and very competitive times to solution demonstrates that modern CFD tools in general, and the ELBE code in particular, can be a helpful design tool for civil and environmental engineers.
GPU accelerated Monte-Carlo simulation of SEM images for metrology
Verduin, T.; Lokhorst, S. R.; Hagen, C. W.
2016-03-01
In this work we address the computation times of numerical studies in dimensional metrology. In particular, full Monte-Carlo simulation programs for scanning electron microscopy (SEM) image acquisition are known to be notoriously slow. Our quest in reducing the computation time of SEM image simulation has led us to investigate the use of graphics processing units (GPUs) for metrology. We have succeeded in creating a full Monte-Carlo simulation program for SEM images, which runs entirely on a GPU. The physical scattering models of this GPU simulator are identical to a previous CPU-based simulator, which includes the dielectric function model for inelastic scattering and also refinements for low-voltage SEM applications. As a case study for the performance, we considered the simulated exposure of a complex feature: an isolated silicon line with rough sidewalls located on a at silicon substrate. The surface of the rough feature is decomposed into 408 012 triangles. We have used an exposure dose of 6 mC/cm2, which corresponds to 6 553 600 primary electrons on average (Poisson distributed). We repeat the simulation for various primary electron energies, 300 eV, 500 eV, 800 eV, 1 keV, 3 keV and 5 keV. At first we run the simulation on a GeForce GTX480 from NVIDIA. The very same simulation is duplicated on our CPU-based program, for which we have used an Intel Xeon X5650. Apart from statistics in the simulation, no difference is found between the CPU and GPU simulated results. The GTX480 generates the images (depending on the primary electron energy) 350 to 425 times faster than a single threaded Intel X5650 CPU. Although this is a tremendous speedup, we actually have not reached the maximum throughput because of the limited amount of available memory on the GTX480. Nevertheless, the speedup enables the fast acquisition of simulated SEM images for metrology. We now have the potential to investigate case studies in CD-SEM metrology, which otherwise would take unreasonable
Assessment of Efficiency and Performance in Tsunami Numerical Modeling with GPU
Yalciner, Bora; Zaytsev, Andrey
2017-04-01
Non-linear shallow water equations (NSWE) are used to solve the propagation and coastal amplification of long waves and tsunamis. Leap Frog scheme of finite difference technique is one of the satisfactory numerical methods which is widely used in these problems. Tsunami numerical models are necessary for not only academic but also operational purposes which need faster and accurate solutions. Recent developments in information technology provide considerably faster numerical solutions in this respect and are becoming one of the crucial requirements. Tsunami numerical code NAMI DANCE uses finite difference numerical method to solve linear and non-linear forms of shallow water equations for long wave problems, specifically for tsunamis. In this study, the new code is structured for Graphical Processing Unit (GPU) using CUDA API. The new code is applied to different (analytical, experimental and field) benchmark problems of tsunamis for tests. One of those applications is 2011 Great East Japan tsunami which was instrumentally recorded on various types of gauges including tide and wave gauges and offshore GPS buoys cabled Ocean Bottom Pressure (OBP) gauges and DART buoys. The accuracy of the results are compared with the measurements and fairly well agreements are obtained. The efficiency and performance of the code is also compared with the version using multi-core Central Processing Unit (CPU). Dependence of simulation speed with GPU on linear or non-linear solutions is also investigated. One of the results is that the simulation speed is increased up to 75 times comparing to the process time in the computer using single 4/8 thread multi-core CPU. The results are presented with comparisons and discussions. Furthermore how multi-dimensional finite difference problems fits towards GPU architecture is also discussed. The research leading to this study has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement No
Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU).
Yang, Owen; Choi, Bernard
2013-01-01
To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPU-based approach still can lead to processing that is ~3400 times faster than other GPU-based approaches.
Lee, Sungyoung; Kwon, Min-Seok; Park, Taesung
2014-01-01
In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene-gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.
Streamlining of the RELAP5-3D Code
Mesina, George L; Hykes, Joshua; Guillen, Donna Post
2007-11-01
RELAP5-3D is widely used by the nuclear community to simulate general thermal hydraulic systems and has proven to be so versatile that the spectrum of transient two-phase problems that can be analyzed has increased substantially over time. To accommodate the many new types of problems that are analyzed by RELAP5-3D, both the physics and numerical methods of the code have been continuously improved. In the area of computational methods and mathematical techniques, many upgrades and improvements have been made decrease code run time and increase solution accuracy. These include vectorization, parallelization, use of improved equation solvers for thermal hydraulics and neutron kinetics, and incorporation of improved library utilities. In the area of applied nuclear engineering, expanded capabilities include boron and level tracking models, radiation/conduction enclosure model, feedwater heater and compressor components, fluids and corresponding correlations for modeling Generation IV reactor designs, and coupling to computational fluid dynamics solvers. Ongoing and proposed future developments include improvements to the two-phase pump model, conversion to FORTRAN 90, and coupling to more computer programs. This paper summarizes the general improvements made to RELAP5-3D, with an emphasis on streamlining the code infrastructure for improved maintenance and development. With all these past, present and planned developments, it is necessary to modify the code infrastructure to incorporate modifications in a consistent and maintainable manner. Modifying a complex code such as RELAP5-3D to incorporate new models, upgrade numerics, and optimize existing code becomes more difficult as the code grows larger. The difficulty of this as well as the chance of introducing errors is significantly reduced when the code is structured. To streamline the code into a structured program, a commercial restructuring tool, FOR_STRUCT, was applied to the RELAP5-3D source files. The
Streamline Integration using MPI-Hybrid Parallelism on a Large Multi-Core Architecture
Camp, David; Garth, Christoph; Childs, Hank; Pugmire, Dave; Joy, Kenneth I.
2010-11-01
Streamline computation in a very large vector field data set represents a significant challenge due to the non-local and datadependentnature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programmingand execution as applied to streamline integration on a large, multicore platform. With multi-core processors now prevalent in clustersand supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice.We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize-over-seeds and parallelize-overblocks,and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing betweencores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication andI/O bandwidth than a traditional, non-hybrid distributed implementation.
Changes in the adiabatic invariant and streamline chaos in confined incompressible Stokes flow
Vainshtein, D. L.; Vasiliev, A. A.; Neishtadt, A. I.
1996-03-01
The steady incompressible flow in a unit sphere introduced by Bajer and Moffatt [J. Fluid Mech. 212, 337 (1990)] is discussed. The velocity field of this flow differs by a small perturbation from an integrable field whose streamlines are almost all closed. The unperturbed flow has two stationary saddle points (poles of the sphere) and a two-dimensional separatrix passing through them. The entire interior of the unit sphere becomes the domain of streamline chaos for an arbitrarily small perturbation. This phenomenon is explained by the nonconservation of a certain adiabatic invariant that undergoes a jump when a streamline crosses a small neighborhood of the separatrix of the unperturbed flow. An asymptotic formula is obtained for the jump in the adiabatic invariant. The accumulation of such jumps in the course of repeated crossings of the separatrix results in the complete breaking of adiabatic invariance and streamline chaos.
Streamline integration using MPI-hybrid parallelism on a large multicore architecture.
Camp, David; Garth, Christoph; Childs, Hank; Pugmire, Dave; Joy, Kenneth I
2011-11-01
Streamline computation in a very large vector field data set represents a significant challenge due to the nonlocal and data-dependent nature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programming and execution as applied to streamline integration on a large, multicore platform. With multicore processors now prevalent in clusters and supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice. We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize over seeds and parallelize over blocks, and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing between cores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication and I/O bandwidth than a traditional, nonhybrid distributed implementation.
Simulation of isothermal multi-phase fuel-coolant interaction using MPS method with GPU acceleration
Gou, W.; Zhang, S.; Zheng, Y. [Zhejiang Univ., Hangzhou (China). Center for Engineering and Scientific Computation
2016-07-15
The energetic fuel-coolant interaction (FCI) has been one of the primary safety concerns in nuclear power plants. Graphical processing unit (GPU) implementation of the moving particle semi-implicit (MPS) method is presented and used to simulate the fuel coolant interaction problem. The governing equations are discretized with the particle interaction model of MPS. Detailed implementation on single-GPU is introduced. The three-dimensional broken dam is simulated to verify the developed GPU acceleration MPS method. The proposed GPU acceleration algorithm and developed code are then used to simulate the FCI problem. As a summary of results, the developed GPU-MPS method showed a good agreement with the experimental observation and theoretical prediction.
Semi-automatic tool to ease the creation and optimization of GPU programs
Jepsen, Jacob
2014-01-01
We present a tool that reduces the development time of GPU-executable code. We implement a catalogue of common optimizations specific to the GPU architecture. Through the tool, the programmer can semi-automatically transform a computationally-intensive code section into GPU-executable form...... and apply optimizations thereto. Based on experiments, the code generated by the tool can be 3-256X faster than code generated by an OpenACC compiler, 4-37X faster than optimized CPU code, and attain up to 25% of peak performance of the GPU. We found that by using pattern-matching rules, many...... of the transformations can be performed automatically, which makes the tool usable for both novices and experts in GPU programming....
A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis.
Nagaoka, Tomoaki; Watanabe, Soichi
2010-01-01
Numerical simulations with the numerical human model using the finite-difference time domain (FDTD) method have recently been performed frequently in a number of fields in biomedical engineering. However, the FDTD calculation runs too slowly. We focus, therefore, on general purpose programming on the graphics processing unit (GPGPU). The three-dimensional FDTD method was implemented on the GPU using Compute Unified Device Architecture (CUDA). In this study, we used the NVIDIA Tesla C1060 as a GPGPU board. The performance of the GPU is evaluated in comparison with the performance of a conventional CPU and a vector supercomputer. The results indicate that three-dimensional FDTD calculations using a GPU can significantly reduce run time in comparison with that using a conventional CPU, even a native GPU implementation of the three-dimensional FDTD method, while the GPU/CPU speed ratio varies with the calculation domain and thread block size.
Streamlined, Inexpensive 3D Printing of the Brain and Skull.
Jason S Naftulin
Full Text Available Neuroimaging technologies such as Magnetic Resonance Imaging (MRI and Computed Tomography (CT collect three-dimensional data (3D that is typically viewed on two-dimensional (2D screens. Actual 3D models, however, allow interaction with real objects such as implantable electrode grids, potentially improving patient specific neurosurgical planning and personalized clinical education. Desktop 3D printers can now produce relatively inexpensive, good quality prints. We describe our process for reliably generating life-sized 3D brain prints from MRIs and 3D skull prints from CTs. We have integrated a standardized, primarily open-source process for 3D printing brains and skulls. We describe how to convert clinical neuroimaging Digital Imaging and Communications in Medicine (DICOM images to stereolithography (STL files, a common 3D object file format that can be sent to 3D printing services. We additionally share how to convert these STL files to machine instruction gcode files, for reliable in-house printing on desktop, open-source 3D printers. We have successfully printed over 19 patient brain hemispheres from 7 patients on two different open-source desktop 3D printers. Each brain hemisphere costs approximately $3-4 in consumable plastic filament as described, and the total process takes 14-17 hours, almost all of which is unsupervised (preprocessing = 4-6 hr; printing = 9-11 hr, post-processing = <30 min. Printing a matching portion of a skull costs $1-5 in consumable plastic filament and takes less than 14 hr, in total. We have developed a streamlined, cost-effective process for 3D printing brain and skull models. We surveyed healthcare providers and patients who confirmed that rapid-prototype patient specific 3D models may help interdisciplinary surgical planning and patient education. The methods we describe can be applied for other clinical, research, and educational purposes.
Streamlined, Inexpensive 3D Printing of the Brain and Skull.
Naftulin, Jason S; Kimchi, Eyal Y; Cash, Sydney S
2015-01-01
Neuroimaging technologies such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) collect three-dimensional data (3D) that is typically viewed on two-dimensional (2D) screens. Actual 3D models, however, allow interaction with real objects such as implantable electrode grids, potentially improving patient specific neurosurgical planning and personalized clinical education. Desktop 3D printers can now produce relatively inexpensive, good quality prints. We describe our process for reliably generating life-sized 3D brain prints from MRIs and 3D skull prints from CTs. We have integrated a standardized, primarily open-source process for 3D printing brains and skulls. We describe how to convert clinical neuroimaging Digital Imaging and Communications in Medicine (DICOM) images to stereolithography (STL) files, a common 3D object file format that can be sent to 3D printing services. We additionally share how to convert these STL files to machine instruction gcode files, for reliable in-house printing on desktop, open-source 3D printers. We have successfully printed over 19 patient brain hemispheres from 7 patients on two different open-source desktop 3D printers. Each brain hemisphere costs approximately $3-4 in consumable plastic filament as described, and the total process takes 14-17 hours, almost all of which is unsupervised (preprocessing = 4-6 hr; printing = 9-11 hr, post-processing = Printing a matching portion of a skull costs $1-5 in consumable plastic filament and takes less than 14 hr, in total. We have developed a streamlined, cost-effective process for 3D printing brain and skull models. We surveyed healthcare providers and patients who confirmed that rapid-prototype patient specific 3D models may help interdisciplinary surgical planning and patient education. The methods we describe can be applied for other clinical, research, and educational purposes.
Liu, Yongchao; Schmidt, Bertil; Maskell, Douglas L
2011-03-29
Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads) at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets. We present DecGPU, the first parallel and distributed error correction algorithm for high-throughput short reads (HTSRs) using a hybrid combination of CUDA and MPI parallel programming models. DecGPU provides CPU-based and GPU-based versions, where the CPU-based version employs coarse-grained and fine-grained parallelism using the MPI and OpenMP parallel programming models, and the GPU-based version takes advantage of the CUDA and MPI parallel programming models and employs a hybrid CPU+GPU computing model to maximize the performance by overlapping the CPU and GPU computation. The distributed feature of our algorithm makes it feasible and flexible for the error correction of large-scale HTSR datasets. Using simulated and real datasets, our algorithm demonstrates superior performance, in terms of error correction quality and execution speed, to the existing error correction algorithms. Furthermore, when combined with Velvet and ABySS, the resulting DecGPU-Velvet and DecGPU-ABySS assemblers demonstrate the potential of our algorithm to improve de novo assembly quality for de-Bruijn-graph-based assemblers. DecGPU is publicly available open-source software, written in CUDA C++ and MPI. The experimental results suggest that DecGPU is an effective and feasible error correction algorithm to tackle the flood of short reads produced by next-generation sequencing technologies.
Schmidt Bertil
2011-03-01
Full Text Available Abstract Background Next-generation sequencing technologies have led to the high-throughput production of sequence data (reads at low cost. However, these reads are significantly shorter and more error-prone than conventional Sanger shotgun reads. This poses a challenge for the de novo assembly in terms of assembly quality and scalability for large-scale short read datasets. Results We present DecGPU, the first parallel and distributed error correction algorithm for high-throughput short reads (HTSRs using a hybrid combination of CUDA and MPI parallel programming models. DecGPU provides CPU-based and GPU-based versions, where the CPU-based version employs coarse-grained and fine-grained parallelism using the MPI and OpenMP parallel programming models, and the GPU-based version takes advantage of the CUDA and MPI parallel programming models and employs a hybrid CPU+GPU computing model to maximize the performance by overlapping the CPU and GPU computation. The distributed feature of our algorithm makes it feasible and flexible for the error correction of large-scale HTSR datasets. Using simulated and real datasets, our algorithm demonstrates superior performance, in terms of error correction quality and execution speed, to the existing error correction algorithms. Furthermore, when combined with Velvet and ABySS, the resulting DecGPU-Velvet and DecGPU-ABySS assemblers demonstrate the potential of our algorithm to improve de novo assembly quality for de-Bruijn-graph-based assemblers. Conclusions DecGPU is publicly available open-source software, written in CUDA C++ and MPI. The experimental results suggest that DecGPU is an effective and feasible error correction algorithm to tackle the flood of short reads produced by next-generation sequencing technologies.
SwiftLink: parallel MCMC linkage analysis using multicore CPU and GPU.
Medlar, Alan; Głowacka, Dorota; Stanescu, Horia; Bryson, Kevin; Kleta, Robert
2013-02-15
Linkage analysis remains an important tool in elucidating the genetic component of disease and has become even more important with the advent of whole exome sequencing, enabling the user to focus on only those genomic regions co-segregating with Mendelian traits. Unfortunately, methods to perform multipoint linkage analysis scale poorly with either the number of markers or with the size of the pedigree. Large pedigrees with many markers can only be evaluated with Markov chain Monte Carlo (MCMC) methods that are slow to converge and, as no attempts have been made to exploit parallelism, massively underuse available processing power. Here, we describe SWIFTLINK, a novel application that performs MCMC linkage analysis by spreading the computational burden between multiple processor cores and a graphics processing unit (GPU) simultaneously. SWIFTLINK was designed around the concept of explicitly matching the characteristics of an algorithm with the underlying computer architecture to maximize performance. We implement our approach using existing Gibbs samplers redesigned for parallel hardware. We applied SWIFTLINK to a real-world dataset, performing parametric multipoint linkage analysis on a highly consanguineous pedigree with EAST syndrome, containing 28 members, where a subset of individuals were genotyped with single nucleotide polymorphisms (SNPs). In our experiments with a four core CPU and GPU, SWIFTLINK achieves a 8.5× speed-up over the single-threaded version and a 109× speed-up over the popular linkage analysis program SIMWALK. SWIFTLINK is available at https://github.com/ajm/swiftlink. All source code is licensed under GPLv3.
A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.
Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein
2017-01-01
Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts' Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2-100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms.
A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms
Emrani, Zahra; Bateni, Soroosh; Rabbani, Hossein
2017-01-01
Real-time image processing is used in a wide variety of applications like those in medical care and industrial processes. This technique in medical care has the ability to display important patient information graphi graphically, which can supplement and help the treatment process. Medical decisions made based on real-time images are more accurate and reliable. According to the recent researches, graphic processing unit (GPU) programming is a useful method for improving the speed and quality of medical image processing and is one of the ways of real-time image processing. Edge detection is an early stage in most of the image processing methods for the extraction of features and object segments from a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique are some examples of edge detection algorithms that are widely used in image processing and machine vision. In this work, these algorithms are implemented using the Compute Unified Device Architecture (CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. An existing parallel method for Canny approach has been modified further to run in a fully parallel manner. This has been achieved by replacing the breadth- first search procedure with a parallel method. These algorithms have been compared by testing them on a database of optical coherence tomography images. The comparison of results shows that the proposed implementation of the Canny method on GPU using the CUDA platform improves the speed of execution by 2–100× compared to the central processing unit-based implementation using the OpenCV and MATLAB platforms. PMID:28487831
A GPU-based Monte Carlo dose calculation code for photon transport in a voxel phantom
Bellezzo, M.; Do Nascimento, E.; Yoriyaz, H., E-mail: mbellezzo@gmail.br [Instituto de Pesquisas Energeticas e Nucleares / CNEN, Av. Lineu Prestes 2242, Cidade Universitaria, 05508-000 Sao Paulo (Brazil)
2014-08-15
As the most accurate method to estimate absorbed dose in radiotherapy, Monte Carlo method has been widely used in radiotherapy treatment planning. Nevertheless, its efficiency can be improved for clinical routine applications. In this paper, we present the CUBMC code, a GPU-based Mc photon transport algorithm for dose calculation under the Compute Unified Device Architecture platform. The simulation of physical events is based on the algorithm used in Penelope, and the cross section table used is the one generated by the Material routine, als present in Penelope code. Photons are transported in voxel-based geometries with different compositions. To demonstrate the capabilities of the algorithm developed in the present work four 128 x 128 x 128 voxel phantoms have been considered. One of them is composed by a homogeneous water-based media, the second is composed by bone, the third is composed by lung and the fourth is composed by a heterogeneous bone and vacuum geometry. Simulations were done considering a 6 MeV monoenergetic photon point source. There are two distinct approaches that were used for transport simulation. The first of them forces the photon to stop at every voxel frontier, the second one is the Woodcock method, where the photon stop in the frontier will be considered depending on the material changing across the photon travel line. Dose calculations using these methods are compared for validation with Penelope and MCNP5 codes. Speed-up factors are compared using a NVidia GTX 560-Ti GPU card against a 2.27 GHz Intel Xeon CPU processor. (Author)
Department of pharmacy-initiated program for streamlining empirical antibiotic therapy.
Pastel, D A; Chang, S; Nessim, S; Shane, R; Morgan, M A
1992-07-01
The outcome of a department of pharmacy-initiated "streamlining" study designed to promote cost-conscious modifications of empirically selected antibiotic therapy is described. Two hundred forty-one evaluable adult patients started on restricted-use antibiotics at this university-affiliated community private teaching hospital were enrolled in a 9-week prospective streamlining study. Patients were alternately assigned to a Control (i.e., no pharmacist-initiated streamlining recommendations offered based on culture and susceptibility reports) or a Pharmacist Intervention group (i.e., pharmacist offers recommendations to streamline therapy). A statistically significant greater number of patients had their empiric antibiotic treatment courses modified to more appropriate antibiotic choices after receipt of culture and susceptibility reports among private prescribers in the Pharmacist Intervention group (83%) than in the Control group (38%) (p = .006). Additionally, pharmacists were overall successful in gaining prescriber acceptance for 64% of recommended changes of empiric antibiotic treatment courses before the receipt of culture and susceptibility reports (e.g., dose and/or frequency changes). There was no program effect observed with respect to improved physician response to microbiologic data that would allow streamlining empirical antibiotic choices in the Housestaff (i.e., medical or surgical residents), or infectious disease consultant prescriber groups. Projected overall annual cost savings that would be achieved as a result of continued efforts by pharmacists directed at streamlining empirical "restricted" antibiotic regimens is approximately +40,000.
Work-Efficient Parallel Skyline Computation for the GPU
Bøgh, Kenneth Sejdenfaden; Chester, Sean; Assent, Ira
2015-01-01
The skyline operator returns records in a dataset that provide optimal trade-offs of multiple dimensions. State-of-the-art skyline computation involves complex tree traversals, data-ordering, and conditional branching to minimize the number of point-to-point comparisons. Meanwhile, GPGPU computing...... a global, static partitioning scheme. With the partitioning, we can permit controlled branching to exploit transitive relationships and avoid most point-to-point comparisons. The result is a non-traditional GPU algorithm, SkyAlign, that prioritizes work-effciency and respectable throughput, rather than...... maximal throughput, to achieve orders of magnitude faster performance....
Runtime Analysis of GPU-Based Stereo Matching
Christian Zentner
2015-11-01
Full Text Available This paper elaborates on the possibility to leverage the highly parallel nature of GPUs to implement more efficient stereo matching algorithms. Different algorithms have been implemented and compared on the CPU and the GPU in order to show the speedup gained by moving the computation to the graphics card. The results were evaluated for accuracy using the test available on the Middlebury website for stereo vision. An assessment of the runtime performance was done by a script which examined the runtime behaviour of the individual steps of the stereo matching algorithm.
Finding Convex Hulls Using Quickhull on the GPU
Tzeng, Stanley
2012-01-01
We present a convex hull algorithm that is accelerated on commodity graphics hardware. We analyze and identify the hurdles of writing a recursive divide and conquer algorithm on the GPU and divise a framework for representing this class of problems. Our framework transforms the recursive splitting step into a permutation step that is well-suited for graphics hardware. Our convex hull algorithm of choice is Quickhull. Our parallel Quickhull implementation (for both 2D and 3D cases) achieves an order of magnitude speedup over standard computational geometry libraries.
A Modular Framework for Deformation and Fracture using GPU Shaders
Morris, D J; Anderson, Eike F.; Peters, C.
2012-01-01
Advances in the graphical realism of modern video\\ud games have been achieved mainly through the development of\\ud the GPU (Graphics Processing Unit), providing a dedicated\\ud graphics co-processor and framebuffer. The most recent GPU’s\\ud are extremely capable and so flexible that it is now possible to\\ud implement a wide range of algorithms on graphics hardware\\ud that were previously confined to the computer’s CPU (Central\\ud Processing Unit). We present a modular framework for real-time\\u...
Parallelized Local Volatility Estimation Using GP-GPU Hardware Acceleration
Douglas, Craig C.
2010-01-01
We introduce an inverse problem for the local volatility model in option pricing. We solve the problem using the Levenberg-Marquardt algorithm and use the notion of the Fréchet derivative when calculating the Jacobian matrix. We analyze the existence of the Fréchet derivative and its numerical computation. To reduce the computational time of the inverse problem, a GP-GPU environment is considered for parallel computation. Numerical results confirm the validity and efficiency of the proposed method. ©2010 IEEE.
GPU-Based Techniques for Global Illumination Effects
Szirmay-Kalos, László; Sbert, Mateu
2008-01-01
This book presents techniques to render photo-realistic images by programming the Graphics Processing Unit (GPU). We discuss effects such as mirror reflections, refractions, caustics, diffuse or glossy indirect illumination, radiosity, single or multiple scattering in participating media, tone reproduction, glow, and depth of field. This book targets game developers, graphics programmers, and also students with some basic understanding of computer graphics algorithms, rendering APIs like Direct3D or OpenGL, and shader programming. In order to make this book self-contained, the most important c
Horan, Thomas A; Daniels, Susan M; Feldman, Sue S
2009-07-01
The disability community could benefit significantly from the widespread adoption of health information technology, in particular from its ability to streamline and accelerate processing of the estimated 3 million disability benefits applications filed with the Social Security Administration each year. Disability determination is an inefficient, largely paper-based process requiring large volumes of clinical data compiled from multiple provider sources. That, coupled with a lack of transparency within the process, adds unnecessary delays and expense. The objective of this paper is to outline the case for how personal health records, particularly those populated with information from provider-held electronic health records and payer claims data, offer a means to achieve financial savings from shortened disability determination processes, as well as a tool for disability health self-management and care coordination. Drawing from research and policy forums and testimony before the American Health Information Community, the importance of including the disability community as the nation moves forward with health information technology initiatives is explored. Our research suggests that systemwide improvements such as the Nationwide Health Information Network and other such health information technology initiatives could be used to bring benefits to the disability community. The time has come to use health information technology initiatives so that federal policy makers can takes steps to reduce the inefficiencies in the Social Security Administration disability determination process while improving the program's value to those who need it the most.
Azizian, Morvarid; Grant, Stanley B; Kessler, Adam J; Cook, Perran L M; Rippy, Megan A; Stewardson, Michael J
2015-09-15
Bedforms are a focal point of carbon and nitrogen cycling in streams and coastal marine ecosystems. In this paper, we develop and test a mechanistic model, the "pumping and streamline segregation" or PASS model, for nitrate removal in bedforms. The PASS model dramatically reduces computational overhead associated with modeling nitrogen transformations in bedforms and reproduces (within a factor of 2 or better) previously published measurements and models of biogeochemical reaction rates, benthic fluxes, and in-sediment nutrient and oxygen concentrations. Application of the PASS model to a diverse set of marine and freshwater environments indicates that (1) physical controls on nitrate removal in a bedform include the pore water flushing rate, residence time distribution, and relative rates of respiration and transport (as represented by the Damkohler number); (2) the biogeochemical pathway for nitrate removal is an environment-specific combination of direct denitrification of stream nitrate and coupled nitrification-denitrification of stream and/or sediment ammonium; and (3) permeable sediments are almost always a net source of dissolved inorganic nitrogen. The PASS model also provides a mechanistic explanation for previously published empirical correlations showing denitrification velocity (N2 flux divided by nitrate concentration) declines as a power law of nitrate concentration in a stream (Mulholland et al. Nature, 2008, 452, 202-205).
Managing Written Directives: A Software Solution to Streamline Workflow.
Wagner, Robert H; Savir-Baruch, Bital; Gabriel, Medhat Sam; Halama, James; Bova, Davide
2017-03-09
retrieve active and prior completed directives at any stage of completion and at time. Conclusion: A software solution for the management of WDs streamlines and structures the workflow in the department. Implementation of this solution saves time, centralizes the information for all staff to share and decreases any confusion surrounding the creation, completion, filing, and retrieval of WDs.
LHCb: A GPU offloading mechanism for LHCb
Badalov, A; Zvyagin, A; Neufeld, N; Vilasis Cardona, X
2013-01-01
The LHCb Software Infrastructure is built around a flexible, extensible, single-process, single-threaded framework named Gaudi. One way to optimise the overall usage of a multi-core server, which is used for example in the Online world, is running multiple instances of Gaudi-based applications concurrently. For LHCb, this solution has been shown to work well up to 32 cores and is expected to scale up a bit further. The appearance of many-core architectures such as GPGPUs and the Intel Xeon/Phi poses a new challenge for LHCb. Since the individual data sets are so small (about 60 kB raw event size), many events must be processed in parallel for optimum efficiency. This is, however, not possible with the current framework, which allows only a single event at a time. Exploiting the fact that we always have many instances of the same application running, we have developed an offloading mechanism, based on a client-server design. The server runs outside the Gaudi framework and thus imposes no additional dependencie...
耗散粒子动力学图像处理器并行运算的实现%GPU-accelerated dissipative particle dynamics simulations
刘俊骅; 郭坤琨; 陈安琪; 马瑶
2014-01-01
计算机模拟的时空尺度限制了它的进一步应用和发展，而高性能图像处理器( GPU)得以充分利用的现实将会使计算机模拟突破该限制。为此使用图像处理器实现耗散粒子动力学( DPD)模拟方法的并行运算。通过模拟结果发现图像处理器的并行运算将会大幅度提高计算效率。为了检验自编的代码，研究了并行运算模拟中压力和体系密度的关系，二元共混体中Flory-Huggins参数、保守力参数和链长之间的关系，模拟结果和以前的报道一致。%Due to the limitation of spatial and temporal scales, computer simulations have significant barriers to their further applications and developments. However, computer simulations would break through the limitations because the further acceleration is possible through the advanced computer hardware of Graphic Processing Units ( GPU ) , which is the high performance computer processor designed to accelerate graphical applications. In this study, dissipative particle dynamics simulation was accelerated by GPUs. The simulation results show that a single GPU can give performance competitive with a much more expensive CPU cluster. To verify the GPU-accelerated code, the relation of pressure and destiny, and of the Flory-Huggins parameter, the parameter of the conservative force and polymer length in the simulations were discussed and compared with the previous publications.
A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors
Dennis Akos
2011-09-01
Full Text Available Due to their weak received signal power, Global Positioning System (GPS signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs. However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU coupled with a new generation Graphics Processing Unit (GPU having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities.
Heru Suhartanto
2011-07-01
Full Text Available One of application that needs high performance computing resources is molecular dynamic. There is some software available that perform molecular dynamic, one of these is a well known GROMACS. Our previous experiment simulating molecular dynamics of Indonesian grown herbal compounds show sufficient speed up on 32 nodes Cluster computing environment. In order to obtain a reliable simulation, one usually needs to run the experiment on the scale of hundred nodes. But this is expensive to develop and maintain. Since the invention of Graphical Processing Units that is also useful for general programming, many applications have been developed to run on this. This paper reports our experiments that evaluate the performance of GROMACS that runs on two different environment, Cluster computing resources and GPU based PCs. We run the experiment on BRV-1 and REM2 compounds. Four different GPUs are installed on the same type of PCs of quad cores; they are Gefore GTS 250, GTX 465, GTX 470 and Quadro 4000. We build a cluster of 16 nodes based on these four quad cores PCs. The preliminary experiment shows that those run on GTX 470 is the best among the other type of GPUs and as well as the cluster computing resource. A speed up around 11 and 12 is gained, while the cost of computer with GPU is only about 25 percent that of Cluster we built.
A real-time capable software-defined receiver using GPU for adaptive anti-jam GPS sensors.
Seo, Jiwon; Chen, Yu-Hsuan; De Lorenzo, David S; Lo, Sherman; Enge, Per; Akos, Dennis; Lee, Jiyun
2011-01-01
Due to their weak received signal power, Global Positioning System (GPS) signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs). However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR) with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU) coupled with a new generation Graphics Processing Unit (GPU) having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities.
Park, Justin C; Park, Sung Ho; Kim, Jin Sung; Han, Youngyih; Cho, Min Kook; Kim, Ho Kyung; Liu, Zhaowei; Jiang, Steve B; Song, Bongyong; Song, William Y
2011-08-01
-fan and half-fan modes, respectively. Since commercial CBCT system nominally acquires 11 pps (with 1 gantry-revolution-per-minute), our GPU-based implementation is sufficient to handle the incoming projections data as they are acquired and reconstruct the entire volume immediately after completing the scan. In addition, on increasing the number of slices (hence volume) to be reconstructed from 16 to 256, only minimal increases in reconstruction time were observed for the GPU-based implementation where from 0.73 to 1.27 seconds and 1.42 to 2.47 seconds increase were observed for the full-fan and half-fan modes, respectively. This resulted in speed improvement of up to 87 times compared with the CPU-based implementation (for 256 slices case), with visually identical images and small pixel-value discrepancies (< 6.3%), and CNR differences (< 2.3%). With this achievement, we have shown that time allocation for DTS image reconstruction is virtually eliminated and that clinical implementation of this approach has become quite appealing. In addition, with the speed achievement, further image processing and real-time applications that was prohibited prior due to time restrictions can now be tempered with.