Development of parallel GPU based algorithms for problems in nuclear area; Desenvolvimento de algoritmos paralelos baseados em GPU para solucao de problemas na area nuclear

Energy Technology Data Exchange (ETDEWEB)

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)

SU-E-T-395: Multi-GPU-Based VMAT Treatment Plan Optimization Using a Column-Generation Approach

International Nuclear Information System (INIS)

Tian, Z; Shi, F; Jia, X; Jiang, S; Peng, F

2014-01-01

Purpose: GPU has been employed to speed up VMAT optimizations from hours to minutes. However, its limited memory capacity makes it difficult to handle cases with a huge dose-deposition-coefficient (DDC) matrix, e.g. those with a large target size, multiple arcs, small beam angle intervals and/or small beamlet size. We propose multi-GPU-based VMAT optimization to solve this memory issue to make GPU-based VMAT more practical for clinical use. Methods: Our column-generation-based method generates apertures sequentially by iteratively searching for an optimal feasible aperture (referred as pricing problem, PP) and optimizing aperture intensities (referred as master problem, MP). The PP requires access to the large DDC matrix, which is implemented on a multi-GPU system. Each GPU stores a DDC sub-matrix corresponding to one fraction of beam angles and is only responsible for calculation related to those angles. Broadcast and parallel reduction schemes are adopted for inter-GPU data transfer. MP is a relatively small-scale problem and is implemented on one GPU. One headand- neck cancer case was used for test. Three different strategies for VMAT optimization on single GPU were also implemented for comparison: (S1) truncating DDC matrix to ignore its small value entries for optimization; (S2) transferring DDC matrix part by part to GPU during optimizations whenever needed; (S3) moving DDC matrix related calculation onto CPU. Results: Our multi-GPU-based implementation reaches a good plan within 1 minute. Although S1 was 10 seconds faster than our method, the obtained plan quality is worse. Both S2 and S3 handle the full DDC matrix and hence yield the same plan as in our method. However, the computation time is longer, namely 4 minutes and 30 minutes, respectively. Conclusion: Our multi-GPU-based VMAT optimization can effectively solve the limited memory issue with good plan quality and high efficiency, making GPUbased ultra-fast VMAT planning practical for real clinical use

SU-E-T-395: Multi-GPU-Based VMAT Treatment Plan Optimization Using a Column-Generation Approach

Energy Technology Data Exchange (ETDEWEB)

Tian, Z; Shi, F; Jia, X; Jiang, S [UT Southwestern Medical Ctr at Dallas, Dallas, TX (United States); Peng, F [Carnegie Mellon University, Pittsburgh, PA (United States)

2014-06-01

Purpose: GPU has been employed to speed up VMAT optimizations from hours to minutes. However, its limited memory capacity makes it difficult to handle cases with a huge dose-deposition-coefficient (DDC) matrix, e.g. those with a large target size, multiple arcs, small beam angle intervals and/or small beamlet size. We propose multi-GPU-based VMAT optimization to solve this memory issue to make GPU-based VMAT more practical for clinical use. Methods: Our column-generation-based method generates apertures sequentially by iteratively searching for an optimal feasible aperture (referred as pricing problem, PP) and optimizing aperture intensities (referred as master problem, MP). The PP requires access to the large DDC matrix, which is implemented on a multi-GPU system. Each GPU stores a DDC sub-matrix corresponding to one fraction of beam angles and is only responsible for calculation related to those angles. Broadcast and parallel reduction schemes are adopted for inter-GPU data transfer. MP is a relatively small-scale problem and is implemented on one GPU. One headand- neck cancer case was used for test. Three different strategies for VMAT optimization on single GPU were also implemented for comparison: (S1) truncating DDC matrix to ignore its small value entries for optimization; (S2) transferring DDC matrix part by part to GPU during optimizations whenever needed; (S3) moving DDC matrix related calculation onto CPU. Results: Our multi-GPU-based implementation reaches a good plan within 1 minute. Although S1 was 10 seconds faster than our method, the obtained plan quality is worse. Both S2 and S3 handle the full DDC matrix and hence yield the same plan as in our method. However, the computation time is longer, namely 4 minutes and 30 minutes, respectively. Conclusion: Our multi-GPU-based VMAT optimization can effectively solve the limited memory issue with good plan quality and high efficiency, making GPUbased ultra-fast VMAT planning practical for real clinical use.

Accelerating large-scale phase-field simulations with GPU

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Xiaoming Shi

2017-10-01

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

Parallelization of the model-based iterative reconstruction algorithm DIRA

International Nuclear Information System (INIS)

Oertenberg, A.; Sandborg, M.; Alm Carlsson, G.; Malusek, A.; Magnusson, M.

2016-01-01

New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelization of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelization of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelized using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelization of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelization with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained. (authors)

Accelerating Computation of DCM for ERP in MATLAB by External Function Calls to the GPU

Science.gov (United States)

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

Application and performance of an ML-EM algorithm in NEXT

Science.gov (United States)

Simón, A.; Lerche, C.; Monrabal, F.; Gómez-Cadenas, J. J.; Álvarez, V.; Azevedo, C. D. R.; Benlloch-Rodríguez, J. M.; Borges, F. I. G. M.; Botas, A.; Cárcel, S.; Carrión, J. V.; Cebrián, S.; Conde, C. A. N.; Díaz, J.; Diesburg, M.; Escada, J.; Esteve, R.; Felkai, R.; Fernandes, L. M. P.; Ferrario, P.; Ferreira, A. L.; Freitas, E. D. C.; Goldschmidt, A.; González-Díaz, D.; Gutiérrez, R. M.; Hauptman, J.; Henriques, C. A. O.; Hernandez, A. I.; Hernando Morata, J. A.; Herrero, V.; Jones, B. J. P.; Labarga, L.; Laing, A.; Lebrun, P.; Liubarsky, I.; López-March, N.; Losada, M.; Martín-Albo, J.; Martínez-Lema, G.; Martínez, A.; McDonald, A. D.; Monteiro, C. M. B.; Mora, F. J.; Moutinho, L. M.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Nygren, D. R.; Palmeiro, B.; Para, A.; Pérez, J.; Querol, M.; Renner, J.; Ripoll, L.; Rodríguez, J.; Rogers, L.; Santos, F. P.; dos Santos, J. M. F.; Sofka, C.; Sorel, M.; Stiegler, T.; Toledo, J. F.; Torrent, J.; Tsamalaidze, Z.; Veloso, J. F. C. A.; Webb, R.; White, J. T.; Yahlali, N.

2017-08-01

The goal of the NEXT experiment is the observation of neutrinoless double beta decay in 136Xe using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.

Robust Cell Detection for Large-Scale 3D Microscopy Using GPU-Accelerated Iterative Voting

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Leila Saadatifard

2018-04-01

Full Text Available High-throughput imaging techniques, such as Knife-Edge Scanning Microscopy (KESM,are capable of acquiring three-dimensional whole-organ images at sub-micrometer resolution. These images are challenging to segment since they can exceed several terabytes (TB in size, requiring extremely fast and fully automated algorithms. Staining techniques are limited to contrast agents that can be applied to large samples and imaged in a single pass. This requires maximizing the number of structures labeled in a single channel, resulting in images that are densely packed with spatial features. In this paper, we propose a three-dimensional approach for locating cells based on iterative voting. Due to the computational complexity of this algorithm, a highly efficient GPU implementation is required to make it practical on large data sets. The proposed algorithm has a limited number of input parameters and is highly parallel.

The performances of R GPU implementations of the GMRES method

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Bogdan Oancea

2018-03-01

Full Text Available Although the performance of commodity computers has improved drastically with the introduction of multicore processors and GPU computing, the standard R distribution is still based on single-threaded model of computation, using only a small fraction of the computational power available now for most desktops and laptops. Modern statistical software packages rely on high performance implementations of the linear algebra routines there are at the core of several important leading edge statistical methods. In this paper we present a GPU implementation of the GMRES iterative method for solving linear systems. We compare the performance of this implementation with a pure single threaded version of the CPU. We also investigate the performance of our implementation using different GPU packages available now for R such as gmatrix, gputools or gpuR which are based on CUDA or OpenCL frameworks.

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

International Nuclear Information System (INIS)

Jia Xun; Lou Yifei; Li Ruijiang; Song, William Y.; Jiang, Steve B.

2010-01-01

Purpose: Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose. Methods: The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. Results: It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of ∼360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm. Conclusions: This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation.

Science.gov (United States)

Jia, Xun; Lou, Yifei; Li, Ruijiang; Song, William Y; Jiang, Steve B

2010-04-01

Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm. This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

Processamento da rede neocognitron para reconhecimento facial em ambiente de alto desempenho GPU

OpenAIRE

Gustavo Poli Lameirão da Silva

2007-01-01

Neste trabalho é apresentada a implementação da Rede Neural Neocognitron, usando uma arquitetura de computação de alto desempenho baseada em GPU (Graphics Processing Unit). O Neocognitron é uma rede neural artificial, proposta por Fukushima e colaboradores, constituída de vários estágios de camadas de neurônios, organizados em matrizes bidimensionais denominadas planos celulares. Para o processamento de alto desempenho da aplicação de reconhecimento facial usando neocognitron foi utilizado o ...

High-performance blob-based iterative three-dimensional reconstruction in electron tomography using multi-GPUs

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Wan Xiaohua

2012-06-01

Full Text Available Abstract Background Three-dimensional (3D reconstruction in electron tomography (ET has emerged as a leading technique to elucidate the molecular structures of complex biological specimens. Blob-based iterative methods are advantageous reconstruction methods for 3D reconstruction in ET, but demand huge computational costs. Multiple graphic processing units (multi-GPUs offer an affordable platform to meet these demands. However, a synchronous communication scheme between multi-GPUs leads to idle GPU time, and a weighted matrix involved in iterative methods cannot be loaded into GPUs especially for large images due to the limited available memory of GPUs. Results In this paper we propose a multilevel parallel strategy combined with an asynchronous communication scheme and a blob-ELLR data structure to efficiently perform blob-based iterative reconstructions on multi-GPUs. The asynchronous communication scheme is used to minimize the idle GPU time so as to asynchronously overlap communications with computations. The blob-ELLR data structure only needs nearly 1/16 of the storage space in comparison with ELLPACK-R (ELLR data structure and yields significant acceleration. Conclusions Experimental results indicate that the multilevel parallel scheme combined with the asynchronous communication scheme and the blob-ELLR data structure allows efficient implementations of 3D reconstruction in ET on multi-GPUs.

Validation of GPU based TomoTherapy dose calculation engine.

Science.gov (United States)

Chen, Quan; Lu, Weiguo; Chen, Yu; Chen, Mingli; Henderson, Douglas; Sterpin, Edmond

2012-04-01

The graphic processing unit (GPU) based TomoTherapy convolution/superposition(C/S) dose engine (GPU dose engine) achieves a dramatic performance improvement over the traditional CPU-cluster based TomoTherapy dose engine (CPU dose engine). Besides the architecture difference between the GPU and CPU, there are several algorithm changes from the CPU dose engine to the GPU dose engine. These changes made the GPU dose slightly different from the CPU-cluster dose. In order for the commercial release of the GPU dose engine, its accuracy has to be validated. Thirty eight TomoTherapy phantom plans and 19 patient plans were calculated with both dose engines to evaluate the equivalency between the two dose engines. Gamma indices (Γ) were used for the equivalency evaluation. The GPU dose was further verified with the absolute point dose measurement with ion chamber and film measurements for phantom plans. Monte Carlo calculation was used as a reference for both dose engines in the accuracy evaluation in heterogeneous phantom and actual patients. The GPU dose engine showed excellent agreement with the current CPU dose engine. The majority of cases had over 99.99% of voxels with Γ(1%, 1 mm) engine also showed similar degree of accuracy in heterogeneous media as the current TomoTherapy dose engine. It is verified and validated that the ultrafast TomoTherapy GPU dose engine can safely replace the existing TomoTherapy cluster based dose engine without degradation in dose accuracy.

GPU-based high performance Monte Carlo simulation in neutron transport

Energy Technology Data Exchange (ETDEWEB)

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)

GPU-based high performance Monte Carlo simulation in neutron transport

International Nuclear Information System (INIS)

Heimlich, Adino; Mol, Antonio C.A.; Pereira, Claudio M.N.A.

2009-01-01

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)

Haptic Feedback for the GPU-based Surgical Simulator

DEFF Research Database (Denmark)

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....

On the use of successive data in the ML-EM algorithm in Positron Emission Tomography

Energy Technology Data Exchange (ETDEWEB)

Desmedt, P; Lemahieu, I [University of Ghent, ELIS Department, SInt-Pietersnieuwstraat 41, B-9000 Gent, (Belgium)

1994-12-31

The Maximum Likelihood-Expectation Maximization (ML-EM) algorithm is the most popular statistical reconstruction technique for Positron Emission Tomography (PET). The ML-EM algorithm is however also renowned for its long reconstruction times. An acceleration technique for this algorithm is studied in this paper. The proposed technique starts the ML-EM algorithm before the measurement process is completed. Since the reconstruction is initiated during the scan of the patient, the time elapsed before a reconstruction becomes available is reduced. Experiments with software phantoms indicate that the quality of the reconstructed image using successive data is comparable to the quality of the reconstruction with the normal ML-EM algorithm. (authors). 7 refs, 3 figs.

Noniterative Multireference Coupled Cluster Methods on Heterogeneous CPU-GPU Systems

Energy Technology Data Exchange (ETDEWEB)

Bhaskaran-Nair, Kiran; Ma, Wenjing; Krishnamoorthy, Sriram; Villa, Oreste; van Dam, Hubertus JJ; Apra, Edoardo; Kowalski, Karol

2013-04-09

A novel parallel algorithm for non-iterative multireference coupled cluster (MRCC) theories, which merges recently introduced reference-level parallelism (RLP) [K. Bhaskaran-Nair, J.Brabec, E. Aprà, H.J.J. van Dam, J. Pittner, K. Kowalski, J. Chem. Phys. 137, 094112 (2012)] with the possibility of accelerating numerical calculations using graphics processing unit (GPU) is presented. We discuss the performance of this algorithm on the example of the MRCCSD(T) method (iterative singles and doubles and perturbative triples), where the corrections due to triples are added to the diagonal elements of the MRCCSD (iterative singles and doubles) effective Hamiltonian matrix. The performance of the combined RLP/GPU algorithm is illustrated on the example of the Brillouin-Wigner (BW) and Mukherjee (Mk) state-specific MRCCSD(T) formulations.

GPU-based high-performance computing for radiation therapy

International Nuclear Information System (INIS)

Jia, Xun; Jiang, Steve B; Ziegenhein, Peter

2014-01-01

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. (topical review)

VDE/disruption EM analysis for ITER in-vessel components

International Nuclear Information System (INIS)

Miki, N.; Ioki, K.; Ilio, F.; Kodama, T.; Chiocchio, S.; Williamson, D.; Roccella, M.; Barabaschi, P.; Sayer, R.S.

1998-01-01

This paper summarises the results of EM analyses for ITER in-vessel components, such as blanket modules, backplate and divertor modules. In the ITER design the following two disruption scenarios are taken into account: centered or radial disruption, and vertical displacement event (VDE). Eddy currents and forces due to plasma disruption were calculated using the 3D shell element code EDDYCUFF and the 3D solid element code EMAS. The plasma motion and current decay used in the EM analysis was supplied by 2-D axisymmetric plasma equilibrium codes, TSC and MAXFEA. (authors)

CUDA GPU based full-Stokes finite difference modelling of glaciers

Science.gov (United States)

Brædstrup, C. F.; Egholm, D. L.

2012-04-01

Many have stressed the limitations of using the shallow shelf and shallow ice approximations when modelling ice streams or surging glaciers. Using a full-stokes approach requires either large amounts of computer power or time and is therefore seldom an option for most glaciologists. Recent advances in graphics card (GPU) technology for high performance computing have proven extremely efficient in accelerating many large scale scientific computations. The general purpose GPU (GPGPU) technology is cheap, has a low power consumption and fits into a normal desktop computer. It could therefore provide a powerful tool for many glaciologists. Our full-stokes ice sheet model implements a Red-Black Gauss-Seidel iterative linear solver to solve the full stokes equations. This technique has proven very effective when applied to the stokes equation in geodynamics problems, and should therefore also preform well in glaciological flow probems. The Gauss-Seidel iterator is known to be robust but several other linear solvers have a much faster convergence. To aid convergence, the solver uses a multigrid approach where values are interpolated and extrapolated between different grid resolutions to minimize the short wavelength errors efficiently. This reduces the iteration count by several orders of magnitude. The run-time is further reduced by using the GPGPU technology where each card has up to 448 cores. Researchers utilizing the GPGPU technique in other areas have reported between 2 - 11 times speedup compared to multicore CPU implementations on similar problems. The goal of these initial investigations into the possible usage of GPGPU technology in glacial modelling is to apply the enhanced resolution of a full-stokes solver to ice streams and surging glaciers. This is a area of growing interest because ice streams are the main drainage conjugates for large ice sheets. It is therefore crucial to understand this streaming behavior and it's impact up-ice.

A real-time spike sorting method based on the embedded GPU.

Science.gov (United States)

Zelan Yang; Kedi Xu; Xiang Tian; Shaomin Zhang; Xiaoxiang Zheng

2017-07-01

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

Accelerating the XGBoost algorithm using GPU computing

Directory of Open Access Journals (Sweden)

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.

An efficient spectral crystal plasticity solver for GPU architectures

Science.gov (United States)

Malahe, Michael

2018-03-01

We present a spectral crystal plasticity (CP) solver for graphics processing unit (GPU) architectures that achieves a tenfold increase in efficiency over prior GPU solvers. The approach makes use of a database containing a spectral decomposition of CP simulations performed using a conventional iterative solver over a parameter space of crystal orientations and applied velocity gradients. The key improvements in efficiency come from reducing global memory transactions, exposing more instruction-level parallelism, reducing integer instructions and performing fast range reductions on trigonometric arguments. The scheme also makes more efficient use of memory than prior work, allowing for larger problems to be solved on a single GPU. We illustrate these improvements with a simulation of 390 million crystal grains on a consumer-grade GPU, which executes at a rate of 2.72 s per strain step.

Multi-GPU based acceleration of a list-mode DRAMA toward real-time OpenPET imaging

Energy Technology Data Exchange (ETDEWEB)

Kinouchi, Shoko [Chiba Univ. (Japan); National Institute of Radiological Sciences, Chiba (Japan); Yamaya, Taiga; Yoshida, Eiji; Tashima, Hideaki [National Institute of Radiological Sciences, Chiba (Japan); Kudo, Hiroyuki [Tsukuba Univ., Ibaraki (Japan); Suga, Mikio [Chiba Univ. (Japan)

2011-07-01

OpenPET, which has a physical gap between two detector rings, is our new PET geometry. In order to realize future radiation therapy guided by OpenPET, real-time imaging is required. Therefore we developed a list-mode image reconstruction method using general purpose graphic processing units (GPUs). For GPU implementation, the efficiency of acceleration depends on the implementation method which is required to avoid conditional statements. Therefore, in our previous study, we developed a new system model which was suited for the GPU implementation. In this paper, we implemented our image reconstruction method using 4 GPUs to get further acceleration. We applied the developed reconstruction method to a small OpenPET prototype. We obtained calculation times of total iteration using 4 GPUs that were 3.4 times faster than using a single GPU. Compared to using a single CPU, we achieved the reconstruction time speed-up of 142 times using 4 GPUs. (orig.)

Continuous Analog of Accelerated OS-EM Algorithm for Computed Tomography

Directory of Open Access Journals (Sweden)

Kiyoko Tateishi

2017-01-01

Full Text Available The maximum-likelihood expectation-maximization (ML-EM algorithm is used for an iterative image reconstruction (IIR method and performs well with respect to the inverse problem as cross-entropy minimization in computed tomography. For accelerating the convergence rate of the ML-EM, the ordered-subsets expectation-maximization (OS-EM with a power factor is effective. In this paper, we propose a continuous analog to the power-based accelerated OS-EM algorithm. The continuous-time image reconstruction (CIR system is described by nonlinear differential equations with piecewise smooth vector fields by a cyclic switching process. A numerical discretization of the differential equation by using the geometric multiplicative first-order expansion of the nonlinear vector field leads to an exact equivalent iterative formula of the power-based OS-EM. The convergence of nonnegatively constrained solutions to a globally stable equilibrium is guaranteed by the Lyapunov theorem for consistent inverse problems. We illustrate through numerical experiments that the convergence characteristics of the continuous system have the highest quality compared with that of discretization methods. We clarify how important the discretization method approximates the solution of the CIR to design a better IIR method.

GPU-BSM: a GPU-based tool to map bisulfite-treated reads.

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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-Based Point Cloud Superpositioning for Structural Comparisons of Protein Binding Sites.

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Leinweber, Matthias; Fober, Thomas; Freisleben, Bernd

2018-01-01

In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations. Binary classification tests based on the ATP, NADH, and FAD protein subsets of CavBase, a database containing putative binding sites, show average classification rate improvements from about 92 percent (CPU) to 96 percent (GPU). Further experiments indicate that the proposed GPU-based labeled point cloud superpositioning approach can be superior to traditional protein comparison approaches based on sequence alignments.

Implementation of GPU parallel equilibrium reconstruction for plasma control in EAST

Energy Technology Data Exchange (ETDEWEB)

Huang, Yao, E-mail: yaohuang@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei (China); Xiao, B.J. [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei (China); School of Nuclear Science & Technology, University of Science & Technology of China (China); Luo, Z.P.; Yuan, Q.P.; Pei, X.F. [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei (China); Yue, X.N. [School of Nuclear Science & Technology, University of Science & Technology of China (China)

2016-11-15

Highlights: • We described parallel equilibrium reconstruction code P-EFIT running on GPU was integrated with EAST plasma control system. • Compared with RT-EFIT used in EAST, P-EFIT has better spatial resolution and full algorithm of EFIT per iteration. • With the data interface through RFM, 65 × 65 spatial grids P-EFIT can satisfy the accuracy and time feasibility requirements for plasma control. • Successful control using ISOFLUX/P-EFIT was established in the dedicated experiment during the EAST 2014 campaign. • This work is a stepping-stone towards versatile ISOFLUX/P-EFIT control, such as real-time equilibrium reconstruction with more diagnostics. - Abstract: Implementation of P-EFIT code for plasma control in EAST is described. P-EFIT is based on the EFIT framework, but built with the CUDA™ architecture to take advantage of massively parallel Graphical Processing Unit (GPU) cores to significantly accelerate the computation. 65 × 65 grid size P-EFIT can complete one reconstruction iteration in 300 μs, with one iteration strategy, it can satisfy the needs of real-time plasma shape control. Data interface between P-EFIT and PCS is realized and developed by transferring data through RFM. First application of P-EFIT to discharge control in EAST is described.

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson's Correlation Coefficients for Time Series Data-fMRI Study.

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Eslami, Taban; Saeed, Fahad

2018-04-20

Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns N ( N − 1 ) / 2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods.

A CFD Heterogeneous Parallel Solver Based on Collaborating CPU and GPU

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Lai, Jianqi; Tian, Zhengyu; Li, Hua; Pan, Sha

2018-03-01

Since Graphic Processing Unit (GPU) has a strong ability of floating-point computation and memory bandwidth for data parallelism, it has been widely used in the areas of common computing such as molecular dynamics (MD), computational fluid dynamics (CFD) and so on. The emergence of compute unified device architecture (CUDA), which reduces the complexity of compiling program, brings the great opportunities to CFD. There are three different modes for parallel solution of NS equations: parallel solver based on CPU, parallel solver based on GPU and heterogeneous parallel solver based on collaborating CPU and GPU. As we can see, GPUs are relatively rich in compute capacity but poor in memory capacity and the CPUs do the opposite. We need to make full use of the GPUs and CPUs, so a CFD heterogeneous parallel solver based on collaborating CPU and GPU has been established. Three cases are presented to analyse the solver’s computational accuracy and heterogeneous parallel efficiency. The numerical results agree well with experiment results, which demonstrate that the heterogeneous parallel solver has high computational precision. The speedup on a single GPU is more than 40 for laminar flow, it decreases for turbulent flow, but it still can reach more than 20. What’s more, the speedup increases as the grid size becomes larger.

3D dictionary learning based iterative cone beam CT reconstruction

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Ti Bai

2014-03-01

Full Text Available Purpose: This work is to develop a 3D dictionary learning based cone beam CT (CBCT reconstruction algorithm on graphic processing units (GPU to improve the quality of sparse-view CBCT reconstruction with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms of 3 × 3 × 3 was trained from a large number of blocks extracted from a high quality volume image. On the basis, we utilized cholesky decomposition based orthogonal matching pursuit algorithm to find the sparse representation of each block. To accelerate the time-consuming sparse coding in the 3D case, we implemented the sparse coding in a parallel fashion by taking advantage of the tremendous computational power of GPU. Conjugate gradient least square algorithm was adopted to minimize the data fidelity term. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with tight frame (TF by performing reconstructions on a subset data of 121 projections. Results: Compared to TF based CBCT reconstruction that shows good overall performance, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, remove more streaking artifacts and also induce less blocky artifacts. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppress the noise, and hence to achieve high quality reconstruction under the case of sparse view. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application.-------------------------------Cite this article as: Bai T, Yan H, Shi F, Jia X, Lou Y, Xu Q, Jiang S, Mou X. 3D dictionary learning based iterative cone beam CT reconstruction. Int J Cancer Ther Oncol 2014; 2(2:020240. DOI: 10

GPU based numerical simulation of core shooting process

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Yi-zhong Zhang

2017-11-01

Full Text Available Core shooting process is the most widely used technique to make sand cores and it plays an important role in the quality of sand cores. Although numerical simulation can hopefully optimize the core shooting process, research on numerical simulation of the core shooting process is very limited. Based on a two-fluid model (TFM and a kinetic-friction constitutive correlation, a program for 3D numerical simulation of the core shooting process has been developed and achieved good agreements with in-situ experiments. To match the needs of engineering applications, a graphics processing unit (GPU has also been used to improve the calculation efficiency. The parallel algorithm based on the Compute Unified Device Architecture (CUDA platform can significantly decrease computing time by multi-threaded GPU. In this work, the program accelerated by CUDA parallelization method was developed and the accuracy of the calculations was ensured by comparing with in-situ experimental results photographed by a high-speed camera. The design and optimization of the parallel algorithm were discussed. The simulation result of a sand core test-piece indicated the improvement of the calculation efficiency by GPU. The developed program has also been validated by in-situ experiments with a transparent core-box, a high-speed camera, and a pressure measuring system. The computing time of the parallel program was reduced by nearly 95% while the simulation result was still quite consistent with experimental data. The GPU parallelization method can successfully solve the problem of low computational efficiency of the 3D sand shooting simulation program, and thus the developed GPU program is appropriate for engineering applications.

GPU-Monte Carlo based fast IMRT plan optimization

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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

Development of parallel GPU based algorithms for problems in nuclear area

International Nuclear Information System (INIS)

Almeida, Adino Americo Heimlich

2009-01-01

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)

Implementation and Optimization of GPU-Based Static State Security Analysis in Power Systems

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Yong Chen

2017-01-01

Full Text Available Static state security analysis (SSSA is one of the most important computations to check whether a power system is in normal and secure operating state. It is a challenge to satisfy real-time requirements with CPU-based concurrent methods due to the intensive computations. A sensitivity analysis-based method with Graphics processing unit (GPU is proposed for power systems, which can reduce calculation time by 40% compared to the execution on a 4-core CPU. The proposed method involves load flow analysis and sensitivity analysis. In load flow analysis, a multifrontal method for sparse LU factorization is explored on GPU through dynamic frontal task scheduling between CPU and GPU. The varying matrix operations during sensitivity analysis on GPU are highly optimized in this study. The results of performance evaluations show that the proposed GPU-based SSSA with optimized matrix operations can achieve a significant reduction in computation time.

GPU Parallel Bundle Block Adjustment

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ZHENG Maoteng

2017-09-01

Full Text Available To deal with massive data in photogrammetry, we introduce the GPU parallel computing technology. The preconditioned conjugate gradient and inexact Newton method are also applied to decrease the iteration times while solving the normal equation. A brand new workflow of bundle adjustment is developed to utilize GPU parallel computing technology. Our method can avoid the storage and inversion of the big normal matrix, and compute the normal matrix in real time. The proposed method can not only largely decrease the memory requirement of normal matrix, but also largely improve the efficiency of bundle adjustment. It also achieves the same accuracy as the conventional method. Preliminary experiment results show that the bundle adjustment of a dataset with about 4500 images and 9 million image points can be done in only 1.5 minutes while achieving sub-pixel accuracy.

GPU based contouring method on grid DEM data

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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.

GPU-accelerated 3D neutron diffusion code based on finite difference method

Energy Technology Data Exchange (ETDEWEB)

Xu, Q.; Yu, G.; Wang, K. [Dept. of Engineering Physics, Tsinghua Univ. (China)

2012-07-01

Finite difference method, as a traditional numerical solution to neutron diffusion equation, although considered simpler and more precise than the coarse mesh nodal methods, has a bottle neck to be widely applied caused by the huge memory and unendurable computation time it requires. In recent years, the concept of General-Purpose computation on GPUs has provided us with a powerful computational engine for scientific research. In this study, a GPU-Accelerated multi-group 3D neutron diffusion code based on finite difference method was developed. First, a clean-sheet neutron diffusion code (3DFD-CPU) was written in C++ on the CPU architecture, and later ported to GPUs under NVIDIA's CUDA platform (3DFD-GPU). The IAEA 3D PWR benchmark problem was calculated in the numerical test, where three different codes, including the original CPU-based sequential code, the HYPRE (High Performance Pre-conditioners)-based diffusion code and CITATION, were used as counterpoints to test the efficiency and accuracy of the GPU-based program. The results demonstrate both high efficiency and adequate accuracy of the GPU implementation for neutron diffusion equation. A speedup factor of about 46 times was obtained, using NVIDIA's Geforce GTX470 GPU card against a 2.50 GHz Intel Quad Q9300 CPU processor. Compared with the HYPRE-based code performing in parallel on an 8-core tower server, the speedup of about 2 still could be observed. More encouragingly, without any mathematical acceleration technology, the GPU implementation ran about 5 times faster than CITATION which was speeded up by using the SOR method and Chebyshev extrapolation technique. (authors)

GPU-accelerated 3D neutron diffusion code based on finite difference method

International Nuclear Information System (INIS)

Xu, Q.; Yu, G.; Wang, K.

2012-01-01

Finite difference method, as a traditional numerical solution to neutron diffusion equation, although considered simpler and more precise than the coarse mesh nodal methods, has a bottle neck to be widely applied caused by the huge memory and unendurable computation time it requires. In recent years, the concept of General-Purpose computation on GPUs has provided us with a powerful computational engine for scientific research. In this study, a GPU-Accelerated multi-group 3D neutron diffusion code based on finite difference method was developed. First, a clean-sheet neutron diffusion code (3DFD-CPU) was written in C++ on the CPU architecture, and later ported to GPUs under NVIDIA's CUDA platform (3DFD-GPU). The IAEA 3D PWR benchmark problem was calculated in the numerical test, where three different codes, including the original CPU-based sequential code, the HYPRE (High Performance Pre-conditioners)-based diffusion code and CITATION, were used as counterpoints to test the efficiency and accuracy of the GPU-based program. The results demonstrate both high efficiency and adequate accuracy of the GPU implementation for neutron diffusion equation. A speedup factor of about 46 times was obtained, using NVIDIA's Geforce GTX470 GPU card against a 2.50 GHz Intel Quad Q9300 CPU processor. Compared with the HYPRE-based code performing in parallel on an 8-core tower server, the speedup of about 2 still could be observed. More encouragingly, without any mathematical acceleration technology, the GPU implementation ran about 5 times faster than CITATION which was speeded up by using the SOR method and Chebyshev extrapolation technique. (authors)

Bayer image parallel decoding based on GPU

Science.gov (United States)

Hu, Rihui; Xu, Zhiyong; Wei, Yuxing; Sun, Shaohua

2012-11-01

In the photoelectrical tracking system, Bayer image is decompressed in traditional method, which is CPU-based. However, it is too slow when the images become large, for example, 2K×2K×16bit. In order to accelerate the Bayer image decoding, this paper introduces a parallel speedup method for NVIDA's Graphics Processor Unit (GPU) which supports CUDA architecture. The decoding procedure can be divided into three parts: the first is serial part, the second is task-parallelism part, and the last is data-parallelism part including inverse quantization, inverse discrete wavelet transform (IDWT) as well as image post-processing part. For reducing the execution time, the task-parallelism part is optimized by OpenMP techniques. The data-parallelism part could advance its efficiency through executing on the GPU as CUDA parallel program. The optimization techniques include instruction optimization, shared memory access optimization, the access memory coalesced optimization and texture memory optimization. In particular, it can significantly speed up the IDWT by rewriting the 2D (Tow-dimensional) serial IDWT into 1D parallel IDWT. Through experimenting with 1K×1K×16bit Bayer image, data-parallelism part is 10 more times faster than CPU-based implementation. Finally, a CPU+GPU heterogeneous decompression system was designed. The experimental result shows that it could achieve 3 to 5 times speed increase compared to the CPU serial method.

Parallelizing flow-accumulation calculations on graphics processing units—From iterative DEM preprocessing algorithm to recursive multiple-flow-direction algorithm

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Qin, Cheng-Zhi; Zhan, Lijun

2012-06-01

As one of the important tasks in digital terrain analysis, the calculation of flow accumulations from gridded digital elevation models (DEMs) usually involves two steps in a real application: (1) using an iterative DEM preprocessing algorithm to remove the depressions and flat areas commonly contained in real DEMs, and (2) using a recursive flow-direction algorithm to calculate the flow accumulation for every cell in the DEM. Because both algorithms are computationally intensive, quick calculation of the flow accumulations from a DEM (especially for a large area) presents a practical challenge to personal computer (PC) users. In recent years, rapid increases in hardware capacity of the graphics processing units (GPUs) provided in modern PCs have made it possible to meet this challenge in a PC environment. Parallel computing on GPUs using a compute-unified-device-architecture (CUDA) programming model has been explored to speed up the execution of the single-flow-direction algorithm (SFD). However, the parallel implementation on a GPU of the multiple-flow-direction (MFD) algorithm, which generally performs better than the SFD algorithm, has not been reported. Moreover, GPU-based parallelization of the DEM preprocessing step in the flow-accumulation calculations has not been addressed. This paper proposes a parallel approach to calculate flow accumulations (including both iterative DEM preprocessing and a recursive MFD algorithm) on a CUDA-compatible GPU. For the parallelization of an MFD algorithm (MFD-md), two different parallelization strategies using a GPU are explored. The first parallelization strategy, which has been used in the existing parallel SFD algorithm on GPU, has the problem of computing redundancy. Therefore, we designed a parallelization strategy based on graph theory. The application results show that the proposed parallel approach to calculate flow accumulations on a GPU performs much faster than either sequential algorithms or other parallel GPU-based

A distributed multi-GPU system for high speed electron microscopic tomographic reconstruction

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Zheng, Shawn Q.; Branlund, Eric; Kesthelyi, Bettina; Braunfeld, Michael B.; Cheng, Yifan; Sedat, John W. [The Howard Hughes Medical Institute and the W.M. Keck Advanced Microscopy Laboratory, Department of Biochemistry and Biophysics, University of California, San Francisco, 600, 16th Street, Room S412D, CA 94158-2517 (United States); Agard, David A., E-mail: agard@msg.ucsf.edu [The Howard Hughes Medical Institute and the W.M. Keck Advanced Microscopy Laboratory, Department of Biochemistry and Biophysics, University of California, San Francisco, 600, 16th Street, Room S412D, CA 94158-2517 (United States)

2011-07-15

Full resolution electron microscopic tomographic (EMT) reconstruction of large-scale tilt series requires significant computing power. The desire to perform multiple cycles of iterative reconstruction and realignment dramatically increases the pressing need to improve reconstruction performance. This has motivated us to develop a distributed multi-GPU (graphics processing unit) system to provide the required computing power for rapid constrained, iterative reconstructions of very large three-dimensional (3D) volumes. The participating GPUs reconstruct segments of the volume in parallel, and subsequently, the segments are assembled to form the complete 3D volume. Owing to its power and versatility, the CUDA (NVIDIA, USA) platform was selected for GPU implementation of the EMT reconstruction. For a system containing 10 GPUs provided by 5 GTX295 cards, 10 cycles of SIRT reconstruction for a tomogram of 4096{sup 2}x512 voxels from an input tilt series containing 122 projection images of 4096{sup 2} pixels (single precision float) takes a total of 1845 s of which 1032 s are for computation with the remainder being the system overhead. The same system takes only 39 s total to reconstruct 1024{sup 2}x256 voxels from 122 1024{sup 2} pixel projections. While the system overhead is non-trivial, performance analysis indicates that adding extra GPUs to the system would lead to steadily enhanced overall performance. Therefore, this system can be easily expanded to generate superior computing power for very large tomographic reconstructions and especially to empower iterative cycles of reconstruction and realignment. -- Highlights: {yields} A distributed multi-GPU system has been developed for electron microscopic tomography (EMT). {yields} This system allows for rapid constrained, iterative reconstruction of very large volumes. {yields} This system can be easily expanded to generate superior computing power for large-scale iterative EMT realignment.

A distributed multi-GPU system for high speed electron microscopic tomographic reconstruction

International Nuclear Information System (INIS)

Zheng, Shawn Q.; Branlund, Eric; Kesthelyi, Bettina; Braunfeld, Michael B.; Cheng, Yifan; Sedat, John W.; Agard, David A.

2011-01-01

Full resolution electron microscopic tomographic (EMT) reconstruction of large-scale tilt series requires significant computing power. The desire to perform multiple cycles of iterative reconstruction and realignment dramatically increases the pressing need to improve reconstruction performance. This has motivated us to develop a distributed multi-GPU (graphics processing unit) system to provide the required computing power for rapid constrained, iterative reconstructions of very large three-dimensional (3D) volumes. The participating GPUs reconstruct segments of the volume in parallel, and subsequently, the segments are assembled to form the complete 3D volume. Owing to its power and versatility, the CUDA (NVIDIA, USA) platform was selected for GPU implementation of the EMT reconstruction. For a system containing 10 GPUs provided by 5 GTX295 cards, 10 cycles of SIRT reconstruction for a tomogram of 4096 2 x512 voxels from an input tilt series containing 122 projection images of 4096 2 pixels (single precision float) takes a total of 1845 s of which 1032 s are for computation with the remainder being the system overhead. The same system takes only 39 s total to reconstruct 1024 2 x256 voxels from 122 1024 2 pixel projections. While the system overhead is non-trivial, performance analysis indicates that adding extra GPUs to the system would lead to steadily enhanced overall performance. Therefore, this system can be easily expanded to generate superior computing power for very large tomographic reconstructions and especially to empower iterative cycles of reconstruction and realignment. -- Highlights: → A distributed multi-GPU system has been developed for electron microscopic tomography (EMT). → This system allows for rapid constrained, iterative reconstruction of very large volumes. → This system can be easily expanded to generate superior computing power for large-scale iterative EMT realignment.

Moving-Target Position Estimation Using GPU-Based Particle Filter for IoT Sensing Applications

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Seongseop Kim

2017-11-01

Full Text Available A particle filter (PF has been introduced for effective position estimation of moving targets for non-Gaussian and nonlinear systems. The time difference of arrival (TDOA method using acoustic sensor array has normally been used to for estimation by concealing the location of a moving target, especially underwater. In this paper, we propose a GPU -based acceleration of target position estimation using a PF and propose an efficient system and software architecture. The proposed graphic processing unit (GPU-based algorithm has more advantages in applying PF signal processing to a target system, which consists of large-scale Internet of Things (IoT-driven sensors because of the parallelization which is scalable. For the TDOA measurement from the acoustic sensor array, we use the generalized cross correlation phase transform (GCC-PHAT method to obtain the correlation coefficient of the signal using Fast Fourier Transform (FFT, and we try to accelerate the calculations of GCC-PHAT based TDOA measurements using FFT with GPU compute unified device architecture (CUDA. The proposed approach utilizes a parallelization method in the target position estimation algorithm using GPU-based PF processing. In addition, it could efficiently estimate sudden movement change of the target using GPU-based parallel computing which also can be used for multiple target tracking. It also provides scalability in extending the detection algorithm according to the increase of the number of sensors. Therefore, the proposed architecture can be applied in IoT sensing applications with a large number of sensors. The target estimation algorithm was verified using MATLAB and implemented using GPU CUDA. We implemented the proposed signal processing acceleration system using target GPU to analyze in terms of execution time. The execution time of the algorithm is reduced by 55% from to the CPU standalone operation in target embedded board, NVIDIA Jetson TX1. Also, to apply large

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

International Nuclear Information System (INIS)

Tian, Zhen; Folkerts, Michael; Tan, Jun; Jia, Xun; Jiang, Steve B.; Peng, Fei

2015-01-01

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is

Multi-GPU implementation of a VMAT treatment plan optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Tian, Zhen, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Folkerts, Michael; Tan, Jun; Jia, Xun, E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu; Jiang, Steve B., E-mail: Zhen.Tian@UTSouthwestern.edu, E-mail: Xun.Jia@UTSouthwestern.edu, E-mail: Steve.Jiang@UTSouthwestern.edu [Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75390 (United States); Peng, Fei [Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 (United States)

2015-06-15

Purpose: Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU’s relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors’ group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. Methods: The column-generation approach generates VMAT apertures sequentially by solving a pricing problem (PP) and a master problem (MP) iteratively. In the authors’ method, the sparse DDC matrix is first stored on a CPU in coordinate list format (COO). On the GPU side, this matrix is split into four submatrices according to beam angles, which are stored on four GPUs in compressed sparse row format. Computation of beamlet price, the first step in PP, is accomplished using multi-GPUs. A fast inter-GPU data transfer scheme is accomplished using peer-to-peer access. The remaining steps of PP and MP problems are implemented on CPU or a single GPU due to their modest problem scale and computational loads. Barzilai and Borwein algorithm with a subspace step scheme is adopted here to solve the MP problem. A head and neck (H and N) cancer case is

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

International Nuclear Information System (INIS)

Modiri, A; Hagan, A; Gu, X; Sawant, A

2015-01-01

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results

SU-E-T-500: Initial Implementation of GPU-Based Particle Swarm Optimization for 4D IMRT Planning in Lung SBRT

Energy Technology Data Exchange (ETDEWEB)

Modiri, A; Hagan, A; Gu, X; Sawant, A [UT Southwestern Medical Center, Dallas, TX (United States)

2015-06-15

Purpose 4D-IMRT planning, combined with dynamic MLC tracking delivery, utilizes the temporal dimension as an additional degree of freedom to achieve improved OAR-sparing. The computational complexity for such optimization increases exponentially with increase in dimensionality. In order to accomplish this task in a clinically-feasible time frame, we present an initial implementation of GPU-based 4D-IMRT planning based on particle swarm optimization (PSO). Methods The target and normal structures were manually contoured on ten phases of a 4DCT scan of a NSCLC patient with a 54cm3 right-lower-lobe tumor (1.5cm motion). Corresponding ten 3D-IMRT plans were created in the Eclipse treatment planning system (Ver-13.6). A vendor-provided scripting interface was used to export 3D-dose matrices corresponding to each control point (10 phases × 9 beams × 166 control points = 14,940), which served as input to PSO. The optimization task was to iteratively adjust the weights of each control point and scale the corresponding dose matrices. In order to handle the large amount of data in GPU memory, dose matrices were sparsified and placed in contiguous memory blocks with the 14,940 weight-variables. PSO was implemented on CPU (dual-Xeon, 3.1GHz) and GPU (dual-K20 Tesla, 2496 cores, 3.52Tflops, each) platforms. NiftyReg, an open-source deformable image registration package, was used to calculate the summed dose. Results The 4D-PSO plan yielded PTV coverage comparable to the clinical ITV-based plan and significantly higher OAR-sparing, as follows: lung Dmean=33%; lung V20=27%; spinal cord Dmax=26%; esophagus Dmax=42%; heart Dmax=0%; heart Dmean=47%. The GPU-PSO processing time for 14940 variables and 7 PSO-particles was 41% that of CPU-PSO (199 vs. 488 minutes). Conclusion Truly 4D-IMRT planning can yield significant OAR dose-sparing while preserving PTV coverage. The corresponding optimization problem is large-scale, non-convex and computationally rigorous. Our initial results

NMF-mGPU: non-negative matrix factorization on multi-GPU systems.

Science.gov (United States)

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

GPU-based Scalable Volumetric Reconstruction for Multi-view Stereo

Energy Technology Data Exchange (ETDEWEB)

Kim, H; Duchaineau, M; Max, N

2011-09-21

We present a new scalable volumetric reconstruction algorithm for multi-view stereo using a graphics processing unit (GPU). It is an effectively parallelized GPU algorithm that simultaneously uses a large number of GPU threads, each of which performs voxel carving, in order to integrate depth maps with images from multiple views. Each depth map, triangulated from pair-wise semi-dense correspondences, represents a view-dependent surface of the scene. This algorithm also provides scalability for large-scale scene reconstruction in a high resolution voxel grid by utilizing streaming and parallel computation. The output is a photo-realistic 3D scene model in a volumetric or point-based representation. We demonstrate the effectiveness and the speed of our algorithm with a synthetic scene and real urban/outdoor scenes. Our method can also be integrated with existing multi-view stereo algorithms such as PMVS2 to fill holes or gaps in textureless regions.

Clinical implementation of a GPU-based simplified Monte Carlo method for a treatment planning system of proton beam therapy

International Nuclear Information System (INIS)

Kohno, R; Hotta, K; Nishioka, S; Matsubara, K; Tansho, R; Suzuki, T

2011-01-01

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. (note)

Optimization of PET image quality by means of 3D data acquisition and iterative image reconstruction

International Nuclear Information System (INIS)

Doll, J.; Zaers, J.; Trojan, H.; Bellemann, M.E.; Adam, L.E.; Haberkorn, U.; Brix, G.

1998-01-01

The experiments were performed at the latest-generation whole-body PET system ECAT EXACT HR + . For 2D data acquisition, a collimator of thin tungsten septa was positioned in the field-of-view. Prior to image reconstruction, the measured 3D data were sorted into 2D sinograms by using the Fourier rebinning (FORE) algorithm developed by M. Defrise. The standard filtered backprojection (FBP) method and an optimized ML/EM algorithm with overrelaxation for accelerated convergence were employed for image reconstruction. The spatial resolution of both methods as well as the convergence and noise properties of the ML/EM algorithm were studied in phantom measurements. Furthermore, patient data were acquired in the 2D mode as well as in the 3D mode and reconstructed with both techniques. At the same spatial resolution, the ML/EM-reconstructed images showed fewer and less prominent artefacts than the FBP-reconstructed images. The resulting improved detail conspicuously was achieved for the data acquired in the 2D mode as well as in the 3D mode. The best image quality was obtained by iterative 2D reconstruction of 3D data sets which were previously rebinned into 2D sinograms with help of the FORE algorithm. The phantom measurements revealed that 50 iteration steps with the otpimized ML/EM algorithm were sufficient to keep the relative quantitation error below 5%. (orig./MG) [de

The ITER EC H and CD upper launcher: EM disruption analyses

International Nuclear Information System (INIS)

Vaccaro, A.; Aiello, G.; Grossetti, G.; Meier, A.; Scherer, T.A.; Schreck, S.; Späh, P.; Strauß, D.; Saibene, G.; Cavinato, M.

2013-01-01

In the frame of the new grant started in November 2011 between Fusion for Energy (F4E) and the ECHUL-CA consortium, the development process of the Electron Cyclotron Heating and Current Drive (EC H and CD) upper launcher (UL) in ITER has moved a step toward the final design phase. Based on the 2009 preliminary design review version, the new configuration of the UL now features a thicker single-wall mainframe (up to 90 mm), a recessed first wall panel (100 mm, to reduce the impact of halo currents) and a new arrangement of the internal shield blocks. The main design drivers for the structural components are still the electromagnetic (EM) loads, which need to be reassessed for the new configuration of the UL. In this paper the results of a new EM 20° sector model of ITER, specialized for the UL, are shown. Six different disruption scenarios are considered in this work: upward linear (36 ms) and exponential (36 ms) vertical displacement events (VDE), upward linear (36 ms) and exponential (16 ms) major disruptions (MD), category II upward slow and slow–fast VDEs. Comparing the analyses’ results allowed to define a set of structural loads to be used as a reference for the forthcoming structural calculations

Efficient approach to simulate EM loads on massive structures in ITER machine

Energy Technology Data Exchange (ETDEWEB)

Alekseev, A. [ITER Organization, Route de Vinon sur Verdon, 13115 St. Paul-Lez-Durance (France); Andreeva, Z.; Belov, A.; Belyakov, V.; Filatov, O. [D.V. Efremov Scientific Research Institute, 196641 St. Petersburg (Russian Federation); Gribov, Yu.; Ioki, K. [ITER Organization, Route de Vinon sur Verdon, 13115 St. Paul-Lez-Durance (France); Kukhtin, V.; Labusov, A.; Lamzin, E.; Lyublin, B.; Malkov, A.; Mazul, I. [D.V. Efremov Scientific Research Institute, 196641 St. Petersburg (Russian Federation); Rozov, V.; Sugihara, M. [ITER Organization, Route de Vinon sur Verdon, 13115 St. Paul-Lez-Durance (France); Sychevsky, S., E-mail: sytch@sintez.niiefa.spb.su [D.V. Efremov Scientific Research Institute, 196641 St. Petersburg (Russian Federation)

2013-10-15

Highlights: ► A modelling technique to predict EM loads in ITER conducting structures is presented. ► The technique provides low computational cost and parallel computations. ► Detailed models were built for the system “vacuum vessel, cryostat, thermal shields”. ► EM loads on massive in-vessel structures were simulated with the use of local models. ► A flexible combination of models enables desired accuracy of load distributions. -- Abstract: Operation of the ITER machine is associated with high electromagnetic (EM) loads. An essential contributor to EM loads is eddy currents induced in passive conductive structures. Reasoning from the ITER construction, a modelling technique has been developed and applied in computations to efficiently predict anticipated loads. The technique allows us to avoid building a global 3D finite-element (FE) model that requires meshing of the conducting structures and their vacuum environment into 3D solid elements that leads to high computational cost. The key features of the proposed technique are: (i) the use of an existing shell model for the system “vacuum vessel (VV), cryostat, and thermal shields (TS)” implementing the magnetic shell approach. A solution is obtained in terms of a single-component, in this case, vector electric potential taken within the conducting shells of the “VV + cryostat + TS” system. (ii) EM loads on in-vessel conducting structures are simulated with the use of local FE models. The local models use either the 3D solid body or shell approximations. Reasoning from the simulation efficiency, the local boundary conditions are put with respect to the total field or an external field. The use of an integral-differential formulation and special procedures ensures smooth and accurate simulated distributions of fields from current sources of any geometry. The local FE models have been developed and applied for EM analyses of a variety of the ITER components including the diagnostic systems

GPU-based cone beam computed tomography.

Science.gov (United States)

Noël, Peter B; Walczak, Alan M; Xu, Jinhui; Corso, Jason J; Hoffmann, Kenneth R; Schafer, Sebastian

2010-06-01

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Distributed GPU Computing in GIScience

Science.gov (United States)

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

SU-E-T-806: Very Fast GPU-Based IMPT Dose Computation

Energy Technology Data Exchange (ETDEWEB)

Sullivan, A; Brand, M [Mitsubishi Electric Research Lab, Cambridge, MA (United States)

2015-06-15

Purpose: Designing particle therapy treatment plans is a dosimetrist-in-the-loop optimization wherein the conflicting constraints of achieving a desired tumor dose distribution must be balanced against the need to minimize the dose to nearby OARs. IMPT introduces an additional, inner, numerical optimization step in which the dosimetrist’s current set of constraints are used to determine the weighting of beam spots. Very fast dose calculations are needed to enable the dosimetrist to perform many iterations of the outer optimization in a commercially reasonable time. Methods: We have developed a GPU-based convolution-type dose computation algorithm that more accurately handles heterogeneities than earlier algorithms by redistributing energy from dose computed in a water volume. The depth dependence of the beam size is handled by pre-processing Bragg curves using a weighted superposition of Gaussian bases. Additionally, scattering, the orientation of treatment ports, and the non-parallel propagation of beams are handled by large, but sparse, energy-redistribution matrices that implement affine transforms. Results: We tested our algorithm using a brain tumor dataset with 1 mm voxels and a single treatment port from the patient’s anterior through the sinuses. The resulting dose volume is 100 × 100 × 230 mm with 66,200 beam spots on a 3 × 3 × 2 mm grid. The dose computation takes <1 msec on a GeForce GTX Titan GPU with the Gamma passing rate for 2mm/2% criterion of 99.1% compared to dose calculated by an alternative dose algorithm based on pencil beams. We will present comparisons to Monte Carlo dose calculations. Conclusion: Our high-speed dose computation method enables the IMPT spot weights to be optimized in <1 second, resulting in a nearly instantaneous response to user changes to dose constraints. This permits the creation of higher quality plans by allowing the dosimetrist to evaluate more alternatives in a short period of time.

irGPU.proton.Net: Irregular strong charge interaction networks of protonatable groups in protein molecules--a GPU solver using the fast multipole method and statistical thermodynamics.

Science.gov (United States)

Kantardjiev, Alexander A

2015-04-05

A cluster of strongly interacting ionization groups in protein molecules with irregular ionization behavior is suggestive for specific structure-function relationship. However, their computational treatment is unconventional (e.g., lack of convergence in naive self-consistent iterative algorithm). The stringent evaluation requires evaluation of Boltzmann averaged statistical mechanics sums and electrostatic energy estimation for each microstate. irGPU: Irregular strong interactions in proteins--a GPU solver is novel solution to a versatile problem in protein biophysics--atypical protonation behavior of coupled groups. The computational severity of the problem is alleviated by parallelization (via GPU kernels) which is applied for the electrostatic interaction evaluation (including explicit electrostatics via the fast multipole method) as well as statistical mechanics sums (partition function) estimation. Special attention is given to the ease of the service and encapsulation of theoretical details without sacrificing rigor of computational procedures. irGPU is not just a solution-in-principle but a promising practical application with potential to entice community into deeper understanding of principles governing biomolecule mechanisms. © 2015 Wiley Periodicals, Inc.

GPU-accelerated adjoint algorithmic differentiation

Science.gov (United States)

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.

A study of reconstruction artifacts in cone beam tomography using filtered backprojection and iterative EM algorithms

International Nuclear Information System (INIS)

Zeng, G.L.; Gullberg, G.T.

1990-01-01

Reconstruction artifacts in cone beam tomography are studied for filtered backprojection (Feldkamp) and iterative EM algorithms. The filtered backprojection algorithm uses a voxel-driven, interpolated backprojection to reconstruct the cone beam data; whereas, the iterative EM algorithm performs ray-driven projection and backprojection operations for each iteration. Two weight in schemes for the projection and backprojection operations in the EM algorithm are studied. One weights each voxel by the length of the ray through the voxel and the other equates the value of a voxel to the functional value of the midpoint of the line intersecting the voxel, which is obtained by interpolating between eight neighboring voxels. Cone beam reconstruction artifacts such as rings, bright vertical extremities, and slice-to slice cross talk are not found with parallel beam and fan beam geometries

GPU-based Branchless Distance-Driven Projection and Backprojection.

Science.gov (United States)

Liu, Rui; Fu, Lin; De Man, Bruno; Yu, Hengyong

2017-12-01

Projection and backprojection operations are essential in a variety of image reconstruction and physical correction algorithms in CT. The distance-driven (DD) projection and backprojection are widely used for their highly sequential memory access pattern and low arithmetic cost. However, a typical DD implementation has an inner loop that adjusts the calculation depending on the relative position between voxel and detector cell boundaries. The irregularity of the branch behavior makes it inefficient to be implemented on massively parallel computing devices such as graphics processing units (GPUs). Such irregular branch behaviors can be eliminated by factorizing the DD operation as three branchless steps: integration, linear interpolation, and differentiation, all of which are highly amenable to massive vectorization. In this paper, we implement and evaluate a highly parallel branchless DD algorithm for 3D cone beam CT. The algorithm utilizes the texture memory and hardware interpolation on GPUs to achieve fast computational speed. The developed branchless DD algorithm achieved 137-fold speedup for forward projection and 188-fold speedup for backprojection relative to a single-thread CPU implementation. Compared with a state-of-the-art 32-thread CPU implementation, the proposed branchless DD achieved 8-fold acceleration for forward projection and 10-fold acceleration for backprojection. GPU based branchless DD method was evaluated by iterative reconstruction algorithms with both simulation and real datasets. It obtained visually identical images as the CPU reference algorithm.

Fast GPU-based spot extraction for energy-dispersive X-ray Laue diffraction

International Nuclear Information System (INIS)

Alghabi, F.; Schipper, U.; Kolb, A.; Send, S.; Abboud, A.; Pashniak, N.; Pietsch, U.

2014-01-01

This paper describes a novel method for fast online analysis of X-ray Laue spots taken by means of an energy-dispersive X-ray 2D detector. Current pnCCD detectors typically operate at some 100 Hz (up to a maximum of 400 Hz) and have a resolution of 384 × 384 pixels, future devices head for even higher pixel counts and frame rates. The proposed online data analysis is based on a computer utilizing multiple Graphics Processing Units (GPUs), which allow for fast and parallel data processing. Our multi-GPU based algorithm is compliant with the rules of stream-based data processing, for which GPUs are optimized. The paper's main contribution is therefore an alternative algorithm for the determination of spot positions and energies over the full sequence of pnCCD data frames. Furthermore, an improved background suppression algorithm is presented.The resulting system is able to process data at the maximum acquisition rate of 400 Hz. We present a detailed analysis of the spot positions and energies deduced from a prior (single-core) CPU-based and the novel GPU-based data processing, showing that the parallel computed results using the GPU implementation are at least of the same quality as prior CPU-based results. Furthermore, the GPU-based algorithm is able to speed up the data processing by a factor of 7 (in comparison to single-core CPU-based algorithm) which effectively makes the detector system more suitable for online data processing

GPU acceleration of 3D forward and backward projection using separable footprints for X-ray CT image reconstruction

Energy Technology Data Exchange (ETDEWEB)

Wu, Meng; Fessler, Jeffrey A. [Michigan Univ., Ann Arbor, MI (United States). Dept. of Electrical Engineering and Computer Science

2011-07-01

Iterative 3D image reconstruction methods can improve image quality over conventional filtered back projection (FBP) in X-ray computed tomography. However, high computational costs deter the routine use of iterative reconstruction clinically. The separable footprint method for forward and back-projection simplifies the integrals over a detector cell in a way that is quite accurate and also has a relatively efficient CPU implementation. In this project, we implemented the separable footprints method for both forward and backward projection on a graphics processing unit (GPU) with NVDIA's parallel computing architecture (CUDA). This paper describes our GPU kernels for the separable footprint method and simulation results. (orig.)

Procedural generation of aesthetic patterns from dynamics and iteration processes

Directory of Open Access Journals (Sweden)

Gdawiec Krzysztof

2017-12-01

Full Text Available Aesthetic patterns are widely used nowadays, e.g., in jewellery design, carpet design, as textures and patterns on wallpapers, etc. Most of the work during the design stage is carried out by a designer manually. Therefore, it is highly useful to develop methods for aesthetic pattern generation. In this paper, we present methods for generating aesthetic patterns using the dynamics of a discrete dynamical system. The presented methods are based on the use of various iteration processes from fixed point theory (Mann, S, Noor, etc. and the application of an affine combination of these iterations. Moreover, we propose new convergence tests that enrich the obtained patterns. The proposed methods generate patterns in a procedural way and can be easily implemented on the GPU. The presented examples show that using the proposed methods we are able to obtain a variety of interesting patterns. Moreover, the numerical examples show that the use of the GPU implementation with shaders allows the generation of patterns in real time and the speed-up (compared with a CPU implementation ranges from about 1000 to 2500 times.

GPU-based relative fuzzy connectedness image segmentation

International Nuclear Information System (INIS)

Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

2013-01-01

Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ ∞ -based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

GPU-based relative fuzzy connectedness image segmentation

Energy Technology Data Exchange (ETDEWEB)

Zhuge Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W. [Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States); Department of Mathematics, West Virginia University, Morgantown, West Virginia 26506 (United States) and Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892 (United States)

2013-01-15

Purpose:Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an Script-Small-L {sub {infinity}}-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8 Multiplication-Sign , 22.9 Multiplication-Sign , 20.9 Multiplication-Sign , and 17.5 Multiplication-Sign , correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

GPU-based relative fuzzy connectedness image segmentation.

Science.gov (United States)

Zhuge, Ying; Ciesielski, Krzysztof C; Udupa, Jayaram K; Miller, Robert W

2013-01-01

Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. The most common FC segmentations, optimizing an [script-l](∞)-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA's Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology.

GPU-based relative fuzzy connectedness image segmentation

Science.gov (United States)

Zhuge, Ying; Ciesielski, Krzysztof C.; Udupa, Jayaram K.; Miller, Robert W.

2013-01-01

Purpose: Recently, clinical radiological research and practice are becoming increasingly quantitative. Further, images continue to increase in size and volume. For quantitative radiology to become practical, it is crucial that image segmentation algorithms and their implementations are rapid and yield practical run time on very large data sets. The purpose of this paper is to present a parallel version of an algorithm that belongs to the family of fuzzy connectedness (FC) algorithms, to achieve an interactive speed for segmenting large medical image data sets. Methods: The most common FC segmentations, optimizing an ℓ∞-based energy, are known as relative fuzzy connectedness (RFC) and iterative relative fuzzy connectedness (IRFC). Both RFC and IRFC objects (of which IRFC contains RFC) can be found via linear time algorithms, linear with respect to the image size. The new algorithm, P-ORFC (for parallel optimal RFC), which is implemented by using NVIDIA’s Compute Unified Device Architecture (CUDA) platform, considerably improves the computational speed of the above mentioned CPU based IRFC algorithm. Results: Experiments based on four data sets of small, medium, large, and super data size, achieved speedup factors of 32.8×, 22.9×, 20.9×, and 17.5×, correspondingly, on the NVIDIA Tesla C1060 platform. Although the output of P-ORFC need not precisely match that of IRFC output, it is very close to it and, as the authors prove, always lies between the RFC and IRFC objects. Conclusions: A parallel version of a top-of-the-line algorithm in the family of FC has been developed on the NVIDIA GPUs. An interactive speed of segmentation has been achieved, even for the largest medical image data set. Such GPU implementations may play a crucial role in automatic anatomy recognition in clinical radiology. PMID:23298094

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

Directory of Open Access Journals (Sweden)

Qiang Liu

2018-05-01

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

The development of GPU-based parallel PRNG for Monte Carlo applications in CUDA Fortran

Directory of Open Access Journals (Sweden)

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

Energy Technology Data Exchange (ETDEWEB)

Kargaran, Hamed, E-mail: h-kargaran@sbu.ac.ir; Minuchehr, Abdolhamid; Zolfaghari, Ahmad [Department of nuclear engineering, Shahid Behesti University, Tehran, 1983969411 (Iran, Islamic Republic of)

2016-04-15

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.

A Comparison of Sequential and GPU Implementations of Iterative Methods to Compute Reachability Probabilities

Directory of Open Access Journals (Sweden)

Elise Cormie-Bowins

2012-10-01

Full Text Available We consider the problem of computing reachability probabilities: given a Markov chain, an initial state of the Markov chain, and a set of goal states of the Markov chain, what is the probability of reaching any of the goal states from the initial state? This problem can be reduced to solving a linear equation Ax = b for x, where A is a matrix and b is a vector. We consider two iterative methods to solve the linear equation: the Jacobi method and the biconjugate gradient stabilized (BiCGStab method. For both methods, a sequential and a parallel version have been implemented. The parallel versions have been implemented on the compute unified device architecture (CUDA so that they can be run on a NVIDIA graphics processing unit (GPU. From our experiments we conclude that as the size of the matrix increases, the CUDA implementations outperform the sequential implementations. Furthermore, the BiCGStab method performs better than the Jacobi method for dense matrices, whereas the Jacobi method does better for sparse ones. Since the reachability probabilities problem plays a key role in probabilistic model checking, we also compared the implementations for matrices obtained from a probabilistic model checker. Our experiments support the conjecture by Bosnacki et al. that the Jacobi method is superior to Krylov subspace methods, a class to which the BiCGStab method belongs, for probabilistic model checking.

Performance of Сellular Automata-based Stream Ciphers in GPU Implementation

Directory of Open Access Journals (Sweden)

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.

GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method

International Nuclear Information System (INIS)

Gong Chunye; Liu Jie; Chi Lihua; Huang Haowei; Fang Jingyue; Gong Zhenghu

2011-01-01

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 (S n ) 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.

GPU accelerated simulations of 3D deterministic particle transport using discrete ordinates method

Science.gov (United States)

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.

R-GPU : A reconfigurable GPU architecture

NARCIS (Netherlands)

van den Braak, G.J.; Corporaal, H.

2016-01-01

Over the last decade, Graphics Processing Unit (GPU) architectures have evolved from a fixed-function graphics pipeline to a programmable, energy-efficient compute accelerator for massively parallel applications. The compute power arises from the GPU's Single Instruction/Multiple Threads

A new simple iterative reconstruction algorithm for SPECT transmission measurement

International Nuclear Information System (INIS)

Hwang, D.S.; Zeng, G.L.

2005-01-01

This paper proposes a new iterative reconstruction algorithm for transmission tomography and compares this algorithm with several other methods. The new algorithm is simple and resembles the emission ML-EM algorithm in form. Due to its simplicity, it is easy to implement and fast to compute a new update at each iteration. The algorithm also always guarantees non-negative solutions. Evaluations are performed using simulation studies and real phantom data. Comparisons with other algorithms such as convex, gradient, and logMLEM show that the proposed algorithm is as good as others and performs better in some cases

Pinhole single-photon emission tomography reconstruction based on median root prior

International Nuclear Information System (INIS)

Sohlberg, Antti; Kuikka, Jyrki T.; Ruotsalainen, Ulla

2003-01-01

The maximum likelihood expectation maximisation (ML-EM) algorithm can be used to reduce reconstruction artefacts produced by filtered backprojection (FBP) methods in pinhole single-photon emission tomography (SPET). However, ML-EM suffers from noise propagation along iterations, which leads to quantitatively unpleasant reconstruction results. To avoid this increase in noise, the median root prior (MRP) algorithm for pinhole SPET was implemented. Projection data of a line source and Picker's thyroid phantom were collected using a single-head gamma camera with a pinhole collimator. MRP was added to existing pinhole ML-EM reconstruction algorithm and the phantom studies were reconstructed using MRP, ML-EM and FBP for comparison. Coefficients of variation, contrasts and full-widths at half-maximum were calculated and showed a clear reduction in noise without significant loss of resolution or decrease in contrast when MRP was applied. MRP also produced visually pleasing images even with high iteration numbers, free of the checkerboard-type noise patterns which are typical of ML-EM images. (orig.)

GPU Computing Gems Emerald Edition

CERN Document Server

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

The CUBLAS and CULA based GPU acceleration of adaptive finite element framework for bioluminescence tomography.

Science.gov (United States)

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.

Multi-GPU Accelerated Admittance Method for High-Resolution Human Exposure Evaluation.

Science.gov (United States)

Xiong, Zubiao; Feng, Shi; Kautz, Richard; Chandra, Sandeep; Altunyurt, Nevin; Chen, Ji

2015-12-01

A multi-graphics processing unit (GPU) accelerated admittance method solver is presented for solving the induced electric field in high-resolution anatomical models of human body when exposed to external low-frequency magnetic fields. In the solver, the anatomical model is discretized as a three-dimensional network of admittances. The conjugate orthogonal conjugate gradient (COCG) iterative algorithm is employed to take advantage of the symmetric property of the complex-valued linear system of equations. Compared against the widely used biconjugate gradient stabilized method, the COCG algorithm can reduce the solving time by 3.5 times and reduce the storage requirement by about 40%. The iterative algorithm is then accelerated further by using multiple NVIDIA GPUs. The computations and data transfers between GPUs are overlapped in time by using asynchronous concurrent execution design. The communication overhead is well hidden so that the acceleration is nearly linear with the number of GPU cards. Numerical examples show that our GPU implementation running on four NVIDIA Tesla K20c cards can reach 90 times faster than the CPU implementation running on eight CPU cores (two Intel Xeon E5-2603 processors). The implemented solver is able to solve large dimensional problems efficiently. A whole adult body discretized in 1-mm resolution can be solved in just several minutes. The high efficiency achieved makes it practical to investigate human exposure involving a large number of cases with a high resolution that meets the requirements of international dosimetry guidelines.

ITER Fast Plant System Controller prototype based on PXIe platform

International Nuclear Information System (INIS)

Ruiz, M.; Vega, J.; Castro, R.; Sanz, D.; López, J.M.; Arcas, G. de; Barrera, E.; Nieto, J.; Gonçalves, B.; Sousa, J.; Carvalho, B.; Utzel, N.; Makijarvi, P.

2012-01-01

Highlights: ► Implementation of Fast Plant System Controller (FPSC) for ITER CODAC. ► Efficient data acquisition and data movement using EPICS. ► Performance of PCIe technologies in the implementation of FPSC. - Abstract: The ITER Fast Plant System Controller (FPSC) is based on embedded technologies. The FPSC will be devoted to both data acquisition tasks (sampling rates higher than 1 kHz) and control purposes (feedback loop actuators). Some of the essential requirements of these systems are: (a) data acquisition and data preprocessing; (b) interfacing with different networks and high speed links (Plant Operation Network, timing network based on IEEE1588, synchronous data transference and streaming/archiving networks); and (c) system setup and operation using EPICS (Experimental Physics and Industrial Control System) process variables. CIEMAT and UPM have implemented a prototype of FPSC using a PXIe (PCI eXtension for Instrumentation) form factor in a R and D project developed in two phases. The paper presents the main features of the two prototypes developed that have been named alpha and beta. The former was implemented using LabVIEW development tools as it was focused on modeling the FPSC software modules, using the graphical features of LabVIEW applications, and measuring the basic performance in the system. The alpha version prototype implements data acquisition with time-stamping, EPICS monitoring using waveform process variables (PVs), and archiving. The beta version prototype is a complete IOC implemented using EPICS with different software functional blocks. These functional blocks are integrated and managed using an ASYN driver solution and provide the basic functionalities required by ITER FPSC such as data acquisition, data archiving, data pre-processing (using both CPU and GPU) and streaming.

GPU Based Software Correlators - Perspectives for VLBI2010

Science.gov (United States)

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.

SU-F-T-256: 4D IMRT Planning Using An Early Prototype GPU-Enabled Eclipse Workstation

Energy Technology Data Exchange (ETDEWEB)

Hagan, A; Modiri, A; Sawant, A [University of Maryland in Baltimore, Baltimore, MD (United States); Svatos, M [Varian Medical Systems, Palo Alto, CA (United States)

2016-06-15

Purpose: True 4D IMRT planning, based on simultaneous spatiotemporal optimization has been shown to significantly improve plan quality in lung radiotherapy. However, the high computational complexity associated with such planning represents a significant barrier to widespread clinical deployment. We introduce an early prototype GPU-enabled Eclipse workstation for inverse planning. To our knowledge, this is the first GPUintegrated Eclipse system demonstrating the potential for clinical translation of GPU computing on a major commercially-available TPS. Methods: The prototype system comprised of four NVIDIA Tesla K80 GPUs, with a maximum processing capability of 8.5 Tflops per K80 card. The system architecture consisted of three key modules: (i) a GPU-based inverse planning module using a highly-parallelizable, swarm intelligence-based global optimization algorithm, (ii) a GPU-based open-source b-spline deformable image registration module, Elastix, and (iii) a CUDA-based data management module. For evaluation, aperture fluence weights in an IMRT plan were optimized over 9 beams,166 apertures and 10 respiratory phases (14940 variables) for a lung cancer case (GTV = 95 cc, right lower lobe, 15 mm cranio-caudal motion). Sensitivity of the planning time and memory expense to parameter variations was quantified. Results: GPU-based inverse planning was significantly accelerated compared to its CPU counterpart (36 vs 488 min, for 10 phases, 10 search agents and 10 iterations). The optimized IMRT plan significantly improved OAR sparing compared to the original internal target volume (ITV)-based clinical plan, while maintaining prescribed tumor coverage. The dose-sparing improvements were: Esophagus Dmax 50%, Heart Dmax 42% and Spinal cord Dmax 25%. Conclusion: Our early prototype system demonstrates that through massive parallelization, computationally intense tasks such as 4D treatment planning can be accomplished in clinically feasible timeframes. With further

SU-F-T-256: 4D IMRT Planning Using An Early Prototype GPU-Enabled Eclipse Workstation

International Nuclear Information System (INIS)

Hagan, A; Modiri, A; Sawant, A; Svatos, M

2016-01-01

Purpose: True 4D IMRT planning, based on simultaneous spatiotemporal optimization has been shown to significantly improve plan quality in lung radiotherapy. However, the high computational complexity associated with such planning represents a significant barrier to widespread clinical deployment. We introduce an early prototype GPU-enabled Eclipse workstation for inverse planning. To our knowledge, this is the first GPUintegrated Eclipse system demonstrating the potential for clinical translation of GPU computing on a major commercially-available TPS. Methods: The prototype system comprised of four NVIDIA Tesla K80 GPUs, with a maximum processing capability of 8.5 Tflops per K80 card. The system architecture consisted of three key modules: (i) a GPU-based inverse planning module using a highly-parallelizable, swarm intelligence-based global optimization algorithm, (ii) a GPU-based open-source b-spline deformable image registration module, Elastix, and (iii) a CUDA-based data management module. For evaluation, aperture fluence weights in an IMRT plan were optimized over 9 beams,166 apertures and 10 respiratory phases (14940 variables) for a lung cancer case (GTV = 95 cc, right lower lobe, 15 mm cranio-caudal motion). Sensitivity of the planning time and memory expense to parameter variations was quantified. Results: GPU-based inverse planning was significantly accelerated compared to its CPU counterpart (36 vs 488 min, for 10 phases, 10 search agents and 10 iterations). The optimized IMRT plan significantly improved OAR sparing compared to the original internal target volume (ITV)-based clinical plan, while maintaining prescribed tumor coverage. The dose-sparing improvements were: Esophagus Dmax 50%, Heart Dmax 42% and Spinal cord Dmax 25%. Conclusion: Our early prototype system demonstrates that through massive parallelization, computationally intense tasks such as 4D treatment planning can be accomplished in clinically feasible timeframes. With further

Development of High-speed Visualization System of Hypocenter Data Using CUDA-based GPU computing

Science.gov (United States)

Kumagai, T.; Okubo, K.; Uchida, N.; Matsuzawa, T.; Kawada, N.; Takeuchi, N.

2014-12-01

After the Great East Japan Earthquake on March 11, 2011, intelligent visualization of seismic information is becoming important to understand the earthquake phenomena. On the other hand, to date, the quantity of seismic data becomes enormous as a progress of high accuracy observation network; we need to treat many parameters (e.g., positional information, origin time, magnitude, etc.) to efficiently display the seismic information. Therefore, high-speed processing of data and image information is necessary to handle enormous amounts of seismic data. Recently, GPU (Graphic Processing Unit) is used as an acceleration tool for data processing and calculation in various study fields. This movement is called GPGPU (General Purpose computing on GPUs). In the last few years the performance of GPU keeps on improving rapidly. GPU computing gives us the high-performance computing environment at a lower cost than before. Moreover, use of GPU has an advantage of visualization of processed data, because GPU is originally architecture for graphics processing. In the GPU computing, the processed data is always stored in the video memory. Therefore, we can directly write drawing information to the VRAM on the video card by combining CUDA and the graphics API. In this study, we employ CUDA and OpenGL and/or DirectX to realize full-GPU implementation. This method makes it possible to write drawing information to the VRAM on the video card without PCIe bus data transfer: It enables the high-speed processing of seismic data. The present study examines the GPU computing-based high-speed visualization and the feasibility for high-speed visualization system of hypocenter data.

GPU-based Parallel Application Design for Emerging Mobile Devices

Science.gov (United States)

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

TH-E-BRE-08: GPU-Monte Carlo Based Fast IMRT Plan Optimization

Energy Technology Data Exchange (ETDEWEB)

Li, Y; Tian, Z; Shi, F; Jiang, S; Jia, X [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States)

2014-06-15

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, rough beamlet dose calculations is conducted with only a small number of particles 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, 10{sup 5} particles per beamlet in the first round, and 10{sup 8} 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.

Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics

Directory of Open Access Journals (Sweden)

Carlos González-Gutiérrez

2018-01-01

Full Text Available Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks.

Improvement of brain perfusion SPET using iterative reconstruction with scatter and non-uniform attenuation correction

Energy Technology Data Exchange (ETDEWEB)

Kauppinen, T.; Vanninen, E.; Kuikka, J.T. [Kuopio Central Hospital (Finland). Dept. of Clinical Physiology; Koskinen, M.O. [Dept. of Clinical Physiology and Nuclear Medicine, Tampere Univ. Hospital, Tampere (Finland); Alenius, S. [Signal Processing Lab., Tampere Univ. of Technology, Tampere (Finland)

2000-09-01

Filtered back-projection (FBP) is generally used as the reconstruction method for single-photon emission tomography although it produces noisy images with apparent streak artefacts. It is possible to improve the image quality by using an algorithm with iterative correction steps. The iterative reconstruction technique also has an additional benefit in that computation of attenuation correction can be included in the process. A commonly used iterative method, maximum-likelihood expectation maximisation (ML-EM), can be accelerated using ordered subsets (OS-EM). We have applied to the OS-EM algorithm a Bayesian one-step late correction method utilising median root prior (MRP). Methodological comparison was performed by means of measurements obtained with a brain perfusion phantom and using patient data. The aim of this work was to quantitate the accuracy of iterative reconstruction with scatter and non-uniform attenuation corrections and post-filtering in SPET brain perfusion imaging. SPET imaging was performed using a triple-head gamma camera with fan-beam collimators. Transmission and emission scans were acquired simultaneously. The brain phantom used was a high-resolution three-dimensional anthropomorphic JB003 phantom. Patient studies were performed in ten chronic pain syndrome patients. The images were reconstructed using conventional FBP and iterative OS-EM and MRP techniques including scatter and nonuniform attenuation corrections. Iterative reconstructions were individually post-filtered. The quantitative results obtained with the brain perfusion phantom were compared with the known actual contrast ratios. The calculated difference from the true values was largest with the FBP method; iteratively reconstructed images proved closer to the reality. Similar findings were obtained in the patient studies. The plain OS-EM method improved the contrast whereas in the case of the MRP technique the improvement in contrast was not so evident with post-filtering. (orig.)

Improvement of brain perfusion SPET using iterative reconstruction with scatter and non-uniform attenuation correction

International Nuclear Information System (INIS)

Kauppinen, T.; Vanninen, E.; Kuikka, J.T.; Alenius, S.

2000-01-01

Filtered back-projection (FBP) is generally used as the reconstruction method for single-photon emission tomography although it produces noisy images with apparent streak artefacts. It is possible to improve the image quality by using an algorithm with iterative correction steps. The iterative reconstruction technique also has an additional benefit in that computation of attenuation correction can be included in the process. A commonly used iterative method, maximum-likelihood expectation maximisation (ML-EM), can be accelerated using ordered subsets (OS-EM). We have applied to the OS-EM algorithm a Bayesian one-step late correction method utilising median root prior (MRP). Methodological comparison was performed by means of measurements obtained with a brain perfusion phantom and using patient data. The aim of this work was to quantitate the accuracy of iterative reconstruction with scatter and non-uniform attenuation corrections and post-filtering in SPET brain perfusion imaging. SPET imaging was performed using a triple-head gamma camera with fan-beam collimators. Transmission and emission scans were acquired simultaneously. The brain phantom used was a high-resolution three-dimensional anthropomorphic JB003 phantom. Patient studies were performed in ten chronic pain syndrome patients. The images were reconstructed using conventional FBP and iterative OS-EM and MRP techniques including scatter and nonuniform attenuation corrections. Iterative reconstructions were individually post-filtered. The quantitative results obtained with the brain perfusion phantom were compared with the known actual contrast ratios. The calculated difference from the true values was largest with the FBP method; iteratively reconstructed images proved closer to the reality. Similar findings were obtained in the patient studies. The plain OS-EM method improved the contrast whereas in the case of the MRP technique the improvement in contrast was not so evident with post-filtering. (orig.)

A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC)

Science.gov (United States)

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).

Science.gov (United States)

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

A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC)

International Nuclear Information System (INIS)

Tian, Zhen; Shi, Feng; Folkerts, Michael; Qin, Nan; Jiang, Steve B; Jia, Xun

2015-01-01

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

A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation

International Nuclear Information System (INIS)

Gu Xuejun; Jia Xun; Jiang, Steve B; Jelen, Urszula; Li Jinsheng

2011-01-01

Targeting at the development of an accurate and efficient dose calculation engine for online adaptive radiotherapy, we have implemented a finite-size pencil beam (FSPB) algorithm with a 3D-density correction method on graphics processing unit (GPU). This new GPU-based dose engine is built on our previously published ultrafast FSPB computational framework (Gu et al 2009 Phys. Med. Biol. 54 6287-97). Dosimetric evaluations against Monte Carlo dose calculations are conducted on ten IMRT treatment plans (five head-and-neck cases and five lung cases). For all cases, there is improvement with the 3D-density correction over the conventional FSPB algorithm and for most cases the improvement is significant. Regarding the efficiency, because of the appropriate arrangement of memory access and the usage of GPU intrinsic functions, the dose calculation for an IMRT plan can be accomplished well within 1 s (except for one case) with this new GPU-based FSPB algorithm. Compared to the previous GPU-based FSPB algorithm without 3D-density correction, this new algorithm, though slightly sacrificing the computational efficiency (∼5-15% lower), has significantly improved the dose calculation accuracy, making it more suitable for online IMRT replanning.

The Research and Test of Fast Radio Burst Real-time Search Algorithm Based on GPU Acceleration

Science.gov (United States)

Wang, J.; Chen, M. Z.; Pei, X.; Wang, Z. Q.

2017-03-01

In order to satisfy the research needs of Nanshan 25 m radio telescope of Xinjiang Astronomical Observatory (XAO) and study the key technology of the planned QiTai radio Telescope (QTT), the receiver group of XAO studied the GPU (Graphics Processing Unit) based real-time FRB searching algorithm which developed from the original FRB searching algorithm based on CPU (Central Processing Unit), and built the FRB real-time searching system. The comparison of the GPU system and the CPU system shows that: on the basis of ensuring the accuracy of the search, the speed of the GPU accelerated algorithm is improved by 35-45 times compared with the CPU algorithm.

Edge-preserving image denoising via group coordinate descent on the GPU.

Science.gov (United States)

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.

GPU-accelerated micromagnetic simulations using cloud computing

Energy Technology Data Exchange (ETDEWEB)

Jermain, C.L., E-mail: clj72@cornell.edu [Cornell University, Ithaca, NY 14853 (United States); Rowlands, G.E.; Buhrman, R.A. [Cornell University, Ithaca, NY 14853 (United States); Ralph, D.C. [Cornell University, Ithaca, NY 14853 (United States); Kavli Institute at Cornell, Ithaca, NY 14853 (United States)

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. - Highlights: • The benefits of cloud computing for GPU-accelerated micromagnetics are examined. • We present the MuCloud software for running simulations on cloud computing. • Simulation run times are measured to benchmark cloud computing performance. • Comparison benchmarks are analyzed between CPU and GPU based solvers.

GPU-accelerated micromagnetic simulations using cloud computing

International Nuclear Information System (INIS)

Jermain, C.L.; Rowlands, G.E.; Buhrman, R.A.; Ralph, D.C.

2016-01-01

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. - Highlights: • The benefits of cloud computing for GPU-accelerated micromagnetics are examined. • We present the MuCloud software for running simulations on cloud computing. • Simulation run times are measured to benchmark cloud computing performance. • Comparison benchmarks are analyzed between CPU and GPU based solvers.

Real-Time Incompressible Fluid Simulation on the GPU

Directory of Open Access Journals (Sweden)

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.

GPU-accelerated brain connectivity reconstruction and visualization in large-scale electron micrographs

KAUST Repository

Jeong, Wonki

2011-01-01

This chapter introduces a GPU-accelerated interactive, semiautomatic axon segmentation and visualization system. Two challenging problems have been addressed: the interactive 3D axon segmentation and the interactive 3D image filtering and rendering of implicit surfaces. The reconstruction of neural connections to understand the function of the brain is an emerging and active research area in neuroscience. With the advent of high-resolution scanning technologies, such as 3D light microscopy and electron microscopy (EM), reconstruction of complex 3D neural circuits from large volumes of neural tissues has become feasible. Among them, only EM data can provide sufficient resolution to identify synapses and to resolve extremely narrow neural processes. These high-resolution, large-scale datasets pose challenging problems, for example, how to process and manipulate large datasets to extract scientifically meaningful information using a compact representation in a reasonable processing time. The running time of the multiphase level set segmentation method has been measured on the CPU and GPU. The CPU version is implemented using the ITK image class and the ITK distance transform filter. The numerical part of the CPU implementation is similar to the GPU implementation for fair comparison. The main focus of this chapter is introducing the GPU algorithms and their implementation details, which are the core components of the interactive segmentation and visualization system. © 2011 Copyright © 2011 NVIDIA Corporation and Wen-mei W. Hwu Published by Elsevier Inc. All rights reserved..

System matrix computation vs storage on GPU: A comparative study in cone beam CT.

Science.gov (United States)

Matenine, Dmitri; Côté, Geoffroi; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2018-02-01

Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersection distances between the trajectories of photons and the object, also called ray tracing or system matrix computation. This work focused on the thin-ray model is aimed at comparing different system matrix handling strategies using graphical processing units (GPUs). In this work, the system matrix is modeled by thin rays intersecting a regular grid of box-shaped voxels, known to be an accurate representation of the forward projection operator in CT. However, an uncompressed system matrix exceeds the random access memory (RAM) capacities of typical computers by one order of magnitude or more. Considering the RAM limitations of GPU hardware, several system matrix handling methods were compared: full storage of a compressed system matrix, on-the-fly computation of its coefficients, and partial storage of the system matrix with partial on-the-fly computation. These methods were tested on geometries mimicking a cone beam CT (CBCT) acquisition of a human head. Execution times of three routines of interest were compared: forward projection, backprojection, and ordered-subsets convex (OSC) iteration. A fully stored system matrix yielded the shortest backprojection and OSC iteration times, with a 1.52× acceleration for OSC when compared to the on-the-fly approach. Nevertheless, the maximum problem size was bound by the available GPU RAM and geometrical symmetries. On-the-fly coefficient computation did not require symmetries and was shown to be the fastest for forward projection. It also offered reasonable execution times of about 176.4 ms per view per OSC iteration for a detector of 512 × 448 pixels and a volume of 384 3 voxels, using commodity GPU hardware. Partial system matrix storage has shown a performance similar to the on-the-fly approach, while still relying on symmetries. Partial system matrix storage was shown to yield the lowest relative

A cache-friendly sampling strategy for texture-based volume rendering on GPU

Directory of Open Access Journals (Sweden)

Junpeng Wang

2017-06-01

Full Text Available The texture-based volume rendering is a memory-intensive algorithm. Its performance relies heavily on the performance of the texture cache. However, most existing texture-based volume rendering methods blindly map computational resources to texture memory and result in incoherent memory access patterns, causing low cache hit rates in certain cases. The distance between samples taken by threads of an atomic scheduling unit (e.g. a warp of 32 threads in CUDA of the GPU is a crucial factor that affects the texture cache performance. Based on this fact, we present a new sampling strategy, called Warp Marching, for the ray-casting algorithm of texture-based volume rendering. The effects of different sample organizations and different thread-pixel mappings in the ray-casting algorithm are thoroughly analyzed. Also, a pipeline manner color blending approach is introduced and the power of warp-level GPU operations is leveraged to improve the efficiency of parallel executions on the GPU. In addition, the rendering performance of the Warp Marching is view-independent, and it outperforms existing empty space skipping techniques in scenarios that need to render large dynamic volumes in a low resolution image. Through a series of micro-benchmarking and real-life data experiments, we rigorously analyze our sampling strategies and demonstrate significant performance enhancements over existing sampling methods.

Parallel CT image reconstruction based on GPUs

International Nuclear Information System (INIS)

Flores, Liubov A.; Vidal, Vicent; Mayo, Patricia; Rodenas, Francisco; Verdú, Gumersindo

2014-01-01

In X-ray computed tomography (CT) iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions from a small number of projections. However, in practice, these methods are not widely used due to the high computational cost of their implementation. Nowadays technology provides the possibility to reduce effectively this drawback. It is the goal of this work to develop a fast GPU-based algorithm to reconstruct high quality images from under sampled and noisy projection data. - Highlights: • We developed GPU-based iterative algorithm to reconstruct images. • Iterative algorithms are capable to reconstruct images from under sampled set of projections. • The computer cost of the implementation of the developed algorithm is low. • The efficiency of the algorithm increases for the large scale problems

Mean-variance analysis of block-iterative reconstruction algorithms modeling 3D detector response in SPECT

Science.gov (United States)

Lalush, D. S.; Tsui, B. M. W.

1998-06-01

We study the statistical convergence properties of two fast iterative reconstruction algorithms, the rescaled block-iterative (RBI) and ordered subset (OS) EM algorithms, in the context of cardiac SPECT with 3D detector response modeling. The Monte Carlo method was used to generate nearly noise-free projection data modeling the effects of attenuation, detector response, and scatter from the MCAT phantom. One thousand noise realizations were generated with an average count level approximating a typical T1-201 cardiac study. Each noise realization was reconstructed using the RBI and OS algorithms for cases with and without detector response modeling. For each iteration up to twenty, we generated mean and variance images, as well as covariance images for six specific locations. Both OS and RBI converged in the mean to results that were close to the noise-free ML-EM result using the same projection model. When detector response was not modeled in the reconstruction, RBI exhibited considerably lower noise variance than OS for the same resolution. When 3D detector response was modeled, the RBI-EM provided a small improvement in the tradeoff between noise level and resolution recovery, primarily in the axial direction, while OS required about half the number of iterations of RBI to reach the same resolution. We conclude that OS is faster than RBI, but may be sensitive to errors in the projection model. Both OS-EM and RBI-EM are effective alternatives to the EVIL-EM algorithm, but noise level and speed of convergence depend on the projection model used.

Qualitative and quantitative improvements of PET reconstruction on GPU architecture

International Nuclear Information System (INIS)

Autret, Awen

2016-01-01

In positron emission tomography, reconstructed images suffer from a high noise level and a low resolution. Iterative reconstruction processes require an estimation of the system response (scanner and patient) and the quality of the images depends on the accuracy of this estimate. Accurate and fast to compute models already exists for the attenuation, scattering, random coincidences and dead times. Thus, this thesis focuses on modeling the system components associated with the detector response and the positron range. A new multi-GPU parallelization of the reconstruction based on a cutting of the volume is also proposed to speed up the reconstruction exploiting the computing power of such architectures. The proposed detector response model is based on a multi-ray approach that includes all the detector effects as the geometry and the scattering in the crystals. An evaluation study based on data obtained through Mote Carlo simulation (MCS) showed this model provides reconstructed images with a better contrast to noise ratio and resolution compared with those of the methods from the state of the art. The proposed positron range model is based on a simplified MCS, integrated into the forward projector during the reconstruction. A GPU implementation of this method allows running MCS three order of magnitude faster than the same simulation on GATE, while providing similar results. An evaluation study shows this model integrated in the reconstruction gives images with better contrast recovery and resolution while avoiding artifacts. (author)

CMFD and GPU acceleration on method of characteristics for hexagonal cores

International Nuclear Information System (INIS)

Han, Yu; Jiang, Xiaofeng; Wang, Dezhong

2014-01-01

Highlights: • A merged hex-mesh CMFD method solved via tri-diagonal matrix inversion. • Alternative hardware acceleration of using inexpensive GPU. • A hex-core benchmark with solution to confirm two acceleration methods. - Abstract: Coarse Mesh Finite Difference (CMFD) has been widely adopted as an effective way to accelerate the source iteration of transport calculation. However in a core with hexagonal assemblies there are non-hexagonal meshes around the edges of assemblies, causing a problem for CMFD if the CMFD equations are still to be solved via tri-diagonal matrix inversion by simply scanning the whole core meshes in different directions. To solve this problem, we propose an unequal mesh CMFD formulation that combines the non-hexagonal cells on the boundary of neighboring assemblies into non-regular hexagonal cells. We also investigated the alternative hardware acceleration of using graphics processing units (GPU) with graphics card in a personal computer. The tool CUDA is employed, which is a parallel computing platform and programming model invented by the company NVIDIA for harnessing the power of GPU. To investigate and implement these two acceleration methods, a 2-D hexagonal core transport code using the method of characteristics (MOC) is developed. A hexagonal mini-core benchmark problem is established to confirm the accuracy of the MOC code and to assess the effectiveness of CMFD and GPU parallel acceleration. For this benchmark problem, the CMFD acceleration increases the speed 16 times while the GPU acceleration speeds it up 25 times. When used simultaneously, they provide a speed gain of 292 times

CMFD and GPU acceleration on method of characteristics for hexagonal cores

Energy Technology Data Exchange (ETDEWEB)

Han, Yu, E-mail: hanyu1203@gmail.com [School of Nuclear Science and Engineering, Shanghai Jiaotong University, Shanghai 200240 (China); Jiang, Xiaofeng [Shanghai NuStar Nuclear Power Technology Co., Ltd., No. 81 South Qinzhou Road, XuJiaHui District, Shanghai 200000 (China); Wang, Dezhong [School of Nuclear Science and Engineering, Shanghai Jiaotong University, Shanghai 200240 (China)

2014-12-15

Highlights: • A merged hex-mesh CMFD method solved via tri-diagonal matrix inversion. • Alternative hardware acceleration of using inexpensive GPU. • A hex-core benchmark with solution to confirm two acceleration methods. - Abstract: Coarse Mesh Finite Difference (CMFD) has been widely adopted as an effective way to accelerate the source iteration of transport calculation. However in a core with hexagonal assemblies there are non-hexagonal meshes around the edges of assemblies, causing a problem for CMFD if the CMFD equations are still to be solved via tri-diagonal matrix inversion by simply scanning the whole core meshes in different directions. To solve this problem, we propose an unequal mesh CMFD formulation that combines the non-hexagonal cells on the boundary of neighboring assemblies into non-regular hexagonal cells. We also investigated the alternative hardware acceleration of using graphics processing units (GPU) with graphics card in a personal computer. The tool CUDA is employed, which is a parallel computing platform and programming model invented by the company NVIDIA for harnessing the power of GPU. To investigate and implement these two acceleration methods, a 2-D hexagonal core transport code using the method of characteristics (MOC) is developed. A hexagonal mini-core benchmark problem is established to confirm the accuracy of the MOC code and to assess the effectiveness of CMFD and GPU parallel acceleration. For this benchmark problem, the CMFD acceleration increases the speed 16 times while the GPU acceleration speeds it up 25 times. When used simultaneously, they provide a speed gain of 292 times.

GPU computing and applications

CERN Document Server

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-based prompt gamma ray imaging from boron neutron capture therapy

International Nuclear Information System (INIS)

Yoon, Do-Kun; Jung, Joo-Young; Suk Suh, Tae; Jo Hong, Key; Sil Lee, Keum

2015-01-01

Purpose: The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. Methods: To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU). Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. Results: The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). Conclusions: The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray image reconstruction using the GPU computation for BNCT simulations

A GPU Implementation of Local Search Operators for Symmetric Travelling Salesman Problem

Directory of Open Access Journals (Sweden)

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.

GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration

International Nuclear Information System (INIS)

Sharp, G C; Kandasamy, N; Singh, H; Folkert, M

2007-01-01

This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup-up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration

Blaze-DEMGPU: Modular high performance DEM framework for the GPU architecture

Directory of Open Access Journals (Sweden)

Nicolin Govender

2016-01-01

Full Text Available Blaze-DEMGPU is a modular GPU based discrete element method (DEM framework that supports polyhedral shaped particles. The high level performance is attributed to the light weight and Single Instruction Multiple Data (SIMD that the GPU architecture offers. Blaze-DEMGPU offers suitable algorithms to conduct DEM simulations on the GPU and these algorithms can be extended and modified. Since a large number of scientific simulations are particle based, many of the algorithms and strategies for GPU implementation present in Blaze-DEMGPU can be applied to other fields. Blaze-DEMGPU will make it easier for new researchers to use high performance GPU computing as well as stimulate wider GPU research efforts by the DEM community.

A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures

Energy Technology Data Exchange (ETDEWEB)

Neylon, J., E-mail: jneylon@mednet.ucla.edu; Sheng, K.; Yu, V.; Low, D. A.; Kupelian, P.; Santhanam, A. [Department of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, California 90095 (United States); Chen, Q. [Department of Radiation Oncology, University of Virginia, 1300 Jefferson Park Avenue, Charlottesville, California 22908 (United States)

2014-10-15

Purpose: Real-time adaptive planning and treatment has been infeasible due in part to its high computational complexity. There have been many recent efforts to utilize graphics processing units (GPUs) to accelerate the computational performance and dose accuracy in radiation therapy. Data structure and memory access patterns are the key GPU factors that determine the computational performance and accuracy. In this paper, the authors present a nonvoxel-based (NVB) approach to maximize computational and memory access efficiency and throughput on the GPU. Methods: The proposed algorithm employs a ray-tracing mechanism to restructure the 3D data sets computed from the CT anatomy into a nonvoxel-based framework. In a process that takes only a few milliseconds of computing time, the algorithm restructured the data sets by ray-tracing through precalculated CT volumes to realign the coordinate system along the convolution direction, as defined by zenithal and azimuthal angles. During the ray-tracing step, the data were resampled according to radial sampling and parallel ray-spacing parameters making the algorithm independent of the original CT resolution. The nonvoxel-based algorithm presented in this paper also demonstrated a trade-off in computational performance and dose accuracy for different coordinate system configurations. In order to find the best balance between the computed speedup and the accuracy, the authors employed an exhaustive parameter search on all sampling parameters that defined the coordinate system configuration: zenithal, azimuthal, and radial sampling of the convolution algorithm, as well as the parallel ray spacing during ray tracing. The angular sampling parameters were varied between 4 and 48 discrete angles, while both radial sampling and parallel ray spacing were varied from 0.5 to 10 mm. The gamma distribution analysis method (γ) was used to compare the dose distributions using 2% and 2 mm dose difference and distance-to-agreement criteria

A nonvoxel-based dose convolution/superposition algorithm optimized for scalable GPU architectures

International Nuclear Information System (INIS)

Neylon, J.; Sheng, K.; Yu, V.; Low, D. A.; Kupelian, P.; Santhanam, A.; Chen, Q.

2014-01-01

Purpose: Real-time adaptive planning and treatment has been infeasible due in part to its high computational complexity. There have been many recent efforts to utilize graphics processing units (GPUs) to accelerate the computational performance and dose accuracy in radiation therapy. Data structure and memory access patterns are the key GPU factors that determine the computational performance and accuracy. In this paper, the authors present a nonvoxel-based (NVB) approach to maximize computational and memory access efficiency and throughput on the GPU. Methods: The proposed algorithm employs a ray-tracing mechanism to restructure the 3D data sets computed from the CT anatomy into a nonvoxel-based framework. In a process that takes only a few milliseconds of computing time, the algorithm restructured the data sets by ray-tracing through precalculated CT volumes to realign the coordinate system along the convolution direction, as defined by zenithal and azimuthal angles. During the ray-tracing step, the data were resampled according to radial sampling and parallel ray-spacing parameters making the algorithm independent of the original CT resolution. The nonvoxel-based algorithm presented in this paper also demonstrated a trade-off in computational performance and dose accuracy for different coordinate system configurations. In order to find the best balance between the computed speedup and the accuracy, the authors employed an exhaustive parameter search on all sampling parameters that defined the coordinate system configuration: zenithal, azimuthal, and radial sampling of the convolution algorithm, as well as the parallel ray spacing during ray tracing. The angular sampling parameters were varied between 4 and 48 discrete angles, while both radial sampling and parallel ray spacing were varied from 0.5 to 10 mm. The gamma distribution analysis method (γ) was used to compare the dose distributions using 2% and 2 mm dose difference and distance-to-agreement criteria

Noise properties of the EM algorithm. Pt. 1

International Nuclear Information System (INIS)

Barrett, H.H.; Wilson, D.W.; Tsui, B.M.W.

1994-01-01

The expectation-maximisation (EM) algorithm is an important tool for maximum-likelihood (ML) estimation and image reconstruction, especially in medical imaging. It is a non-linear iterative algorithm that attempts to find the ML estimate of the object that produced a data set. The convergence of the algorithm and other deterministic properties are well established, but relatively little is known about how noise in the data influences noise in the final reconstructed image. In this paper we present a detailed treatment of these statistical properties. The specific application we have in mind is image reconstruction in emission tomography, but the results are valid for any application of the EM algorithm in which the data set can be described by Poisson statistics. We show that the probability density function for the grey level at a pixel in the image is well approximated by a log-normal law. An expression is derived for the variance of the grey level and for pixel-to-pixel covariance. The variance increases rapidly with iteration number at first, but eventually saturates as the ML estimate is approached. Moreover, the variance at any iteration number has a factor proportional to the square of the mean image (though other factors may also depend on the mean image), so a map of the standard deviation resembles the object itself. Thus low-intensity regions of the image tend to have low noise. (author)

APEnet+: a 3D Torus network optimized for GPU-based HPC Systems

International Nuclear Information System (INIS)

Ammendola, R; Biagioni, A; Frezza, O; Lo Cicero, F; Lonardo, A; Paolucci, P S; Rossetti, D; Simula, F; Tosoratto, L; Vicini, P

2012-01-01

In the supercomputing arena, the strong rise of GPU-accelerated clusters is a matter of fact. Within INFN, we proposed an initiative — the QUonG project — whose aim is to deploy a high performance computing system dedicated to scientific computations leveraging on commodity multi-core processors coupled with latest generation GPUs. The inter-node interconnection system is based on a point-to-point, high performance, low latency 3D torus network which is built in the framework of the APEnet+ project. It takes the form of an FPGA-based PCIe network card exposing six full bidirectional links running at 34 Gbps each that implements the RDMA protocol. In order to enable significant access latency reduction for inter-node data transfer, a direct network-to-GPU interface was built. The specialized hardware blocks, integrated in the APEnet+ board, provide support for GPU-initiated communications using the so called PCIe peer-to-peer (P2P) transactions. This development is made in close collaboration with the GPU vendor NVIDIA. The final shape of a complete QUonG deployment is an assembly of standard 42U racks, each one capable of 80 TFLOPS/rack of peak performance, at a cost of 5 k€/T F LOPS and for an estimated power consumption of 25 kW/rack. In this paper we report on the status of final rack deployment and on the R and D activities for 2012 that will focus on performance enhancement of the APEnet+ hardware through the adoption of new generation 28 nm FPGAs allowing the implementation of PCIe Gen3 host interface and the addition of new fault tolerance-oriented capabilities.

APEnet+: a 3D Torus network optimized for GPU-based HPC Systems

Energy Technology Data Exchange (ETDEWEB)

Ammendola, R [INFN Tor Vergata (Italy); Biagioni, A; Frezza, O; Lo Cicero, F; Lonardo, A; Paolucci, P S; Rossetti, D; Simula, F; Tosoratto, L; Vicini, P [INFN Roma (Italy)

2012-12-13

In the supercomputing arena, the strong rise of GPU-accelerated clusters is a matter of fact. Within INFN, we proposed an initiative - the QUonG project - whose aim is to deploy a high performance computing system dedicated to scientific computations leveraging on commodity multi-core processors coupled with latest generation GPUs. The inter-node interconnection system is based on a point-to-point, high performance, low latency 3D torus network which is built in the framework of the APEnet+ project. It takes the form of an FPGA-based PCIe network card exposing six full bidirectional links running at 34 Gbps each that implements the RDMA protocol. In order to enable significant access latency reduction for inter-node data transfer, a direct network-to-GPU interface was built. The specialized hardware blocks, integrated in the APEnet+ board, provide support for GPU-initiated communications using the so called PCIe peer-to-peer (P2P) transactions. This development is made in close collaboration with the GPU vendor NVIDIA. The final shape of a complete QUonG deployment is an assembly of standard 42U racks, each one capable of 80 TFLOPS/rack of peak performance, at a cost of 5 k Euro-Sign /T F LOPS and for an estimated power consumption of 25 kW/rack. In this paper we report on the status of final rack deployment and on the R and D activities for 2012 that will focus on performance enhancement of the APEnet+ hardware through the adoption of new generation 28 nm FPGAs allowing the implementation of PCIe Gen3 host interface and the addition of new fault tolerance-oriented capabilities.

GPU-based RFA simulation for minimally invasive cancer treatment of liver tumours.

Science.gov (United States)

Mariappan, Panchatcharam; Weir, Phil; Flanagan, Ronan; Voglreiter, Philip; Alhonnoro, Tuomas; Pollari, Mika; Moche, Michael; Busse, Harald; Futterer, Jurgen; Portugaller, Horst Rupert; Sequeiros, Roberto Blanco; Kolesnik, Marina

2017-01-01

Radiofrequency ablation (RFA) is one of the most popular and well-standardized minimally invasive cancer treatments (MICT) for liver tumours, employed where surgical resection has been contraindicated. Less-experienced interventional radiologists (IRs) require an appropriate planning tool for the treatment to help avoid incomplete treatment and so reduce the tumour recurrence risk. Although a few tools are available to predict the ablation lesion geometry, the process is computationally expensive. Also, in our implementation, a few patient-specific parameters are used to improve the accuracy of the lesion prediction. Advanced heterogeneous computing using personal computers, incorporating the graphics processing unit (GPU) and the central processing unit (CPU), is proposed to predict the ablation lesion geometry. The most recent GPU technology is used to accelerate the finite element approximation of Penne's bioheat equation and a three state cell model. Patient-specific input parameters are used in the bioheat model to improve accuracy of the predicted lesion. A fast GPU-based RFA solver is developed to predict the lesion by doing most of the computational tasks in the GPU, while reserving the CPU for concurrent tasks such as lesion extraction based on the heat deposition at each finite element node. The solver takes less than 3 min for a treatment duration of 26 min. When the model receives patient-specific input parameters, the deviation between real and predicted lesion is below 3 mm. A multi-centre retrospective study indicates that the fast RFA solver is capable of providing the IR with the predicted lesion in the short time period before the intervention begins when the patient has been clinically prepared for the treatment.

Singular value decomposition for collaborative filtering on a GPU

Science.gov (United States)

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.

Singular value decomposition for collaborative filtering on a GPU

International Nuclear Information System (INIS)

Kato, Kimikazu; Hosino, Tikara

2010-01-01

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.

GPU-based simulation of optical propagation through turbulence for active and passive imaging

Science.gov (United States)

Monnier, Goulven; Duval, François-Régis; Amram, Solène

2014-10-01

IMOTEP is a GPU-based (Graphical Processing Units) software relying on a fast parallel implementation of Fresnel diffraction through successive phase screens. Its applications include active imaging, laser telemetry and passive imaging through turbulence with anisoplanatic spatial and temporal fluctuations. Thanks to parallel implementation on GPU, speedups ranging from 40X to 70X are achieved. The present paper gives a brief overview of IMOTEP models, algorithms, implementation and user interface. It then focuses on major improvements recently brought to the anisoplanatic imaging simulation method. Previously, we took advantage of the computational power offered by the GPU to develop a simulation method based on large series of deterministic realisations of the PSF distorted by turbulence. The phase screen propagation algorithm, by reproducing higher moments of the incident wavefront distortion, provides realistic PSFs. However, we first used a coarse gaussian model to fit the numerical PSFs and characterise there spatial statistics through only 3 parameters (two-dimensional displacements of centroid and width). Meanwhile, this approach was unable to reproduce the effects related to the details of the PSF structure, especially the "speckles" leading to prominent high-frequency content in short-exposure images. To overcome this limitation, we recently implemented a new empirical model of the PSF, based on Principal Components Analysis (PCA), ought to catch most of the PSF complexity. The GPU implementation allows estimating and handling efficiently the numerous (up to several hundreds) principal components typically required under the strong turbulence regime. A first demanding computational step involves PCA, phase screen propagation and covariance estimates. In a second step, realistic instantaneous images, fully accounting for anisoplanatic effects, are quickly generated. Preliminary results are presented.

Development of a GPU-based high-performance radiative transfer model for the Infrared Atmospheric Sounding Interferometer (IASI)

International Nuclear Information System (INIS)

Huang Bormin; Mielikainen, Jarno; Oh, Hyunjong; Allen Huang, Hung-Lung

2011-01-01

Satellite-observed radiance is a nonlinear functional of surface properties and atmospheric temperature and absorbing gas profiles as described by the radiative transfer equation (RTE). In the era of hyperspectral sounders with thousands of high-resolution channels, the computation of the radiative transfer model becomes more time-consuming. The radiative transfer model performance in operational numerical weather prediction systems still limits the number of channels we can use in hyperspectral sounders to only a few hundreds. To take the full advantage of such high-resolution infrared observations, a computationally efficient radiative transfer model is needed to facilitate satellite data assimilation. In recent years the programmable commodity graphics processing unit (GPU) has evolved into a highly parallel, multi-threaded, many-core processor with tremendous computational speed and very high memory bandwidth. The radiative transfer model is very suitable for the GPU implementation to take advantage of the hardware's efficiency and parallelism where radiances of many channels can be calculated in parallel in GPUs. In this paper, we develop a GPU-based high-performance radiative transfer model for the Infrared Atmospheric Sounding Interferometer (IASI) launched in 2006 onboard the first European meteorological polar-orbiting satellites, METOP-A. Each IASI spectrum has 8461 spectral channels. The IASI radiative transfer model consists of three modules. The first module for computing the regression predictors takes less than 0.004% of CPU time, while the second module for transmittance computation and the third module for radiance computation take approximately 92.5% and 7.5%, respectively. Our GPU-based IASI radiative transfer model is developed to run on a low-cost personal supercomputer with four GPUs with total 960 compute cores, delivering near 4 TFlops theoretical peak performance. By massively parallelizing the second and third modules, we reached 364x

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Directory of Open Access Journals (Sweden)

Zhou Zhe

2017-04-01

Full Text Available According to previous reports, information could be leaked from GPU memory; however, the security implications of such a threat were mostly over-looked, because only limited information could be indirectly extracted through side-channel attacks. In this paper, we propose a novel algorithm for recovering raw data directly from the GPU memory residues of many popular applications such as Google Chrome and Adobe PDF reader. Our algorithm enables harvesting highly sensitive information including credit card numbers and email contents from GPU memory residues. Evaluation results also indicate that nearly all GPU-accelerated applications are vulnerable to such attacks, and adversaries can launch attacks without requiring any special privileges both on traditional multi-user operating systems, and emerging cloud computing scenarios.

GPU Computing For Particle Tracking

International Nuclear Information System (INIS)

Nishimura, Hiroshi; Song, Kai; Muriki, Krishna; Sun, Changchun; James, Susan; Qin, Yong

2011-01-01

This is a feasibility study of using a modern Graphics Processing Unit (GPU) to parallelize the accelerator particle tracking code. To demonstrate the massive parallelization features provided by GPU computing, a simplified TracyGPU program is developed for dynamic aperture calculation. Performances, issues, and challenges from introducing GPU are also discussed. General purpose Computation on Graphics Processing Units (GPGPU) bring massive parallel computing capabilities to numerical calculation. However, the unique architecture of GPU requires a comprehensive understanding of the hardware and programming model to be able to well optimize existing applications. In the field of accelerator physics, the dynamic aperture calculation of a storage ring, which is often the most time consuming part of the accelerator modeling and simulation, can benefit from GPU due to its embarrassingly parallel feature, which fits well with the GPU programming model. In this paper, we use the Tesla C2050 GPU which consists of 14 multi-processois (MP) with 32 cores on each MP, therefore a total of 448 cores, to host thousands ot threads dynamically. Thread is a logical execution unit of the program on GPU. In the GPU programming model, threads are grouped into a collection of blocks Within each block, multiple threads share the same code, and up to 48 KB of shared memory. Multiple thread blocks form a grid, which is executed as a GPU kernel. A simplified code that is a subset of Tracy++ (2) is developed to demonstrate the possibility of using GPU to speed up the dynamic aperture calculation by having each thread track a particle.

Segurança de nebulização com 3 a 5 ml de adrenalina (1:1000 em crianças: uma revisão baseada em evidência The safety of nebulization with 3 to 5 ml of adrenaline (1:1000 in children: an evidence based review

Directory of Open Access Journals (Sweden)

Linjie Zhang

2005-06-01

Full Text Available OBJETIVO: Apresentar evidências sobre a segurança da nebulização com 3 a 5 ml de adrenalina (1:1000 no tratamento das crianças com obstrução inflamatória aguda das vias aéreas. FONTES DE DADOS: Uma busca eletrônica foi feita, utilizando-se, principalmente, o banco de dados do MEDLINE (janeiro de 1949 a julho de 2004. Os critérios de inclusão do estudo para esta revisão foram: 1 ensaio clínico randomizado; 2 pacientes (até 12 anos com diagnós tico de bronquiolite ou laringotraqueobronquite; 3 uso de adrenalina (1:1000 através de nebulização. Os principais dados extraídos dos ensaios dizem respeito a doses de adrenalina e seus efeitos sobre a freqüência cardíaca e a pressão arterial sistêmica, bem como outros efeitos colaterais. SÍNTESE DOS DADOS: Sete ensaios clínicos, com um total de 238 pacientes, foram incluídos para esta revisão. Dos cinco ensaios clínicos nos quais a maior dose (> 3 ml de adrenalina foi usada, dois demonstraram aumento significativo de freqüência cardíaca. O aumento médio de freqüência cardíaca variou de sete a 21 batimentos por minuto, até 60 minutos após o tratamento. A maior incidência de palidez foi observada em um ensaio clínico com 21 crianças tratadas com 3 ml de adrenalina através de nebulização (47,6% no grupo de adrenalina versus 14,3% no grupo de salbutamol, 30 minutos após o tratamento. Não foram observados, em dois ensaios clínicos, efeitos significativos em nebulização com adrenalina (4 e 5 ml na pressão arterial sistêmica. CONCLUSÃO: As evidências mostram que nebulização com 3 a 5 ml de adrenalina (1:1000 é uma terapia segura, com poucos efeitos colaterais, em crianças com obstrução inflamatória aguda das vias aéreas.OBJECTIVE:To present the evidence regarding the safety of nebulization with 3-5 ml of adrenaline (1:1000 for the treatment of children with acute inflammatory airway obstruction. SOURCES OF DATA: An electronic search was undertaken

Parallelization and checkpointing of GPU applications through program transformation

Energy Technology Data Exchange (ETDEWEB)

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

A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

Science.gov (United States)

Guerrero, Ginés D.; Imbernón, Baldomero; García, José M.

2014-01-01

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. PMID:25025055

GPU accelerated manifold correction method for spinning compact binaries

Science.gov (United States)

Ran, Chong-xi; Liu, Song; Zhong, Shuang-ying

2018-04-01

The graphics processing unit (GPU) acceleration of the manifold correction algorithm based on the compute unified device architecture (CUDA) technology is designed to simulate the dynamic evolution of the Post-Newtonian (PN) Hamiltonian formulation of spinning compact binaries. The feasibility and the efficiency of parallel computation on GPU have been confirmed by various numerical experiments. The numerical comparisons show that the accuracy on GPU execution of manifold corrections method has a good agreement with the execution of codes on merely central processing unit (CPU-based) method. The acceleration ability when the codes are implemented on GPU can increase enormously through the use of shared memory and register optimization techniques without additional hardware costs, implying that the speedup is nearly 13 times as compared with the codes executed on CPU for phase space scan (including 314 × 314 orbits). In addition, GPU-accelerated manifold correction method is used to numerically study how dynamics are affected by the spin-induced quadrupole-monopole interaction for black hole binary system.

Synergia CUDA: GPU-accelerated accelerator modeling package

International Nuclear Information System (INIS)

Lu, Q; Amundson, J

2014-01-01

Synergia is a parallel, 3-dimensional space-charge particle-in-cell accelerator modeling code. We present our work porting the purely MPI-based version of the code to a hybrid of CPU and GPU computing kernels. The hybrid code uses the CUDA platform in the same framework as the pure MPI solution. We have implemented a lock-free collaborative charge-deposition algorithm for the GPU, as well as other optimizations, including local communication avoidance for GPUs, a customized FFT, and fine-tuned memory access patterns. On a small GPU cluster (up to 4 Tesla C1070 GPUs), our benchmarks exhibit both superior peak performance and better scaling than a CPU cluster with 16 nodes and 128 cores. We also compare the code performance on different GPU architectures, including C1070 Tesla and K20 Kepler.

A distributed multi-GPU system for high speed electron microscopic tomographic reconstruction.

Science.gov (United States)

Zheng, Shawn Q; Branlund, Eric; Kesthelyi, Bettina; Braunfeld, Michael B; Cheng, Yifan; Sedat, John W; Agard, David A

2011-07-01

Full resolution electron microscopic tomographic (EMT) reconstruction of large-scale tilt series requires significant computing power. The desire to perform multiple cycles of iterative reconstruction and realignment dramatically increases the pressing need to improve reconstruction performance. This has motivated us to develop a distributed multi-GPU (graphics processing unit) system to provide the required computing power for rapid constrained, iterative reconstructions of very large three-dimensional (3D) volumes. The participating GPUs reconstruct segments of the volume in parallel, and subsequently, the segments are assembled to form the complete 3D volume. Owing to its power and versatility, the CUDA (NVIDIA, USA) platform was selected for GPU implementation of the EMT reconstruction. For a system containing 10 GPUs provided by 5 GTX295 cards, 10 cycles of SIRT reconstruction for a tomogram of 4096(2) × 512 voxels from an input tilt series containing 122 projection images of 4096(2) pixels (single precision float) takes a total of 1845 s of which 1032 s are for computation with the remainder being the system overhead. The same system takes only 39 s total to reconstruct 1024(2) × 256 voxels from 122 1024(2) pixel projections. While the system overhead is non-trivial, performance analysis indicates that adding extra GPUs to the system would lead to steadily enhanced overall performance. Therefore, this system can be easily expanded to generate superior computing power for very large tomographic reconstructions and especially to empower iterative cycles of reconstruction and realignment. Copyright © 2011 Elsevier B.V. All rights reserved.

GPU-based real-time triggering in the NA62 experiment

CERN Document Server

Ammendola, R.; 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.; Pontisso, L.; Rossetti, D.; Simula, F.; Sozzi, M.; Vicini, P.

2016-01-01

Over the last few years the GPGPU (General-Purpose computing on Graphics Processing Units) paradigm represented a remarkable development in the world of computing. Computing for High-Energy Physics is no exception: several works have demonstrated the effectiveness of the integration of GPU-based systems in high level trigger of different experiments. On the other hand the use of GPUs in the low level trigger systems, characterized by stringent real-time constraints, such as tight time budget and high throughput, poses several challenges. In this paper we focus on the low level trigger in the CERN NA62 experiment, investigating the use of real-time computing on GPUs in this synchronous system. Our approach aimed at harvesting the GPU computing power to build in real-time refined physics-related trigger primitives for the RICH detector, as the the knowledge of Cerenkov rings parameters allows to build stringent conditions for data selection at trigger level. Latencies of all components of the trigger chain have...

A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

Directory of Open Access Journals (Sweden)

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.

PIConGPU - A highly-scalable particle-in-cell implementation for GPU clusters

Energy Technology Data Exchange (ETDEWEB)

Bussmann, Michael; Burau, Heiko; Debus, Alexander; Huebl, Axel; Kluge, Thomas; Pausch, Richard; Schmeisser, Nils; Steiniger, Klaus; Widera, Rene; Wyderka, Nikolai; Schramm, Ulrich; Cowan, Thomas [HZDR, Dresden (Germany); Schneider, Benjamin [HZDR, Dresden (Germany); TU Dresden (Germany); Schmitt, Felix [NVIDIA, Austin, TX (United States); Grottel, Sebastian; Gumhold, Stefan [TU Dresden (Germany); Juckeland, Guido; Angel, Wolfgang [TU Dresden (Germany); ZIH, Dresden (Germany)

2013-07-01

PIConGPU can handle large-scale simulations of laser plasma and astrophysical plasma dynamics on GPU clusters with thousands of GPUs. High data throughput allows to conduct large parameter surveys but makes it necessary to rethink data analysis and look for new ways of analyzing large simulation data sets. The speedup seen on GPUs enables scientists to add physical effects to their code that up until recently have been too computationally demanding. We present recent results obtained with PIConGPU, discuss scaling behaviour, the most important building blocks of the code and new physics modules recently added. In addition we give an outlook on data analysis, resiliance and load balancing with PIConGPU.

SU-E-T-29: A Web Application for GPU-Based Monte Carlo IMRT/VMAT QA with Delivered Dose Verification

International Nuclear Information System (INIS)

Folkerts, M; Graves, Y; Tian, Z; Gu, X; Jia, X; Jiang, S

2014-01-01

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

SU-E-T-29: A Web Application for GPU-Based Monte Carlo IMRT/VMAT QA with Delivered Dose Verification

Energy Technology Data Exchange (ETDEWEB)

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.

Employing multi-GPU power for molecular dynamics simulation: an extension of GALAMOST

Science.gov (United States)

Zhu, You-Liang; Pan, Deng; Li, Zhan-Wei; Liu, Hong; Qian, Hu-Jun; Zhao, Yang; Lu, Zhong-Yuan; Sun, Zhao-Yan

2018-04-01

We describe the algorithm of employing multi-GPU power on the basis of Message Passing Interface (MPI) domain decomposition in a molecular dynamics code, GALAMOST, which is designed for the coarse-grained simulation of soft matters. The code of multi-GPU version is developed based on our previous single-GPU version. In multi-GPU runs, one GPU takes charge of one domain and runs single-GPU code path. The communication between neighbouring domains takes a similar algorithm of CPU-based code of LAMMPS, but is optimised specifically for GPUs. We employ a memory-saving design which can enlarge maximum system size at the same device condition. An optimisation algorithm is employed to prolong the update period of neighbour list. We demonstrate good performance of multi-GPU runs on the simulation of Lennard-Jones liquid, dissipative particle dynamics liquid, polymer and nanoparticle composite, and two-patch particles on workstation. A good scaling of many nodes on cluster for two-patch particles is presented.

Avaliação de desempenho e consumo energético para configurações de Wavefront pools de uma GPU AMD

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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.

a method of gravity and seismic sequential inversion and its GPU implementation

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Liu, G.; Meng, X.

2011-12-01

In this abstract, we introduce a gravity and seismic sequential inversion method to invert for density and velocity together. For the gravity inversion, we use an iterative method based on correlation imaging algorithm; for the seismic inversion, we use the full waveform inversion. The link between the density and velocity is an empirical formula called Gardner equation, for large volumes of data, we use the GPU to accelerate the computation. For the gravity inversion method , we introduce a method based on correlation imaging algorithm,it is also a interative method, first we calculate the correlation imaging of the observed gravity anomaly, it is some value between -1 and +1, then we multiply this value with a little density ,this value become the initial density model. We get a forward reuslt with this initial model and also calculate the correaltion imaging of the misfit of observed data and the forward data, also multiply the correaltion imaging result a little density and add it to the initial model, then do the same procedure above , at last ,we can get a inversion density model. For the seismic inveron method ,we use a mothod base on the linearity of acoustic wave equation written in the frequency domain,with a intial velociy model, we can get a good velocity result. In the sequential inversion of gravity and seismic , we need a link formula to convert between density and velocity ,in our method , we use the Gardner equation. Driven by the insatiable market demand for real time, high-definition 3D images, the programmable NVIDIA Graphic Processing Unit (GPU) as co-processor of CPU has been developed for high performance computing. Compute Unified Device Architecture (CUDA) is a parallel programming model and software environment provided by NVIDIA designed to overcome the challenge of using traditional general purpose GPU while maintaining a low learn curve for programmers familiar with standard programming languages such as C. In our inversion processing

Development of GPU Based Parallel Computing Module for Solving Pressure Equation in the CUPID Component Thermo-Fluid Analysis Code

International Nuclear Information System (INIS)

Lee, Jin Pyo; Joo, Han Gyu

2010-01-01

In the thermo-fluid analysis code named CUPID, the linear system of pressure equations must be solved in each iteration step. The time for repeatedly solving the linear system can be quite significant because large sparse matrices of Rank more than 50,000 are involved and the diagonal dominance of the system is hardly hold. Therefore parallelization of the linear system solver is essential to reduce the computing time. Meanwhile, Graphics Processing Units (GPU) have been developed as highly parallel, multi-core processors for the global demand of high quality 3D graphics. If a suitable interface is provided, parallelization using GPU can be available to engineering computing. NVIDIA provides a Software Development Kit(SDK) named CUDA(Compute Unified Device Architecture) to code developers so that they can manage GPUs for parallelization using the C language. In this research, we implement parallel routines for the linear system solver using CUDA, and examine the performance of the parallelization. In the next section, we will describe the method of CUDA parallelization for the CUPID code, and then the performance of the CUDA parallelization will be discussed

Conditional probability distribution associated to the E-M image reconstruction algorithm for neutron stimulated emission tomography

International Nuclear Information System (INIS)

Viana, R.S.; Yoriyaz, H.; Santos, A.

2011-01-01

The Expectation-Maximization (E-M) algorithm is an iterative computational method for maximum likelihood (M-L) estimates, useful in a variety of incomplete-data problems. Due to its stochastic nature, one of the most relevant applications of E-M algorithm is the reconstruction of emission tomography images. In this paper, the statistical formulation of the E-M algorithm was applied to the in vivo spectrographic imaging of stable isotopes called Neutron Stimulated Emission Computed Tomography (NSECT). In the process of E-M algorithm iteration, the conditional probability distribution plays a very important role to achieve high quality image. This present work proposes an alternative methodology for the generation of the conditional probability distribution associated to the E-M reconstruction algorithm, using the Monte Carlo code MCNP5 and with the application of the reciprocity theorem. (author)

Conditional probability distribution associated to the E-M image reconstruction algorithm for neutron stimulated emission tomography

Energy Technology Data Exchange (ETDEWEB)

Viana, R.S.; Yoriyaz, H.; Santos, A., E-mail: rodrigossviana@gmail.com, E-mail: hyoriyaz@ipen.br, E-mail: asantos@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2011-07-01

The Expectation-Maximization (E-M) algorithm is an iterative computational method for maximum likelihood (M-L) estimates, useful in a variety of incomplete-data problems. Due to its stochastic nature, one of the most relevant applications of E-M algorithm is the reconstruction of emission tomography images. In this paper, the statistical formulation of the E-M algorithm was applied to the in vivo spectrographic imaging of stable isotopes called Neutron Stimulated Emission Computed Tomography (NSECT). In the process of E-M algorithm iteration, the conditional probability distribution plays a very important role to achieve high quality image. This present work proposes an alternative methodology for the generation of the conditional probability distribution associated to the E-M reconstruction algorithm, using the Monte Carlo code MCNP5 and with the application of the reciprocity theorem. (author)

Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation

Directory of Open Access Journals (Sweden)

Yuan Xu

2014-03-01

Full Text Available Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT. We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstruction within 30 seconds.Methods: The method consists of six steps: 1 FDK reconstruction using raw projection data; 2 Rigid Registration of planning CT to the FDK results; 3 MC scatter calculation at sparse view angles using the planning CT; 4 Interpolation of the calculated scatter signals to other angles; 5 Removal of scatter from the raw projections; 6 FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC noise from the simulated scatter images caused by low photon numbers. The method is validated on one simulated head-and-neck case with 364 projection angles.Results: We have examined variation of the scatter signal among projection angles using Fourier analysis. It is found that scatter images at 31 angles are sufficient to restore those at all angles with < 0.1% error. For the simulated patient case with a resolution of 512 × 512 × 100, we simulated 5 × 106 photons per angle. The total computation time is 20.52 seconds on a Nvidia GTX Titan GPU, and the time at each step is 2.53, 0.64, 14.78, 0.13, 0.19, and 2.25 seconds, respectively. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU.Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. It accomplished the whole procedure of scatter correction and reconstruction within 30 seconds.----------------------------Cite this

Similarity-regulation of OS-EM for accelerated SPECT reconstruction

Science.gov (United States)

Vaissier, P. E. B.; Beekman, F. J.; Goorden, M. C.

2016-06-01

Ordered subsets expectation maximization (OS-EM) is widely used to accelerate image reconstruction in single photon emission computed tomography (SPECT). Speedup of OS-EM over maximum likelihood expectation maximization (ML-EM) is close to the number of subsets used. Although a high number of subsets can shorten reconstruction times significantly, it can also cause severe image artifacts such as improper erasure of reconstructed activity if projections contain few counts. We recently showed that such artifacts can be prevented by using a count-regulated OS-EM (CR-OS-EM) algorithm which automatically adapts the number of subsets for each voxel based on the estimated number of counts that the voxel contributed to the projections. While CR-OS-EM reached high speed-up over ML-EM in high-activity regions of images, speed in low-activity regions could still be very slow. In this work we propose similarity-regulated OS-EM (SR-OS-EM) as a much faster alternative to CR-OS-EM. SR-OS-EM also automatically and locally adapts the number of subsets, but it uses a different criterion for subset regulation: the number of subsets that is used for updating an individual voxel depends on how similar the reconstruction algorithm would update the estimated activity in that voxel with different subsets. Reconstructions of an image quality phantom and in vivo scans show that SR-OS-EM retains all of the favorable properties of CR-OS-EM, while reconstruction speed can be up to an order of magnitude higher in low-activity regions. Moreover our results suggest that SR-OS-EM can be operated with identical reconstruction parameters (including the number of iterations) for a wide range of count levels, which can be an additional advantage from a user perspective since users would only have to post-filter an image to present it at an appropriate noise level.

GGEMS-Brachy: GPU GEant4-based Monte Carlo simulation for brachytherapy applications

Science.gov (United States)

Lemaréchal, Yannick; Bert, Julien; Falconnet, Claire; Després, Philippe; Valeri, Antoine; Schick, Ulrike; Pradier, Olivier; Garcia, Marie-Paule; Boussion, Nicolas; Visvikis, Dimitris

2015-07-01

In brachytherapy, plans are routinely calculated using the AAPM TG43 formalism which considers the patient as a simple water object. An accurate modeling of the physical processes considering patient heterogeneity using Monte Carlo simulation (MCS) methods is currently too time-consuming and computationally demanding to be routinely used. In this work we implemented and evaluated an accurate and fast MCS on Graphics Processing Units (GPU) for brachytherapy low dose rate (LDR) applications. A previously proposed Geant4 based MCS framework implemented on GPU (GGEMS) was extended to include a hybrid GPU navigator, allowing navigation within voxelized patient specific images and analytically modeled 125I seeds used in LDR brachytherapy. In addition, dose scoring based on track length estimator including uncertainty calculations was incorporated. The implemented GGEMS-brachy platform was validated using a comparison with Geant4 simulations and reference datasets. Finally, a comparative dosimetry study based on the current clinical standard (TG43) and the proposed platform was performed on twelve prostate cancer patients undergoing LDR brachytherapy. Considering patient 3D CT volumes of 400  × 250  × 65 voxels and an average of 58 implanted seeds, the mean patient dosimetry study run time for a 2% dose uncertainty was 9.35 s (≈500 ms 10-6 simulated particles) and 2.5 s when using one and four GPUs, respectively. The performance of the proposed GGEMS-brachy platform allows envisaging the use of Monte Carlo simulation based dosimetry studies in brachytherapy compatible with clinical practice. Although the proposed platform was evaluated for prostate cancer, it is equally applicable to other LDR brachytherapy clinical applications. Future extensions will allow its application in high dose rate brachytherapy applications.

GGEMS-Brachy: GPU GEant4-based Monte Carlo simulation for brachytherapy applications

International Nuclear Information System (INIS)

Lemaréchal, Yannick; Bert, Julien; Schick, Ulrike; Pradier, Olivier; Garcia, Marie-Paule; Boussion, Nicolas; Visvikis, Dimitris; Falconnet, Claire; Després, Philippe; Valeri, Antoine

2015-01-01

In brachytherapy, plans are routinely calculated using the AAPM TG43 formalism which considers the patient as a simple water object. An accurate modeling of the physical processes considering patient heterogeneity using Monte Carlo simulation (MCS) methods is currently too time-consuming and computationally demanding to be routinely used. In this work we implemented and evaluated an accurate and fast MCS on Graphics Processing Units (GPU) for brachytherapy low dose rate (LDR) applications. A previously proposed Geant4 based MCS framework implemented on GPU (GGEMS) was extended to include a hybrid GPU navigator, allowing navigation within voxelized patient specific images and analytically modeled 125 I seeds used in LDR brachytherapy. In addition, dose scoring based on track length estimator including uncertainty calculations was incorporated. The implemented GGEMS-brachy platform was validated using a comparison with Geant4 simulations and reference datasets. Finally, a comparative dosimetry study based on the current clinical standard (TG43) and the proposed platform was performed on twelve prostate cancer patients undergoing LDR brachytherapy. Considering patient 3D CT volumes of 400  × 250  × 65 voxels and an average of 58 implanted seeds, the mean patient dosimetry study run time for a 2% dose uncertainty was 9.35 s (≈500 ms 10 −6 simulated particles) and 2.5 s when using one and four GPUs, respectively. The performance of the proposed GGEMS-brachy platform allows envisaging the use of Monte Carlo simulation based dosimetry studies in brachytherapy compatible with clinical practice. Although the proposed platform was evaluated for prostate cancer, it is equally applicable to other LDR brachytherapy clinical applications. Future extensions will allow its application in high dose rate brachytherapy applications. (paper)

A GPU-based calculation using the three-dimensional FDTD method for electromagnetic field analysis.

Science.gov (United States)

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.

Rapid simulation of X-ray transmission imaging for baggage inspection via GPU-based ray-tracing

Science.gov (United States)

Gong, Qian; Stoian, Razvan-Ionut; Coccarelli, David S.; Greenberg, Joel A.; Vera, Esteban; Gehm, Michael E.

2018-01-01

We present a pipeline that rapidly simulates X-ray transmission imaging for arbitrary system architectures using GPU-based ray-tracing techniques. The purpose of the pipeline is to enable statistical analysis of threat detection in the context of airline baggage inspection. As a faster alternative to Monte Carlo methods, we adopt a deterministic approach for simulating photoelectric absorption-based imaging. The highly-optimized NVIDIA OptiX API is used to implement ray-tracing, greatly speeding code execution. In addition, we implement the first hierarchical representation structure to determine the interaction path length of rays traversing heterogeneous media described by layered polygons. The accuracy of the pipeline has been validated by comparing simulated data with experimental data collected using a heterogenous phantom and a laboratory X-ray imaging system. On a single computer, our approach allows us to generate over 400 2D transmission projections (125 × 125 pixels per frame) per hour for a bag packed with hundreds of everyday objects. By implementing our approach on cloud-based GPU computing platforms, we find that the same 2D projections of approximately 3.9 million bags can be obtained in a single day using 400 GPU instances, at a cost of only 0.001 per bag.

Parallel iterative solution of the Hermite Collocation equations on GPUs II

International Nuclear Information System (INIS)

Vilanakis, N; Mathioudakis, E

2014-01-01

Hermite Collocation is a high order finite element method for Boundary Value Problems modelling applications in several fields of science and engineering. Application of this integration free numerical solver for the solution of linear BVPs results in a large and sparse general system of algebraic equations, suggesting the usage of an efficient iterative solver especially for realistic simulations. In part I of this work an efficient parallel algorithm of the Schur complement method coupled with Bi-Conjugate Gradient Stabilized (BiCGSTAB) iterative solver has been designed for multicore computing architectures with a Graphics Processing Unit (GPU). In the present work the proposed algorithm has been extended for high performance computing environments consisting of multiprocessor machines with multiple GPUs. Since this is a distributed GPU and shared CPU memory parallel architecture, a hybrid memory treatment is needed for the development of the parallel algorithm. The realization of the algorithm took place on a multiprocessor machine HP SL390 with Tesla M2070 GPUs using the OpenMP and OpenACC standards. Execution time measurements reveal the efficiency of the parallel implementation

The GPU implementation of micro - Doppler period estimation

Science.gov (United States)

Yang, Liyuan; Wang, Junling; Bi, Ran

2018-03-01

Aiming at the problem that the computational complexity and the deficiency of real-time of the wideband radar echo signal, a program is designed to improve the performance of real-time extraction of micro-motion feature in this paper based on the CPU-GPU heterogeneous parallel structure. Firstly, we discuss the principle of the micro-Doppler effect generated by the rolling of the scattering points on the orbiting satellite, analyses how to use Kalman filter to compensate the translational motion of tumbling satellite and how to use the joint time-frequency analysis and inverse Radon transform to extract the micro-motion features from the echo after compensation. Secondly, the advantages of GPU in terms of real-time processing and the working principle of CPU-GPU heterogeneous parallelism are analysed, and a program flow based on GPU to extract the micro-motion feature from the radar echo signal of rolling satellite is designed. At the end of the article the results of extraction are given to verify the correctness of the program and algorithm.

Acoustic reverse-time migration using GPU card and POSIX thread based on the adaptive optimal finite-difference scheme and the hybrid absorbing boundary condition

Science.gov (United States)

Cai, Xiaohui; Liu, Yang; Ren, Zhiming

2018-06-01

Reverse-time migration (RTM) is a powerful tool for imaging geologically complex structures such as steep-dip and subsalt. However, its implementation is quite computationally expensive. Recently, as a low-cost solution, the graphic processing unit (GPU) was introduced to improve the efficiency of RTM. In the paper, we develop three ameliorative strategies to implement RTM on GPU card. First, given the high accuracy and efficiency of the adaptive optimal finite-difference (FD) method based on least squares (LS) on central processing unit (CPU), we study the optimal LS-based FD method on GPU. Second, we develop the CPU-based hybrid absorbing boundary condition (ABC) to the GPU-based one by addressing two issues of the former when introduced to GPU card: time-consuming and chaotic threads. Third, for large-scale data, the combinatorial strategy for optimal checkpointing and efficient boundary storage is introduced for the trade-off between memory and recomputation. To save the time of communication between host and disk, the portable operating system interface (POSIX) thread is utilized to create the other CPU core at the checkpoints. Applications of the three strategies on GPU with the compute unified device architecture (CUDA) programming language in RTM demonstrate their efficiency and validity.

GPU based acceleration of first principles calculation

International Nuclear Information System (INIS)

Tomono, H; Tsumuraya, K; Aoki, M; Iitaka, T

2010-01-01

We present a Graphics Processing Unit (GPU) accelerated simulations of first principles electronic structure calculations. The FFT, which is the most time-consuming part, is about 10 times accelerated. As the result, the total computation time of a first principles calculation is reduced to 15 percent of that of the CPU.

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

Science.gov (United States)

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

2011-12-01

comtinue to perform 3D correlation imaging for the redisual gravity data. After several iterations, we can obtain a satisfactoy results. Newly developed general purpose computing technology from Nvidia GPU (Graphics Processing Unit) has been put into practice and received widespread attention in many areas. Based on the GPU programming mode and two parallel levels, five CPU loops for the main computation of 3D correlation imaging are converted into three loops in GPU kernel functions, thus achieving GPU/CPU collaborative computing. The two inner loops are defined as the dimensions of blocks and the three outer loops are defined as the dimensions of threads, thus realizing the double loop block calculation. Theoretical and real gravity data tests show that results are reliable and the computing time is greatly reduced. Acknowledgments We acknowledge the financial support of Sinoprobe project (201011039 and 201011049-03), the Fundamental Research Funds for the Central Universities (2010ZY26 and 2011PY0183), the National Natural Science Foundation of China (41074095) and the Open Project of State Key Laboratory of Geological Processes and Mineral Resources (GPMR0945).

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing

Directory of Open Access Journals (Sweden)

Fan Zhang

2016-04-01

Full Text Available With the development of synthetic aperture radar (SAR technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO. However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

Accelerating Spaceborne SAR Imaging Using Multiple CPU/GPU Deep Collaborative Computing.

Science.gov (United States)

Zhang, Fan; Li, Guojun; Li, Wei; Hu, Wei; Hu, Yuxin

2016-04-07

With the development of synthetic aperture radar (SAR) technologies in recent years, the huge amount of remote sensing data brings challenges for real-time imaging processing. Therefore, high performance computing (HPC) methods have been presented to accelerate SAR imaging, especially the GPU based methods. In the classical GPU based imaging algorithm, GPU is employed to accelerate image processing by massive parallel computing, and CPU is only used to perform the auxiliary work such as data input/output (IO). However, the computing capability of CPU is ignored and underestimated. In this work, a new deep collaborative SAR imaging method based on multiple CPU/GPU is proposed to achieve real-time SAR imaging. Through the proposed tasks partitioning and scheduling strategy, the whole image can be generated with deep collaborative multiple CPU/GPU computing. In the part of CPU parallel imaging, the advanced vector extension (AVX) method is firstly introduced into the multi-core CPU parallel method for higher efficiency. As for the GPU parallel imaging, not only the bottlenecks of memory limitation and frequent data transferring are broken, but also kinds of optimized strategies are applied, such as streaming, parallel pipeline and so on. Experimental results demonstrate that the deep CPU/GPU collaborative imaging method enhances the efficiency of SAR imaging on single-core CPU by 270 times and realizes the real-time imaging in that the imaging rate outperforms the raw data generation rate.

CellML, SED-ML, and the Physiome Model Repository

OpenAIRE

Nickerson, David

2016-01-01

Invited presentation delivered at COMBINE 2016.CellML, SED-ML, and the Physiome Model Repository.David Nickerson, Auckland Bioengineering Institute, University of Auckland, New Zealand.CellML is an XML-based protocol for storing and exchanging computer-based mathematical models in an unambiguous, modular, and reusable manner. In addition to introducing CellML, in this presentation I will provide some of physiological examples that have help drive the development and adoption of CellML. I will...

GPU Pro 5 advanced rendering techniques

CERN Document Server

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

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

International Nuclear Information System (INIS)

Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

2014-01-01

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

Energy Technology Data Exchange (ETDEWEB)

Bai, T [Xi' an Jiaotong University, Xi' an (China); UT Southwestern Medical Center, Dallas, TX (United States); Yan, H; Shi, F; Jia, X; Jiang, Steve B. [UT Southwestern Medical Center, Dallas, TX (United States); Lou, Y [University of California Irvine, Irvine, CA (United States); Xu, Q; Mou, X [Xi' an Jiaotong University, Xi' an (China)

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

The orthorectified technology for UAV aerial remote sensing image based on the Programmable GPU

International Nuclear Information System (INIS)

Jin, Liu; Ying-cheng, Li; De-long, Li; Chang-sheng, Teng; Wen-hao, Zhang

2014-01-01

Considering the time requirements of the disaster emergency aerial remote sensing data acquisition and processing, this paper introduced the GPU parallel processing in orthorectification algorithm. Meanwhile, our experiments verified the correctness and feasibility of CUDA parallel processing algorithm, and the algorithm can effectively solve the problem of calculation large, time-consuming for ortho rectification process, realized fast processing of UAV airborne remote sensing image orthorectification based on GPU. The experimental results indicate that using the assumption of same accuracy of proposed method with CPU, the processing time is reduced obviously, maximum acceleration can reach more than 12 times, which greatly enhances the emergency surveying and mapping processing of rapid reaction rate, and has a broad application

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU

Directory of Open Access Journals (Sweden)

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.

A high performance image processing platform based on CPU-GPU heterogeneous cluster with parallel image reconstroctions for micro-CT

International Nuclear Information System (INIS)

Ding Yu; Qi Yujin; Zhang Xuezhu; Zhao Cuilan

2011-01-01

In this paper, we report the development of a high-performance image processing platform, which is based on CPU-GPU heterogeneous cluster. Currently, it consists of a Dell Precision T7500 and HP XW8600 workstations with parallel programming and runtime environment, using the message-passing interface (MPI) and CUDA (Compute Unified Device Architecture). We succeeded in developing parallel image processing techniques for 3D image reconstruction of X-ray micro-CT imaging. The results show that a GPU provides a computing efficiency of about 194 times faster than a single CPU, and the CPU-GPU clusters provides a computing efficiency of about 46 times faster than the CPU clusters. These meet the requirements of rapid 3D image reconstruction and real time image display. In conclusion, the use of CPU-GPU heterogeneous cluster is an effective way to build high-performance image processing platform. (authors)

gPGA: GPU Accelerated Population Genetics Analyses.

Directory of Open Access Journals (Sweden)

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.

AMITIS: A 3D GPU-Based Hybrid-PIC Model for Space and Plasma Physics

Science.gov (United States)

Fatemi, Shahab; Poppe, Andrew R.; Delory, Gregory T.; Farrell, William M.

2017-05-01

We have developed, for the first time, an advanced modeling infrastructure in space simulations (AMITIS) with an embedded three-dimensional self-consistent grid-based hybrid model of plasma (kinetic ions and fluid electrons) that runs entirely on graphics processing units (GPUs). The model uses NVIDIA GPUs and their associated parallel computing platform, CUDA, developed for general purpose processing on GPUs. The model uses a single CPU-GPU pair, where the CPU transfers data between the system and GPU memory, executes CUDA kernels, and writes simulation outputs on the disk. All computations, including moving particles, calculating macroscopic properties of particles on a grid, and solving hybrid model equations are processed on a single GPU. We explain various computing kernels within AMITIS and compare their performance with an already existing well-tested hybrid model of plasma that runs in parallel using multi-CPU platforms. We show that AMITIS runs ∼10 times faster than the parallel CPU-based hybrid model. We also introduce an implicit solver for computation of Faraday’s Equation, resulting in an explicit-implicit scheme for the hybrid model equation. We show that the proposed scheme is stable and accurate. We examine the AMITIS energy conservation and show that the energy is conserved with an error < 0.2% after 500,000 timesteps, even when a very low number of particles per cell is used.

SU-F-BRF-01: A GPU Framework for Developing Interactive High-Resolution Patient-Specific Biomechanical Models

International Nuclear Information System (INIS)

Neylon, J; Qi, S; Sheng, K; Kupelian, P; Santhanam, A

2014-01-01

Purpose: To develop a GPU-based framework that can generate highresolution and patient-specific biomechanical models from a given simulation CT and contoured structures, optimized to run at interactive speeds, for addressing adaptive radiotherapy objectives. Method: A Massspring-damping (MSD) model was generated from a given simulation CT. The model's mass elements were generated for every voxel of anatomy, and positioned in a deformation space in the GPU memory. MSD connections were established between neighboring mass elements in a dense distribution. Contoured internal structures allowed control over elastic material properties of different tissues. Once the model was initialized in GPU memory, skeletal anatomy was actuated using rigid-body transformations, while soft tissues were governed by elastic corrective forces and constraints, which included tensile forces, shear forces, and spring damping forces. The model was validated by applying a known load to a soft tissue block and comparing the observed deformation to ground truth calculations from established elastic mechanics. Results: Our analyses showed that both local and global load experiments yielded results with a correlation coefficient R 2 > 0.98 compared to ground truth. Models were generated for several anatomical regions. Head and neck models accurately simulated posture changes by rotating the skeletal anatomy in three dimensions. Pelvic models were developed for realistic deformations for changes in bladder volume. Thoracic models demonstrated breast deformation due to gravity when changing treatment position from supine to prone. The GPU framework performed at greater than 30 iterations per second for over 1 million mass elements with up to 26 MSD connections each. Conclusions: Realistic simulations of site-specific, complex posture and physiological changes were simulated at interactive speeds using patient data. Incorporating such a model with live patient tracking would facilitate real

gWEGA: GPU-accelerated WEGA for molecular superposition and shape comparison.

Science.gov (United States)

Yan, Xin; Li, Jiabo; Gu, Qiong; Xu, Jun

2014-06-05

Virtual screening of a large chemical library for drug lead identification requires searching/superimposing a large number of three-dimensional (3D) chemical structures. This article reports a graphic processing unit (GPU)-accelerated weighted Gaussian algorithm (gWEGA) that expedites shape or shape-feature similarity score-based virtual screening. With 86 GPU nodes (each node has one GPU card), gWEGA can screen 110 million conformations derived from an entire ZINC drug-like database with diverse antidiabetic agents as query structures within 2 s (i.e., screening more than 55 million conformations per second). The rapid screening speed was accomplished through the massive parallelization on multiple GPU nodes and rapid prescreening of 3D structures (based on their shape descriptors and pharmacophore feature compositions). Copyright © 2014 Wiley Periodicals, Inc.

Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images.

Science.gov (United States)

Rydén, T; Heydorn Lagerlöf, J; Hemmingsson, J; Marin, I; Svensson, J; Båth, M; Gjertsson, P; Bernhardt, P

2018-01-04

Full Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (128 3 or 256 3 ). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom 177 Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC). The performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 128 3 voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was

GPU accelerated likelihoods for stereo-based articulated tracking

DEFF Research Database (Denmark)

Friborg, Rune Møllegaard; Hauberg, Søren; Erleben, Kenny

2010-01-01

than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously required several minutes, are now performed in few seconds....

Compact multimode fiber beam-shaping system based on GPU accelerated digital holography.

Science.gov (United States)

Plöschner, Martin; Čižmár, Tomáš

2015-01-15

Real-time, on-demand, beam shaping at the end of the multimode fiber has recently been made possible by exploiting the computational power of rapidly evolving graphics processing unit (GPU) technology [Opt. Express 22, 2933 (2014)]. However, the current state-of-the-art system requires the presence of an acousto-optic deflector (AOD) to produce images at the end of the fiber without interference effects between neighboring output points. Here, we present a system free from the AOD complexity where we achieve the removal of the undesired interference effects computationally using GPU implemented Gerchberg-Saxton and Yang-Gu algorithms. The GPU implementation is two orders of magnitude faster than the CPU implementation which allows video-rate image control at the distal end of the fiber virtually free of interference effects.

A parallel finite element procedure for contact-impact problems using edge-based smooth triangular element and GPU

Science.gov (United States)

Cai, Yong; Cui, Xiangyang; Li, Guangyao; Liu, Wenyang

2018-04-01

The edge-smooth finite element method (ES-FEM) can improve the computational accuracy of triangular shell elements and the mesh partition efficiency of complex models. In this paper, an approach is developed to perform explicit finite element simulations of contact-impact problems with a graphical processing unit (GPU) using a special edge-smooth triangular shell element based on ES-FEM. Of critical importance for this problem is achieving finer-grained parallelism to enable efficient data loading and to minimize communication between the device and host. Four kinds of parallel strategies are then developed to efficiently solve these ES-FEM based shell element formulas, and various optimization methods are adopted to ensure aligned memory access. Special focus is dedicated to developing an approach for the parallel construction of edge systems. A parallel hierarchy-territory contact-searching algorithm (HITA) and a parallel penalty function calculation method are embedded in this parallel explicit algorithm. Finally, the program flow is well designed, and a GPU-based simulation system is developed, using Nvidia's CUDA. Several numerical examples are presented to illustrate the high quality of the results obtained with the proposed methods. In addition, the GPU-based parallel computation is shown to significantly reduce the computing time.

PIConGPU - How to build one of the fastest GPU particle-in-cell codes in the world

Energy Technology Data Exchange (ETDEWEB)

Burau, Heiko; Debus, Alexander; Helm, Anton; Huebl, Axel; Kluge, Thomas; Widera, Rene; Bussmann, Michael; Schramm, Ulrich; Cowan, Thomas [HZDR, Dresden (Germany); Juckeland, Guido; Nagel, Wolfgang [TU Dresden (Germany); ZIH, Dresden (Germany); Schmitt, Felix [NVIDIA (United States)

2013-07-01

We present the algorithmic building blocks of PIConGPU, one of the fastest implementations of the particle-in-cell algortihm on GPU clusters. PIConGPU is a highly-scalable, 3D3V electromagnetic PIC code that is used in laser plasma and astrophysical plasma simulations.

TU-FG-BRB-07: GPU-Based Prompt Gamma Ray Imaging From Boron Neutron Capture Therapy

Energy Technology Data Exchange (ETDEWEB)

Kim, S; Suh, T; Yoon, D; Jung, J; Shin, H; Kim, M [The catholic university of Korea, Seoul (Korea, Republic of)

2016-06-15

Purpose: The purpose of this research is to perform the fast reconstruction of a prompt gamma ray image using a graphics processing unit (GPU) computation from boron neutron capture therapy (BNCT) simulations. Methods: To evaluate the accuracy of the reconstructed image, a phantom including four boron uptake regions (BURs) was used in the simulation. After the Monte Carlo simulation of the BNCT, the modified ordered subset expectation maximization reconstruction algorithm using the GPU computation was used to reconstruct the images with fewer projections. The computation times for image reconstruction were compared between the GPU and the central processing unit (CPU). Also, the accuracy of the reconstructed image was evaluated by a receiver operating characteristic (ROC) curve analysis. Results: The image reconstruction time using the GPU was 196 times faster than the conventional reconstruction time using the CPU. For the four BURs, the area under curve values from the ROC curve were 0.6726 (A-region), 0.6890 (B-region), 0.7384 (C-region), and 0.8009 (D-region). Conclusion: The tomographic image using the prompt gamma ray event from the BNCT simulation was acquired using the GPU computation in order to perform a fast reconstruction during treatment. The authors verified the feasibility of the prompt gamma ray reconstruction using the GPU computation for BNCT simulations.

GPU PRO 3 Advanced rendering techniques

CERN Document Server

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

NaNet: a low-latency NIC enabling GPU-based, real-time low level trigger systems

International Nuclear Information System (INIS)

Ammendola, Roberto; Biagioni, Andrea; Frezza, Ottorino; Cicero, Francesca Lo; Lonardo, Alessandro; Paolucci, Pier Stanislao; Rossetti, Davide; Simula, Francesco; Tosoratto, Laura; Vicini, Piero; Fantechi, Riccardo; Lamanna, Gianluca; Pantaleo, Felice; Piandani, Roberto; Sozzi, Marco; Pontisso, Luca

2014-01-01

We implemented the NaNet FPGA-based PCIe Gen2 GbE/APElink NIC, featuring GPUDirect RDMA capabilities and UDP protocol management offloading. NaNet is able to receive a UDP input data stream from its GbE interface and redirect it, without any intermediate buffering or CPU intervention, to the memory of a Fermi/Kepler GPU hosted on the same PCIe bus, provided that the two devices share the same upstream root complex. Synthetic benchmarks for latency and bandwidth are presented. We describe how NaNet can be employed in the prototype of the GPU-based RICH low-level trigger processor of the NA62 CERN experiment, to implement the data link between the TEL62 readout boards and the low level trigger processor. Results for the throughput and latency of the integrated system are presented and discussed.

NaNet: a low-latency NIC enabling GPU-based, real-time low level trigger systems

Energy Technology Data Exchange (ETDEWEB)

Ammendola, Roberto [INFN, Rome – Tor Vergata (Italy); Biagioni, Andrea; Frezza, Ottorino; Cicero, Francesca Lo; Lonardo, Alessandro; Paolucci, Pier Stanislao; Rossetti, Davide; Simula, Francesco; Tosoratto, Laura; Vicini, Piero [INFN, Rome – Sapienza (Italy); Fantechi, Riccardo [CERN, Geneve (Switzerland); Lamanna, Gianluca; Pantaleo, Felice; Piandani, Roberto; Sozzi, Marco [INFN, Pisa (Italy); Pontisso, Luca [University, Rome (Italy)

2014-06-11

We implemented the NaNet FPGA-based PCIe Gen2 GbE/APElink NIC, featuring GPUDirect RDMA capabilities and UDP protocol management offloading. NaNet is able to receive a UDP input data stream from its GbE interface and redirect it, without any intermediate buffering or CPU intervention, to the memory of a Fermi/Kepler GPU hosted on the same PCIe bus, provided that the two devices share the same upstream root complex. Synthetic benchmarks for latency and bandwidth are presented. We describe how NaNet can be employed in the prototype of the GPU-based RICH low-level trigger processor of the NA62 CERN experiment, to implement the data link between the TEL62 readout boards and the low level trigger processor. Results for the throughput and latency of the integrated system are presented and discussed.

NaNet:a low-latency NIC enabling GPU-based, real-time low level trigger systems

CERN Document Server

INSPIRE-00646837; Biagioni, Andrea; Fantechi, Riccardo; Frezza, Ottorino; Lamanna, Gianluca; Lo Cicero, Francesca; Lonardo, Alessandro; Paolucci, Pier Stanislao; Pantaleo, Felice; Piandani, Roberto; Pontisso, Luca; Rossetti, Davide; Simula, Francesco; Sozzi, Marco; Tosoratto, Laura; Vicini, Piero

2014-01-01

We implemented the NaNet FPGA-based PCI2 Gen2 GbE/APElink NIC, featuring GPUDirect RDMA capabilities and UDP protocol management offloading. NaNet is able to receive a UDP input data stream from its GbE interface and redirect it, without any intermediate buffering or CPU intervention, to the memory of a Fermi/Kepler GPU hosted on the same PCIe bus, provided that the two devices share the same upstream root complex. Synthetic benchmarks for latency and bandwidth are presented. We describe how NaNet can be employed in the prototype of the GPU-based RICH low-level trigger processor of the NA62 CERN experiment, to implement the data link between the TEL62 readout boards and the low level trigger processor. Results for the throughput and latency of the integrated system are presented and discussed.

Parallel generation of architecture on the GPU

KAUST Repository

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.

Comparison of GPU-Based Numerous Particles Simulation and Experiment

International Nuclear Information System (INIS)

Park, Sang Wook; Jun, Chul Woong; Sohn, Jeong Hyun; Lee, Jae Wook

2014-01-01

The dynamic behavior of numerous grains interacting with each other can be easily observed. In this study, this dynamic behavior was analyzed based on the contact between numerous grains. The discrete element method was used for analyzing the dynamic behavior of each particle and the neighboring-cell algorithm was employed for detecting their contact. The Hertzian and tangential sliding friction contact models were used for calculating the contact force acting between the particles. A GPU-based parallel program was developed for conducting the computer simulation and calculating the numerous contacts. The dam break experiment was performed to verify the simulation results. The reliability of the program was verified by comparing the results of the simulation with those of the experiment

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system

Energy Technology Data Exchange (ETDEWEB)

Ma, Jiasen, E-mail: ma.jiasen@mayo.edu; Beltran, Chris; Seum Wan Chan Tseung, Hok; Herman, Michael G. [Department of Radiation Oncology, Division of Medical Physics, Mayo Clinic, 200 First Street Southwest, Rochester, Minnesota 55905 (United States)

2014-12-15

Purpose: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. Methods: An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. Results: For relatively large and complex three-field head and neck cases, i.e., >100 000 spots with a target volume of ∼1000 cm{sup 3} and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. Conclusions: A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45�

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system.

Science.gov (United States)

Ma, Jiasen; Beltran, Chris; Seum Wan Chan Tseung, Hok; Herman, Michael G

2014-12-01

Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. For relatively large and complex three-field head and neck cases, i.e., >100,000 spots with a target volume of ∼ 1000 cm(3) and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45,000 dollars. The fast calculation and

cellGPU: Massively parallel simulations of dynamic vertex models

Science.gov (United States)

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

Region of interest evaluation of SPECT image reconstruction methods using a realistic brain phantom

International Nuclear Information System (INIS)

Xia, Weishi; Glick, S.J.; Soares, E.J.

1996-01-01

A realistic numerical brain phantom, developed by Zubal et al, was used for a region-of-interest evaluation of the accuracy and noise variance of the following SPECT reconstruction methods: (1) Maximum-Likelihood reconstruction using the Expectation-Maximization (ML-EM) algorithm; (2) an EM algorithm using ordered-subsets (OS-EM); (3) a re-scaled block iterative EM algorithm (RBI-EM); and (4) a filtered backprojection algorithm that uses a combination of the Bellini method for attenuation compensation and an iterative spatial blurring correction method using the frequency-distance principle (FDP). The Zubal phantom was made from segmented MRI slices of the brain, so that neuro-anatomical structures are well defined and indexed. Small regions-of-interest (ROIs) from the white matter, grey matter in the center of the brain and grey matter from the peripheral area of the brain were selected for the evaluation. Photon attenuation and distance-dependent collimator blurring were modeled. Multiple independent noise realizations were generated for two different count levels. The simulation study showed that the ROI bias measured for the EM-based algorithms decreased as the iteration number increased, and that the OS-EM and RBI-EM algorithms (16 and 64 subsets were used) achieved the equivalent accuracy of the ML-EM algorithm at about the same noise variance, with much fewer number of iterations. The Bellini-FDP restoration algorithm converged fast and required less computation per iteration. The ML-EM algorithm had a slightly better ROI bias vs. variance trade-off than the other algorithms

Acceleration of PIC simulation with GPU

International Nuclear Information System (INIS)

Suzuki, Junya; Shimazu, Hironori; Fukazawa, Keiichiro; Den, Mitsue

2011-01-01

Particle-in-cell (PIC) is a simulation technique for plasma physics. The large number of particles in high-resolution plasma simulation increases the volume computation required, making it vital to increase computation speed. In this study, we attempt to accelerate computation speed on graphics processing units (GPUs) using KEMPO, a PIC simulation code package. We perform two tests for benchmarking, with small and large grid sizes. In these tests, we run KEMPO1 code using a CPU only, both a CPU and a GPU, and a GPU only. The results showed that performance using only a GPU was twice that of using a CPU alone. While, execution time for using both a CPU and GPU is comparable to the tests with a CPU alone, because of the significant bottleneck in communication between the CPU and GPU. (author)

GPU Accelerated Vector Median Filter

Science.gov (United States)

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 .

The Extended-Window Channel Estimator for Iterative Channel-and-Symbol Estimation

Directory of Open Access Journals (Sweden)

Barry John R

2005-01-01

Full Text Available The application of the expectation-maximization (EM algorithm to channel estimation results in a well-known iterative channel-and-symbol estimator (ICSE. The EM-ICSE iterates between a symbol estimator based on the forward-backward recursion (BCJR equalizer and a channel estimator, and may provide approximate maximum-likelihood blind or semiblind channel estimates. Nevertheless, the EM-ICSE has high complexity, and it is prone to misconvergence. In this paper, we propose the extended-window (EW estimator, a novel channel estimator for ICSE that can be used with any soft-output symbol estimator. Therefore, the symbol estimator may be chosen according to performance or complexity specifications. We show that the EW-ICSE, an ICSE that uses the EW estimator and the BCJR equalizer, is less complex and less susceptible to misconvergence than the EM-ICSE. Simulation results reveal that the EW-ICSE may converge faster than the EM-ICSE.

Application of GPU to computational multiphase fluid dynamics

International Nuclear Information System (INIS)

Nagatake, T; Kunugi, T

2010-01-01

The MARS (Multi-interfaces Advection and Reconstruction Solver) [1] is one of the surface volume tracking methods for multi-phase flows. Nowadays, the performance of GPU (Graphics Processing Unit) is much higher than the CPU (Central Processing Unit). In this study, the GPU was applied to the MARS in order to accelerate the computation of multi-phase flows (GPU-MARS), and the performance of the GPU-MARS was discussed. From the performance of the interface tracking method for the analyses of one-directional advection problem, it is found that the computing time of GPU(single GTX280) was around 4 times faster than that of the CPU (Xeon 5040, 4 threads parallelized). From the performance of Poisson Solver by using the algorithm developed in this study, it is found that the performance of the GPU showed around 30 times faster than that of the CPU. Finally, it is confirmed that the GPU showed the large acceleration of the fluid flow computation (GPU-MARS) compared to the CPU. However, it is also found that the double-precision computation of the GPU must perform with very high precision.

Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System

Directory of Open Access Journals (Sweden)

Yu Liu

2015-01-01

Full Text Available The Smith-Waterman (SW algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively.

Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System.

Science.gov (United States)

Liu, Yu; Hong, Yang; Lin, Chun-Yuan; Hung, Che-Lun

2015-01-01

The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively.

GPU-based large-scale visualization

KAUST Repository

Hadwiger, Markus

2013-11-19

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

GPU seeks new funding for TMI cleanup

International Nuclear Information System (INIS)

Utroska, D.

1982-01-01

General Public Utilities (GPU) wants approval for annual transfer of money from base rate increases in other accounts to pay for the cleanup at Three Mile Island (TMI) until TMI-1 returns to service or the public utility commission takes further action. This proposal confirms fears of a delay in TMI-1 startup and demonstrates that the January negotiated settlement will produce little funding for TMI-2 cleanup. A review of the settlement terms outlines the three-step process for base rate increases and revenue adjustments after the startup of TMI-1, and points out where controversy and delays due to psychological stress make a new source of money essential. GPU thinks customer funding will motivate other parties to a broad-based cost-sharing agreement

GPU Acceleration of DSP for Communication Receivers.

Science.gov (United States)

Gunther, Jake; Gunther, Hyrum; Moon, Todd

2017-09-01

Graphics processing unit (GPU) implementations of signal processing algorithms can outperform CPU-based implementations. This paper describes the GPU implementation of several algorithms encountered in a wide range of high-data rate communication receivers including filters, multirate filters, numerically controlled oscillators, and multi-stage digital down converters. These structures are tested by processing the 20 MHz wide FM radio band (88-108 MHz). Two receiver structures are explored: a single channel receiver and a filter bank channelizer. Both run in real time on NVIDIA GeForce GTX 1080 graphics card.

A FPGA-based Network Interface Card with GPUDirect enabling realtime GPU computing in HEP experiments

CERN Document Server

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...

Cost-effective GPU-grid for genome-wide epistasis calculations.

Science.gov (United States)

Pütz, B; Kam-Thong, T; Karbalai, N; Altmann, A; Müller-Myhsok, B

2013-01-01

Until recently, genotype studies were limited to the investigation of single SNP effects due to the computational burden incurred when studying pairwise interactions of SNPs. However, some genetic effects as simple as coloring (in plants and animals) cannot be ascribed to a single locus but only understood when epistasis is taken into account [1]. It is expected that such effects are also found in complex diseases where many genes contribute to the clinical outcome of affected individuals. Only recently have such problems become feasible computationally. The inherently parallel structure of the problem makes it a perfect candidate for massive parallelization on either grid or cloud architectures. Since we are also dealing with confidential patient data, we were not able to consider a cloud-based solution but had to find a way to process the data in-house and aimed to build a local GPU-based grid structure. Sequential epistatsis calculations were ported to GPU using CUDA at various levels. Parallelization on the CPU was compared to corresponding GPU counterparts with regards to performance and cost. A cost-effective solution was created by combining custom-built nodes equipped with relatively inexpensive consumer-level graphics cards with highly parallel GPUs in a local grid. The GPU method outperforms current cluster-based systems on a price/performance criterion, as a single GPU shows speed performance comparable up to 200 CPU cores. The outlined approach will work for problems that easily lend themselves to massive parallelization. Code for various tasks has been made available and ongoing development of tools will further ease the transition from sequential to parallel algorithms.

GPU's for event reconstruction in the FairRoot framework

International Nuclear Information System (INIS)

Al-Turany, M; Uhlig, F; Karabowicz, R

2010-01-01

FairRoot is the simulation and analysis framework used by CBM and PANDA experiments at FAIR/GSI. The use of graphics processor units (GPUs) for event reconstruction in FairRoot will be presented. The fact that CUDA (Nvidia's Compute Unified Device Architecture) development tools work alongside the conventional C/C++ compiler, makes it possible to mix GPU code with general-purpose code for the host CPU, based on this some of the reconstruction tasks can be send to the graphic cards. Moreover, tasks that run on the GPU's can also run in emulation mode on the host CPU, which has the advantage that the same code is used on both CPU and GPU.

Web-based Tsunami Early Warning System with instant Tsunami Propagation Calculations in the GPU Cloud

Science.gov (United States)

Hammitzsch, M.; Spazier, J.; Reißland, S.

2014-12-01

Usually, tsunami early warning and mitigation systems (TWS or TEWS) are based on several software components deployed in a client-server based infrastructure. The vast majority of systems importantly include desktop-based clients with a graphical user interface (GUI) for the operators in early warning centers. However, in times of cloud computing and ubiquitous computing the use of concepts and paradigms, introduced by continuously evolving approaches in information and communications technology (ICT), have to be considered even for early warning systems (EWS). Based on the experiences and the knowledge gained in three research projects - 'German Indonesian Tsunami Early Warning System' (GITEWS), 'Distant Early Warning System' (DEWS), and 'Collaborative, Complex, and Critical Decision-Support in Evolving Crises' (TRIDEC) - new technologies are exploited to implement a cloud-based and web-based prototype to open up new prospects for EWS. This prototype, named 'TRIDEC Cloud', merges several complementary external and in-house cloud-based services into one platform for automated background computation with graphics processing units (GPU), for web-mapping of hazard specific geospatial data, and for serving relevant functionality to handle, share, and communicate threat specific information in a collaborative and distributed environment. The prototype in its current version addresses tsunami early warning and mitigation. The integration of GPU accelerated tsunami simulation computations have been an integral part of this prototype to foster early warning with on-demand tsunami predictions based on actual source parameters. However, the platform is meant for researchers around the world to make use of the cloud-based GPU computation to analyze other types of geohazards and natural hazards and react upon the computed situation picture with a web-based GUI in a web browser at remote sites. The current website is an early alpha version for demonstration purposes to give the

GPU-Based Block-Wise Nonlocal Means Denoising for 3D Ultrasound Images

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Liu Li

2013-01-01

Full Text Available Speckle suppression plays an important role in improving ultrasound (US image quality. While lots of algorithms have been proposed for 2D US image denoising with remarkable filtering quality, there is relatively less work done on 3D ultrasound speckle suppression, where the whole volume data rather than just one frame needs to be considered. Then, the most crucial problem with 3D US denoising is that the computational complexity increases tremendously. The nonlocal means (NLM provides an effective method for speckle suppression in US images. In this paper, a programmable graphic-processor-unit- (GPU- based fast NLM filter is proposed for 3D ultrasound speckle reduction. A Gamma distribution noise model, which is able to reliably capture image statistics for Log-compressed ultrasound images, was used for the 3D block-wise NLM filter on basis of Bayesian framework. The most significant aspect of our method was the adopting of powerful data-parallel computing capability of GPU to improve the overall efficiency. Experimental results demonstrate that the proposed method can enormously accelerate the algorithm.

Hypergraph partitioning implementation for parallelizing matrix-vector multiplication using CUDA GPU-based parallel computing

Science.gov (United States)

Murni, Bustamam, A.; Ernastuti, Handhika, T.; Kerami, D.

2017-07-01

Calculation of the matrix-vector multiplication in the real-world problems often involves large matrix with arbitrary size. Therefore, parallelization is needed to speed up the calculation process that usually takes a long time. Graph partitioning techniques that have been discussed in the previous studies cannot be used to complete the parallelized calculation of matrix-vector multiplication with arbitrary size. This is due to the assumption of graph partitioning techniques that can only solve the square and symmetric matrix. Hypergraph partitioning techniques will overcome the shortcomings of the graph partitioning technique. This paper addresses the efficient parallelization of matrix-vector multiplication through hypergraph partitioning techniques using CUDA GPU-based parallel computing. CUDA (compute unified device architecture) is a parallel computing platform and programming model that was created by NVIDIA and implemented by the GPU (graphics processing unit).

SU-E-I-93: Improved Imaging Quality for Multislice Helical CT Via Sparsity Regularized Iterative Image Reconstruction Method Based On Tensor Framelet

International Nuclear Information System (INIS)

Nam, H; Guo, M; Lee, K; Li, R; Xing, L; Gao, H

2014-01-01

Purpose: Inspired by compressive sensing, sparsity regularized iterative reconstruction method has been extensively studied. However, its utility pertinent to multislice helical 4D CT for radiotherapy with respect to imaging quality, dose, and time has not been thoroughly addressed. As the beginning of such an investigation, this work carries out the initial comparison of reconstructed imaging quality between sparsity regularized iterative method and analytic method through static phantom studies using a state-of-art 128-channel multi-slice Siemens helical CT scanner. Methods: In our iterative method, tensor framelet (TF) is chosen as the regularization method for its superior performance from total variation regularization in terms of reduced piecewise-constant artifacts and improved imaging quality that has been demonstrated in our prior work. On the other hand, X-ray transforms and its adjoints are computed on-the-fly through GPU implementation using our previous developed fast parallel algorithms with O(1) complexity per computing thread. For comparison, both FDK (approximate analytic method) and Katsevich algorithm (exact analytic method) are used for multislice helical CT image reconstruction. Results: The phantom experimental data with different imaging doses were acquired using a state-of-art 128-channel multi-slice Siemens helical CT scanner. The reconstructed image quality was compared between TF-based iterative method, FDK and Katsevich algorithm with the quantitative analysis for characterizing signal-to-noise ratio, image contrast, and spatial resolution of high-contrast and low-contrast objects. Conclusion: The experimental results suggest that our tensor framelet regularized iterative reconstruction algorithm improves the helical CT imaging quality from FDK and Katsevich algorithm for static experimental phantom studies that have been performed

Efficient GPU-based texture interpolation using uniform B-splines

NARCIS (Netherlands)

Ruijters, D.; Haar Romenij, ter B.M.; Suetens, P.

2008-01-01

This article presents uniform B-spline interpolation, completely contained on the graphics processing unit (GPU). This implies that the CPU does not need to compute any lookup tables or B-spline basis functions. The cubic interpolation can be decomposed into several linear interpolations [Sigg and

SU-D-207-04: GPU-Based 4D Cone-Beam CT Reconstruction Using Adaptive Meshing Method

International Nuclear Information System (INIS)

Zhong, Z; Gu, X; Iyengar, P; Mao, W; Wang, J; Guo, X

2015-01-01

Purpose: Due to the limited number of projections at each phase, the image quality of a four-dimensional cone-beam CT (4D-CBCT) is often degraded, which decreases the accuracy of subsequent motion modeling. One of the promising methods is the simultaneous motion estimation and image reconstruction (SMEIR) approach. The objective of this work is to enhance the computational speed of the SMEIR algorithm using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step is to generate the tetrahedral mesh based on the features of a reference phase 4D-CBCT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. After the mesh generation, the updated motion model and other phases of 4D-CBCT can be obtained by matching the 4D-CBCT projection images at each phase with the corresponding forward projections of the deformed reference phase of 4D-CBCT. The entire process of this 4D-CBCT reconstruction method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its tremendous parallel computing ability. Results: A 4D XCAT digital phantom was used to test the proposed mesh-based image reconstruction algorithm. The image Result shows both bone structures and inside of the lung are well-preserved and the tumor position can be well captured. Compared to the previous voxel-based CPU implementation of SMEIR, the proposed method is about 157 times faster for reconstructing a 10 -phase 4D-CBCT with dimension 256×256×150. Conclusion: The GPU-based parallel 4D CBCT reconstruction method uses the feature-based mesh for estimating motion model and demonstrates equivalent image Result with previous voxel-based SMEIR approach, with significantly improved computational speed

Massive parallelization of a 3D finite difference electromagnetic forward solution using domain decomposition methods on multiple CUDA enabled GPUs

Science.gov (United States)

Schultz, A.

2010-12-01

describe our ongoing efforts to achieve massive parallelization on a novel hybrid GPU testbed machine currently configured with 12 Intel Westmere Xeon CPU cores (or 24 parallel computational threads) with 96 GB DDR3 system memory, 4 GPU subsystems which in aggregate contain 960 NVidia Tesla GPU cores with 16 GB dedicated DDR3 GPU memory, and a second interleved bank of 4 GPU subsystems containing in aggregate 1792 NVidia Fermi GPU cores with 12 GB dedicated DDR5 GPU memory. We are applying domain decomposition methods to a modified version of Weiss' (2001) 3D frequency domain full physics EM finite difference code, an open source GPL licensed f90 code available for download from www.OpenEM.org. This will be the core of a new hybrid 3D inversion that parallelizes frequencies across CPUs and individual forward solutions across GPUs. We describe progress made in modifying the code to use direct solvers in GPU cores dedicated to each small subdomain, iteratively improving the solution by matching adjacent subdomain boundary solutions, rather than iterative Krylov space sparse solvers as currently applied to the whole domain.

Multi-GPU hybrid programming accelerated three-dimensional phase-field model in binary alloy

Directory of Open Access Journals (Sweden)

Changsheng Zhu

2018-03-01

Full Text Available In the process of dendritic growth simulation, the computational efficiency and the problem scales have extremely important influence on simulation efficiency of three-dimensional phase-field model. Thus, seeking for high performance calculation method to improve the computational efficiency and to expand the problem scales has a great significance to the research of microstructure of the material. A high performance calculation method based on MPI+CUDA hybrid programming model is introduced. Multi-GPU is used to implement quantitative numerical simulations of three-dimensional phase-field model in binary alloy under the condition of multi-physical processes coupling. The acceleration effect of different GPU nodes on different calculation scales is explored. On the foundation of multi-GPU calculation model that has been introduced, two optimization schemes, Non-blocking communication optimization and overlap of MPI and GPU computing optimization, are proposed. The results of two optimization schemes and basic multi-GPU model are compared. The calculation results show that the use of multi-GPU calculation model can improve the computational efficiency of three-dimensional phase-field obviously, which is 13 times to single GPU, and the problem scales have been expanded to 8193. The feasibility of two optimization schemes is shown, and the overlap of MPI and GPU computing optimization has better performance, which is 1.7 times to basic multi-GPU model, when 21 GPUs are used.

Visualization of Astronomical Nebulae via Distributed Multi-GPU Compressed Sensing Tomography.

Science.gov (United States)

Wenger, S; Ament, M; Guthe, S; Lorenz, D; Tillmann, A; Weiskopf, D; Magnor, M

2012-12-01

The 3D visualization of astronomical nebulae is a challenging problem since only a single 2D projection is observable from our fixed vantage point on Earth. We attempt to generate plausible and realistic looking volumetric visualizations via a tomographic approach that exploits the spherical or axial symmetry prevalent in some relevant types of nebulae. Different types of symmetry can be implemented by using different randomized distributions of virtual cameras. Our approach is based on an iterative compressed sensing reconstruction algorithm that we extend with support for position-dependent volumetric regularization and linear equality constraints. We present a distributed multi-GPU implementation that is capable of reconstructing high-resolution datasets from arbitrary projections. Its robustness and scalability are demonstrated for astronomical imagery from the Hubble Space Telescope. The resulting volumetric data is visualized using direct volume rendering. Compared to previous approaches, our method preserves a much higher amount of detail and visual variety in the 3D visualization, especially for objects with only approximate symmetry.

Computational models for electromagnetic transients in ITER vacuum vessel, cryostat and thermal shield

International Nuclear Information System (INIS)

Alekseev, A.; Arslanova, D.; Belov, A.; Belyakov, V.; Gapionok, E.; Gornikel, I.; Gribov, Y.; Ioki, K.; Kukhtin, V.; Lamzin, E.; Sugihara, M.; Sychevsky, S.; Terasawa, A.; Utin, Y.

2013-01-01

A set of detailed computational models are reviewed that covers integrally the system “vacuum vessel (VV), cryostat, and thermal shields (TS)” to study transient electromagnetics (EMs) in the ITER machine. The models have been developed in the course of activities requested and supervised by the ITER Organization. EM analysis is enabled for all ITER operational scenarios. The input data are derived from results of DINA code simulations. The external EM fields are modeled accurate to the input data description. The known magnetic shell approach can be effectively applied to simulate thin-walled structures of the ITER machine. Using an integral–differential formulation, a single unknown is determined within the shells in terms of the vector electric potential taken only at the nodes of a finite-element (FE) mesh of the conducting structures. As a result, the FE mesh encompasses only the system “VV + Cryostat + TS”. The 3D model requires much higher computational resources as compared to a shell model based on the equivalent approximation. The shell models have been developed for all principal conducting structures in the system “VV + Cryostat + TS” including regular ports and neutral beam ports. The structures are described in details in accordance with the latest design. The models have also been applied for simulations of EM transients in components of diagnostic systems and cryopumps and estimation of the 3D effects of the ITER structures on the plasma performance. The developed models have been elaborated and applied for the last 15 years to support the ITER design activities. The finalization of the ITER VV design enables this set of models to be considered ready to use in plasma-physics computations and the development of ITER simulators

Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform

Directory of Open Access Journals (Sweden)

Syed Tahir Hussain Rizvi

2017-10-01

Full Text Available The realization of a deep neural architecture on a mobile platform is challenging, but can open up a number of possibilities for visual analysis applications. A neural network can be realized on a mobile platform by exploiting the computational power of the embedded GPU and simplifying the flow of a neural architecture trained on the desktop workstation or a GPU server. This paper presents an embedded platform-based Italian license plate detection and recognition system using deep neural classifiers. In this work, trained parameters of a highly precise automatic license plate recognition (ALPR system are imported and used to replicate the same neural classifiers on a Nvidia Shield K1 tablet. A CUDA-based framework is used to realize these neural networks. The flow of the trained architecture is simplified to perform the license plate recognition in real-time. Results show that the tasks of plate and character detection and localization can be performed in real-time on a mobile platform by simplifying the flow of the trained architecture. However, the accuracy of the simplified architecture would be decreased accordingly.

Acceleration for 2D time-domain elastic full waveform inversion using a single GPU card

Science.gov (United States)

Jiang, Jinpeng; Zhu, Peimin

2018-05-01

Full waveform inversion (FWI) is a challenging procedure due to the high computational cost related to the modeling, especially for the elastic case. The graphics processing unit (GPU) has become a popular device for the high-performance computing (HPC). To reduce the long computation time, we design and implement the GPU-based 2D elastic FWI (EFWI) in time domain using a single GPU card. We parallelize the forward modeling and gradient calculations using the CUDA programming language. To overcome the limitation of relatively small global memory on GPU, the boundary saving strategy is exploited to reconstruct the forward wavefield. Moreover, the L-BFGS optimization method used in the inversion increases the convergence of the misfit function. A multiscale inversion strategy is performed in the workflow to obtain the accurate inversion results. In our tests, the GPU-based implementations using a single GPU device achieve >15 times speedup in forward modeling, and about 12 times speedup in gradient calculation, compared with the eight-core CPU implementations optimized by OpenMP. The test results from the GPU implementations are verified to have enough accuracy by comparing the results obtained from the CPU implementations.

Travel Software using GPU Hardware

CERN Document Server

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...

Parallel Computer System for 3D Visualization Stereo on GPU

Science.gov (United States)

Al-Oraiqat, Anas M.; Zori, Sergii A.

2018-03-01

This paper proposes the organization of a parallel computer system based on Graphic Processors Unit (GPU) for 3D stereo image synthesis. The development is based on the modified ray tracing method developed by the authors for fast search of tracing rays intersections with scene objects. The system allows significant increase in the productivity for the 3D stereo synthesis of photorealistic quality. The generalized procedure of 3D stereo image synthesis on the Graphics Processing Unit/Graphics Processing Clusters (GPU/GPC) is proposed. The efficiency of the proposed solutions by GPU implementation is compared with single-threaded and multithreaded implementations on the CPU. The achieved average acceleration in multi-thread implementation on the test GPU and CPU is about 7.5 and 1.6 times, respectively. Studying the influence of choosing the size and configuration of the computational Compute Unified Device Archi-tecture (CUDA) network on the computational speed shows the importance of their correct selection. The obtained experimental estimations can be significantly improved by new GPUs with a large number of processing cores and multiprocessors, as well as optimized configuration of the computing CUDA network.

GPU-Accelerated Parallel FDTD on Distributed Heterogeneous Platform

Directory of Open Access Journals (Sweden)

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.

Cobalt: A GPU-based correlator and beamformer for LOFAR

Science.gov (United States)

Broekema, P. Chris; Mol, J. Jan David; Nijboer, R.; van Amesfoort, A. S.; Brentjens, M. A.; Loose, G. Marcel; Klijn, W. F. A.; Romein, J. W.

2018-04-01

For low-frequency radio astronomy, software correlation and beamforming on general purpose hardware is a viable alternative to custom designed hardware. LOFAR, a new-generation radio telescope centered in the Netherlands with international stations in Germany, France, Ireland, Poland, Sweden and the UK, has successfully used software real-time processors based on IBM Blue Gene technology since 2004. Since then, developments in technology have allowed us to build a system based on commercial off-the-shelf components that combines the same capabilities with lower operational cost. In this paper, we describe the design and implementation of a GPU-based correlator and beamformer with the same capabilities as the Blue Gene based systems. We focus on the design approach taken, and show the challenges faced in selecting an appropriate system. The design, implementation and verification of the software system show the value of a modern test-driven development approach. Operational experience, based on three years of operations, demonstrates that a general purpose system is a good alternative to the previous supercomputer-based system or custom-designed hardware.

An overview of the CellML API and its implementation

Directory of Open Access Journals (Sweden)

Halstead Matt

2010-04-01

Full Text Available Abstract Background CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models. However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API, and a good implementation of that API, upon which tools can base their support for CellML. Results We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages. We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance. Conclusions Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions.

Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU.

Science.gov (United States)

Arefan, D; Talebpour, A; Ahmadinejhad, N; Kamali Asl, A

2015-06-01

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).

Ultra-Fast Image Reconstruction of Tomosynthesis Mammography Using GPU

Directory of Open Access Journals (Sweden)

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.

Computing treewidth on the GPU

NARCIS (Netherlands)

van der Zanden, T.C.; Bodlaender, Hans L.

2017-01-01

We present a parallel algorithm for computing the treewidth of a graph on a GPU. We implement this algorithm in OpenCL, and experimentally evaluate its performance. Our algorithm is based on an $O^*(2^{n})$-time algorithm that explores the elimination orderings of the graph using a Held-Karp like

Optimizing a mobile robot control system using GPU acceleration

Science.gov (United States)

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.

Allelism of Genes in the Ml-a locus

DEFF Research Database (Denmark)

Giese, Nanna Henriette; Jensen, Hans Peter; Jørgensen, Jørgen Helms

1980-01-01

Seven barley lines or varieties, each with a different gene at the Ml-a locus for resistance to Erysiphe graminis were intercrossed. Progeny testing of the F2s using two different fungal isolates per cross provided evidence that there are two or more loci in the Ml-a region. Apparent recombinants...... were also screened for recombination between the Hor1 and Hor2 loci which are situated either side of the Ml-a locus. The cross between Ricardo and Iso42R (Rupee) yielded one possible recombinant, with Ml-a3 and Ml-a(Rul) in the coupling phase; other recombinants had wild-type genes in the coupling...... phase. Iso20R, derived from Hordeum spontaneum 'H204', carrying Ml-a6, had an additional gene, in close coupling with Ml-a6, tentatively named Ml-aSp2 or Reglv, causing an intermediate infection type with isolate EmA30. It is suggested that Ml-a(Ar) in Emir and Ml-a(Rul), shown to differ from other Ml...

GPU-accelerated back-projection revisited. Squeezing performance by careful tuning

Energy Technology Data Exchange (ETDEWEB)

Papenhausen, Eric; Zheng, Ziyi; Mueller, Klaus [Stony Brook Univ., NY (United States). Computer Science Dept.

2011-07-01

In recent years, GPUs have become an increasingly popular tool in computed tomography (CT) reconstruction. In this paper, we discuss performance optimization techniques for a GPU-based filtered-backprojection reconstruction implementation. We explore the different optimization techniques we used and explain how those techniques affected performance. Our results show a nearly 50% increase in performance when compared to the current top ranked GPU implementation. (orig.)

GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method

Science.gov (United States)

Kim, Byungyeon; Park, Byungjun; Lee, Seungrag; Won, Youngjae

2016-01-01

We demonstrated GPU accelerated real-time confocal fluorescence lifetime imaging microscopy (FLIM) based on the analog mean-delay (AMD) method. Our algorithm was verified for various fluorescence lifetimes and photon numbers. The GPU processing time was faster than the physical scanning time for images up to 800 × 800, and more than 149 times faster than a single core CPU. The frame rate of our system was demonstrated to be 13 fps for a 200 × 200 pixel image when observing maize vascular tissue. This system can be utilized for observing dynamic biological reactions, medical diagnosis, and real-time industrial inspection. PMID:28018724

TH-A-19A-12: A GPU-Accelerated and Monte Carlo-Based Intensity Modulated Proton Therapy Optimization System

Energy Technology Data Exchange (ETDEWEB)

Ma, J; Wan Chan Tseung, H; Beltran, C [Mayo Clinic, Rochester, MN (United States)

2014-06-15

Purpose: To develop a clinically applicable intensity modulated proton therapy (IMPT) optimization system that utilizes more accurate Monte Carlo (MC) dose calculation, rather than analytical dose calculation. Methods: A very fast in-house graphics processing unit (GPU) based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified gradient based optimization method was used to achieve the desired dose volume histograms (DVH). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve the spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that Result from maintaining the intrinsic CT resolution and large number of proton spots. The dose effects were studied particularly in cases with heterogeneous materials in comparison with the commercial treatment planning system (TPS). Results: For a relatively large and complex three-field bi-lateral head and neck case (i.e. >100K spots with a target volume of ∼1000 cc and multiple surrounding critical structures), the optimization together with the initial MC dose influence map calculation can be done in a clinically viable time frame (i.e. less than 15 minutes) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The DVHs of the MC TPS plan compare favorably with those of a commercial treatment planning system. Conclusion: A GPU accelerated and MC-based IMPT optimization system was developed. The dose calculation and plan optimization can be performed in less than 15 minutes on a hardware system costing less than 45,000 dollars. The fast calculation and optimization makes the system easily expandable to robust and multi-criteria optimization. This work was funded in part by a grant from Varian Medical Systems, Inc.

TH-A-19A-12: A GPU-Accelerated and Monte Carlo-Based Intensity Modulated Proton Therapy Optimization System

International Nuclear Information System (INIS)

Ma, J; Wan Chan Tseung, H; Beltran, C

2014-01-01

Purpose: To develop a clinically applicable intensity modulated proton therapy (IMPT) optimization system that utilizes more accurate Monte Carlo (MC) dose calculation, rather than analytical dose calculation. Methods: A very fast in-house graphics processing unit (GPU) based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified gradient based optimization method was used to achieve the desired dose volume histograms (DVH). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve the spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that Result from maintaining the intrinsic CT resolution and large number of proton spots. The dose effects were studied particularly in cases with heterogeneous materials in comparison with the commercial treatment planning system (TPS). Results: For a relatively large and complex three-field bi-lateral head and neck case (i.e. >100K spots with a target volume of ∼1000 cc and multiple surrounding critical structures), the optimization together with the initial MC dose influence map calculation can be done in a clinically viable time frame (i.e. less than 15 minutes) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The DVHs of the MC TPS plan compare favorably with those of a commercial treatment planning system. Conclusion: A GPU accelerated and MC-based IMPT optimization system was developed. The dose calculation and plan optimization can be performed in less than 15 minutes on a hardware system costing less than 45,000 dollars. The fast calculation and optimization makes the system easily expandable to robust and multi-criteria optimization. This work was funded in part by a grant from Varian Medical Systems, Inc

GPU-Based FFT Computation for Multi-Gigabit WirelessHD Baseband Processing

Directory of Open Access Journals (Sweden)

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.

Computing treewidth on the GPU

NARCIS (Netherlands)

Van Der Zanden, Tom C.; Bodlaender, Hans L.

2018-01-01

We present a parallel algorithm for computing the treewidth of a graph on a GPU. We implement this algorithm in OpenCL, and experimentally evaluate its performance. Our algorithm is based on an O∗(2n)-time algorithm that explores the elimination orderings of the graph using a Held-Karp like dynamic

QuakeML: Status of the XML-based Seismological Data Exchange Format

Science.gov (United States)

Euchner, Fabian; Schorlemmer, Danijel; Kästli, Philipp; Quakeml Working Group

2010-05-01

QuakeML is an XML-based data exchange standard for seismology that is in its fourth year of active community-driven development. The current release (version 1.2) is based on a public Request for Comments process that included contributions from ETH, GFZ, USC, SCEC, USGS, IRIS DMC, EMSC, ORFEUS, GNS, ZAMG, BRGM, Nanometrics, and ISTI. QuakeML has mainly been funded through the EC FP6 infrastructure project NERIES, in which it was endorsed as the preferred data exchange format. Currently, QuakeML services are being installed at several institutions around the globe, including EMSC, ORFEUS, ETH, Geoazur (Europe), NEIC, ANSS, SCEC/SCSN (USA), and GNS Science (New Zealand). Some of these institutions already provide QuakeML earthquake catalog web services. Several implementations of the QuakeML data model have been made. QuakePy, an open-source Python-based seismicity analysis toolkit using the QuakeML data model, is being developed at ETH. QuakePy is part of the software stack used in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing center installations, developed by SCEC. Furthermore, the QuakeML data model is part of the SeisComP3 package from GFZ Potsdam. QuakeML is designed as an umbrella schema under which several sub-packages are collected. The present scope of QuakeML 1.2 covers a basic description of seismic events including picks, arrivals, amplitudes, magnitudes, origins, focal mechanisms, and moment tensors. Work on additional packages (macroseismic information, seismic inventory, and resource metadata) has been started, but is at an early stage. Contributions from the community that help to widen the thematic coverage of QuakeML are highly welcome. Online resources: http://www.quakeml.org, http://www.quakepy.org

GPU-Based Techniques for Global Illumination Effects

CERN Document Server

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

High performance cellular level agent-based simulation with FLAME for the GPU.

Science.gov (United States)

Richmond, Paul; Walker, Dawn; Coakley, Simon; Romano, Daniela

2010-05-01

Driven by the availability of experimental data and ability to simulate a biological scale which is of immediate interest, the cellular scale is fast emerging as an ideal candidate for middle-out modelling. As with 'bottom-up' simulation approaches, cellular level simulations demand a high degree of computational power, which in large-scale simulations can only be achieved through parallel computing. The flexible large-scale agent modelling environment (FLAME) is a template driven framework for agent-based modelling (ABM) on parallel architectures ideally suited to the simulation of cellular systems. It is available for both high performance computing clusters (www.flame.ac.uk) and GPU hardware (www.flamegpu.com) and uses a formal specification technique that acts as a universal modelling format. This not only creates an abstraction from the underlying hardware architectures, but avoids the steep learning curve associated with programming them. In benchmarking tests and simulations of advanced cellular systems, FLAME GPU has reported massive improvement in performance over more traditional ABM frameworks. This allows the time spent in the development and testing stages of modelling to be drastically reduced and creates the possibility of real-time visualisation for simple visual face-validation.

SU-E-T-37: A GPU-Based Pencil Beam Algorithm for Dose Calculations in Proton Radiation Therapy

International Nuclear Information System (INIS)

Kalantzis, G; Leventouri, T; Tachibana, H; Shang, C

2015-01-01

Purpose: Recent developments in radiation therapy have been focused on applications of charged particles, especially protons. Over the years several dose calculation methods have been proposed in proton therapy. A common characteristic of all these methods is their extensive computational burden. In the current study we present for the first time, to our best knowledge, a GPU-based PBA for proton dose calculations in Matlab. Methods: In the current study we employed an analytical expression for the protons depth dose distribution. The central-axis term is taken from the broad-beam central-axis depth dose in water modified by an inverse square correction while the distribution of the off-axis term was considered Gaussian. The serial code was implemented in MATLAB and was launched on a desktop with a quad core Intel Xeon X5550 at 2.67GHz with 8 GB of RAM. For the parallelization on the GPU, the parallel computing toolbox was employed and the code was launched on a GTX 770 with Kepler architecture. The performance comparison was established on the speedup factors. Results: The performance of the GPU code was evaluated for three different energies: low (50 MeV), medium (100 MeV) and high (150 MeV). Four square fields were selected for each energy, and the dose calculations were performed with both the serial and parallel codes for a homogeneous water phantom with size 300×300×300 mm3. The resolution of the PBs was set to 1.0 mm. The maximum speedup of ∼127 was achieved for the highest energy and the largest field size. Conclusion: A GPU-based PB algorithm for proton dose calculations in Matlab was presented. A maximum speedup of ∼127 was achieved. Future directions of the current work include extension of our method for dose calculation in heterogeneous phantoms

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations.

Science.gov (United States)

Hallock, Michael J; Stone, John E; Roberts, Elijah; Fry, Corey; Luthey-Schulten, Zaida

2014-05-01

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli . Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.

A GPU-based mipmapping method for water surface visualization

Science.gov (United States)

Li, Hua; Quan, Wei; Xu, Chao; Wu, Yan

2018-03-01

Visualization of water surface is a hot topic in computer graphics. In this paper, we presented a fast method to generate wide range of water surface with good image quality both near and far from the viewpoint. This method utilized uniform mesh and Fractal Perlin noise to model water surface. Mipmapping technology was enforced to the surface textures, which adjust the resolution with respect to the distance from the viewpoint and reduce the computing cost. Lighting effect was computed based on shadow mapping technology, Snell's law and Fresnel term. The render pipeline utilizes a CPU-GPU shared memory structure, which improves the rendering efficiency. Experiment results show that our approach visualizes water surface with good image quality at real-time frame rates performance.

Projector and backprojector for iterative CT reconstruction with blobs using CUDA

Energy Technology Data Exchange (ETDEWEB)

Bippus, Rolf-Dieter; Koehler, Thomas; Bergner, Frank; Brendel, Bernhard; Proksa, Roland [Philips Research Laboratories, Hamburg (Germany); Hansis, Eberhard [Philips Healthcare, Nuclear Medicine, San Jose, CA (United States)

2011-07-01

Using blobs allows modeling the CT system's geometry more correctly within an iterative reconstruction framework. However their application comes with an increased computational demand. This led us to use blobs for image representation and a dedicated GPU hardware implementation to counteract their computational demand. Making extensive use of the texture interpolation capabilities of CUDA and implementing an asymmetric projector/backprojector pair we achieve reasonable processing times and good system modeling at the same time. (orig.)

Implementation and optimization of ultrasound signal processing algorithms on mobile GPU

Science.gov (United States)

Kong, Woo Kyu; Lee, Wooyoul; Kim, Kyu Cheol; Yoo, Yangmo; Song, Tai-Kyong

2014-03-01

A general-purpose graphics processing unit (GPGPU) has been used for improving computing power in medical ultrasound imaging systems. Recently, a mobile GPU becomes powerful to deal with 3D games and videos at high frame rates on Full HD or HD resolution displays. This paper proposes the method to implement ultrasound signal processing on a mobile GPU available in the high-end smartphone (Galaxy S4, Samsung Electronics, Seoul, Korea) with programmable shaders on the OpenGL ES 2.0 platform. To maximize the performance of the mobile GPU, the optimization of shader design and load sharing between vertex and fragment shader was performed. The beamformed data were captured from a tissue mimicking phantom (Model 539 Multipurpose Phantom, ATS Laboratories, Inc., Bridgeport, CT, USA) by using a commercial ultrasound imaging system equipped with a research package (Ultrasonix Touch, Ultrasonix, Richmond, BC, Canada). The real-time performance is evaluated by frame rates while varying the range of signal processing blocks. The implementation method of ultrasound signal processing on OpenGL ES 2.0 was verified by analyzing PSNR with MATLAB gold standard that has the same signal path. CNR was also analyzed to verify the method. From the evaluations, the proposed mobile GPU-based processing method has no significant difference with the processing using MATLAB (i.e., PSNRe., 11.31). From the mobile GPU implementation, the frame rates of 57.6 Hz were achieved. The total execution time was 17.4 ms that was faster than the acquisition time (i.e., 34.4 ms). These results indicate that the mobile GPU-based processing method can support real-time ultrasound B-mode processing on the smartphone.

Graphics processing unit (GPU) real-time infrared scene generation

Science.gov (United States)

Christie, Chad L.; Gouthas, Efthimios (Themie); Williams, Owen M.

2007-04-01

VIRSuite, the GPU-based suite of software tools developed at DSTO for real-time infrared scene generation, is described. The tools include the painting of scene objects with radiometrically-associated colours, translucent object generation, polar plot validation and versatile scene generation. Special features include radiometric scaling within the GPU and the presence of zoom anti-aliasing at the core of VIRSuite. Extension of the zoom anti-aliasing construct to cover target embedding and the treatment of translucent objects is described.

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

Science.gov (United States)

Wen, Tiexiang; Li, Ling; Zhu, Qingsong; Qin, Wenjian; Gu, Jia; Yang, Feng; Xie, Yaoqin

2017-07-01

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor-based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Electromagnetic Computation and Visualization of Transmission Particle Model and Its Simulation Based on GPU

Directory of Open Access Journals (Sweden)

Yingnian Wu

2014-01-01

Full Text Available Electromagnetic calculation plays an important role in both military and civic fields. Some methods and models proposed for calculation of electromagnetic wave propagation in a large range bring heavy burden in CPU computation and also require huge amount of memory. Using the GPU to accelerate computation and visualization can reduce the computational burden on the CPU. Based on forward ray-tracing method, a transmission particle model (TPM for calculating electromagnetic field is presented to combine the particle method. The movement of a particle obeys the principle of the propagation of electromagnetic wave, and then the particle distribution density in space reflects the electromagnetic distribution status. The algorithm with particle transmission, movement, reflection, and diffraction is described in detail. Since the particles in TPM are completely independent, it is very suitable for the parallel computing based on GPU. Deduction verification of TPM with the electric dipole antenna as the transmission source is conducted to prove that the particle movement itself represents the variation of electromagnetic field intensity caused by diffusion. Finally, the simulation comparisons are made against the forward and backward ray-tracing methods. The simulation results verified the effectiveness of the proposed method.

State-of-the-Art in GPU-Based Large-Scale Volume Visualization

KAUST Repository

Beyer, Johanna

2015-05-01

This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera- and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. \\'output-sensitive\\' algorithms and system designs. This leads to recent output-sensitive approaches that are \\'ray-guided\\', \\'visualization-driven\\' or \\'display-aware\\'. In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks-the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context-the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

State-of-the-Art in GPU-Based Large-Scale Volume Visualization

KAUST Repository

Beyer, Johanna; Hadwiger, Markus; Pfister, Hanspeter

2015-01-01

This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera- and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e. 'output-sensitive' algorithms and system designs. This leads to recent output-sensitive approaches that are 'ray-guided', 'visualization-driven' or 'display-aware'. In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks-the current subset of data that is minimally required to produce an output image of the desired display resolution. Furthermore, we discuss the differences and similarities of different rendering and data traversal strategies in volume rendering by putting them into a common context-the notion of address translation. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we present in this survey. © 2015 The Eurographics Association and John Wiley & Sons Ltd.

A Hybrid CPU/GPU Pattern-Matching Algorithm for Deep Packet Inspection.

Directory of Open Access Journals (Sweden)

Chun-Liang Lee

Full Text Available The large quantities of data now being transferred via high-speed networks have made deep packet inspection indispensable for security purposes. Scalable and low-cost signature-based network intrusion detection systems have been developed for deep packet inspection for various software platforms. Traditional approaches that only involve central processing units (CPUs are now considered inadequate in terms of inspection speed. Graphic processing units (GPUs have superior parallel processing power, but transmission bottlenecks can reduce optimal GPU efficiency. In this paper we describe our proposal for a hybrid CPU/GPU pattern-matching algorithm (HPMA that divides and distributes the packet-inspecting workload between a CPU and GPU. All packets are initially inspected by the CPU and filtered using a simple pre-filtering algorithm, and packets that might contain malicious content are sent to the GPU for further inspection. Test results indicate that in terms of random payload traffic, the matching speed of our proposed algorithm was 3.4 times and 2.7 times faster than those of the AC-CPU and AC-GPU algorithms, respectively. Further, HPMA achieved higher energy efficiency than the other tested algorithms.

Fast Simulation of Dynamic Ultrasound Images Using the GPU.

Science.gov (United States)

Storve, Sigurd; Torp, Hans

2017-10-01

Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development.

A multi-GPU implementation of a D2Q37 lattice Boltzmann code

NARCIS (Netherlands)

Biferale, L.; Mantovani, F.; Pivanti, M.; Pozzati, F.; Sbragaglia, M.; Scagliarini, Andrea; Schifano, S.F.; Toschi, F.; Tripiccione, R.; Wyrzykowski, R.; Dongarra, J.; Karczewski, K.; Wasniewski, J.

2012-01-01

We describe a parallel implementation of a compressible Lattice Boltzmann code on a multi-GPU cluster based on Nvidia Fermi processors. We analyze how to optimize the algorithm for GP-GPU architectures, describe the implementation choices that we have adopted and compare our performance results with

Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection

Directory of Open Access Journals (Sweden)

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.

Accelerating image reconstruction in dual-head PET system by GPU and symmetry properties.

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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.

Fast 3D elastic micro-seismic source location using new GPU features

Science.gov (United States)

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.

GPU Accelerated Chemical Similarity Calculation for Compound Library Comparison

Science.gov (United States)

Ma, Chao; Wang, Lirong; Xie, Xiang-Qun

2012-01-01

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 multi-core 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 minutes to complete the calculation of Tanimoto coefficients between 32M PubChem compounds and 10K Active Probes compounds, i.e., 324G Tanimoto coefficients, on a 128-CUDA-core GPU. PMID:21692447

THEWASP library. Thermodynamic water and steam properties library in GPU

International Nuclear Information System (INIS)

Waintraub, M.; Lapa, C.M.F.; Mol, A.C.A.; Heimlich, A.

2011-01-01

In this paper we present a new library for thermodynamic evaluation of water properties, THEWASP. This library consists of a C++ and CUDA based programs used to accelerate a function evaluation using GPU and GPU clusters. Global optimization problems need thousands of evaluations of the objective functions to nd the global optimum implying in several days of expensive processing. This problem motivates to seek a way to speed up our code, as well as to use MPI on Beowulf clusters, which however increases the cost in terms of electricity, air conditioning and others. The GPU based programming can accelerate the implementation up to 100 times and help increase the number of evaluations in global optimization problems using, for example, the PSO or DE Algorithms. THEWASP is based on Water-Steam formulations publish by the International Association for the properties of water and steam, Lucerne - Switzerland, and provides several temperature and pressure function evaluations, such as specific heat, specific enthalpy, specific entropy and also some inverse maps. In this study we evaluated the gain in speed and performance and compared it a CPU based processing library. (author)

QuakeML: status of the XML-based seismological data exchange format

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Joachim Saul

2011-04-01

Full Text Available QuakeML is an XML-based data exchange standard for seismology that is in its fourth year of active community-driven development. Its development was motivated by the need to consolidate existing data formats for applications in statistical seismology, as well as setting a cutting-edge, community-agreed standard to foster interoperability of distributed infrastructures. The current release (version 1.2 is based on a public Request for Comments process and accounts for suggestions and comments provided by a broad international user community. QuakeML is designed as an umbrella schema under which several sub-packages are collected. The present scope of QuakeML 1.2 covers a basic description of seismic events including picks, arrivals, amplitudes, magnitudes, origins, focal mechanisms, and moment tensors. Work on additional packages (macroseismic information, ground motion, seismic inventory, and resource metadata has been started, but is at an early stage. Several applications based on the QuakeML data model have been created so far. Among these are earthquake catalog web services at the European Mediterranean Seismological Centre (EMSC, GNS Science, and the Southern California Earthquake Data Center (SCEDC, and QuakePy, an open-source Python-based seismicity analysis toolkit. Furthermore, QuakeML is being used in the SeisComP3 system from GFZ Potsdam, and in the Collaboratory for the Study of Earthquake Predictability (CSEP testing center installations, developed by Southern California Earthquake Center (SCEC. QuakeML is still under active and dynamic development. Further contributions from the community are crucial to its success and are highly welcome.

GPU: the biggest key processor for AI and parallel processing

Science.gov (United States)

Baji, Toru

2017-07-01

Two types of processors exist in the market. One is the conventional CPU and the other is Graphic Processor Unit (GPU). Typical CPU is composed of 1 to 8 cores while GPU has thousands of cores. CPU is good for sequential processing, while GPU is good to accelerate software with heavy parallel executions. GPU was initially dedicated for 3D graphics. However from 2006, when GPU started to apply general-purpose cores, it was noticed that this architecture can be used as a general purpose massive-parallel processor. NVIDIA developed a software framework Compute Unified Device Architecture (CUDA) that make it possible to easily program the GPU for these application. With CUDA, GPU started to be used in workstations and supercomputers widely. Recently two key technologies are highlighted in the industry. The Artificial Intelligence (AI) and Autonomous Driving Cars. AI requires a massive parallel operation to train many-layers of neural networks. With CPU alone, it was impossible to finish the training in a practical time. The latest multi-GPU system with P100 makes it possible to finish the training in a few hours. For the autonomous driving cars, TOPS class of performance is required to implement perception, localization, path planning processing and again SoC with integrated GPU will play a key role there. In this paper, the evolution of the GPU which is one of the biggest commercial devices requiring state-of-the-art fabrication technology will be introduced. Also overview of the GPU demanding key application like the ones described above will be introduced.

GPU Implementation of High Rayleigh Number Three-Dimensional Mantle Convection

Science.gov (United States)

Sanchez, D. A.; Yuen, D. A.; Wright, G. B.; Barnett, G. A.

2010-12-01

Although we have entered the age of petascale computing, many factors are still prohibiting high-performance computing (HPC) from infiltrating all suitable scientific disciplines. For this reason and others, application of GPU to HPC is gaining traction in the scientific world. With its low price point, high performance potential, and competitive scalability, GPU has been an option well worth considering for the last few years. Moreover with the advent of NVIDIA's Fermi architecture, which brings ECC memory, better double-precision performance, and more RAM to GPU, there is a strong message of corporate support for GPU in HPC. However many doubts linger concerning the practicality of using GPU for scientific computing. In particular, GPU has a reputation for being difficult to program and suitable for only a small subset of problems. Although inroads have been made in addressing these concerns, for many scientists GPU still has hurdles to clear before becoming an acceptable choice. We explore the applicability of GPU to geophysics by implementing a three-dimensional, second-order finite-difference model of Rayleigh-Benard thermal convection on an NVIDIA GPU using C for CUDA. Our code reaches sufficient resolution, on the order of 500x500x250 evenly-spaced finite-difference gridpoints, on a single GPU. We make extensive use of highly optimized CUBLAS routines, allowing us to achieve performance on the order of O( 0.1 ) µs per timestep*gridpoint at this resolution. This performance has allowed us to study high Rayleigh number simulations, on the order of 2x10^7, on a single GPU.

GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.

Science.gov (United States)

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

Visualizing whole-brain DTI tractography with GPU-based Tuboids and LoD management.

Science.gov (United States)

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.

ACL2 Meets the GPU: Formalizing a CUDA-based Parallelizable All-Pairs Shortest Path Algorithm in ACL2

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David S. Hardin

2013-04-01

Full Text Available As Graphics Processing Units (GPUs have gained in capability and GPU development environments have matured, developers are increasingly turning to the GPU to off-load the main host CPU of numerically-intensive, parallelizable computations. Modern GPUs feature hundreds of cores, and offer programming niceties such as double-precision floating point, and even limited recursion. This shift from CPU to GPU, however, raises the question: how do we know that these new GPU-based algorithms are correct? In order to explore this new verification frontier, we formalized a parallelizable all-pairs shortest path (APSP algorithm for weighted graphs, originally coded in NVIDIA's CUDA language, in ACL2. The ACL2 specification is written using a single-threaded object (stobj and tail recursion, as the stobj/tail recursion combination yields the most straightforward translation from imperative programming languages, as well as efficient, scalable executable specifications within ACL2 itself. The ACL2 version of the APSP algorithm can process millions of vertices and edges with little to no garbage generation, and executes at one-sixth the speed of a host-based version of APSP coded in C – a very respectable result for a theorem prover. In addition to formalizing the APSP algorithm (which uses Dijkstra's shortest path algorithm at its core, we have also provided capability that the original APSP code lacked, namely shortest path recovery. Path recovery is accomplished using a secondary ACL2 stobj implementing a LIFO stack, which is proven correct. To conclude the experiment, we ported the ACL2 version of the APSP kernels back to C, resulting in a less than 5% slowdown, and also performed a partial back-port to CUDA, which, surprisingly, yielded a slight performance increase.

TU-AB-202-05: GPU-Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling

International Nuclear Information System (INIS)

Zhong, Z; Zhuang, L; Gu, X; Wang, J; Chen, H; Zhen, X

2016-01-01

Purpose: Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real-time and high-quality image segmentation, especially in a large datasets of 4D-CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step is to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D-CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D-CT can be obtained by matching each phase of the target 4D-CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability. Results: A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel-based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10-phase 4D-CT with a phase dimension of 256×256×150. Conclusion: The proposed 4D DIR method uses feature-based mesh and GPU-based parallelism, which demonstrates the capability to compute both high-quality image and motion results, with significant improvement on the computational speed.

TU-AB-202-05: GPU-Based 4D Deformable Image Registration Using Adaptive Tetrahedral Mesh Modeling

Energy Technology Data Exchange (ETDEWEB)

Zhong, Z; Zhuang, L [Wayne State University, Detroit, MI (United States); Gu, X; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States); Chen, H; Zhen, X [Southern Medical University, Guangzhou, Guangdong (China)

2016-06-15

Purpose: Deformable image registration (DIR) has been employed today as an automated and effective segmentation method to transfer tumor or organ contours from the planning image to daily images, instead of manual segmentation. However, the computational time and accuracy of current DIR approaches are still insufficient for online adaptive radiation therapy (ART), which requires real-time and high-quality image segmentation, especially in a large datasets of 4D-CT images. The objective of this work is to propose a new DIR algorithm, with fast computational speed and high accuracy, by using adaptive feature-based tetrahedral meshing and GPU-based parallelization. Methods: The first step is to generate the adaptive tetrahedral mesh based on the image features of a reference phase of 4D-CT, so that the deformation can be well captured and accurately diffused from the mesh vertices to voxels of the image volume. Subsequently, the deformation vector fields (DVF) and other phases of 4D-CT can be obtained by matching each phase of the target 4D-CT images with the corresponding deformed reference phase. The proposed 4D DIR method is implemented on GPU, resulting in significantly increasing the computational efficiency due to its parallel computing ability. Results: A 4D NCAT digital phantom was used to test the efficiency and accuracy of our method. Both the image and DVF results show that the fine structures and shapes of lung are well preserved, and the tumor position is well captured, i.e., 3D distance error is 1.14 mm. Compared to the previous voxel-based CPU implementation of DIR, such as demons, the proposed method is about 160x faster for registering a 10-phase 4D-CT with a phase dimension of 256×256×150. Conclusion: The proposed 4D DIR method uses feature-based mesh and GPU-based parallelism, which demonstrates the capability to compute both high-quality image and motion results, with significant improvement on the computational speed.

Ramses-GPU: Second order MUSCL-Handcock finite volume fluid solver

Science.gov (United States)

Kestener, Pierre

2017-10-01

RamsesGPU is a reimplementation of RAMSES (ascl:1011.007) which drops the adaptive mesh refinement (AMR) features to optimize 3D uniform grid algorithms for modern graphics processor units (GPU) to provide an efficient software package for astrophysics applications that do not need AMR features but do require a very large number of integration time steps. RamsesGPU provides an very efficient C++/CUDA/MPI software implementation of a second order MUSCL-Handcock finite volume fluid solver for compressible hydrodynamics as a magnetohydrodynamics solver based on the constraint transport technique. Other useful modules includes static gravity, dissipative terms (viscosity, resistivity), and forcing source term for turbulence studies, and special care was taken to enhance parallel input/output performance by using state-of-the-art libraries such as HDF5 and parallel-netcdf.

A GPU-paralleled implementation of an enhanced face recognition algorithm

Science.gov (United States)

Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo

2013-03-01

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

GPU applications for data processing

Energy Technology Data Exchange (ETDEWEB)

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.

GRay: A MASSIVELY PARALLEL GPU-BASED CODE FOR RAY TRACING IN RELATIVISTIC SPACETIMES

Energy Technology Data Exchange (ETDEWEB)

Chan, Chi-kwan; Psaltis, Dimitrios; Özel, Feryal [Department of Astronomy, University of Arizona, 933 N. Cherry Ave., Tucson, AZ 85721 (United States)

2013-11-01

We introduce GRay, a massively parallel integrator designed to trace the trajectories of billions of photons in a curved spacetime. This graphics-processing-unit (GPU)-based integrator employs the stream processing paradigm, is implemented in CUDA C/C++, and runs on nVidia graphics cards. The peak performance of GRay using single-precision floating-point arithmetic on a single GPU exceeds 300 GFLOP (or 1 ns per photon per time step). For a realistic problem, where the peak performance cannot be reached, GRay is two orders of magnitude faster than existing central-processing-unit-based ray-tracing codes. This performance enhancement allows more effective searches of large parameter spaces when comparing theoretical predictions of images, spectra, and light curves from the vicinities of compact objects to observations. GRay can also perform on-the-fly ray tracing within general relativistic magnetohydrodynamic algorithms that simulate accretion flows around compact objects. Making use of this algorithm, we calculate the properties of the shadows of Kerr black holes and the photon rings that surround them. We also provide accurate fitting formulae of their dependencies on black hole spin and observer inclination, which can be used to interpret upcoming observations of the black holes at the center of the Milky Way, as well as M87, with the Event Horizon Telescope.

NLSEmagic: Nonlinear Schrödinger equation multi-dimensional Matlab-based GPU-accelerated integrators using compact high-order schemes

Science.gov (United States)

Caplan, R. M.

2013-04-01

We present a simple to use, yet powerful code package called NLSEmagic to numerically integrate the nonlinear Schrödinger equation in one, two, and three dimensions. NLSEmagic is a high-order finite-difference code package which utilizes graphic processing unit (GPU) parallel architectures. The codes running on the GPU are many times faster than their serial counterparts, and are much cheaper to run than on standard parallel clusters. The codes are developed with usability and portability in mind, and therefore are written to interface with MATLAB utilizing custom GPU-enabled C codes with the MEX-compiler interface. The packages are freely distributed, including user manuals and set-up files. Catalogue identifier: AEOJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOJ_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.: 124453 No. of bytes in distributed program, including test data, etc.: 4728604 Distribution format: tar.gz Programming language: C, CUDA, MATLAB. Computer: PC, MAC. Operating system: Windows, MacOS, Linux. Has the code been vectorized or parallelized?: Yes. Number of processors used: Single CPU, number of GPU processors dependent on chosen GPU card (max is currently 3072 cores on GeForce GTX 690). Supplementary material: Setup guide, Installation guide. RAM: Highly dependent on dimensionality and grid size. For typical medium-large problem size in three dimensions, 4GB is sufficient. Keywords: Nonlinear Schröodinger Equation, GPU, high-order finite difference, Bose-Einstien condensates. Classification: 4.3, 7.7. Nature of problem: Integrate solutions of the time-dependent one-, two-, and three-dimensional cubic nonlinear Schrödinger equation. Solution method: The integrators utilize a fully-explicit fourth-order Runge-Kutta scheme in time

Fast plane wave density functional theory molecular dynamics calculations on multi-GPU machines

International Nuclear Information System (INIS)

Jia, Weile; Fu, Jiyun; Cao, Zongyan; Wang, Long; Chi, Xuebin; Gao, Weiguo; Wang, Lin-Wang

2013-01-01

Plane wave pseudopotential (PWP) density functional theory (DFT) calculation is the most widely used method for material simulations, but its absolute speed stagnated due to the inability to use large scale CPU based computers. By a drastic redesign of the algorithm, and moving all the major computation parts into GPU, we have reached a speed of 12 s per molecular dynamics (MD) step for a 512 atom system using 256 GPU cards. This is about 20 times faster than the CPU version of the code regardless of the number of CPU cores used. Our tests and analysis on different GPU platforms and configurations shed lights on the optimal GPU deployments for PWP-DFT calculations. An 1800 step MD simulation is used to study the liquid phase properties of GaInP

A survey and measurement study of GPU DVFS on energy conservation

Directory of Open Access Journals (Sweden)

Xinxin Mei

2017-05-01

Full Text Available Energy efficiency has become one of the top design criteria for current computing systems. The dynamic voltage and frequency scaling (DVFS has been widely adopted by laptop computers, servers, and mobile devices to conserve energy, while the GPU DVFS is still at a certain early age. This paper aims at exploring the impact of GPU DVFS on the application performance and power consumption, and furthermore, on energy conservation. We survey the state-of-the-art GPU DVFS characterizations, and then summarize recent research works on GPU power and performance models. We also conduct real GPU DVFS experiments on NVIDIA Fermi and Maxwell GPUs. According to our experimental results, GPU DVFS has significant potential for energy saving. The effect of scaling core voltage/frequency and memory voltage/frequency depends on not only the GPU architectures, but also the characteristic of GPU applications.

Low Complexity Tree Searching-Based Iterative Precoding Techniques for Multiuser MIMO Broadcast Channel

Science.gov (United States)

Cha, Jongsub; Park, Kyungho; Kang, Joonhyuk; Park, Hyuncheol

In this letter, we propose two computationally efficient precoding algorithms that achieve near-ML performance for multiuser MIMO downlink. The proposed algorithms perform tree expansion after lattice reduction. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find an optimal sequence, they iteratively visit each node and terminate the expansion by comparing node metrics with the calculated reference metric. By doing this, they significantly reduce the number of undesirable node visit. Monte-Carlo simulations show that both proposed algorithms yield near-ML performance with considerable reduction in complexity compared with that of the conventional schemes such as sphere encoding.

ODYSSEY: A PUBLIC GPU-BASED CODE FOR GENERAL RELATIVISTIC RADIATIVE TRANSFER IN KERR SPACETIME

Energy Technology Data Exchange (ETDEWEB)

Pu, Hung-Yi [Institute of Astronomy and Astrophysics, Academia Sinica, 11F of Astronomy-Mathematics Building, AS/NTU No. 1, Taipei 10617, Taiwan (China); Yun, Kiyun; Yoon, Suk-Jin [Department of Astronomy and Center for Galaxy Evolution Research, Yonsei University, Seoul 120-749 (Korea, Republic of); Younsi, Ziri [Institut für Theoretische Physik, Max-von-Laue-Straße 1, D-60438 Frankfurt am Main (Germany)

2016-04-01

General relativistic radiative transfer calculations coupled with the calculation of geodesics in the Kerr spacetime are an essential tool for determining the images, spectra, and light curves from matter in the vicinity of black holes. Such studies are especially important for ongoing and upcoming millimeter/submillimeter very long baseline interferometry observations of the supermassive black holes at the centers of Sgr A* and M87. To this end we introduce Odyssey, a graphics processing unit (GPU) based code for ray tracing and radiative transfer in the Kerr spacetime. On a single GPU, the performance of Odyssey can exceed 1 ns per photon, per Runge–Kutta integration step. Odyssey is publicly available, fast, accurate, and flexible enough to be modified to suit the specific needs of new users. Along with a Graphical User Interface powered by a video-accelerated display architecture, we also present an educational software tool, Odyssey-Edu, for showing in real time how null geodesics around a Kerr black hole vary as a function of black hole spin and angle of incidence onto the black hole.

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

Science.gov (United States)

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

GPU in Physics Computation: Case Geant4 Navigation

CERN Document Server

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 Linear algebra extensions for GNU/Octave

International Nuclear Information System (INIS)

Bosi, L B; Mariotti, M; Santocchia, A

2012-01-01

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.

Advantages of GPU technology in DFT calculations of intercalated graphene

Science.gov (United States)

Pešić, J.; Gajić, R.

2014-09-01

Over the past few years, the expansion of general-purpose graphic-processing unit (GPGPU) technology has had a great impact on computational science. GPGPU is the utilization of a graphics-processing unit (GPU) to perform calculations in applications usually handled by the central processing unit (CPU). Use of GPGPUs as a way to increase computational power in the material sciences has significantly decreased computational costs in already highly demanding calculations. A level of the acceleration and parallelization depends on the problem itself. Some problems can benefit from GPU acceleration and parallelization, such as the finite-difference time-domain algorithm (FTDT) and density-functional theory (DFT), while others cannot take advantage of these modern technologies. A number of GPU-supported applications had emerged in the past several years (www.nvidia.com/object/gpu-applications.html). Quantum Espresso (QE) is reported as an integrated suite of open source computer codes for electronic-structure calculations and materials modeling at the nano-scale. It is based on DFT, the use of a plane-waves basis and a pseudopotential approach. Since the QE 5.0 version, it has been implemented as a plug-in component for standard QE packages that allows exploiting the capabilities of Nvidia GPU graphic cards (www.qe-forge.org/gf/proj). In this study, we have examined the impact of the usage of GPU acceleration and parallelization on the numerical performance of DFT calculations. Graphene has been attracting attention worldwide and has already shown some remarkable properties. We have studied an intercalated graphene, using the QE package PHonon, which employs GPU. The term ‘intercalation’ refers to a process whereby foreign adatoms are inserted onto a graphene lattice. In addition, by intercalating different atoms between graphene layers, it is possible to tune their physical properties. Our experiments have shown there are benefits from using GPUs, and we reached an

Advantages of GPU technology in DFT calculations of intercalated graphene

International Nuclear Information System (INIS)

Pešić, J; Gajić, R

2014-01-01

Over the past few years, the expansion of general-purpose graphic-processing unit (GPGPU) technology has had a great impact on computational science. GPGPU is the utilization of a graphics-processing unit (GPU) to perform calculations in applications usually handled by the central processing unit (CPU). Use of GPGPUs as a way to increase computational power in the material sciences has significantly decreased computational costs in already highly demanding calculations. A level of the acceleration and parallelization depends on the problem itself. Some problems can benefit from GPU acceleration and parallelization, such as the finite-difference time-domain algorithm (FTDT) and density-functional theory (DFT), while others cannot take advantage of these modern technologies. A number of GPU-supported applications had emerged in the past several years (www.nvidia.com/object/gpu-applications.html). Quantum Espresso (QE) is reported as an integrated suite of open source computer codes for electronic-structure calculations and materials modeling at the nano-scale. It is based on DFT, the use of a plane-waves basis and a pseudopotential approach. Since the QE 5.0 version, it has been implemented as a plug-in component for standard QE packages that allows exploiting the capabilities of Nvidia GPU graphic cards (www.qe-forge.org/gf/proj). In this study, we have examined the impact of the usage of GPU acceleration and parallelization on the numerical performance of DFT calculations. Graphene has been attracting attention worldwide and has already shown some remarkable properties. We have studied an intercalated graphene, using the QE package PHonon, which employs GPU. The term ‘intercalation’ refers to a process whereby foreign adatoms are inserted onto a graphene lattice. In addition, by intercalating different atoms between graphene layers, it is possible to tune their physical properties. Our experiments have shown there are benefits from using GPUs, and we reached an

GPU-based implementation of an accelerated SR-NLUT based on N-point one-dimensional sub-principal fringe patterns in computer-generated holograms

Directory of Open Access Journals (Sweden)

Hee-Min Choi

2015-06-01

Full Text Available An accelerated spatial redundancy-based novel-look-up-table (A-SR-NLUT method based on a new concept of the N-point one-dimensional sub-principal fringe pattern (N-point1-D sub-PFP is implemented on a graphics processing unit (GPU for fast calculation of computer-generated holograms (CGHs of three-dimensional (3-Dobjects. Since the proposed method can generate the N-point two-dimensional (2-D PFPs for CGH calculation from the pre-stored N-point 1-D PFPs, the loading time of the N-point PFPs on the GPU can be dramatically reduced, which results in a great increase of the computational speed of the proposed method. Experimental results confirm that the average calculation time for one-object point has been reduced by 49.6% and 55.4% compared to those of the conventional 2-D SR-NLUT methods for each case of the 2-point and 3-point SR maps, respectively.

Using GPU to calculate electron dose for hybrid pencil beam model

International Nuclear Information System (INIS)

Guo Chengjun; Li Xia; Hou Qing; Wu Zhangwen

2011-01-01

Hybrid pencil beam model (HPBM) offers an efficient approach to calculate the three-dimension dose distribution from a clinical electron beam. Still, clinical radiation treatment activity desires faster treatment plan process. Our work presented the fast implementation of HPBM-based electron dose calculation using graphics processing unit (GPU). The HPBM algorithm was implemented in compute unified device architecture running on the GPU, and C running on the CPU, respectively. Several tests with various sizes of the field, beamlet and voxel were used to evaluate our implementation. On an NVIDIA GeForce GTX470 GPU card, we achieved speedup factors of 2.18- 98.23 with acceptable accuracy, compared with the results from a Pentium E5500 2.80 GHz Dual-core CPU. (authors)

GPU based Monte Carlo for PET image reconstruction: parameter optimization

International Nuclear Information System (INIS)

Cserkaszky, Á; Légrády, D.; Wirth, A.; Bükki, T.; Patay, G.

2011-01-01

This paper presents the optimization of a fully Monte Carlo (MC) based iterative image reconstruction of Positron Emission Tomography (PET) measurements. With our MC re- construction method all the physical effects in a PET system are taken into account thus superior image quality is achieved in exchange for increased computational effort. The method is feasible because we utilize the enormous processing power of Graphical Processing Units (GPUs) to solve the inherently parallel problem of photon transport. The MC approach regards the simulated positron decays as samples in mathematical sums required in the iterative reconstruction algorithm, so to complement the fast architecture, our work of optimization focuses on the number of simulated positron decays required to obtain sufficient image quality. We have achieved significant results in determining the optimal number of samples for arbitrary measurement data, this allows to achieve the best image quality with the least possible computational effort. Based on this research recommendations can be given for effective partitioning of computational effort into the iterations in limited time reconstructions. (author)

GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

Directory of Open Access Journals (Sweden)

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.

Structural analysis of the ITER Vacuum Vessel regarding 2012 ITER Project-Level Loads

Energy Technology Data Exchange (ETDEWEB)

Martinez, J.-M., E-mail: jean-marc.martinez@live.fr [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul lez Durance (France); Jun, C.H.; Portafaix, C.; Choi, C.-H.; Ioki, K.; Sannazzaro, G.; Sborchia, C. [ITER Organization, Route de Vinon sur Verdon, 13115 St Paul lez Durance (France); Cambazar, M.; Corti, Ph.; Pinori, K.; Sfarni, S.; Tailhardat, O. [Assystem EOS, 117 rue Jacquard, L' Atrium, 84120 Pertuis (France); Borrelly, S. [Sogeti High Tech, RE2, 180 rue René Descartes, Le Millenium – Bat C, 13857 Aix en Provence (France); Albin, V.; Pelletier, N. [SOM Calcul – Groupe ORTEC, 121 ancien Chemin de Cassis – Immeuble Grand Pré, 13009 Marseille (France)

2014-10-15

Highlights: • ITER Vacuum Vessel is a part of the first barrier to confine the plasma. • ITER Vacuum Vessel as Nuclear Pressure Equipment (NPE) necessitates a third party organization authorized by the French nuclear regulator to assure design, fabrication, conformance testing and quality assurance, i.e. Agreed Notified Body (ANB). • A revision of the ITER Project-Level Load Specification was implemented in April 2012. • ITER Vacuum Vessel Loads (seismic, pressure, thermal and electromagnetic loads) were summarized. • ITER Vacuum Vessel Structural Margins with regards to RCC-MR code were summarized. - Abstract: A revision of the ITER Project-Level Load Specification (to be used for all systems of the ITER machine) was implemented in April 2012. This revision supports ITER's licensing by accommodating requests from the French regulator to maintain consistency with the plasma physics database and our present understanding of plasma transients and electro-magnetic (EM) loads, to investigate the possibility of removing unnecessary conservatism in the load requirements and to review the list and definition of incidental cases. The purpose of this paper is to present the impact of this 2012 revision of the ITER Project-Level Load Specification (LS) on the ITER Vacuum Vessel (VV) loads and the main structural margins required by the applicable French code, RCC-MR.

GPU-Based Computation of Formation Pressure for Multistage Hydraulically Fractured Horizontal Wells in Tight Oil and Gas Reservoirs

Directory of Open Access Journals (Sweden)

Rongwang Yin

2018-01-01

Full Text Available A mathematical model for multistage hydraulically fractured horizontal wells (MFHWs in tight oil and gas reservoirs was derived by considering the variations in the permeability and porosity of tight oil and gas reservoirs that depend on formation pressure and mixed fluid properties and introducing the pseudo-pressure; analytical solutions were presented using the Newman superposition principle. The CPU-GPU asynchronous computing model was designed based on the CUDA platform, and the analytic solution was decomposed into infinite summation and integral forms for parallel computation. Implementation of this algorithm on an Intel i5 4590 CPU and NVIDIA GT 730 GPU demonstrates that computation speed increased by almost 80 times, which meets the requirement for real-time calculation of the formation pressure of MFHWs.

NaNet-10: a 10GbE network interface card for the GPU-based low-level trigger of the NA62 RICH detector

International Nuclear Information System (INIS)

Ammendola, R.; Biagioni, A.; Frezza, O.; Lonardo, A.; Cicero, F. Lo; Martinelli, M.; Paolucci, P.S.; Pastorelli, E.; Simula, F.; Tosoratto, L.; Vicini, P.; Fiorini, M.; Neri, I.; Lamanna, G.; Piandani, R.; Pontisso, L.; Sozzi, M.; Rossetti, D.

2016-01-01

A GPU-based low level (L0) trigger is currently integrated in the experimental setup of the RICH detector of the NA62 experiment to assess the feasibility of building more refined physics-related trigger primitives and thus improve the trigger discriminating power. To ensure the real-time operation of the system, a dedicated data transport mechanism has been implemented: an FPGA-based Network Interface Card (NaNet-10) receives data from detectors and forwards them with low, predictable latency to the memory of the GPU performing the trigger algorithms. Results of the ring-shaped hit patterns reconstruction will be reported and discussed

Accelerating three-dimensional FDTD calculations on GPU clusters for electromagnetic field simulation.

Science.gov (United States)

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.

TH-E-17A-02: High-Pitch and Sparse-View Helical 4D CT Via Iterative Image Reconstruction Method Based On Tensor Framelet

International Nuclear Information System (INIS)

Guo, M; Nam, H; Li, R; Xing, L; Gao, H

2014-01-01

Purpose: 4D CT is routinely performed during radiation therapy treatment planning of thoracic and abdominal cancers. Compared with the cine mode, the helical mode is advantageous in temporal resolution. However, a low pitch (∼0.1) for 4D CT imaging is often required instead of the standard pitch (∼1) for static imaging, since standard image reconstruction based on analytic method requires the low-pitch scanning in order to satisfy the data sufficient condition when reconstructing each temporal frame individually. In comparison, the flexible iterative method enables the reconstruction of all temporal frames simultaneously, so that the image similarity among frames can be utilized to possibly perform high-pitch and sparse-view helical 4D CT imaging. The purpose of this work is to investigate such an exciting possibility for faster imaging with lower dose. Methods: A key for highpitch and sparse-view helical 4D CT imaging is the simultaneous reconstruction of all temporal frames using the prior that temporal frames are continuous along the temporal direction. In this work, such a prior is regularized through the sparsity transform based on spatiotemporal tensor framelet (TF) as a multilevel and high-order extension of total variation transform. Moreover, GPU-based fast parallel computing of X-ray transform and its adjoint together with split Bregman method is utilized for solving the 4D image reconstruction problem efficiently and accurately. Results: The simulation studies based on 4D NCAT phantoms were performed with various pitches (i.e., 0.1, 0.2, 0.5, and 1) and sparse views (i.e., 400 views per rotation instead of standard >2000 views per rotation), using 3D iterative individual reconstruction method based on 3D TF and 4D iterative simultaneous reconstruction method based on 4D TF respectively. Conclusion: The proposed TF-based simultaneous 4D image reconstruction method enables high-pitch and sparse-view helical 4D CT with lower dose and faster speed

RED-ML

DEFF Research Database (Denmark)

Xiong, Heng; Liu, Dongbing; Li, Qiye

2017-01-01

using diverse RNA-seq datasets, we have developed a software tool, RED-ML: RNA Editing Detection based on Machine learning (pronounced as "red ML"). The input to RED-ML can be as simple as a single BAM file, while it can also take advantage of matched genomic variant information when available...... accurately detect novel RNA editing sites without relying on curated RNA editing databases. We have also made this tool freely available via GitHub . We have developed a highly accurate, speedy and general-purpose tool for RNA editing detection using RNA-seq data....... With the availability of RED-ML, it is now possible to conveniently make RNA editing a routine analysis of RNA-seq. We believe this can greatly benefit the RNA editing research community and has profound impact to accelerate our understanding of this intriguing posttranscriptional modification process....

GPU-accelerated computation of electron transfer.

Science.gov (United States)

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.

Mielografia em cães sadios com o meio de contraste ioversol 240 mg I/ml: Resultados clínicos e radiológicos

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Luciana Virgínia Costa Sarmento

2001-01-01

Full Text Available Este trabalho teve por objetivo avaliar o período de opacificação e a qualidade diagnóstica do ioversol 240 mg I/ml, nas regiões torácica e lombar após sua injeção na cisterna magna, e verificar seus efeitos sobre o sistema nervoso por meio de observações clínicas e neurológicas. Utilizaram-se 30 cães sadios, sem distinção de sexo ou raça, com peso entre 9 e 12 kg, divididos em três grupos de 10 animais cada. Nos animais anestesiados com diazepam e tiopental sódico, o ioversol foi injetado na dose de 0,5 ml/kg. Foram obtidas radiografias cervicais aos cinco minutos para confirmar a injeção do contraste e toracolombares aos 30, 40, 50 e 60 minutos para avaliar o período de opacificação. Exames clínicos e neurológicos foram realizados diariamente até os 7, 14 e 28 dias para os grupos I, II e III respectivamente. Efeitos adversos como movimentos de pedalagem, início de convulsão, espasmos musculares, apnéia, dispnéia e ânsia de vômito foram observados apenas durante o procedimento mielográfico. O ioversol apresentou boas radiopacidade, difusão e miscibilidade com o líquor, oferecendo também a possibilidade de redução de custos, já que pode ser autoclavado. O tempo de opacificação do contraste com valor diagnóstico atingiu 60 minutos em 60% das radiografias torácicas e em 80% das radiografias lombares. Havendo baixa incidência de alterações clínicas e neurológicas, e apropriadas qualidades radiográficas, concluiu-se que o ioversol na concentração de 240 mg I/ml é adequado e seguro para ser utilizado em mielografia de cães.

Gating-ML: XML-based gating descriptions in flow cytometry.

Science.gov (United States)

Spidlen, Josef; Leif, Robert C; Moore, Wayne; Roederer, Mario; Brinkman, Ryan R

2008-12-01

The lack of software interoperability with respect to gating due to lack of a standardized mechanism for data exchange has traditionally been a bottleneck, preventing reproducibility of flow cytometry (FCM) data analysis and the usage of multiple analytical tools. To facilitate interoperability among FCM data analysis tools, members of the International Society for the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) have developed an XML-based mechanism to formally describe gates (Gating-ML). Gating-ML, an open specification for encoding gating, data transformations and compensation, has been adopted by the ISAC DSTF as a Candidate Recommendation. Gating-ML can facilitate exchange of gating descriptions the same way that FCS facilitated for exchange of raw FCM data. Its adoption will open new collaborative opportunities as well as possibilities for advanced analyses and methods development. The ISAC DSTF is satisfied that the standard addresses the requirements for a gating exchange standard.

SU-E-J-60: Efficient Monte Carlo Dose Calculation On CPU-GPU Heterogeneous Systems

Energy Technology Data Exchange (ETDEWEB)

Xiao, K; Chen, D. Z; Hu, X. S [University of Notre Dame, Notre Dame, IN (United States); Zhou, B [Altera Corp., San Jose, CA (United States)

2014-06-01

Purpose: It is well-known that the performance of GPU-based Monte Carlo dose calculation implementations is bounded by memory bandwidth. One major cause of this bottleneck is the random memory writing patterns in dose deposition, which leads to several memory efficiency issues on GPU such as un-coalesced writing and atomic operations. We propose a new method to alleviate such issues on CPU-GPU heterogeneous systems, which achieves overall performance improvement for Monte Carlo dose calculation. Methods: Dose deposition is to accumulate dose into the voxels of a dose volume along the trajectories of radiation rays. Our idea is to partition this procedure into the following three steps, which are fine-tuned for CPU or GPU: (1) each GPU thread writes dose results with location information to a buffer on GPU memory, which achieves fully-coalesced and atomic-free memory transactions; (2) the dose results in the buffer are transferred to CPU memory; (3) the dose volume is constructed from the dose buffer on CPU. We organize the processing of all radiation rays into streams. Since the steps within a stream use different hardware resources (i.e., GPU, DMA, CPU), we can overlap the execution of these steps for different streams by pipelining. Results: We evaluated our method using a Monte Carlo Convolution Superposition (MCCS) program and tested our implementation for various clinical cases on a heterogeneous system containing an Intel i7 quad-core CPU and an NVIDIA TITAN GPU. Comparing with a straightforward MCCS implementation on the same system (using both CPU and GPU for radiation ray tracing), our method gained 2-5X speedup without losing dose calculation accuracy. Conclusion: The results show that our new method improves the effective memory bandwidth and overall performance for MCCS on the CPU-GPU systems. Our proposed method can also be applied to accelerate other Monte Carlo dose calculation approaches. This research was supported in part by NSF under Grants CCF

A Generic High-performance GPU-based Library for PDE solvers

DEFF Research Database (Denmark)

Glimberg, Stefan Lemvig; Engsig-Karup, Allan Peter

, the privilege of high-performance parallel computing is now in principle accessible for many scientific users, no matter their economic resources. Though being highly effective units, GPUs and parallel architectures in general, pose challenges for software developers to utilize their efficiency. Sequential...... legacy codes are not always easily parallelized and the time spent on conversion might not pay o in the end. We present a highly generic C++ library for fast assembling of partial differential equation (PDE) solvers, aiming at utilizing the computational resources of GPUs. The library requires a minimum...... of GPU computing knowledge, while still oering the possibility to customize user-specic solvers at kernel level if desired. Spatial dierential operators are based on matrix free exible order nite dierence approximations. These matrix free operators minimize both memory consumption and main memory access...

GPU-accelerated FDTD modeling of radio-frequency field-tissue interactions in high-field MRI.

Science.gov (United States)

Chi, Jieru; Liu, Feng; Weber, Ewald; Li, Yu; Crozier, Stuart

2011-06-01

The analysis of high-field RF field-tissue interactions requires high-performance finite-difference time-domain (FDTD) computing. Conventional CPU-based FDTD calculations offer limited computing performance in a PC environment. This study presents a graphics processing unit (GPU)-based parallel-computing framework, producing substantially boosted computing efficiency (with a two-order speedup factor) at a PC-level cost. Specific details of implementing the FDTD method on a GPU architecture have been presented and the new computational strategy has been successfully applied to the design of a novel 8-element transceive RF coil system at 9.4 T. Facilitated by the powerful GPU-FDTD computing, the new RF coil array offers optimized fields (averaging 25% improvement in sensitivity, and 20% reduction in loop coupling compared with conventional array structures of the same size) for small animal imaging with a robust RF configuration. The GPU-enabled acceleration paves the way for FDTD to be applied for both detailed forward modeling and inverse design of MRI coils, which were previously impractical.

GPU-accelerated ray-tracing for real-time treatment planning

International Nuclear Information System (INIS)

Heinrich, H; Ziegenhein, P; Kamerling, C P; Oelfke, U; Froening, H

2014-01-01

Dose calculation methods in radiotherapy treatment planning require the radiological depth information of the voxels that represent the patient volume to correct for tissue inhomogeneities. This information is acquired by time consuming ray-tracing-based calculations. For treatment planning scenarios with changing geometries and real-time constraints this is a severe bottleneck. We implemented an algorithm for the graphics processing unit (GPU) which implements a ray-matrix approach to reduce the number of rays to trace. Furthermore, we investigated the impact of different strategies of accessing memory in kernel implementations as well as strategies for rapid data transfers between main memory and memory of the graphics device. Our study included the overlapping of computations and memory transfers to reduce the overall runtime using Hyper-Q. We tested our approach on a prostate case (9 beams, coplanar). The measured execution times for a complete ray-tracing range from 28 msec for the computations on the GPU to 99 msec when considering data transfers to and from the graphics device. Our GPU-based algorithm performed the ray-tracing in real-time. The strategies efficiently reduce the time consumption of memory accesses and data transfer overhead. The achieved runtimes demonstrate the viability of this approach and allow improved real-time performance for dose calculation methods in clinical routine.

Work-Efficient Parallel Skyline Computation for the GPU

DEFF Research Database (Denmark)

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...

permGPU: Using graphics processing units in RNA microarray association studies

Directory of Open Access Journals (Sweden)

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.

Bayesian image reconstruction for emission tomography based on median root prior

International Nuclear Information System (INIS)

Alenius, S.

1997-01-01

The aim of the present study was to investigate a new type of Bayesian one-step late reconstruction method which utilizes a median root prior (MRP). The method favours images which have locally monotonous radioactivity concentrations. The new reconstruction algorithm was applied to ideal simulated data, phantom data and some patient examinations with PET. The same projection data were reconstructed with filtered back-projection (FBP) and maximum likelihood-expectation maximization (ML-EM) methods for comparison. The MRP method provided good-quality images with a similar resolution to the FBP method with a ramp filter, and at the same time the noise properties were as good as with Hann-filtered FBP images. The typical artefacts seen in FBP reconstructed images outside of the object were completely removed, as was the grainy noise inside the object. Quantitativley, the resulting average regional radioactivity concentrations in a large region of interest in images produced by the MRP method corresponded to the FBP and ML-EM results but at the pixel by pixel level the MRP method proved to be the most accurate of the tested methods. In contrast to other iterative reconstruction methods, e.g. ML-EM, the MRP method was not sensitive to the number of iterations nor to the adjustment of reconstruction parameters. Only the Bayesian parameter β had to be set. The proposed MRP method is much more simple to calculate than the methods described previously, both with regard to the parameter settings and in terms of general use. The new MRP reconstruction method was shown to produce high-quality quantitative emission images with only one parameter setting in addition to the number of iterations. (orig.)

GPU-accelerated Gibbs ensemble Monte Carlo simulations of Lennard-Jonesium

Science.gov (United States)

Mick, Jason; Hailat, Eyad; Russo, Vincent; Rushaidat, Kamel; Schwiebert, Loren; Potoff, Jeffrey

2013-12-01

This work describes an implementation of canonical and Gibbs ensemble Monte Carlo simulations on graphics processing units (GPUs). The pair-wise energy calculations, which consume the majority of the computational effort, are parallelized using the energetic decomposition algorithm. While energetic decomposition is relatively inefficient for traditional CPU-bound codes, the algorithm is ideally suited to the architecture of the GPU. The performance of the CPU and GPU codes are assessed for a variety of CPU and GPU combinations for systems containing between 512 and 131,072 particles. For a system of 131,072 particles, the GPU-enabled canonical and Gibbs ensemble codes were 10.3 and 29.1 times faster (GTX 480 GPU vs. i5-2500K CPU), respectively, than an optimized serial CPU-bound code. Due to overhead from memory transfers from system RAM to the GPU, the CPU code was slightly faster than the GPU code for simulations containing less than 600 particles. The critical temperature Tc∗=1.312(2) and density ρc∗=0.316(3) were determined for the tail corrected Lennard-Jones potential from simulations of 10,000 particle systems, and found to be in exact agreement with prior mixed field finite-size scaling calculations [J.J. Potoff, A.Z. Panagiotopoulos, J. Chem. Phys. 109 (1998) 10914].

GPU-Vote: A Framework for Accelerating Voting Algorithms on GPU.

NARCIS (Netherlands)

Braak, van den G.J.W.; Nugteren, C.; Mesman, B.; Corporaal, H.; Kaklamanis, C.; Papatheodorou, T.; Spirakis, P.G.

2012-01-01

Voting algorithms, such as histogram and Hough transforms, are frequently used algorithms in various domains, such as statistics and image processing. Algorithms in these domains may be accelerated using GPUs. Implementing voting algorithms efficiently on a GPU however is far from trivial due to

Fatores associados à soropositividade do teste ML Flow em pacientes e contatos de pacientes com hanseníase menores de 18 anos Factors associated with ML Flow test seropositivity in leprosy patients and household contacts under the age of 18

Directory of Open Access Journals (Sweden)

Maria Aparecida Alves Ferreira

2008-01-01

Full Text Available Estudo transversal em menores de 18 anos, sendo 115 casos novos de hanseníase e 1.011 contatos intradomiciliares. Determinaram-se as proporções da soropositividade do ML Flow e fatores associados ao teste positivo. Observou-se soropositividade em 21,7% dos pacientes e 19,7% dos contatos. Nos pacientes, a regressão logística indicou associação com baciloscopia positiva e número de lesões cutâneas maior que cinco. A análise por árvore de decisão mostrou associação com baciloscopia, classificação de Madri, número de nervos acometidos e idade. Nos contatos, as duas análises indicaram as mesmas associações: classificação do caso-índice, idade e tipo de serviço de saúde. As variáveis que explicaram melhor a soropositividade, em menores de 18 anos, são aquelas associadas à maior carga bacilar. Assim, o teste ML Flow poderia ser utilizado também na infância para ajudar na correta classificação dos pacientes para tratamento e na identificação dos contatos com maior risco de desenvolver hanseníase.This article presents the results of a cross-sectional study involving 115 newly-diagnosed leprosy patients and 1,011 household contacts, all under the age of 18. Seropositivity ratios and factors associated with a positive ML Flow were determined. The test was positive in 21.7% of patients and in 19.7% of contacts. Among patients, logistic regression indicated the association of seropositivity with positive slit skin smear and having more than five skin lesions. The tree method analysis indicated associations with skin smear, Madrid classification, number of nerves affected and age. In the contact group, index case classification, age and type of health service were associated with seropositivity in both analyses. The variables that best explained seropositivity are those associated with higher bacterial load. Therefore, the ML Flow test could be used in children to aid correct classification of patients for treatment and to

GPU-based fast pencil beam algorithm for proton therapy

International Nuclear Information System (INIS)

Fujimoto, Rintaro; Nagamine, Yoshihiko; Kurihara, Tsuneya

2011-01-01

Performance of a treatment planning system is an essential factor in making sophisticated plans. The dose calculation is a major time-consuming process in planning operations. The standard algorithm for proton dose calculations is the pencil beam algorithm which produces relatively accurate results, but is time consuming. In order to shorten the computational time, we have developed a GPU (graphics processing unit)-based pencil beam algorithm. We have implemented this algorithm and calculated dose distributions in the case of a water phantom. The results were compared to those obtained by a traditional method with respect to the computational time and discrepancy between the two methods. The new algorithm shows 5-20 times faster performance using the NVIDIA GeForce GTX 480 card in comparison with the Intel Core-i7 920 processor. The maximum discrepancy of the dose distribution is within 0.2%. Our results show that GPUs are effective for proton dose calculations.

SU-E-T-558: Monte Carlo Photon Transport Simulations On GPU with Quadric Geometry

International Nuclear Information System (INIS)

Chi, Y; Tian, Z; Jiang, S; Jia, X

2015-01-01

Purpose: Monte Carlo simulation on GPU has experienced rapid advancements over the past a few years and tremendous accelerations have been achieved. Yet existing packages were developed only in voxelized geometry. In some applications, e.g. radioactive seed modeling, simulations in more complicated geometry are needed. This abstract reports our initial efforts towards developing a quadric geometry module aiming at expanding the application scope of GPU-based MC simulations. Methods: We defined the simulation geometry consisting of a number of homogeneous bodies, each specified by its material composition and limiting surfaces characterized by quadric functions. A tree data structure was utilized to define geometric relationship between different bodies. We modified our GPU-based photon MC transport package to incorporate this geometry. Specifically, geometry parameters were loaded into GPU’s shared memory for fast access. Geometry functions were rewritten to enable the identification of the body that contains the current particle location via a fast searching algorithm based on the tree data structure. Results: We tested our package in an example problem of HDR-brachytherapy dose calculation for shielded cylinder. The dose under the quadric geometry and that under the voxelized geometry agreed in 94.2% of total voxels within 20% isodose line based on a statistical t-test (95% confidence level), where the reference dose was defined to be the one at 0.5cm away from the cylinder surface. It took 243sec to transport 100million source photons under this quadric geometry on an NVidia Titan GPU card. Compared with simulation time of 99.6sec in the voxelized geometry, including quadric geometry reduced efficiency due to the complicated geometry-related computations. Conclusion: Our GPU-based MC package has been extended to support photon transport simulation in quadric geometry. Satisfactory accuracy was observed with a reduced efficiency. Developments for charged

SU-E-T-558: Monte Carlo Photon Transport Simulations On GPU with Quadric Geometry

Energy Technology Data Exchange (ETDEWEB)

Chi, Y; Tian, Z; Jiang, S; Jia, X [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States)

2015-06-15

Purpose: Monte Carlo simulation on GPU has experienced rapid advancements over the past a few years and tremendous accelerations have been achieved. Yet existing packages were developed only in voxelized geometry. In some applications, e.g. radioactive seed modeling, simulations in more complicated geometry are needed. This abstract reports our initial efforts towards developing a quadric geometry module aiming at expanding the application scope of GPU-based MC simulations. Methods: We defined the simulation geometry consisting of a number of homogeneous bodies, each specified by its material composition and limiting surfaces characterized by quadric functions. A tree data structure was utilized to define geometric relationship between different bodies. We modified our GPU-based photon MC transport package to incorporate this geometry. Specifically, geometry parameters were loaded into GPU’s shared memory for fast access. Geometry functions were rewritten to enable the identification of the body that contains the current particle location via a fast searching algorithm based on the tree data structure. Results: We tested our package in an example problem of HDR-brachytherapy dose calculation for shielded cylinder. The dose under the quadric geometry and that under the voxelized geometry agreed in 94.2% of total voxels within 20% isodose line based on a statistical t-test (95% confidence level), where the reference dose was defined to be the one at 0.5cm away from the cylinder surface. It took 243sec to transport 100million source photons under this quadric geometry on an NVidia Titan GPU card. Compared with simulation time of 99.6sec in the voxelized geometry, including quadric geometry reduced efficiency due to the complicated geometry-related computations. Conclusion: Our GPU-based MC package has been extended to support photon transport simulation in quadric geometry. Satisfactory accuracy was observed with a reduced efficiency. Developments for charged

Elastography Using Multi-Stream GPU: An Application to Online Tracked Ultrasound Elastography, In-Vivo and the da Vinci Surgical System

Science.gov (United States)

Deshmukh, Nishikant P.; Kang, Hyun Jae; Billings, Seth D.; Taylor, Russell H.; Hager, Gregory D.; Boctor, Emad M.

2014-01-01

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. PMID:25541954

Elastography using multi-stream GPU: an application to online tracked ultrasound elastography, in-vivo and the da Vinci Surgical System.

Directory of Open Access Journals (Sweden)

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.

Elastography using multi-stream GPU: an application to online tracked ultrasound elastography, in-vivo and the da Vinci Surgical System.

Science.gov (United States)

Deshmukh, Nishikant P; Kang, Hyun Jae; Billings, Seth D; Taylor, Russell H; Hager, Gregory D; Boctor, Emad M

2014-01-01

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.

jmzML, an open-source Java API for mzML, the PSI standard for MS data.

Science.gov (United States)

Côté, Richard G; Reisinger, Florian; Martens, Lennart

2010-04-01

We here present jmzML, a Java API for the Proteomics Standards Initiative mzML data standard. Based on the Java Architecture for XML Binding and XPath-based XML indexer random-access XML parser, jmzML can handle arbitrarily large files in minimal memory, allowing easy and efficient processing of mzML files using the Java programming language. jmzML also automatically resolves internal XML references on-the-fly. The library (which includes a viewer) can be downloaded from http://jmzml.googlecode.com.

Criação de um tradutor XML para a linguagem de marcações sobre emoção EmotionML

Directory of Open Access Journals (Sweden)

Marcelo Nichele

2013-11-01

Full Text Available Este artigo visa apresentar e descrever a criação de um tradutor XML para a linguagem EmotionML 1.0. EmotionML é uma linguagem de marcação criada para padronizar a representação das emoções em computadores. O tradutor deve ser capaz de: (i identificar os elementos da EmotionML, em um documento XML, e retorná-los como objetos instanciados; (ii gerar dinamicamente classes a partir da gramática definida para a linguagem EmotionML para a instanciação de objetos; (iii gerar arquivos EmotionML a partir de objetos EmotionML instanciados. Dessa forma, o tradutor proposto pode ser usado nos mais variados tipos de aplicações em Computação Afetiva que envolvam inferência, expressão ou síntese de emoções. A utilização do tradutor permite a um sistema computacional afetivo recuperar informações mantidas em arquivos, modificar os dados do arquivo em tempo real, assim como armazenar novamente as informações no formato EmotionML para futuros acessos ou modificações.

GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy

International Nuclear Information System (INIS)

Ammazzalorso, F; Jelen, U; Bednarz, T

2014-01-01

We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a metric scoring irradiation port robustness through analysis of tissue density patterns prior to dose optimization and computation. Results were benchmarked against an independent native CPU implementation. Numerical results were in agreement between the GPU implementation and native CPU implementation. For 10 skull base cases, the GPU-accelerated implementation was employed to select beam setups for proton and carbon ion treatment plans, which proved to be dosimetrically robust, when recomputed in presence of various simulated positioning errors. From the point of view of performance, average running time on the GPU decreased by at least one order of magnitude compared to the CPU, rendering the GPU-accelerated analysis a feasible step in a clinical treatment planning interactive session. In conclusion, selection of robust particle therapy beam setups can be effectively accelerated on a GPU and become an unintrusive part of the particle therapy treatment planning workflow. Additionally, the speed gain opens new usage scenarios, like interactive analysis manipulation (e.g. constraining of some setup) and re-execution. Finally, through OpenCL portable parallelism, the new implementation is suitable also for CPU-only use, taking advantage of multiple cores, and can potentially exploit types of accelerators other than GPUs.

GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy

Science.gov (United States)

Ammazzalorso, F.; Bednarz, T.; Jelen, U.

2014-03-01

We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a metric scoring irradiation port robustness through analysis of tissue density patterns prior to dose optimization and computation. Results were benchmarked against an independent native CPU implementation. Numerical results were in agreement between the GPU implementation and native CPU implementation. For 10 skull base cases, the GPU-accelerated implementation was employed to select beam setups for proton and carbon ion treatment plans, which proved to be dosimetrically robust, when recomputed in presence of various simulated positioning errors. From the point of view of performance, average running time on the GPU decreased by at least one order of magnitude compared to the CPU, rendering the GPU-accelerated analysis a feasible step in a clinical treatment planning interactive session. In conclusion, selection of robust particle therapy beam setups can be effectively accelerated on a GPU and become an unintrusive part of the particle therapy treatment planning workflow. Additionally, the speed gain opens new usage scenarios, like interactive analysis manipulation (e.g. constraining of some setup) and re-execution. Finally, through OpenCL portable parallelism, the new implementation is suitable also for CPU-only use, taking advantage of multiple cores, and can potentially exploit types of accelerators other than GPUs.

GPU Based N-Gram String Matching Algorithm with Score Table Approach for String Searching in Many Documents

Science.gov (United States)

Srinivasa, K. G.; Shree Devi, B. N.

2017-10-01

String searching in documents has become a tedious task with the evolution of Big Data. Generation of large data sets demand for a high performance search algorithm in areas such as text mining, information retrieval and many others. The popularity of GPU's for general purpose computing has been increasing for various applications. Therefore it is of great interest to exploit the thread feature of a GPU to provide a high performance search algorithm. This paper proposes an optimized new approach to N-gram model for string search in a number of lengthy documents and its GPU implementation. The algorithm exploits GPGPUs for searching strings in many documents employing character level N-gram matching with parallel Score Table approach and search using CUDA API. The new approach of Score table used for frequency storage of N-grams in a document, makes the search independent of the document's length and allows faster access to the frequency values, thus decreasing the search complexity. The extensive thread feature in a GPU has been exploited to enable parallel pre-processing of trigrams in a document for Score Table creation and parallel search in huge number of documents, thus speeding up the whole search process even for a large pattern size. Experiments were carried out for many documents of varied length and search strings from the standard Lorem Ipsum text on NVIDIA's GeForce GT 540M GPU with 96 cores. Results prove that the parallel approach for Score Table creation and searching gives a good speed up than the same approach executed serially.

Parallel hyperbolic PDE simulation on clusters: Cell versus GPU

Science.gov (United States)

Rostrup, Scott; De Sterck, Hans

2010-12-01

Increasingly, high-performance computing is looking towards data-parallel computational devices to enhance computational performance. Two technologies that have received significant attention are IBM's Cell Processor and NVIDIA's CUDA programming model for graphics processing unit (GPU) computing. In this paper we investigate the acceleration of parallel hyperbolic partial differential equation simulation on structured grids with explicit time integration on clusters with Cell and GPU backends. The message passing interface (MPI) is used for communication between nodes at the coarsest level of parallelism. Optimizations of the simulation code at the several finer levels of parallelism that the data-parallel devices provide are described in terms of data layout, data flow and data-parallel instructions. Optimized Cell and GPU performance are compared with reference code performance on a single x86 central processing unit (CPU) core in single and double precision. We further compare the CPU, Cell and GPU platforms on a chip-to-chip basis, and compare performance on single cluster nodes with two CPUs, two Cell processors or two GPUs in a shared memory configuration (without MPI). We finally compare performance on clusters with 32 CPUs, 32 Cell processors, and 32 GPUs using MPI. Our GPU cluster results use NVIDIA Tesla GPUs with GT200 architecture, but some preliminary results on recently introduced NVIDIA GPUs with the next-generation Fermi architecture are also included. This paper provides computational scientists and engineers who are considering porting their codes to accelerator environments with insight into how structured grid based explicit algorithms can be optimized for clusters with Cell and GPU accelerators. It also provides insight into the speed-up that may be gained on current and future accelerator architectures for this class of applications. Program summaryProgram title: SWsolver Catalogue identifier: AEGY_v1_0 Program summary URL

Large Scale Simulations of the Euler Equations on GPU Clusters

KAUST Repository

Liebmann, Manfred

2010-08-01

The paper investigates the scalability of a parallel Euler solver, using the Vijayasundaram method, on a GPU cluster with 32 Nvidia Geforce GTX 295 boards. The aim of this research is to enable large scale fluid dynamics simulations with up to one billion elements. We investigate communication protocols for the GPU cluster to compensate for the slow Gigabit Ethernet network between the GPU compute nodes and to maintain overall efficiency. A diesel engine intake-port and a nozzle, meshed in different resolutions, give good real world examples for the scalability tests on the GPU cluster. © 2010 IEEE.

CPU and GPU (Cuda Template Matching Comparison

Directory of Open Access Journals (Sweden)

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.

Ultrafast convolution/superposition using tabulated and exponential kernels on GPU

Energy Technology Data Exchange (ETDEWEB)

Chen Quan; Chen Mingli; Lu Weiguo [TomoTherapy Inc., 1240 Deming Way, Madison, Wisconsin 53717 (United States)

2011-03-15

Purpose: Collapsed-cone convolution/superposition (CCCS) dose calculation is the workhorse for IMRT dose calculation. The authors present a novel algorithm for computing CCCS dose on the modern graphic processing unit (GPU). Methods: The GPU algorithm includes a novel TERMA calculation that has no write-conflicts and has linear computation complexity. The CCCS algorithm uses either tabulated or exponential cumulative-cumulative kernels (CCKs) as reported in literature. The authors have demonstrated that the use of exponential kernels can reduce the computation complexity by order of a dimension and achieve excellent accuracy. Special attentions are paid to the unique architecture of GPU, especially the memory accessing pattern, which increases performance by more than tenfold. Results: As a result, the tabulated kernel implementation in GPU is two to three times faster than other GPU implementations reported in literature. The implementation of CCCS showed significant speedup on GPU over single core CPU. On tabulated CCK, speedups as high as 70 are observed; on exponential CCK, speedups as high as 90 are observed. Conclusions: Overall, the GPU algorithm using exponential CCK is 1000-3000 times faster over a highly optimized single-threaded CPU implementation using tabulated CCK, while the dose differences are within 0.5% and 0.5 mm. This ultrafast CCCS algorithm will allow many time-sensitive applications to use accurate dose calculation.

Semi-automatic tool to ease the creation and optimization of GPU programs

DEFF Research Database (Denmark)

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...... of the transformations can be performed automatically, which makes the tool usable for both novices and experts in GPU programming....

Benchmarking and Evaluating Unified Memory for OpenMP GPU Offloading

Energy Technology Data Exchange (ETDEWEB)

Mishra, Alok [Stony Brook Univ., Stony Brook, NY (United States); Li, Lingda [Brookhaven National Lab. (BNL), Upton, NY (United States); Kong, Martin [Brookhaven National Lab. (BNL), Upton, NY (United States); Finkel, Hal [Argonne National Lab. (ANL), Argonne, IL (United States); Chapman, Barbara [Stony Brook Univ., Stony Brook, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)

2017-01-01

Here, the latest OpenMP standard offers automatic device offloading capabilities which facilitate GPU programming. Despite this, there remain many challenges. One of these is the unified memory feature introduced in recent GPUs. GPUs in current and future HPC systems have enhanced support for unified memory space. In such systems, CPU and GPU can access each other's memory transparently, that is, the data movement is managed automatically by the underlying system software and hardware. Memory over subscription is also possible in these systems. However, there is a significant lack of knowledge about how this mechanism will perform, and how programmers should use it. We have modified several benchmarks codes, in the Rodinia benchmark suite, to study the behavior of OpenMP accelerator extensions and have used them to explore the impact of unified memory in an OpenMP context. We moreover modified the open source LLVM compiler to allow OpenMP programs to exploit unified memory. The results of our evaluation reveal that, while the performance of unified memory is comparable with that of normal GPU offloading for benchmarks with little data reuse, it suffers from significant overhead when GPU memory is over subcribed for benchmarks with large amount of data reuse. Based on these results, we provide several guidelines for programmers to achieve better performance with unified memory.

High Performance Processing and Analysis of Geospatial Data Using CUDA on GPU

Directory of Open Access Journals (Sweden)

STOJANOVIC, N.

2014-11-01

Full Text Available In this paper, the high-performance processing of massive geospatial data on many-core GPU (Graphic Processing Unit is presented. We use CUDA (Compute Unified Device Architecture programming framework to implement parallel processing of common Geographic Information Systems (GIS algorithms, such as viewshed analysis and map-matching. Experimental evaluation indicates the improvement in performance with respect to CPU-based solutions and shows feasibility of using GPU and CUDA for parallel implementation of GIS algorithms over large-scale geospatial datasets.

SU-D-BRD-03: A Gateway for GPU Computing in Cancer Radiotherapy Research

Energy Technology Data Exchange (ETDEWEB)

Jia, X; Folkerts, M [The University of Texas Southwestern Medical Ctr, Dallas, TX (United States); Shi, F; Yan, H; Yan, Y; Jiang, S [UT Southwestern Medical Center, Dallas, TX (United States); Sivagnanam, S; Majumdar, A [University of California San Diego, La Jolla, CA (United States)

2014-06-01

Purpose: Graphics Processing Unit (GPU) has become increasingly important in radiotherapy. However, it is still difficult for general clinical researchers to access GPU codes developed by other researchers, and for developers to objectively benchmark their codes. Moreover, it is quite often to see repeated efforts spent on developing low-quality GPU codes. The goal of this project is to establish an infrastructure for testing GPU codes, cross comparing them, and facilitating code distributions in radiotherapy community. Methods: We developed a system called Gateway for GPU Computing in Cancer Radiotherapy Research (GCR2). A number of GPU codes developed by our group and other developers can be accessed via a web interface. To use the services, researchers first upload their test data or use the standard data provided by our system. Then they can select the GPU device on which the code will be executed. Our system offers all mainstream GPU hardware for code benchmarking purpose. After the code running is complete, the system automatically summarizes and displays the computing results. We also released a SDK to allow the developers to build their own algorithm implementation and submit their binary codes to the system. The submitted code is then systematically benchmarked using a variety of GPU hardware and representative data provided by our system. The developers can also compare their codes with others and generate benchmarking reports. Results: It is found that the developed system is fully functioning. Through a user-friendly web interface, researchers are able to test various GPU codes. Developers also benefit from this platform by comprehensively benchmarking their codes on various GPU platforms and representative clinical data sets. Conclusion: We have developed an open platform allowing the clinical researchers and developers to access the GPUs and GPU codes. This development will facilitate the utilization of GPU in radiation therapy field.

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

NARCIS (Netherlands)

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

2012-01-01

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

A GPU-based Monte Carlo dose calculation code for photon transport in a voxel phantom

Energy Technology Data Exchange (ETDEWEB)

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)

A GPU-based Monte Carlo dose calculation code for photon transport in a voxel phantom

International Nuclear Information System (INIS)

Bellezzo, M.; Do Nascimento, E.; Yoriyaz, H.

2014-08-01

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)

Survey of using GPU CUDA programming model in medical image analysis

Directory of Open Access Journals (Sweden)

T. Kalaiselvi

2017-01-01

Full Text Available With the technology development of medical industry, processing data is expanding rapidly and computation time also increases due to many factors like 3D, 4D treatment planning, the increasing sophistication of MRI pulse sequences and the growing complexity of algorithms. Graphics processing unit (GPU addresses these problems and gives the solutions for using their features such as, high computation throughput, high memory bandwidth, support for floating-point arithmetic and low cost. Compute unified device architecture (CUDA is a popular GPU programming model introduced by NVIDIA for parallel computing. This review paper briefly discusses the need of GPU CUDA computing in the medical image analysis. The GPU performances of existing algorithms are analyzed and the computational gain is discussed. A few open issues, hardware configurations and optimization principles of existing methods are discussed. This survey concludes the few optimization techniques with the medical imaging algorithms on GPU. Finally, limitation and future scope of GPU programming are discussed.

STEM image simulation with hybrid CPU/GPU programming

International Nuclear Information System (INIS)

Yao, Y.; Ge, B.H.; Shen, X.; Wang, Y.G.; Yu, R.C.

2016-01-01

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. - Highlights: • STEM image simulation is achieved by hybrid CPU/GPU programming under parallel algorithm architecture to speed up the calculation in the personal computer (PC). • In order to fully utilize the calculation power of the PC, the simulation is performed by GPU core and multi-CPU cores at the same time so efficiency is improved significantly. • GaSb and artificial GaSb/InAs interface with atom diffusion have been used to verify the computation. The results reveal some unintuitive phenomena about the contrast variation with the atom numbers.

STEM image simulation with hybrid CPU/GPU programming

Energy Technology Data Exchange (ETDEWEB)

Yao, Y., E-mail: yaoyuan@iphy.ac.cn; Ge, B.H.; Shen, X.; Wang, Y.G.; Yu, R.C.

2016-07-15

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. - Highlights: • STEM image simulation is achieved by hybrid CPU/GPU programming under parallel algorithm architecture to speed up the calculation in the personal computer (PC). • In order to fully utilize the calculation power of the PC, the simulation is performed by GPU core and multi-CPU cores at the same time so efficiency is improved significantly. • GaSb and artificial GaSb/InAs interface with atom diffusion have been used to verify the computation. The results reveal some unintuitive phenomena about the contrast variation with the atom numbers.

Research on GPU-accelerated algorithm in 3D finite difference neutron diffusion calculation method

International Nuclear Information System (INIS)

Xu Qi; Yu Ganglin; Wang Kan; Sun Jialong

2014-01-01

In this paper, the adaptability of the neutron diffusion numerical algorithm on GPUs was studied, and a GPU-accelerated multi-group 3D neutron diffusion code based on finite difference method was developed. The IAEA 3D PWR benchmark problem was calculated in the numerical test. The results demonstrate both high efficiency and adequate accuracy of the GPU implementation for neutron diffusion equation. (authors)

Development of efficient GPU parallelization of WRF Yonsei University planetary boundary layer scheme

Directory of Open Access Journals (Sweden)

M. Huang

2015-09-01

Full Text Available The planetary boundary layer (PBL is the lowest part of the atmosphere and where its character is directly affected by its contact with the underlying planetary surface. The PBL is responsible for vertical sub-grid-scale fluxes due to eddy transport in the whole atmospheric column. It determines the flux profiles within the well-mixed boundary layer and the more stable layer above. It thus provides an evolutionary model of atmospheric temperature, moisture (including clouds, and horizontal momentum in the entire atmospheric column. For such purposes, several PBL models have been proposed and employed in the weather research and forecasting (WRF model of which the Yonsei University (YSU scheme is one. To expedite weather research and prediction, we have put tremendous effort into developing an accelerated implementation of the entire WRF model using graphics processing unit (GPU massive parallel computing architecture whilst maintaining its accuracy as compared to its central processing unit (CPU-based implementation. This paper presents our efficient GPU-based design on a WRF YSU PBL scheme. Using one NVIDIA Tesla K40 GPU, the GPU-based YSU PBL scheme achieves a speedup of 193× with respect to its CPU counterpart running on one CPU core, whereas the speedup for one CPU socket (4 cores with respect to 1 CPU core is only 3.5×. We can even boost the speedup to 360× with respect to 1 CPU core as two K40 GPUs are applied.

Parallel GPU implementation of PWR reactor burnup

International Nuclear Information System (INIS)

Heimlich, A.; Silva, F.C.; Martinez, A.S.

2016-01-01

Highlights: • Three GPU algorithms used to evaluate the burn-up in a PWR reactor. • Exhibit speed improvement exceeding 200 times over the sequential. • The C++ container is expansible to accept new nuclides chains. - Abstract: This paper surveys three methods, implemented for multi-core CPU and graphic processor unit (GPU), to evaluate the fuel burn-up in a pressurized light water nuclear reactor (PWR) using the solutions of a large system of coupled ordinary differential equations. The reactor physics simulation of a PWR reactor spends a long execution time with burnup calculations, so performance improvement using GPU can imply in better core design and thus extended fuel life cycle. The results of this study exhibit speed improvement exceeding 200 times over the sequential solver, within 1% accuracy.

GPU accelerated FDTD solver and its application in MRI.

Science.gov (United States)

Chi, J; Liu, F; Jin, J; Mason, D G; Crozier, S

2010-01-01

The finite difference time domain (FDTD) method is a popular technique for computational electromagnetics (CEM). The large computational power often required, however, has been a limiting factor for its applications. In this paper, we will present a graphics processing unit (GPU)-based parallel FDTD solver and its successful application to the investigation of a novel B1 shimming scheme for high-field magnetic resonance imaging (MRI). The optimized shimming scheme exhibits considerably improved transmit B(1) profiles. The GPU implementation dramatically shortened the runtime of FDTD simulation of electromagnetic field compared with its CPU counterpart. The acceleration in runtime has made such investigation possible, and will pave the way for other studies of large-scale computational electromagnetic problems in modern MRI which were previously impractical.

GPU credit reduced, tie to TMI-1 cheating discounted

International Nuclear Information System (INIS)

Utroska, D.

1981-01-01

The recent reduction of credit available to General Public Utilities (GPU) Nuclear may be linked to a cheating incident involving two reactor operators at the Three Mile Island-1 (TMI-1) reactor. The incident caused the Nuclear Regulatory Commission to reopen the managerial portion of the restart hearings and may delay the restart. The delay and the lower credit line will worsen GPU's financial position. Banks claim that misgivings about TMI-1 influence them more than the cheating, although GPU had been gradually improving its financial situation since the TMI-2 accident. The new agreement gives GPU $150 million in immediate credit, but lowers the interim ceiling from $292 million to $200 million. A spokesman from the Office of Management and Budget acknowledges that administration plans to limit the federal role to research and development softened under political pressure

Frog: Asynchronous Graph Processing on GPU with Hybrid Coloring Model

Energy Technology Data Exchange (ETDEWEB)

Shi, Xuanhua; Luo, Xuan; Liang, Junling; Zhao, Peng; Di, Sheng; He, Bingsheng; Jin, Hai

2018-01-01

GPUs have been increasingly used to accelerate graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or atomic operations, leading to significant penalties/overheads when implemented on GPUs. As such, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. Common coloring algorithms, however, may suffer from low parallelism because of a large number of colors generally required for processing a large-scale graph with billions of vertices. We propose a light-weight asynchronous processing framework called Frog with a preprocessing/hybrid coloring model. The fundamental idea is based on Pareto principle (or 80-20 rule) about coloring algorithms as we observed through masses of realworld graph coloring cases. We find that a majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution separates the processing of the vertices based on the distribution of colors. In this work, we mainly answer three questions: (1) how to partition the vertices in a sparse graph with maximized parallelism, (2) how to process large-scale graphs that cannot fit into GPU memory, and (3) how to reduce the overhead of data transfers on PCIe while processing each partition. We conduct experiments on real-world data (Amazon, DBLP, YouTube, RoadNet-CA, WikiTalk and Twitter) to evaluate our approach and make comparisons with well-known non-preprocessed (such as Totem, Medusa, MapGraph and Gunrock) and preprocessed (Cusha) approaches, by testing four classical algorithms (BFS, PageRank, SSSP and CC). On all the tested applications and

Fast magnetic field computation in fusion technology using GPU technology

Energy Technology Data Exchange (ETDEWEB)

Chiariello, Andrea Gaetano [Ass. EURATOM/ENEA/CREATE, Dipartimento di Ingegneria Industriale e dell’Informazione, Seconda Università di Napoli, Via Roma 29, Aversa (CE) (Italy); Formisano, Alessandro, E-mail: Alessandro.Formisano@unina2.it [Ass. EURATOM/ENEA/CREATE, Dipartimento di Ingegneria Industriale e dell’Informazione, Seconda Università di Napoli, Via Roma 29, Aversa (CE) (Italy); Martone, Raffaele [Ass. EURATOM/ENEA/CREATE, Dipartimento di Ingegneria Industriale e dell’Informazione, Seconda Università di Napoli, Via Roma 29, Aversa (CE) (Italy)

2013-10-15

Highlights: ► The paper deals with high accuracy numerical simulations of high field magnets. ► The porting of existing codes of High Performance Computing architectures allowed to obtain a relevant speedup while not reducing computational accuracy. ► Some examples of applications, referred to ITER-like magnets, are reported. -- Abstract: One of the main issues in the simulation of Tokamaks functioning is the reliable and accurate computation of actual field maps in the plasma chamber. In this paper a tool able to accurately compute magnetic field maps produced by active coils of any 3D shape, wound with high number of conductors, is presented. Under linearity assumption, the coil winding is modeled by means of “sticks”, following each conductor's shape, and the contribution of each stick is computed using high speed Graphic Computing Units (GPU's). Relevant speed enhancements with respect to standard parallel computing environment are achieved in this way.

A Theoretical Framework for Soft-Information-Based Synchronization in Iterative (Turbo Receivers

Directory of Open Access Journals (Sweden)

Lottici Vincenzo

2005-01-01

Full Text Available This contribution considers turbo synchronization, that is to say, the use of soft data information to estimate parameters like carrier phase, frequency, or timing offsets of a modulated signal within an iterative data demodulator. In turbo synchronization, the receiver exploits the soft decisions computed at each turbo decoding iteration to provide a reliable estimate of some signal parameters. The aim of our paper is to show that such "turbo-estimation" approach can be regarded as a special case of the expectation-maximization (EM algorithm. This leads to a general theoretical framework for turbo synchronization that allows to derive parameter estimation procedures for carrier phase and frequency offset, as well as for timing offset and signal amplitude. The proposed mathematical framework is illustrated by simulation results reported for the particular case of carrier phase and frequency offsets estimation of a turbo-coded 16-QAM signal.

GPU Pro 4 advanced rendering techniques

CERN Document Server

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

A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms.

Science.gov (United States)

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.

Application of GPU to Multi-interfaces Advection and Reconstruction Solver (MARS)

International Nuclear Information System (INIS)

Nagatake, Taku; Takase, Kazuyuki; Kunugi, Tomoaki

2010-01-01

In the nuclear engineering fields, a high performance computer system is necessary to perform the large scale computations. Recently, a Graphics Processing Unit (GPU) has been developed as a rendering computational system in order to reduce a Central Processing Unit (CPU) load. In the graphics processing, the high performance computing is needed to render the high-quality 3D objects in some video games. Thus the GPU consists of many processing units and a wide memory bandwidth. In this study, the Multi-interfaces Advection and Reconstruction Solver (MARS) which is one of the interface volume tracking methods for multi-phase flows has been performed. The multi-phase flow computation is very important for the nuclear reactors and other engineering fields. The MARS consists of two computing parts: the interface tracking part and the fluid motion computing part. As for the interface tracking part, the performance of GPU (GTX280) was 6 times faster than that of the CPU (Dual-Xeon 5040), and in the fluid motion computing part the Poisson Solver by the GPU (GTX285) was 22 times faster than that by the CPU(Core i7). As for the Dam Breaking Problem, the result of GPU-MARS showed slightly different from the experimental result. Because the GPU-MARS was developed using the single-precision GPU, it can be considered that the round-off error might be accumulated. (author)

WE-AB-204-11: Development of a Nuclear Medicine Dosimetry Module for the GPU-Based Monte Carlo Code ARCHER

Energy Technology Data Exchange (ETDEWEB)

Liu, T; Lin, H; Xu, X [Rensselaer Polytechnic Institute, Troy, NY (United States); Stabin, M [Vanderbilt Univ Medical Ctr, Nashville, TN (United States)

2015-06-15

Purpose: To develop a nuclear medicine dosimetry module for the GPU-based Monte Carlo code ARCHER. Methods: We have developed a nuclear medicine dosimetry module for the fast Monte Carlo code ARCHER. The coupled electron-photon Monte Carlo transport kernel included in ARCHER is built upon the Dose Planning Method code (DPM). The developed module manages the radioactive decay simulation by consecutively tracking several types of radiation on a per disintegration basis using the statistical sampling method. Optimization techniques such as persistent threads and prefetching are studied and implemented. The developed module is verified against the VIDA code, which is based on Geant4 toolkit and has previously been verified against OLINDA/EXM. A voxelized geometry is used in the preliminary test: a sphere made of ICRP soft tissue is surrounded by a box filled with water. Uniform activity distribution of I-131 is assumed in the sphere. Results: The self-absorption dose factors (mGy/MBqs) of the sphere with varying diameters are calculated by ARCHER and VIDA respectively. ARCHER’s result is in agreement with VIDA’s that are obtained from a previous publication. VIDA takes hours of CPU time to finish the computation, while it takes ARCHER 4.31 seconds for the 12.4-cm uniform activity sphere case. For a fairer CPU-GPU comparison, more effort will be made to eliminate the algorithmic differences. Conclusion: The coupled electron-photon Monte Carlo code ARCHER has been extended to radioactive decay simulation for nuclear medicine dosimetry. The developed code exhibits good performance in our preliminary test. The GPU-based Monte Carlo code is developed with grant support from the National Institute of Biomedical Imaging and Bioengineering through an R01 grant (R01EB015478)

GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model

International Nuclear Information System (INIS)

Takaishi, Tetsuya

2015-01-01

The realized stochastic volatility (RSV) model that utilizes the realized volatility as additional information has been proposed to infer volatility of financial time series. We consider the Bayesian inference of the RSV model by the Hybrid Monte Carlo (HMC) algorithm. The HMC algorithm can be parallelized and thus performed on the GPU for speedup. The GPU code is developed with CUDA Fortran. We compare the computational time in performing the HMC algorithm on GPU (GTX 760) and CPU (Intel i7-4770 3.4GHz) and find that the GPU can be up to 17 times faster than the CPU. We also code the program with OpenACC and find that appropriate coding can achieve the similar speedup with CUDA Fortran

GPU acceleration of Monte Carlo simulations for polarized photon scattering in anisotropic turbid media.

Science.gov (United States)

Li, Pengcheng; Liu, Celong; Li, Xianpeng; He, Honghui; Ma, Hui

2016-09-20

In earlier studies, we developed scattering models and the corresponding CPU-based Monte Carlo simulation programs to study the behavior of polarized photons as they propagate through complex biological tissues. Studying the simulation results in high degrees of freedom that created a demand for massive simulation tasks. In this paper, we report a parallel implementation of the simulation program based on the compute unified device architecture running on a graphics processing unit (GPU). Different schemes for sphere-only simulations and sphere-cylinder mixture simulations were developed. Diverse optimizing methods were employed to achieve the best acceleration. The final-version GPU program is hundreds of times faster than the CPU version. Dependence of the performance on input parameters and precision were also studied. It is shown that using single precision in the GPU simulations results in very limited losses in accuracy. Consumer-level graphics cards, even those in laptop computers, are more cost-effective than scientific graphics cards for single-precision computation.

Personal Supercomputing for Monte Carlo Simulation Using a GPU

Energy Technology Data Exchange (ETDEWEB)

Oh, Jae-Yong; Koo, Yang-Hyun; Lee, Byung-Ho [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2008-05-15

Since the usability, accessibility, and maintenance of a personal computer (PC) are very good, a PC is a useful computer simulation tool for researchers. It has enough calculation power to simulate a small scale system with the improved performance of a PC's CPU. However, if a system is large or long time scale, we need a cluster computer or supercomputer. Recently great changes have occurred in the PC calculation environment. A graphic process unit (GPU) on a graphic card, only used to calculate display data, has a superior calculation capability to a PC's CPU. This GPU calculation performance is a match for the supercomputer in 2000. Although it has such a great calculation potential, it is not easy to program a simulation code for GPU due to difficult programming techniques for converting a calculation matrix to a 3D rendering image using graphic APIs. In 2006, NVIDIA provided the Software Development Kit (SDK) for the programming environment for NVIDIA's graphic cards, which is called the Compute Unified Device Architecture (CUDA). It makes the programming on the GPU easy without knowledge of the graphic APIs. This paper describes the basic architectures of NVIDIA's GPU and CUDA, and carries out a performance benchmark for the Monte Carlo simulation.

Personal Supercomputing for Monte Carlo Simulation Using a GPU

International Nuclear Information System (INIS)

Oh, Jae-Yong; Koo, Yang-Hyun; Lee, Byung-Ho

2008-01-01

Since the usability, accessibility, and maintenance of a personal computer (PC) are very good, a PC is a useful computer simulation tool for researchers. It has enough calculation power to simulate a small scale system with the improved performance of a PC's CPU. However, if a system is large or long time scale, we need a cluster computer or supercomputer. Recently great changes have occurred in the PC calculation environment. A graphic process unit (GPU) on a graphic card, only used to calculate display data, has a superior calculation capability to a PC's CPU. This GPU calculation performance is a match for the supercomputer in 2000. Although it has such a great calculation potential, it is not easy to program a simulation code for GPU due to difficult programming techniques for converting a calculation matrix to a 3D rendering image using graphic APIs. In 2006, NVIDIA provided the Software Development Kit (SDK) for the programming environment for NVIDIA's graphic cards, which is called the Compute Unified Device Architecture (CUDA). It makes the programming on the GPU easy without knowledge of the graphic APIs. This paper describes the basic architectures of NVIDIA's GPU and CUDA, and carries out a performance benchmark for the Monte Carlo simulation

Iterative channel decoding of FEC-based multiple-description codes.

Science.gov (United States)

Chang, Seok-Ho; Cosman, Pamela C; Milstein, Laurence B

2012-03-01

Multiple description coding has been receiving attention as a robust transmission framework for multimedia services. This paper studies the iterative decoding of FEC-based multiple description codes. The proposed decoding algorithms take advantage of the error detection capability of Reed-Solomon (RS) erasure codes. The information of correctly decoded RS codewords is exploited to enhance the error correction capability of the Viterbi algorithm at the next iteration of decoding. In the proposed algorithm, an intradescription interleaver is synergistically combined with the iterative decoder. The interleaver does not affect the performance of noniterative decoding but greatly enhances the performance when the system is iteratively decoded. We also address the optimal allocation of RS parity symbols for unequal error protection. For the optimal allocation in iterative decoding, we derive mathematical equations from which the probability distributions of description erasures can be generated in a simple way. The performance of the algorithm is evaluated over an orthogonal frequency-division multiplexing system. The results show that the performance of the multiple description codes is significantly enhanced.

Efficient two-level preconditionined conjugate gradient method on the GPU

NARCIS (Netherlands)

Gupta, R.; Van Gijzen, M.B.; Vuik, K.

2011-01-01

We present an implementation of Two-Level Preconditioned Conjugate Gradient Method for the GPU. We investigate a Truncated Neumann Series based preconditioner in combination with deflation and compare it with Block Incomplete Cholesky schemes. This combination exhibits fine-grain parallelism and

High performance GPU processing for inversion using uniform grid searches

Science.gov (United States)

Venetis, Ioannis E.; Saltogianni, Vasso; Stiros, Stathis; Gallopoulos, Efstratios

2017-04-01

Many geophysical problems are described by systems of redundant, highly non-linear systems of ordinary equations with constant terms deriving from measurements and hence representing stochastic variables. Solution (inversion) of such problems is based on numerical, optimization methods, based on Monte Carlo sampling or on exhaustive searches in cases of two or even three "free" unknown variables. Recently the TOPological INVersion (TOPINV) algorithm, a grid search-based technique in the Rn space, has been proposed. TOPINV is not based on the minimization of a certain cost function and involves only forward computations, hence avoiding computational errors. The basic concept is to transform observation equations into inequalities on the basis of an optimization parameter k and of their standard errors, and through repeated "scans" of n-dimensional search grids for decreasing values of k to identify the optimal clusters of gridpoints which satisfy observation inequalities and by definition contain the "true" solution. Stochastic optimal solutions and their variance-covariance matrices are then computed as first and second statistical moments. Such exhaustive uniform searches produce an excessive computational load and are extremely time consuming for common computers based on a CPU. An alternative is to use a computing platform based on a GPU, which nowadays is affordable to the research community, which provides a much higher computing performance. Using the CUDA programming language to implement TOPINV allows the investigation of the attained speedup in execution time on such a high performance platform. Based on synthetic data we compared the execution time required for two typical geophysical problems, modeling magma sources and seismic faults, described with up to 18 unknown variables, on both CPU/FORTRAN and GPU/CUDA platforms. The same problems for several different sizes of search grids (up to 1012 gridpoints) and numbers of unknown variables were solved on

Incompressible SPH (ISPH) with fast Poisson solver on a GPU

Science.gov (United States)

Chow, Alex D.; Rogers, Benedict D.; Lind, Steven J.; Stansby, Peter K.

2018-05-01

This paper presents a fast incompressible SPH (ISPH) solver implemented to run entirely on a graphics processing unit (GPU) capable of simulating several millions of particles in three dimensions on a single GPU. The ISPH algorithm is implemented by converting the highly optimised open-source weakly-compressible SPH (WCSPH) code DualSPHysics to run ISPH on the GPU, combining it with the open-source linear algebra library ViennaCL for fast solutions of the pressure Poisson equation (PPE). Several challenges are addressed with this research: constructing a PPE matrix every timestep on the GPU for moving particles, optimising the limited GPU memory, and exploiting fast matrix solvers. The ISPH pressure projection algorithm is implemented as 4 separate stages, each with a particle sweep, including an algorithm for the population of the PPE matrix suitable for the GPU, and mixed precision storage methods. An accurate and robust ISPH boundary condition ideal for parallel processing is also established by adapting an existing WCSPH boundary condition for ISPH. A variety of validation cases are presented: an impulsively started plate, incompressible flow around a moving square in a box, and dambreaks (2-D and 3-D) which demonstrate the accuracy, flexibility, and speed of the methodology. Fragmentation of the free surface is shown to influence the performance of matrix preconditioners and therefore the PPE matrix solution time. The Jacobi preconditioner demonstrates robustness and reliability in the presence of fragmented flows. For a dambreak simulation, GPU speed ups demonstrate up to 10-18 times and 1.1-4.5 times compared to single-threaded and 16-threaded CPU run times respectively.

Paralelização e comparação de métodos iterativos na solução de sistemas lineares grandes e esparsos

Directory of Open Access Journals (Sweden)

Lauro Cássio Martins de Paula

2013-11-01

Full Text Available Apresenta-se neste trabalho uma comparação de desempenho computacional entre métodos iterativos utilizados para solução de sistemas lineares. O objetivo é mostrar que a utilização de processamento paralelo fornecido por uma Graphics Processing Unit (GPU pode ser viável, por viabilizar a solução rápida de sistemas de equações lineares, para que sistemas grandes e esparsos possam ser solucionados em um espaço curto de tempo. Para a validação do trabalho, utilizou-se uma GPU, por meio da arquitetura Compute Unified Device Architecture (CUDA, e comparou-se o desempenho computacional dos métodos iterativos de Jacobi, Gauss-Seidel, BiCGStab e BiCGStab(2 paralelizado na solução de sistemas lineares de tamanhos variados. Foi possível observar uma aceleração significativa nos testes com o método paralelizado, que se acentua consideravelmente na medida em que os sistemas aumentam. Os resultados mostraram que a aplicação de processamento paralelo em um método robusto e eficiente, tal como o BiCGStab(2, se torna muitas vezes indispensável, para que simulações sejam realizadas com qualidade e em tempo não proibitivo.Palavras-chave: CUDA. GPU. BiCGStab(2.Parallelization and comparison of interative methods in solving large and sparse linear systemsAbstractThis paper presents a computational performance comparison between some iterative methods used for linear systems solution. The goal is to show that the use of parallel processing provided by a Graphics Processing Unit (GPU may be more feasible, for making possible the fast solution of linear equations systems in order that complex and sparse problems can be solved in a short time. To validate the paper a GPU through the NVIDIA's Compute Unified Device Architecture (CUDA was employed and the computational performance was compared with Jacobi, Gauss-Seidel, BiCGStab iterative methods and BiCGStab(2 parallelized in the solution of linear systems of varying sizes. There was a

An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU

Directory of Open Access Journals (Sweden)

Hailong Xu

2016-03-01

Full Text Available Nowadays, software-defined radio (SDR has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP and Space-Frequency Adaptive Processing (SFAP are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.

An optimization of a GPU-based parallel wind field module

International Nuclear Information System (INIS)

Pinheiro, André L.S.; Shirru, Roberto

2017-01-01

Atmospheric radionuclide dispersion systems (ARDS) are important tools to predict the impact of radioactive releases from Nuclear Power Plants and guide people evacuation from affected areas. Four modules comprise ARDS: Source Term, Wind Field, Plume Dispersion and Doses Calculations. The slowest is the Wind Field Module that was previously parallelized using the CUDA C language. The statement purpose of this work is to show the speedup gain with the optimization of the already parallel code of the GPU-based Wind Field module, based in WEST model (Extrapolated from Stability and Terrain). Due to the parallelization done in the wind field module, it was observed that some CUDA processors became idle, thus contributing to a reduction in speedup. It was proposed in this work a way of allocating these idle CUDA processors in order to increase the speedup. An acceleration of about 4 times can be seen in the comparative case study between the regular CUDA code and the optimized CUDA code. These results are quite motivating and point out that even after a parallelization of code, a parallel code optimization should be taken into account. (author)

An optimization of a GPU-based parallel wind field module

Energy Technology Data Exchange (ETDEWEB)

Pinheiro, André L.S.; Shirru, Roberto [Coordenacao de Pos-Graduacao e Pesquisa de Engenharia (PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear; Pereira, Cláudio M.N.A., E-mail: apinheiro99@gmail.com, E-mail: schirru@lmp.ufrj.br, E-mail: cmnap@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

2017-07-01

Atmospheric radionuclide dispersion systems (ARDS) are important tools to predict the impact of radioactive releases from Nuclear Power Plants and guide people evacuation from affected areas. Four modules comprise ARDS: Source Term, Wind Field, Plume Dispersion and Doses Calculations. The slowest is the Wind Field Module that was previously parallelized using the CUDA C language. The statement purpose of this work is to show the speedup gain with the optimization of the already parallel code of the GPU-based Wind Field module, based in WEST model (Extrapolated from Stability and Terrain). Due to the parallelization done in the wind field module, it was observed that some CUDA processors became idle, thus contributing to a reduction in speedup. It was proposed in this work a way of allocating these idle CUDA processors in order to increase the speedup. An acceleration of about 4 times can be seen in the comparative case study between the regular CUDA code and the optimized CUDA code. These results are quite motivating and point out that even after a parallelization of code, a parallel code optimization should be taken into account. (author)

Sop-GPU: accelerating biomolecular simulations in the centisecond timescale using graphics processors.

Science.gov (United States)

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. © 2010 Wiley-Liss, Inc.

Design analysis of the ITER divertor

International Nuclear Information System (INIS)

Samuelli, G.; Marin, A.; Roccella, M.; Lucca, F.; Merola, M.; Riccardi, B.; Petrizzi, L.; Villari, R.

2007-01-01

The divertor is one of the most challenging components of the ITER machine. Its function is to reduce the impurity in the plasma and consists essentially of two parts: the plasma facing components (PFCs) and a massive support structure called the cassette body (CB). Considerable R and D effort (developed by EFDA CSU GARCHING and the ITER International Team together with the EU Associations and the EU Industries) has been spent in designing divertor components capable of withstanding the expected electromagnetic (EM) loads and to take into account the latest ITER design conditions. In support of such efforts extensive and very detailed Neutronic, Thermal, EM and Structural analyses have been performed. A summary of the analyses performed will be presented. One of the main result is a typical exercise of integration between the different kind of analyses and the importance of keeping the consistency between the different assumptions and simplifications. The models used for the numerical analyses include a detailed geometrical description of the CB, the inlet, outlet hydraulic manifolds, the CB to vacuum vessel locking system and three configurations of the PFU. The effect of electrical bridging, both in poloidal and toroidal direction, of the PFU castellation, due to a possible melting at the W mono-block or tiles, occurring during the plasma disruptions, has been analyzed. For all these configurations 2 VDE scenarios including the effect of the Toroidal Field Variation and the HaloCurrent with the related out of plane induced EM forces have been extensively analyzed and a detailed poloidal and radial distribution of the nuclear heating has been used for the neutronic flux on the divertor components. The aim of this activity is to produce a comprehensive design and assessment of the ITER divertor via: -The estimation of the neutronic heat deposition and shielding capability; -The calculation of the related thermal and mechanical effects and the comparison of the

Design analysis of the ITER divertor

Energy Technology Data Exchange (ETDEWEB)

Samuelli, G.; Marin, A.; Roccella, M.; Lucca, F. [L.T. Calcoli SaS, Merate (Lecco) (Italy); Merola, M. [ITER Team, Cadarache (France); Riccardi, B. [EFDA CSU Garching (Germany); Petrizzi, L.; Villari, R. [CRE ENEA sulla Fusione Frascati, Roma (Italy)

2007-07-01

The divertor is one of the most challenging components of the ITER machine. Its function is to reduce the impurity in the plasma and consists essentially of two parts: the plasma facing components (PFCs) and a massive support structure called the cassette body (CB). Considerable R and D effort (developed by EFDA CSU GARCHING and the ITER International Team together with the EU Associations and the EU Industries) has been spent in designing divertor components capable of withstanding the expected electromagnetic (EM) loads and to take into account the latest ITER design conditions. In support of such efforts extensive and very detailed Neutronic, Thermal, EM and Structural analyses have been performed. A summary of the analyses performed will be presented. One of the main result is a typical exercise of integration between the different kind of analyses and the importance of keeping the consistency between the different assumptions and simplifications. The models used for the numerical analyses include a detailed geometrical description of the CB, the inlet, outlet hydraulic manifolds, the CB to vacuum vessel locking system and three configurations of the PFU. The effect of electrical bridging, both in poloidal and toroidal direction, of the PFU castellation, due to a possible melting at the W mono-block or tiles, occurring during the plasma disruptions, has been analyzed. For all these configurations 2 VDE scenarios including the effect of the Toroidal Field Variation and the HaloCurrent with the related out of plane induced EM forces have been extensively analyzed and a detailed poloidal and radial distribution of the nuclear heating has been used for the neutronic flux on the divertor components. The aim of this activity is to produce a comprehensive design and assessment of the ITER divertor via: -The estimation of the neutronic heat deposition and shielding capability; -The calculation of the related thermal and mechanical effects and the comparison of the

An Out-of-Core GPU based dimensionality reduction algorithm for Big Mass Spectrometry Data and its application in bottom-up Proteomics.

Science.gov (United States)

Awan, Muaaz Gul; Saeed, Fahad

2017-08-01

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and computational operations inside GPU. These novel data structures include Binary Spectra and Quantized Indexed Spectra (QIS) . The former helps in communicating essential information between CPU and GPU using minimum amount of data while latter enables us to store and process complex 3-D data structure into a 1-D array structure while maintaining the integrity of MS data. Our proposed algorithm also takes into account the limited memory of GPUs and switches between in-core and out-of-core modes based upon the size of input data. G-MSR achieves a peak speed-up of 386x over its sequential counterpart and is shown to process over a million spectra in just 32 seconds. The code for this algorithm is available as a GPL open-source at GitHub at the following link: https://github.com/pcdslab/G-MSR.

jqcML: an open-source java API for mass spectrometry quality control data in the qcML format.

Science.gov (United States)

Bittremieux, Wout; Kelchtermans, Pieter; Valkenborg, Dirk; Martens, Lennart; Laukens, Kris

2014-07-03

The awareness that systematic quality control is an essential factor to enable the growth of proteomics into a mature analytical discipline has increased over the past few years. To this aim, a controlled vocabulary and document structure have recently been proposed by Walzer et al. to store and disseminate quality-control metrics for mass-spectrometry-based proteomics experiments, called qcML. To facilitate the adoption of this standardized quality control routine, we introduce jqcML, a Java application programming interface (API) for the qcML data format. First, jqcML provides a complete object model to represent qcML data. Second, jqcML provides the ability to read, write, and work in a uniform manner with qcML data from different sources, including the XML-based qcML file format and the relational database qcDB. Interaction with the XML-based file format is obtained through the Java Architecture for XML Binding (JAXB), while generic database functionality is obtained by the Java Persistence API (JPA). jqcML is released as open-source software under the permissive Apache 2.0 license and can be downloaded from https://bitbucket.org/proteinspector/jqcml .

Challenges and status of ITER conductor production

International Nuclear Information System (INIS)

Devred, A; Backbier, I; Bessette, D; Bevillard, G; Gardner, M; Jong, C; Lillaz, F; Mitchell, N; Romano, G; Vostner, A

2014-01-01

Taking the relay of the large Hadron collider (LHC) at CERN, ITER has become the largest project in applied superconductivity. In addition to its technical complexity, ITER is also a management challenge as it relies on an unprecedented collaboration of seven partners, representing more than half of the world population, who provide 90% of the components as in-kind contributions. The ITER magnet system is one of the most sophisticated superconducting magnet systems ever designed, with an enormous stored energy of 51 GJ. It involves six of the ITER partners. The coils are wound from cable-in-conduit conductors (CICCs) made up of superconducting and copper strands assembled into a multistage cable, inserted into a conduit of butt-welded austenitic steel tubes. The conductors for the toroidal field (TF) and central solenoid (CS) coils require about 600 t of Nb 3 Sn strands while the poloidal field (PF) and correction coil (CC) and busbar conductors need around 275 t of Nb–Ti strands. The required amount of Nb 3 Sn strands far exceeds pre-existing industrial capacity and has called for a significant worldwide production scale up. The TF conductors are the first ITER components to be mass produced and are more than 50% complete. During its life time, the CS coil will have to sustain several tens of thousands of electromagnetic (EM) cycles to high current and field conditions, way beyond anything a large Nb 3 Sn coil has ever experienced. Following a comprehensive R and D program, a technical solution has been found for the CS conductor, which ensures stable performance versus EM and thermal cycling. Productions of PF, CC and busbar conductors are also underway. After an introduction to the ITER project and magnet system, we describe the ITER conductor procurements and the quality assurance/quality control programs that have been implemented to ensure production uniformity across numerous suppliers. Then, we provide examples of technical challenges that have been

Challenges and status of ITER conductor production

Science.gov (United States)

Devred, A.; Backbier, I.; Bessette, D.; Bevillard, G.; Gardner, M.; Jong, C.; Lillaz, F.; Mitchell, N.; Romano, G.; Vostner, A.

2014-04-01

Taking the relay of the large Hadron collider (LHC) at CERN, ITER has become the largest project in applied superconductivity. In addition to its technical complexity, ITER is also a management challenge as it relies on an unprecedented collaboration of seven partners, representing more than half of the world population, who provide 90% of the components as in-kind contributions. The ITER magnet system is one of the most sophisticated superconducting magnet systems ever designed, with an enormous stored energy of 51 GJ. It involves six of the ITER partners. The coils are wound from cable-in-conduit conductors (CICCs) made up of superconducting and copper strands assembled into a multistage cable, inserted into a conduit of butt-welded austenitic steel tubes. The conductors for the toroidal field (TF) and central solenoid (CS) coils require about 600 t of Nb3Sn strands while the poloidal field (PF) and correction coil (CC) and busbar conductors need around 275 t of Nb-Ti strands. The required amount of Nb3Sn strands far exceeds pre-existing industrial capacity and has called for a significant worldwide production scale up. The TF conductors are the first ITER components to be mass produced and are more than 50% complete. During its life time, the CS coil will have to sustain several tens of thousands of electromagnetic (EM) cycles to high current and field conditions, way beyond anything a large Nb3Sn coil has ever experienced. Following a comprehensive R&D program, a technical solution has been found for the CS conductor, which ensures stable performance versus EM and thermal cycling. Productions of PF, CC and busbar conductors are also underway. After an introduction to the ITER project and magnet system, we describe the ITER conductor procurements and the quality assurance/quality control programs that have been implemented to ensure production uniformity across numerous suppliers. Then, we provide examples of technical challenges that have been encountered and

Fast Multi-Symbol Based Iterative Detectors for UWB Communications

Directory of Open Access Journals (Sweden)

Lottici Vincenzo

2010-01-01

Full Text Available Ultra-wideband (UWB impulse radios have shown great potential in wireless local area networks for localization, coexistence with other services, and low probability of interception and detection. However, low transmission power and high multipath effect make the detection of UWB signals challenging. Recently, multi-symbol based detection has caught attention for UWB communications because it provides good performance and does not require explicit channel estimation. Most of the existing multi-symbol based methods incur a higher computational cost than can be afforded in the envisioned UWB systems. In this paper, we propose an iterative multi-symbol based method that has low complexity and provides near optimal performance. Our method uses only one initial symbol to start and applies a decision directed approach to iteratively update a filter template and information symbols. Simulations show that our method converges in only a few iterations (less than 5, and that when the number of symbols increases, the performance of our method approaches that of the ideal Rake receiver.

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT.

Science.gov (United States)

Matenine, Dmitri; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2015-11-01

The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of

Computing OpenSURF on OpenCL and General Purpose GPU

Directory of Open Access Journals (Sweden)

Wanglong Yan

2013-10-01

Full Text Available Speeded-Up Robust Feature (SURF algorithm is widely used for image feature detecting and matching in computer vision area. Open Computing Language (OpenCL is a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, and other processors. This paper introduces how to implement an open-sourced SURF program, namely OpenSURF, on general purpose GPU by OpenCL, and discusses the optimizations in terms of the thread architectures and memory models in detail. Our final OpenCL implementation of OpenSURF is on average 37% and 64% faster than the OpenCV SURF v2.4.5 CUDA implementation on NVidia's GTX660 and GTX460SE GPUs, repectively. Our OpenCL program achieved real-time performance (>25 Frames Per Second for almost all the input images with different sizes from 320*240 to 1024*768 on NVidia's GTX660 GPU, NVidia's GTX460SE GPU and AMD's Radeon HD 6850 GPU. Our OpenCL approach on NVidia's GTX660 GPU is more than 22.8 times faster than its original CPU version on Intel's Dual-Core E5400 2.7G on average.

A GPU-based incompressible Navier-Stokes solver on moving overset grids

Science.gov (United States)

Chandar, Dominic D. J.; Sitaraman, Jayanarayanan; Mavriplis, Dimitri J.

2013-07-01

In pursuit of obtaining high fidelity solutions to the fluid flow equations in a short span of time, graphics processing units (GPUs) which were originally intended for gaming applications are currently being used to accelerate computational fluid dynamics (CFD) codes. With a high peak throughput of about 1 TFLOPS on a PC, GPUs seem to be favourable for many high-resolution computations. One such computation that involves a lot of number crunching is computing time accurate flow solutions past moving bodies. The aim of the present paper is thus to discuss the development of a flow solver on unstructured and overset grids and its implementation on GPUs. In its present form, the flow solver solves the incompressible fluid flow equations on unstructured/hybrid/overset grids using a fully implicit projection method. The resulting discretised equations are solved using a matrix-free Krylov solver using several GPU kernels such as gradient, Laplacian and reduction. Some of the simple arithmetic vector calculations are implemented using the CU++: An Object Oriented Framework for Computational Fluid Dynamics Applications using Graphics Processing Units, Journal of Supercomputing, 2013, doi:10.1007/s11227-013-0985-9 approach where GPU kernels are automatically generated at compile time. Results are presented for two- and three-dimensional computations on static and moving grids.

Multi-GPU accelerated three-dimensional FDTD method for electromagnetic simulation.

Science.gov (United States)

Nagaoka, Tomoaki; Watanabe, Soichi

2011-01-01

Numerical simulation with a numerical 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 numerical human model, we adapt three-dimensional FDTD code to a multi-GPU environment using Compute Unified Device Architecture (CUDA). In this study, we used NVIDIA Tesla C2070 as GPGPU boards. The performance of multi-GPU is evaluated in comparison with that of a single GPU and vector supercomputer. The calculation speed with four GPUs was approximately 3.5 times faster than with a single GPU, and was slightly (approx. 1.3 times) slower than with the supercomputer. Calculation speed of the three-dimensional FDTD method using GPUs can significantly improve with an expanding number of GPUs.

GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.

Directory of Open Access Journals (Sweden)

Ahmed Shamsul Arefin

Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.

TH-A-18C-04: Ultrafast Cone-Beam CT Scatter Correction with GPU-Based Monte Carlo Simulation

Energy Technology Data Exchange (ETDEWEB)

Xu, Y [UT Southwestern Medical Center, Dallas, TX (United States); Southern Medical University, Guangzhou (China); Bai, T [UT Southwestern Medical Center, Dallas, TX (United States); Xi' an Jiaotong University, Xi' an (China); Yan, H; Ouyang, L; Wang, J; Pompos, A; Jiang, S; Jia, X [UT Southwestern Medical Center, Dallas, TX (United States); Zhou, L [Southern Medical University, Guangzhou (China)

2014-06-15

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstructions within 30 seconds. Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC scatter noise caused by low photon numbers. The method is validated on head-and-neck cases with simulated and clinical data. Results: We have studied impacts of photo histories, volume down sampling factors on the accuracy of scatter estimation. The Fourier analysis was conducted to show that scatter images calculated at 31 angles are sufficient to restore those at all angles with <0.1% error. For the simulated case with a resolution of 512×512×100, we simulated 10M photons per angle. The total computation time is 23.77 seconds on a Nvidia GTX Titan GPU. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU. Similar results were found for a real patient case. Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. The whole process of scatter correction and reconstruction is accomplished within 30 seconds. This study is supported in part by NIH (1R01CA154747-01), The Core Technology Research

TH-AB-BRA-07: PENELOPE-Based GPU-Accelerated Dose Calculation System Applied to MRI-Guided Radiation Therapy

International Nuclear Information System (INIS)

Wang, Y; Mazur, T; Green, O; Hu, Y; Li, H; Rodriguez, V; Wooten, H; Yang, D; Zhao, T; Mutic, S; Li, H

2016-01-01

Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on PENELOPE and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods: We first translated PENELOPE from FORTRAN to C++ and validated that the translation produced equivalent results. Then we adapted the C++ code to CUDA in a workflow optimized for GPU architecture. We expanded upon the original code to include voxelized transport boosted by Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gPENELOPE highly user-friendly. Moreover, we incorporated the vendor-provided MRIdian head model into the code. We performed a set of experimental measurements on MRIdian to examine the accuracy of both the head model and gPENELOPE, and then applied gPENELOPE toward independent validation of patient doses calculated by MRIdian’s KMC. Results: We achieve an average acceleration factor of 152 compared to the original single-thread FORTRAN implementation with the original accuracy preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen (1), mediastinum (1) and breast (1), the MRIdian dose calculation engine agrees with gPENELOPE with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: We developed a Monte Carlo simulation platform based on a GPU-accelerated version of PENELOPE. We validated that both the vendor provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.

TH-AB-BRA-07: PENELOPE-Based GPU-Accelerated Dose Calculation System Applied to MRI-Guided Radiation Therapy

Energy Technology Data Exchange (ETDEWEB)

Wang, Y; Mazur, T; Green, O; Hu, Y; Li, H; Rodriguez, V; Wooten, H; Yang, D; Zhao, T; Mutic, S; Li, H [Washington University School of Medicine, Saint Louis, MO (United States)

2016-06-15

Purpose: The clinical commissioning of IMRT subject to a magnetic field is challenging. The purpose of this work is to develop a GPU-accelerated Monte Carlo dose calculation platform based on PENELOPE and then use the platform to validate a vendor-provided MRIdian head model toward quality assurance of clinical IMRT treatment plans subject to a 0.35 T magnetic field. Methods: We first translated PENELOPE from FORTRAN to C++ and validated that the translation produced equivalent results. Then we adapted the C++ code to CUDA in a workflow optimized for GPU architecture. We expanded upon the original code to include voxelized transport boosted by Woodcock tracking, faster electron/positron propagation in a magnetic field, and several features that make gPENELOPE highly user-friendly. Moreover, we incorporated the vendor-provided MRIdian head model into the code. We performed a set of experimental measurements on MRIdian to examine the accuracy of both the head model and gPENELOPE, and then applied gPENELOPE toward independent validation of patient doses calculated by MRIdian’s KMC. Results: We achieve an average acceleration factor of 152 compared to the original single-thread FORTRAN implementation with the original accuracy preserved. For 16 treatment plans including stomach (4), lung (2), liver (3), adrenal gland (2), pancreas (2), spleen (1), mediastinum (1) and breast (1), the MRIdian dose calculation engine agrees with gPENELOPE with a mean gamma passing rate of 99.1% ± 0.6% (2%/2 mm). Conclusions: We developed a Monte Carlo simulation platform based on a GPU-accelerated version of PENELOPE. We validated that both the vendor provided head model and fast Monte Carlo engine used by the MRIdian system are accurate in modeling radiation transport in a patient using 2%/2 mm gamma criteria. Future applications of this platform will include dose validation and accumulation, IMRT optimization, and dosimetry system modeling for next generation MR-IGRT systems.

TU-EF-304-07: Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy

Energy Technology Data Exchange (ETDEWEB)

Li, Y [Tsinghua University, Beijing, Beijing (China); UT Southwestern Medical Center, Dallas, TX (United States); Tian, Z; Jiang, S; Jia, X [UT Southwestern Medical Center, Dallas, TX (United States); Song, T [Southern Medical University, Guangzhou, Guangdong (China); UT Southwestern Medical Center, Dallas, TX (United States); Wu, Z; Liu, Y [Tsinghua University, Beijing, Beijing (China)

2015-06-15

Purpose: Intensity-modulated proton therapy (IMPT) is increasingly used in proton therapy. For IMPT optimization, Monte Carlo (MC) is desired for spots dose calculations because of its high accuracy, especially in cases with a high level of heterogeneity. It is also preferred in biological optimization problems due to the capability of computing quantities related to biological effects. However, MC simulation is typically too slow to be used for this purpose. Although GPU-based MC engines have become available, the achieved efficiency is still not ideal. The purpose of this work is to develop a new optimization scheme to include GPU-based MC into IMPT. Methods: A conventional approach using MC in IMPT simply calls the MC dose engine repeatedly for each spot dose calculations. However, this is not the optimal approach, because of the unnecessary computations on some spots that turned out to have very small weights after solving the optimization problem. GPU-memory writing conflict occurring at a small beam size also reduces computational efficiency. To solve these problems, we developed a new framework that iteratively performs MC dose calculations and plan optimizations. At each dose calculation step, the particles were sampled from different spots altogether with Metropolis algorithm, such that the particle number is proportional to the latest optimized spot intensity. Simultaneously transporting particles from multiple spots also mitigated the memory writing conflict problem. Results: We have validated the proposed MC-based optimization schemes in one prostate case. The total computation time of our method was ∼5–6 min on one NVIDIA GPU card, including both spot dose calculation and plan optimization, whereas a conventional method naively using the same GPU-based MC engine were ∼3 times slower. Conclusion: A fast GPU-based MC dose calculation method along with a novel optimization workflow is developed. The high efficiency makes it attractive for clinical

TU-EF-304-07: Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy

International Nuclear Information System (INIS)

Li, Y; Tian, Z; Jiang, S; Jia, X; Song, T; Wu, Z; Liu, Y

2015-01-01

Purpose: Intensity-modulated proton therapy (IMPT) is increasingly used in proton therapy. For IMPT optimization, Monte Carlo (MC) is desired for spots dose calculations because of its high accuracy, especially in cases with a high level of heterogeneity. It is also preferred in biological optimization problems due to the capability of computing quantities related to biological effects. However, MC simulation is typically too slow to be used for this purpose. Although GPU-based MC engines have become available, the achieved efficiency is still not ideal. The purpose of this work is to develop a new optimization scheme to include GPU-based MC into IMPT. Methods: A conventional approach using MC in IMPT simply calls the MC dose engine repeatedly for each spot dose calculations. However, this is not the optimal approach, because of the unnecessary computations on some spots that turned out to have very small weights after solving the optimization problem. GPU-memory writing conflict occurring at a small beam size also reduces computational efficiency. To solve these problems, we developed a new framework that iteratively performs MC dose calculations and plan optimizations. At each dose calculation step, the particles were sampled from different spots altogether with Metropolis algorithm, such that the particle number is proportional to the latest optimized spot intensity. Simultaneously transporting particles from multiple spots also mitigated the memory writing conflict problem. Results: We have validated the proposed MC-based optimization schemes in one prostate case. The total computation time of our method was ∼5–6 min on one NVIDIA GPU card, including both spot dose calculation and plan optimization, whereas a conventional method naively using the same GPU-based MC engine were ∼3 times slower. Conclusion: A fast GPU-based MC dose calculation method along with a novel optimization workflow is developed. The high efficiency makes it attractive for clinical

GPU Performance and Power Consumption Analysis: A DCT based denoising application

OpenAIRE

Pi Puig, Martín; De Giusti, Laura Cristina; Naiouf, Marcelo; De Giusti, Armando Eduardo

2017-01-01

It is known that energy and power consumption are becoming serious metrics in the design of high performance workstations because of heat dissipation problems. In the last years, GPU accelerators have been integrating many of these expensive systems despite they are embedding more and more transistors on their chips producing a quick increase of power consumption requirements. This paper analyzes an image processing application, in particular a Discrete Cosine Transform denoising algorithm, i...

GPU-accelerated few-view CT reconstruction using the OSC and TV techniques

Energy Technology Data Exchange (ETDEWEB)

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.)

Image based rendering of iterated function systems

NARCIS (Netherlands)

Wijk, van J.J.; Saupe, D.

2004-01-01

A fast method to generate fractal imagery is presented. Iterated function systems (IFS) are based on repeatedly copying transformed images. We show that this can be directly translated into standard graphics operations: Each image is generated by texture mapping and blending copies of the previous

A GPU-based large-scale Monte Carlo simulation method for systems with long-range interactions

Science.gov (United States)

Liang, Yihao; Xing, Xiangjun; Li, Yaohang

2017-06-01

In this work we present an efficient implementation of Canonical Monte Carlo simulation for Coulomb many body systems on graphics processing units (GPU). Our method takes advantage of the GPU Single Instruction, Multiple Data (SIMD) architectures, and adopts the sequential updating scheme of Metropolis algorithm. It makes no approximation in the computation of energy, and reaches a remarkable 440-fold speedup, compared with the serial implementation on CPU. We further use this method to simulate primitive model electrolytes, and measure very precisely all ion-ion pair correlation functions at high concentrations. From these data, we extract the renormalized Debye length, renormalized valences of constituent ions, and renormalized dielectric constants. These results demonstrate unequivocally physics beyond the classical Poisson-Boltzmann theory.

GPU TECHNOLOGIES EMBODIED IN PARALLEL SOLVERS OF LINEAR ALGEBRAIC EQUATION SYSTEMS

Directory of Open Access Journals (Sweden)

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.

Sub-second pencil beam dose calculation on GPU for adaptive proton therapy.

Science.gov (United States)

da Silva, Joakim; Ansorge, Richard; Jena, Rajesh

2015-06-21

Although proton therapy delivered using scanned pencil beams has the potential to produce better dose conformity than conventional radiotherapy, the created dose distributions are more sensitive to anatomical changes and patient motion. Therefore, the introduction of adaptive treatment techniques where the dose can be monitored as it is being delivered is highly desirable. We present a GPU-based dose calculation engine relying on the widely used pencil beam algorithm, developed for on-line dose calculation. The calculation engine was implemented from scratch, with each step of the algorithm parallelized and adapted to run efficiently on the GPU architecture. To ensure fast calculation, it employs several application-specific modifications and simplifications, and a fast scatter-based implementation of the computationally expensive kernel superposition step. The calculation time for a skull base treatment plan using two beam directions was 0.22 s on an Nvidia Tesla K40 GPU, whereas a test case of a cubic target in water from the literature took 0.14 s to calculate. The accuracy of the patient dose distributions was assessed by calculating the γ-index with respect to a gold standard Monte Carlo simulation. The passing rates were 99.2% and 96.7%, respectively, for the 3%/3 mm and 2%/2 mm criteria, matching those produced by a clinical treatment planning system.

A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

Directory of Open Access Journals (Sweden)

Rui Fan

2017-12-01

Full Text Available Betweenness, a widely employed centrality measure in network science, is a decent proxy for investigating network loads and rankings. However, its extremely high computational cost greatly hinders its applicability in large networks. Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of the betweenness centrality (BC for large weighted networks. We parallelize the traditional Dijkstra algorithm by selecting more than one frontier vertex each time and then inspecting the frontier vertices simultaneously. By combining the parallel SSSP algorithm with the parallel BC framework, our GPU-based betweenness algorithm achieves much better performance than its CPU counterparts. Moreover, to further improve performance, we integrate the work-efficient strategy, and to address the load-imbalance problem, we introduce a warp-centric technique, which assigns many threads rather than one to a single frontier vertex. Experiments on both realistic and synthetic networks demonstrate the efficiency of our solution, which achieves 2.9× to 8.44× speedups over the parallel CPU implementation. Our algorithm is open-source and free to the community; it is publicly available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPUs in personal computers and servers, our solution will offer unprecedented opportunities for exploring betweenness-related problems and will motivate follow-up efforts in network science.

Candidíase em pacientes aidéticos

Directory of Open Access Journals (Sweden)

C.E.O.P. Campos

1992-09-01

Full Text Available Trinta e cinco aidéticos entre 19 e 55 anos admitidos e tratados de candidíase no Hospital Emílio Ribas, SP, com ELISA positivo para HIV e confirmado pelo Western Blot. Tuberculose em 9 sendo 2 com pericardite; neurotoxoplasmose em 6; neurocriptococose em 5; herpes labial em 4; pneumocistose em 3 e sarcoma de Kaposi em 2, achavam-se associadas. A concentração inibitória mínima 50% (MIC 50% para os azoles foi: ketoconazol= 2,2 µg/ml; itraconazol- 21,0 µg/ml; fluconazol = 19,0 µg/ml. O MIC 50% para ospolienos: nistatina- 50,0 µg/ml; anfotericina B= 0,12 µg/ml e para 5 fluorcitosina= 1,6 µg/ml nas 35 amostras de Candida isoladas. Testes não paramétricos de Siegel revelaram significante identificação (80% das Candida albicans na candidíase, e que a dose de AMB não modificou o número de óbitos, precoce e tardio, ocorridos nesses aidéticos. O uso prévio dos azoles e da nistatina explicaria, talvez, o elevado MIC 50% observado nas amostras de Candida isoladas.

A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image registration

Energy Technology Data Exchange (ETDEWEB)

Neylon, J., E-mail: jneylon@mednet.ucla.edu; Qi, X.; Sheng, K.; Low, D. A.; Kupelian, P.; Santhanam, A. [Department of Radiation Oncology, University of California Los Angeles, 200 Medical Plaza, #B265, Los Angeles, California 90095 (United States); Staton, R.; Pukala, J.; Manon, R. [Department of Radiation Oncology, M.D. Anderson Cancer Center, Orlando, 1440 South Orange Avenue, Orlando, Florida 32808 (United States)

2015-01-15

Purpose: Validating the usage of deformable image registration (DIR) for daily patient positioning is critical for adaptive radiotherapy (RT) applications pertaining to head and neck (HN) radiotherapy. The authors present a methodology for generating biomechanically realistic ground-truth data for validating DIR algorithms for HN anatomy by (a) developing a high-resolution deformable biomechanical HN model from a planning CT, (b) simulating deformations for a range of interfraction posture changes and physiological regression, and (c) generating subsequent CT images representing the deformed anatomy. Methods: The biomechanical model was developed using HN kVCT datasets and the corresponding structure contours. The voxels inside a given 3D contour boundary were clustered using a graphics processing unit (GPU) based algorithm that accounted for inconsistencies and gaps in the boundary to form a volumetric structure. While the bony anatomy was modeled as rigid body, the muscle and soft tissue structures were modeled as mass–spring-damper models with elastic material properties that corresponded to the underlying contoured anatomies. Within a given muscle structure, the voxels were classified using a uniform grid and a normalized mass was assigned to each voxel based on its Hounsfield number. The soft tissue deformation for a given skeletal actuation was performed using an implicit Euler integration with each iteration split into two substeps: one for the muscle structures and the other for the remaining soft tissues. Posture changes were simulated by articulating the skeletal structure and enabling the soft structures to deform accordingly. Physiological changes representing tumor regression were simulated by reducing the target volume and enabling the surrounding soft structures to deform accordingly. Finally, the authors also discuss a new approach to generate kVCT images representing the deformed anatomy that accounts for gaps and antialiasing artifacts that may

A GPU based high-resolution multilevel biomechanical head and neck model for validating deformable image registration

International Nuclear Information System (INIS)

Neylon, J.; Qi, X.; Sheng, K.; Low, D. A.; Kupelian, P.; Santhanam, A.; Staton, R.; Pukala, J.; Manon, R.

2015-01-01

Purpose: Validating the usage of deformable image registration (DIR) for daily patient positioning is critical for adaptive radiotherapy (RT) applications pertaining to head and neck (HN) radiotherapy. The authors present a methodology for generating biomechanically realistic ground-truth data for validating DIR algorithms for HN anatomy by (a) developing a high-resolution deformable biomechanical HN model from a planning CT, (b) simulating deformations for a range of interfraction posture changes and physiological regression, and (c) generating subsequent CT images representing the deformed anatomy. Methods: The biomechanical model was developed using HN kVCT datasets and the corresponding structure contours. The voxels inside a given 3D contour boundary were clustered using a graphics processing unit (GPU) based algorithm that accounted for inconsistencies and gaps in the boundary to form a volumetric structure. While the bony anatomy was modeled as rigid body, the muscle and soft tissue structures were modeled as mass–spring-damper models with elastic material properties that corresponded to the underlying contoured anatomies. Within a given muscle structure, the voxels were classified using a uniform grid and a normalized mass was assigned to each voxel based on its Hounsfield number. The soft tissue deformation for a given skeletal actuation was performed using an implicit Euler integration with each iteration split into two substeps: one for the muscle structures and the other for the remaining soft tissues. Posture changes were simulated by articulating the skeletal structure and enabling the soft structures to deform accordingly. Physiological changes representing tumor regression were simulated by reducing the target volume and enabling the surrounding soft structures to deform accordingly. Finally, the authors also discuss a new approach to generate kVCT images representing the deformed anatomy that accounts for gaps and antialiasing artifacts that may

Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA Tesla GPU Cluster

Energy Technology Data Exchange (ETDEWEB)

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.

Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA Tesla GPU Cluster

International Nuclear Information System (INIS)

Allada, Veerendra; Benjegerdes, Troy; Bode, Brett

2009-01-01

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.

MO-C-17A-03: A GPU-Based Method for Validating Deformable Image Registration in Head and Neck Radiotherapy Using Biomechanical Modeling

International Nuclear Information System (INIS)

Neylon, J; Min, Y; Qi, S; Kupelian, P; Santhanam, A

2014-01-01

Purpose: Deformable image registration (DIR) plays a pivotal role in head and neck adaptive radiotherapy but a systematic validation of DIR algorithms has been limited by a lack of quantitative high-resolution groundtruth. We address this limitation by developing a GPU-based framework that provides a systematic DIR validation by generating (a) model-guided synthetic CTs representing posture and physiological changes, and (b) model-guided landmark-based validation. Method: The GPU-based framework was developed to generate massive mass-spring biomechanical models from patient simulation CTs and contoured structures. The biomechanical model represented soft tissue deformations for known rigid skeletal motion. Posture changes were simulated by articulating skeletal anatomy, which subsequently applied elastic corrective forces upon the soft tissue. Physiological changes such as tumor regression and weight loss were simulated in a biomechanically precise manner. Synthetic CT data was then generated from the deformed anatomy. The initial and final positions for one hundred randomly-chosen mass elements inside each of the internal contoured structures were recorded as ground truth data. The process was automated to create 45 synthetic CT datasets for a given patient CT. For instance, the head rotation was varied between +/− 4 degrees along each axis, and tumor volumes were systematically reduced up to 30%. Finally, the original CT and deformed synthetic CT were registered using an optical flow based DIR. Results: Each synthetic data creation took approximately 28 seconds of computation time. The number of landmarks per data set varied between two and three thousand. The validation method is able to perform sub-voxel analysis of the DIR, and report the results by structure, giving a much more in depth investigation of the error. Conclusions: We presented a GPU based high-resolution biomechanical head and neck model to validate DIR algorithms by generating CT equivalent 3D

Parallelized computation for computer simulation of electrocardiograms using personal computers with multi-core CPU and general-purpose GPU.

Science.gov (United States)

Shen, Wenfeng; Wei, Daming; Xu, Weimin; Zhu, Xin; Yuan, Shizhong

2010-10-01

Biological computations like electrocardiological modelling and simulation usually require high-performance computing environments. This paper introduces an implementation of parallel computation for computer simulation of electrocardiograms (ECGs) in a personal computer environment with an Intel CPU of Core (TM) 2 Quad Q6600 and a GPU of Geforce 8800GT, with software support by OpenMP and CUDA. It was tested in three parallelization device setups: (a) a four-core CPU without a general-purpose GPU, (b) a general-purpose GPU plus 1 core of CPU, and (c) a four-core CPU plus a general-purpose GPU. To effectively take advantage of a multi-core CPU and a general-purpose GPU, an algorithm based on load-prediction dynamic scheduling was developed and applied to setting (c). In the simulation with 1600 time steps, the speedup of the parallel computation as compared to the serial computation was 3.9 in setting (a), 16.8 in setting (b), and 20.0 in setting (c). This study demonstrates that a current PC with a multi-core CPU and a general-purpose GPU provides a good environment for parallel computations in biological modelling and simulation studies. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Study on GPU Computing for SCOPE2 with CUDA

International Nuclear Information System (INIS)

Kodama, Yasuhiro; Tatsumi, Masahiro; Ohoka, Yasunori

2011-01-01

For improving safety and cost effectiveness of nuclear power plants, a core calculation code SCOPE2 has been developed, which adopts detailed calculation models such as the multi-group nodal SP3 transport calculation method in three-dimensional pin-by-pin geometry to achieve high predictability. However, it is difficult to apply the code to loading pattern optimizations since it requires much longer computation time than that of codes based on the nodal diffusion method which is widely used in core design calculations. In this study, we studied possibility of acceleration of SCOPE2 with GPU computing capability which has been recognized as one of the most promising direction of high performance computing. In the previous study with an experimental programming framework, it required much effort to convert the algorithms to ones which fit to GPU computation. It was found, however, that this conversion was tremendously difficult because of the complexity of algorithms and restrictions in implementation. In this study, to overcome this complexity, we utilized the CUDA programming environment provided by NVIDIA which is a versatile and flexible language as an extension to the C/C++ languages. It was confirmed that we could enjoy high performance without degradation of maintainability through test implementation of GPU kernels for neutron diffusion/simplified P3 equation solvers. (author)

Simulating spin models on GPU

Science.gov (United States)

Weigel, Martin

2011-09-01

Over the last couple of years it has been realized that the vast computational power of graphics processing units (GPUs) could be harvested for purposes other than the video game industry. This power, which at least nominally exceeds that of current CPUs by large factors, results from the relative simplicity of the GPU architectures as compared to CPUs, combined with a large number of parallel processing units on a single chip. To benefit from this setup for general computing purposes, the problems at hand need to be prepared in a way to profit from the inherent parallelism and hierarchical structure of memory accesses. In this contribution I discuss the performance potential for simulating spin models, such as the Ising model, on GPU as compared to conventional simulations on CPU.

Multi-scenario evaluation and specification of electromagnetic loads on ITER vacuum vessel

International Nuclear Information System (INIS)

Rozov, Vladimir; Martinez, J.-M.; Portafaix, C.; Sannazzaro, G.

2014-01-01

Highlights: • We present the results of multi-scenario analysis of EM loads on ITER vacuum vessel (VV). • The differentiation of models provides the economic way to perform big amount of calculations. • Functional approximation is proposed for distributed data/FE/numerical results specification. • Examples of specification of the load profiles by trigonometric polynomials (DHT) are given. • Principles of accounting for toroidal asymmetry at EM interactions in tokamak are considered. - Abstract: The electro-magnetic (EM) transients cause mechanical forces, which represent one of the most critical loads for the ITER vacuum vessel (VV). The paper is focused on the results of multi-scenario analysis and systematization of these EM loads, including specifically addressed pressures on shells and the net vertical force. The proposed mathematical model and computational technology, based on the use of integral parameters and operational analysis methods, enabled qualitative and quantitative analysis of the problem, time-efficient computations and systematic assessment of a large number of scenarios. The obtained estimates, found envelopes and peak values exemplify the principal loads on the VV and provide a database to support engineering load specifications. Special attention is given to the challenge of specification and documenting of the results in a form, suitable for using the data in engineering applications. The practical aspects of specification of distributed data, such as experimental and finite-element (FE) results, by analytical interpolants are discussed. The example of functional approximation of the load profiles by trigonometric polynomials based on discrete Hartley transform (DHT) is given

Multi-scenario evaluation and specification of electromagnetic loads on ITER vacuum vessel

Energy Technology Data Exchange (ETDEWEB)

Rozov, Vladimir, E-mail: vladimir.rozov@iter.org; Martinez, J.-M.; Portafaix, C.; Sannazzaro, G.

2014-10-15

Highlights: • We present the results of multi-scenario analysis of EM loads on ITER vacuum vessel (VV). • The differentiation of models provides the economic way to perform big amount of calculations. • Functional approximation is proposed for distributed data/FE/numerical results specification. • Examples of specification of the load profiles by trigonometric polynomials (DHT) are given. • Principles of accounting for toroidal asymmetry at EM interactions in tokamak are considered. - Abstract: The electro-magnetic (EM) transients cause mechanical forces, which represent one of the most critical loads for the ITER vacuum vessel (VV). The paper is focused on the results of multi-scenario analysis and systematization of these EM loads, including specifically addressed pressures on shells and the net vertical force. The proposed mathematical model and computational technology, based on the use of integral parameters and operational analysis methods, enabled qualitative and quantitative analysis of the problem, time-efficient computations and systematic assessment of a large number of scenarios. The obtained estimates, found envelopes and peak values exemplify the principal loads on the VV and provide a database to support engineering load specifications. Special attention is given to the challenge of specification and documenting of the results in a form, suitable for using the data in engineering applications. The practical aspects of specification of distributed data, such as experimental and finite-element (FE) results, by analytical interpolants are discussed. The example of functional approximation of the load profiles by trigonometric polynomials based on discrete Hartley transform (DHT) is given.

Dual matrix ordered subsets reconstruction for accelerated 3D scatter compensation in single-photon emission tomography

International Nuclear Information System (INIS)

Kamphuis, C.; Beekman, F.J.; Van Rijk, P.P.; Viergever, M.A.

1998-01-01

Three-dimensional (3D) iterative maximum likelihood expectation maximization (ML-EM) algorithms for single-photon emission tomography (SPET) are capable of correcting image-degrading effects of non-uniform attenuation, distance-dependent camera response and patient shape-dependent scatter. However, the resulting improvements in quantitation, resolution and signal-to-noise ratio (SNR) are obtained at the cost of a huge computational burden. This paper presents a new acceleration method for ML-EM: dual matrix ordered subsets (DM-OS). DM-OS combines two acceleration methods: (a) different matrices for projection and back-projection and (b) ordered subsets of projections. DM-OS was compared with ML-EM on simulated data and on physical thorax phantom data, for both 180 and 360 orbits. Contrast, normalized standard deviation and mean squared error were calculated for the digital phantom experiment. DM-OS resulted in similar image quality to ML-EM, even for speed-up factors of 200 compared to ML-EM in the case of 120 projections. The thorax phantom data could be reconstructed 50 times faster (60 projections) using DM-OS with preservation of image quality. ML-EM and DM-OS with scatter compensation showed significant improvement of SNR compared to ML-EM without scatter compensation. Furthermore, inclusion of complex image formation models in the computer code is simplified in the case of DM-OS. It is thus shown that DM-OS is a fast and relatively simple algorithm for 3D iterative scatter compensation, with similar results to conventional ML-EM, for both 180 and 360 acquired data. (orig.)

Application of the OS-EM method to the 123I-IMP ARG method. Comparison between FBP and OS-EM methods

International Nuclear Information System (INIS)

Sasaki, Kazuko; Satou, Ayuko; Oomura, Tomomi; Ono, Madoka; Hachiya, Takenori

2004-01-01

We investigated application of the ordered subsets-expectation maximization (OS-EM) method to the 123 I-IMP autoradiography (ARG) method to measure regional cerebral blood flow (rCBF). First, scan time and subsets were fixed at 20 min and 16, respectively, and the influence of iteration on the cross calibration factor (CCF) and quantitative rCBF values obtained by the ARG method was investigated when the iteration number was set at 2, 4, 8, 16, 32, and 90. Next, with the number of iterations set at 4, we compared the scanning times of OS-EM and filtered back projection (FBP). We determined that the CCF values remained at the same level irrespective of iteration number. Quantitative rCBF values had no association with iteration number, either. Using the quantitative rCBF values obtained by 20-min. scanning with FBP as a standard, the time period for collecting SPECT data was 10 min, without sacrificing image quality or quantification. Quantitative rCBF obtained by OS-EM was estimated to be higher than that by FBP. (author)

Bridging FPGA and GPU technologies for AO real-time control

Science.gov (United States)

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.

Real-time Deformation of Detailed Geometry Based on Mappings to a Less Detailed Physical Simulation on the GPU

DEFF Research Database (Denmark)

Mosegaard, Jesper; Sørensen, Thomas Sangild

2005-01-01

Modern graphics processing units (GPUs) can be effectively used to solve physical systems. To use the GPUoptimally, the discretization of the physical system is often restricted to a regular grid. When grid values representspatial positions, a direct visualization can result in a jagged appearance....... In this paper we propose todecouple computation and visualization of such systems. We define mappings that enable the deformation of ahigh-resolution surface based on a physical simulation on a lower resolution uniform grid. More specifically weinvestigate new approaches for the visualization of a GPU based...

Efficient parallel implementation of active appearance model fitting algorithm on GPU.

Science.gov (United States)

Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou

2014-01-01

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

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

DEFF Research Database (Denmark)

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

2011-01-01

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

High performance MRI simulations of motion on multi-GPU systems.

Science.gov (United States)

Xanthis, Christos G; Venetis, Ioannis E; Aletras, Anthony H

2014-07-04

MRI physics simulators have been developed in the past for optimizing imaging protocols and for training purposes. However, these simulators have only addressed motion within a limited scope. The purpose of this study was the incorporation of realistic motion, such as cardiac motion, respiratory motion and flow, within MRI simulations in a high performance multi-GPU environment. Three different motion models were introduced in the Magnetic Resonance Imaging SIMULator (MRISIMUL) of this study: cardiac motion, respiratory motion and flow. Simulation of a simple Gradient Echo pulse sequence and a CINE pulse sequence on the corresponding anatomical model was performed. Myocardial tagging was also investigated. In pulse sequence design, software crushers were introduced to accommodate the long execution times in order to avoid spurious echoes formation. The displacement of the anatomical model isochromats was calculated within the Graphics Processing Unit (GPU) kernel for every timestep of the pulse sequence. Experiments that would allow simulation of custom anatomical and motion models were also performed. Last, simulations of motion with MRISIMUL on single-node and multi-node multi-GPU systems were examined. Gradient Echo and CINE images of the three motion models were produced and motion-related artifacts were demonstrated. The temporal evolution of the contractility of the heart was presented through the application of myocardial tagging. Better simulation performance and image quality were presented through the introduction of software crushers without the need to further increase the computational load and GPU resources. Last, MRISIMUL demonstrated an almost linear scalable performance with the increasing number of available GPU cards, in both single-node and multi-node multi-GPU computer systems. MRISIMUL is the first MR physics simulator to have implemented motion with a 3D large computational load on a single computer multi-GPU configuration. The incorporation

Large-Scale Multi-Dimensional Document Clustering on GPU Clusters

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Mueller, Frank [North Carolina State University; Zhang, Yongpeng [ORNL; Potok, Thomas E [ORNL

2010-01-01

Document clustering plays an important role in data mining systems. Recently, a flocking-based document clustering algorithm has been proposed to solve the problem through simulation resembling the flocking behavior of birds in nature. This method is superior to other clustering algorithms, including k-means, in the sense that the outcome is not sensitive to the initial state. One limitation of this approach is that the algorithmic complexity is inherently quadratic in the number of documents. As a result, execution time becomes a bottleneck with large number of documents. In this paper, we assess the benefits of exploiting the computational power of Beowulf-like clusters equipped with contemporary Graphics Processing Units (GPUs) as a means to significantly reduce the runtime of flocking-based document clustering. Our framework scales up to over one million documents processed simultaneously in a sixteennode GPU cluster. Results are also compared to a four-node cluster with higher-end GPUs. On these clusters, we observe 30X-50X speedups, which demonstrates the potential of GPU clusters to efficiently solve massive data mining problems. Such speedups combined with the scalability potential and accelerator-based parallelization are unique in the domain of document-based data mining, to the best of our knowledge.

The Performance Improvement of the Lagrangian Particle Dispersion Model (LPDM) Using Graphics Processing Unit (GPU) Computing

Science.gov (United States)

2017-08-01

used for its GPU computing capability during the experiment. It has Nvidia Tesla K40 GPU accelerators containing 32 GPU nodes consisting of 1024...cores. CUDA is a parallel computing platform and application programming interface (API) model that was created and designed by Nvidia to give direct...Agricultural and Forest Meteorology. 1995:76:277–291, ISSN 0168-1923. 3. GPU vs. CPU? What is GPU computing? Santa Clara (CA): Nvidia Corporation; 2017

Iterating skeletons

DEFF Research Database (Denmark)

Dieterle, Mischa; Horstmeyer, Thomas; Berthold, Jost

2012-01-01

a particular skeleton ad-hoc for repeated execution turns out to be considerably complicated, and raises general questions about introducing state into a stateless parallel computation. In addition, one would strongly prefer an approach which leaves the original skeleton intact, and only uses it as a building...... block inside a bigger structure. In this work, we present a general framework for skeleton iteration and discuss requirements and variations of iteration control and iteration body. Skeleton iteration is expressed by synchronising a parallel iteration body skeleton with a (likewise parallel) state......Skeleton-based programming is an area of increasing relevance with upcoming highly parallel hardware, since it substantially facilitates parallel programming and separates concerns. When parallel algorithms expressed by skeletons involve iterations – applying the same algorithm repeatedly...

ALICE HLT high speed tracking on GPU

CERN Document Server

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...

Simulation of isothermal multi-phase fuel-coolant interaction using MPS method with GPU acceleration

Energy Technology Data Exchange (ETDEWEB)

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.

GeoSciML and EarthResourceML Update, 2012

Science.gov (United States)

Richard, S. M.; Commissionthe Management; Application Inte, I.

2012-12-01

CGI Interoperability Working Group activities during 2012 include deployment of services using the GeoSciML-Portrayal schema, addition of new vocabularies to support properties added in version 3.0, improvements to server software for deploying services, introduction of EarthResourceML v.2 for mineral resources, and collaboration with the IUSS on a markup language for soils information. GeoSciML and EarthResourceML have been used as the basis for the INSPIRE Geology and Mineral Resources specifications respectively. GeoSciML-Portrayal is an OGC GML simple-feature application schema for presentation of geologic map unit, contact, and shear displacement structure (fault and ductile shear zone) descriptions in web map services. Use of standard vocabularies for geologic age and lithology enables map services using shared legends to achieve visual harmonization of maps provided by different services. New vocabularies have been added to the collection of CGI vocabularies provided to support interoperable GeoSciML services, and can be accessed through http://resource.geosciml.org. Concept URIs can be dereferenced to obtain SKOS rdf or html representations using the SISSVoc vocabulary service. New releases of the FOSS GeoServer application greatly improve support for complex XML feature schemas like GeoSciML, and the ArcGIS for INSPIRE extension implements similar complex feature support for ArcGIS Server. These improved server implementations greatly facilitate deploying GeoSciML services. EarthResourceML v2 adds features for information related to mining activities. SoilML provides an interchange format for soil material, soil profile, and terrain information. Work is underway to add GeoSciML to the portfolio of Open Geospatial Consortium (OGC) specifications.

GPU-based low-level trigger system for the standalone reconstruction of the ring-shaped hit patterns in the RICH Cherenkov detector of NA62 experiment

International Nuclear Information System (INIS)

Ammendola, R.; Biagioni, A.; Cretaro, P.; Frezza, O.; Cicero, F. Lo; Lonardo, A.; Martinelli, M.; Paolucci, P.S.; Pastorelli, E.; Chiozzi, S.; Ramusino, A. Cotta; Fiorini, M.; Gianoli, A.; Neri, I.; Lorenzo, S. Di; Fantechi, R.; Piandani, R.; Pontisso, L.; Lamanna, G.; Piccini, M.

2017-01-01

This project aims to exploit the parallel computing power of a commercial Graphics Processing Unit (GPU) to implement fast pattern matching in the Ring Imaging Cherenkov (RICH) detector for the level 0 (L0) trigger of the NA62 experiment. In this approach, the ring-fitting algorithm is seedless, being fed with raw RICH data, with no previous information on the ring position from other detectors. Moreover, since the L0 trigger is provided with a more elaborated information than a simple multiplicity number, it results in a higher selection power. Two methods have been studied in order to reduce the data transfer latency from the readout boards of the detector to the GPU, i.e., the use of a dedicated NIC device driver with very low latency and a direct data transfer protocol from a custom FPGA-based NIC to the GPU. The performance of the system, developed through the FPGA approach, for multi-ring Cherenkov online reconstruction obtained during the NA62 physics runs is presented.

Overview of implementation of DARPA GPU program in SAIC

Science.gov (United States)

Braunreiter, Dennis; Furtek, Jeremy; Chen, Hai-Wen; Healy, Dennis

2008-04-01

This paper reviews the implementation of DARPA MTO STAP-BOY program for both Phase I and II conducted at Science Applications International Corporation (SAIC). The STAP-BOY program conducts fast covariance factorization and tuning techniques for space-time adaptive process (STAP) Algorithm Implementation on Graphics Processor unit (GPU) Architectures for Embedded Systems. The first part of our presentation on the DARPA STAP-BOY program will focus on GPU implementation and algorithm innovations for a prototype radar STAP algorithm. The STAP algorithm will be implemented on the GPU, using stream programming (from companies such as PeakStream, ATI Technologies' CTM, and NVIDIA) and traditional graphics APIs. This algorithm will include fast range adaptive STAP weight updates and beamforming applications, each of which has been modified to exploit the parallel nature of graphics architectures.

GPU-based stochastic-gradient optimization for non-rigid medical image registration in time-critical applications

NARCIS (Netherlands)

Staring, M.; Al-Ars, Z.; Berendsen, Floris; Angelini, Elsa D.; Landman, Bennett A.

2018-01-01

Currently, non-rigid image registration algorithms are too computationally intensive to use in time-critical applications. Existing implementations that focus on speed typically address this by either parallelization on GPU-hardware, or by introducing methodically novel techniques into

Desempenho de idosos brasileiros no teste de deglutição de 100 ml de água Performance of Brazilian elderly on the 100 ml water swallowing test

Directory of Open Access Journals (Sweden)

Graziela Maria Martins Moreira

2012-03-01

Full Text Available OBJETIVO: Comparar o desempenho de idosos brasileiros, residentes em uma instituição de longa permanência, no teste de deglutição de 100 ml de água com os resultados obtidos em idosos ingleses. MÉTODOS: Dezoito idosos residentes numa instituição de longa permanência, considerados normais para a função de deglutição (13 mulheres e cinco homens, com idade média de 83,46 anos foram solicitados a beber 100 ml de água de um copo plástico, reproduzindo o estudo inglês. O avaliador observou lateralmente o número de goles, tempo gasto e intercorrências, gerando três índices: volume por deglutição (ml, tempo por deglutição (s e capacidade de deglutição (ml/s. RESULTADOS: A capacidade de deglutição para homens foi menor do que a das mulheres, divergindo do estudo original. O tempo médio de cada deglutição e o volume médio por deglutição foi semelhante para ambos os gêneros. CONCLUSÃO: A capacidade de deglutição em idosos é inferior à de adultos normais, indicando lentificação da deglutição. A diferença entre gêneros encontrada no estudo original não foi reproduzida, entretanto nossa amostra foi mais idosa.PURPOSE: To compare the performance of Brazilian elderly patients living in a long-term care facility on the 100 ml water swallowing test with the results obtained with British elderly. METHODS: Eighteen elderly subjects (13 women and five men, mean age 83.46, residents in a long-term care facility and considered normal regarding the swallowing function, were selected to take part in this study. As in a British study, they were laterally observed by the examiner while swallowing 100 ml of water from a plastic cup. The examiner observed the number of sips, the time taken, and complications during the test, which generated the following indices: volume per swallow (ml, time per swallow (s, and swallowing capacity (ml/s. RESULTS: The elderly men had lower swallowing capacity than the women in the research

High performance technique for database applicationsusing a hybrid GPU/CPU platform

KAUST Repository

Zidan, Mohammed A.; Bonny, Talal; Salama, Khaled N.

2012-01-01

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

High-speed, multi-input, multi-output control using GPU processing in the HBT-EP tokamak

Energy Technology Data Exchange (ETDEWEB)

Rath, N., E-mail: Nikolaus@rath.org [Columbia University, Rm 200 Mudd, 500 W 120th St, New York, NY - 10027 (United States); Bialek, J.; Byrne, P.J.; DeBono, B.; Levesque, J.P.; Li, B.; Mauel, M.E.; Maurer, D.A.; Navratil, G.A.; Shiraki, D. [Columbia University, Rm 200 Mudd, 500 W 120th St, New York, NY - 10027 (United States)

2012-12-15

Highlights: Black-Right-Pointing-Pointer We present a GPU based system for magnetic control of perturbed equilibria. Black-Right-Pointing-Pointer Cycle times are below 5 {mu}s and I/O latencies below 10 {mu}s for 96 inputs and 64 outputs. Black-Right-Pointing-Pointer A new architecture removes host RAM and CPU from the control cycle. Black-Right-Pointing-Pointer GPU and DA/AD modules operate independently and communicate via PCIe peer-to-peer connections. Black-Right-Pointing-Pointer The Linux host system does not require real-time extensions. - Abstract: We report on the design of a new plasma control system for the HBT-EP tokamak that utilizes a graphical processing unit (GPU) to magnetically control the 3D perturbed equilibrium state [1] of the plasma. The control system achieves cycle times of 5 {mu}s and I/O latencies below 10 {mu}s for up to 96 inputs and 64 outputs. The number of state variables is in the same order. To handle the resulting computational complexity under the given time constraints, the control algorithms are designed for massively parallel processing. The necessary hardware resources are provided by an NVIDIA Tesla M2050 GPU, offering a total of 448 computing cores running at 1.3 GHz each. A new control architecture allows control input from magnetic diagnostics to be pushed directly into GPU memory by a D-TACQ ACQ196 digitizer, and control output to be pulled directly from GPU memory by two D-TACQ AO32 analog output modules. By using peer-to-peer PCI express connections, this technique completely eliminates the use of host RAM and central processing unit (CPU) from the control cycle, permitting single-digit microsecond latencies on a standard Linux host system without any real-time extensions.

Iterative reconstruction with attenuation compensation from cone-beam projections acquired via nonplanar orbits

International Nuclear Information System (INIS)

Zeng, G.L.; Weng, Y.; Gullberg, G.T.

1997-01-01

Single photon emission computed tomography (SPECT) imaging with cone-beam collimators provides improved sensitivity and spatial resolution for imaging small objects with large field-of-view detectors. It is known that Tuy's cone-beam data sufficiency condition must be met to obtain artifact-free reconstructions. Even though Tuy's condition was derived for an attenuation-free situation, the authors hypothesize that an artifact-free reconstruction can be obtained even if the cone-beam data are attenuated, provided the imaging orbit satisfies Tuy's condition and the exact attenuation map is known. In the authors' studies, emission data are acquired using nonplanar circle-and-line orbits to acquire cone-beam data for tomographic reconstructions. An extended iterative ML-EM (maximum likelihood-expectation maximization) reconstruction algorithm is derived and used to reconstruct projection data with either a pre-acquired or assumed attenuation map. Quantitative accuracy of the attenuation corrected emission reconstruction is significantly improved

Simulation-based design process for the verification of ITER remote handling systems

International Nuclear Information System (INIS)

Sibois, Romain; Määttä, Timo; Siuko, Mikko; Mattila, Jouni

2014-01-01

Highlights: •Verification and validation process for ITER remote handling system. •Simulation-based design process for early verification of ITER RH systems. •Design process centralized around simulation lifecycle management system. •Verification and validation roadmap for digital modelling phase. -- Abstract: The work behind this paper takes place in the EFDA's European Goal Oriented Training programme on Remote Handling (RH) “GOT-RH”. The programme aims to train engineers for activities supporting the ITER project and the long-term fusion programme. One of the projects of this programme focuses on the verification and validation (V and V) of ITER RH system requirements using digital mock-ups (DMU). The purpose of this project is to study and develop efficient approach of using DMUs in the V and V process of ITER RH system design utilizing a System Engineering (SE) framework. Complex engineering systems such as ITER facilities lead to substantial rise of cost while manufacturing the full-scale prototype. In the V and V process for ITER RH equipment, physical tests are a requirement to ensure the compliance of the system according to the required operation. Therefore it is essential to virtually verify the developed system before starting the prototype manufacturing phase. This paper gives an overview of the current trends in using digital mock-up within product design processes. It suggests a simulation-based process design centralized around a simulation lifecycle management system. The purpose of this paper is to describe possible improvements in the formalization of the ITER RH design process and V and V processes, in order to increase their cost efficiency and reliability

Collision detection of convex polyhedra on the NVIDIA GPU architecture for the discrete element method

CSIR Research Space (South Africa)

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...

Heterogeneous CPU-GPU moving targets detection for UAV video

Science.gov (United States)

Li, Maowen; Tang, Linbo; Han, Yuqi; Yu, Chunlei; Zhang, Chao; Fu, Huiquan

2017-07-01

Moving targets detection is gaining popularity in civilian and military applications. On some monitoring platform of motion detection, some low-resolution stationary cameras are replaced by moving HD camera based on UAVs. The pixels of moving targets in the HD Video taken by UAV are always in a minority, and the background of the frame is usually moving because of the motion of UAVs. The high computational cost of the algorithm prevents running it at higher resolutions the pixels of frame. Hence, to solve the problem of moving targets detection based UAVs video, we propose a heterogeneous CPU-GPU moving target detection algorithm for UAV video. More specifically, we use background registration to eliminate the impact of the moving background and frame difference to detect small moving targets. In order to achieve the effect of real-time processing, we design the solution of heterogeneous CPU-GPU framework for our method. The experimental results show that our method can detect the main moving targets from the HD video taken by UAV, and the average process time is 52.16ms per frame which is fast enough to solve the problem.

Physics research needs for ITER

International Nuclear Information System (INIS)

Sauthoff, N.R.

1995-01-01

Design of ITER entails the application of physics design tools that have been validated against the world-wide data base of fusion research. In many cases, these tools do not yet exist and must be developed as part of the ITER physics program. ITER's considerable increases in power and size demand significant extrapolations from the current data base; in several cases, new physical effects are projected to dominate the behavior of the ITER plasma. This paper focuses on those design tools and data that have been identified by the ITER team and are not yet available; these needs serve as the basis for the ITER Physics Research Needs, which have been developed jointly by the ITER Physics Expert Groups and the ITER design team. Development of the tools and the supporting data base is an on-going activity that constitutes a significant opportunity for contributions to the ITER program by fusion research programs world-wide

Atomic orbital-based SOS-MP2 with tensor hypercontraction. I. GPU-based tensor construction and exploiting sparsity

Energy Technology Data Exchange (ETDEWEB)

Song, Chenchen; Martínez, Todd J. [Department of Chemistry and the PULSE Institute, Stanford University, Stanford, California 94305 (United States); SLAC National Accelerator Laboratory, Menlo Park, California 94025 (United States)

2016-05-07

We present a tensor hypercontracted (THC) scaled opposite spin second order Møller-Plesset perturbation theory (SOS-MP2) method. By using THC, we reduce the formal scaling of SOS-MP2 with respect to molecular size from quartic to cubic. We achieve further efficiency by exploiting sparsity in the atomic orbitals and using graphical processing units (GPUs) to accelerate integral construction and matrix multiplication. The practical scaling of GPU-accelerated atomic orbital-based THC-SOS-MP2 calculations is found to be N{sup 2.6} for reference data sets of water clusters and alanine polypeptides containing up to 1600 basis functions. The errors in correlation energy with respect to density-fitting-SOS-MP2 are less than 0.5 kcal/mol for all systems tested (up to 162 atoms).

Atomic orbital-based SOS-MP2 with tensor hypercontraction. I. GPU-based tensor construction and exploiting sparsity.

Science.gov (United States)

Song, Chenchen; Martínez, Todd J

2016-05-07

We present a tensor hypercontracted (THC) scaled opposite spin second order Møller-Plesset perturbation theory (SOS-MP2) method. By using THC, we reduce the formal scaling of SOS-MP2 with respect to molecular size from quartic to cubic. We achieve further efficiency by exploiting sparsity in the atomic orbitals and using graphical processing units (GPUs) to accelerate integral construction and matrix multiplication. The practical scaling of GPU-accelerated atomic orbital-based THC-SOS-MP2 calculations is found to be N(2.6) for reference data sets of water clusters and alanine polypeptides containing up to 1600 basis functions. The errors in correlation energy with respect to density-fitting-SOS-MP2 are less than 0.5 kcal/mol for all systems tested (up to 162 atoms).

Variable aperture-based ptychographical iterative engine method

Science.gov (United States)

Sun, Aihui; Kong, Yan; Meng, Xin; He, Xiaoliang; Du, Ruijun; Jiang, Zhilong; Liu, Fei; Xue, Liang; Wang, Shouyu; Liu, Cheng

2018-02-01

A variable aperture-based ptychographical iterative engine (vaPIE) is demonstrated both numerically and experimentally to reconstruct the sample phase and amplitude rapidly. By adjusting the size of a tiny aperture under the illumination of a parallel light beam to change the illumination on the sample step by step and recording the corresponding diffraction patterns sequentially, both the sample phase and amplitude can be faithfully reconstructed with a modified ptychographical iterative engine (PIE) algorithm. Since many fewer diffraction patterns are required than in common PIE and the shape, the size, and the position of the aperture need not to be known exactly, this proposed vaPIE method remarkably reduces the data acquisition time and makes PIE less dependent on the mechanical accuracy of the translation stage; therefore, the proposed technique can be potentially applied for various scientific researches.

Implementation of collisions on GPU architecture in the Vorpal code

Science.gov (United States)

Leddy, Jarrod; Averkin, Sergey; Cowan, Ben; Sides, Scott; Werner, Greg; Cary, John

2017-10-01

The Vorpal code contains a variety of collision operators allowing for the simulation of plasmas containing multiple charge species interacting with neutrals, background gas, and EM fields. These existing algorithms have been improved and reimplemented to take advantage of the massive parallelization allowed by GPU architecture. The use of GPUs is most effective when algorithms are single-instruction multiple-data, so particle collisions are an ideal candidate for this parallelization technique due to their nature as a series of independent processes with the same underlying operation. This refactoring required data memory reorganization and careful consideration of device/host data allocation to minimize memory access and data communication per operation. Successful implementation has resulted in an order of magnitude increase in simulation speed for a test-case involving multiple binary collisions using the null collision method. Work supported by DARPA under contract W31P4Q-16-C-0009.

GASPRNG: GPU accelerated scalable parallel random number generator library

Science.gov (United States)

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

Basket Option Pricing Using GP-GPU Hardware Acceleration

KAUST Repository

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.

Using VS30 to Estimate Station ML Adjustments (dML)

Science.gov (United States)

Yong, A.; Herrick, J.; Cochran, E. S.; Andrews, J. R.; Yu, E.

2017-12-01

Currently, new seismic stations added to a regional seismic network cannot be used to calculate local or Richter magnitude (ML) until a revised region-wide amplitude decay function is developed. The new station must record a minimum number of local and regional events that meet specific amplitude requirements prior to re-calibration of the amplitude decay function. Therefore, there can be significant delay between when a new station starts contributing real-time waveform packets and when the data can be included in magnitude estimation. The station component adjustments (dML; Uhrhammer et al., 2011) are calculated after first inverting for a new regional amplitude decay function, constrained by the sum of dML for long-running stations. Here, we propose a method to calculate an initial dML using known or proxy values of seismic site conditions. For site conditions, we use the time-averaged shear-wave velocity (VS) of the upper 30 m (VS30). We solve for dML as described in Equation (1) by Uhrhammer et al. (2011): ML = log (A) - log A0 (r) + dML, where A is the maximum Wood and Anderson (1925) trace amplitude (mm), r is the distance (km), and dML is the station adjustment. Measured VS30 and estimated dML data are comprised of records from 887 horizontal components (east-west and north-south orientations) from 93 seismic monitoring stations in the California Integrated Seismic Network. VS30 values range from 202 m/s to 1464 m/s and dML range from -1.10 to 0.39. VS30 and dML exhibit a positive correlation coefficient (R = 0.72), indicating that as VS30 increases, dML increases. This implies that greater site amplification (i.e., lower VS30) results in smaller ML. When we restrict VS30 regional network ML estimates immediately without the need to wait until a minimum set of earthquake data has been recorded.

Fast GPU-based computation of the sensitivity matrix for a PET list-mode OSEM algorithm

Energy Technology Data Exchange (ETDEWEB)

Nassiri, Moulay Ali; Carrier, Jean-Francois [Montreal Univ., QC (Canada). Dept. de Radio-Oncologie; Hissoiny, Sami [Ecole Polytechnique de Montreal, QC (Canada). Dept. de Genie Informatique et Genie Logiciel; Despres, Philippe [Quebec Univ. (Canada). Dept. de Radio-Oncologie

2011-07-01

One of the obstacle in introducing a list-mode PET reconstruction algorithm for routine clinical use is the long computation time required for the sensitivity matrix calculation. This matrix must be computed for each study because it depends on the object attenuation map. During the last decade, studies have shown that 3D list-mode OSEM reconstruction algorithms could be effectively performed and considerably accelerated by GPU devices. However, most of that preliminary work (1) was done for pre-clinical PET systems in which the number of LORs is small compared to modern human PET systems and (2) supposed that the sensitivity matrix is pre-calculated. The time required to compute this matrix can however be longer than the reconstruction time itself. The objective of this work is to investigate the performance of sensitivity matrix calculations in terms of computation time with modern GPUs, for clinical fully 3D LM-OSEM for modern PET scanners. For this purpose, sensitivity matrix calculations and full list-mode OSEM reconstruction for human PET systems were implemented on GPUs using the CUDA framework. The system matrices were built on-the-fly by using the multi-ray Siddon algorithm. The time to compute the sensitivity matrix for 288 x 288 x 57 arrays using 3 tangential LORs was 29 seconds. The 3D LM-OSEM algorithm, including the sensitivity matrix calculation, was performed for the same LORs in 71 seconds for 62 millions events, 6 frames and 1 iterations. This work let envision fast reconstructions for advanced PET application such as dynamic studies and parametric image reconstruction. (orig.)

GPU-accelerated denoising of 3D magnetic resonance images

Energy Technology Data Exchange (ETDEWEB)

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.

Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

Directory of Open Access Journals (Sweden)

Jinwei Wang

2014-01-01

Full Text Available The active appearance model (AAM is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

SysML for systems engineering a model-based approach

CERN Document Server

Holt, Jon

2013-01-01

This new edition of this popular text has been fully updated to reflect SysML 1.3, the latest version of the standard, and the discussion has been extended to show the power of SysML as a tool for systems engineering in an MBSE context.

A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment

Energy Technology Data Exchange (ETDEWEB)

Liu, T.; Ding, A.; Ji, W.; Xu, X. G. [Nuclear Engineering and Engineering Physics, Rensselaer Polytechnic Inst., Troy, NY 12180 (United States); Carothers, C. D. [Dept. of Computer Science, Rensselaer Polytechnic Inst. RPI (United States); Brown, F. B. [Los Alamos National Laboratory (LANL) (United States)

2012-07-01

Monte Carlo (MC) method is able to accurately calculate eigenvalues in reactor analysis. Its lengthy computation time can be reduced by general-purpose computing on Graphics Processing Units (GPU), one of the latest parallel computing techniques under development. The method of porting a regular transport code to GPU is usually very straightforward due to the 'embarrassingly parallel' nature of MC code. However, the situation becomes different for eigenvalue calculation in that it will be performed on a generation-by-generation basis and the thread coordination should be explicitly taken care of. This paper presents our effort to develop such a GPU-based MC code in Compute Unified Device Architecture (CUDA) environment. The code is able to perform eigenvalue calculation under simple geometries on a multi-GPU system. The specifics of algorithm design, including thread organization and memory management were described in detail. The original CPU version of the code was tested on an Intel Xeon X5660 2.8 GHz CPU, and the adapted GPU version was tested on NVIDIA Tesla M2090 GPUs. Double-precision floating point format was used throughout the calculation. The result showed that a speedup of 7.0 and 33.3 were obtained for a bare spherical core and a binary slab system respectively. The speedup factor was further increased by a factor of {approx}2 on a dual GPU system. The upper limit of device-level parallelism was analyzed, and a possible method to enhance the thread-level parallelism was proposed. (authors)

A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment

International Nuclear Information System (INIS)

Liu, T.; Ding, A.; Ji, W.; Xu, X. G.; Carothers, C. D.; Brown, F. B.

2012-01-01

Monte Carlo (MC) method is able to accurately calculate eigenvalues in reactor analysis. Its lengthy computation time can be reduced by general-purpose computing on Graphics Processing Units (GPU), one of the latest parallel computing techniques under development. The method of porting a regular transport code to GPU is usually very straightforward due to the 'embarrassingly parallel' nature of MC code. However, the situation becomes different for eigenvalue calculation in that it will be performed on a generation-by-generation basis and the thread coordination should be explicitly taken care of. This paper presents our effort to develop such a GPU-based MC code in Compute Unified Device Architecture (CUDA) environment. The code is able to perform eigenvalue calculation under simple geometries on a multi-GPU system. The specifics of algorithm design, including thread organization and memory management were described in detail. The original CPU version of the code was tested on an Intel Xeon X5660 2.8 GHz CPU, and the adapted GPU version was tested on NVIDIA Tesla M2090 GPUs. Double-precision floating point format was used throughout the calculation. The result showed that a speedup of 7.0 and 33.3 were obtained for a bare spherical core and a binary slab system respectively. The speedup factor was further increased by a factor of ∼2 on a dual GPU system. The upper limit of device-level parallelism was analyzed, and a possible method to enhance the thread-level parallelism was proposed. (authors)

How General-Purpose can a GPU be?

Directory of Open Access Journals (Sweden)

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 GPU accelerated and error-controlled solver for the unbounded Poisson equation in three dimensions

Science.gov (United States)

Exl, Lukas

2017-12-01

An efficient solver for the three dimensional free-space Poisson equation is presented. The underlying numerical method is based on finite Fourier series approximation. While the error of all involved approximations can be fully controlled, the overall computation error is driven by the convergence of the finite Fourier series of the density. For smooth and fast-decaying densities the proposed method will be spectrally accurate. The method scales with O(N log N) operations, where N is the total number of discretization points in the Cartesian grid. The majority of the computational costs come from fast Fourier transforms (FFT), which makes it ideal for GPU computation. Several numerical computations on CPU and GPU validate the method and show efficiency and convergence behavior. Tests are performed using the Vienna Scientific Cluster 3 (VSC3). A free MATLAB implementation for CPU and GPU is provided to the interested community.

GPU-based online track reconstruction for the MuPix-telescope

Energy Technology Data Exchange (ETDEWEB)

Grzesik, Carsten [JGU, Mainz (Germany); Collaboration: Mu3e-Collaboration

2016-07-01

The MuPix telescope is a beam telescope consisting of High Voltage Monolithic Active Pixel Sensors (HV-MAPS). This type of sensor is going to be used for the Mu3e experiment, which is aiming to measure the lepton flavor violating decay μ→ eee with an ultimate sensitivity of 10{sup -16}. This sensitivity requires a high muon decay rate in the order of 1 GHz leading to a data rate of about 1 TBit/s for the whole detector. This needs to be reduced by a factor 1000 using online event selection algorithms on Graphical Processing Units (GPUs) before passing the data to the storage. A test setup for the MuPix sensors and parts of the Mu3e tracking detector readout is realized in a four plane telescope. The telescope can also be used to show the usability of an online track reconstruction using GPUs. As a result the telescope can provide online information about efficiencies of a device under test or the alignment of the telescope itself. This talk discusses the implementation of the GPU based track reconstruction and shows some results from recent testbeam campaigns.

Multi-GPU parallel algorithm design and analysis for improved inversion of probability tomography with gravity gradiometry data

Science.gov (United States)

Hou, Zhenlong; Huang, Danian

2017-09-01

In this paper, we make a study on the inversion of probability tomography (IPT) with gravity gradiometry data at first. The space resolution of the results is improved by multi-tensor joint inversion, depth weighting matrix and the other methods. Aiming at solving the problems brought by the big data in the exploration, we present the parallel algorithm and the performance analysis combining Compute Unified Device Architecture (CUDA) with Open Multi-Processing (OpenMP) based on Graphics Processing Unit (GPU) accelerating. In the test of the synthetic model and real data from Vinton Dome, we get the improved results. It is also proved that the improved inversion algorithm is effective and feasible. The performance of parallel algorithm we designed is better than the other ones with CUDA. The maximum speedup could be more than 200. In the performance analysis, multi-GPU speedup and multi-GPU efficiency are applied to analyze the scalability of the multi-GPU programs. The designed parallel algorithm is demonstrated to be able to process larger scale of data and the new analysis method is practical.

PENGARUH PENAMBAHAN EFFECTIVE MICROORGANISM-4 (EM-4 PADA PENGOMPOSAN TERHADAP PRODUKSI JAMUR MERANG

Directory of Open Access Journals (Sweden)

Adisti Yuliastrin

2016-09-01

Full Text Available This research was aimed for knowing the influence of  EM-4 (effective microorganism-4 addition and the most effective concentration of EM-4 in compost media for straw mushroom (Volvariella volvacea (Bull. ex Fr. Sing production. The treatment examined the use of EM-4 solution divided into six treatments and four repetitions. EM-4 concentration from 0 ml/l – 12,5 ml/l with an interval increasing of 2,5 ml/l. The addition of 7,5 ml/l – 12,5 ml/l concentration of EM-4 did not give so different result with the control media. Composting with addition of 5 ml/l EM-4 gave the highest straw mushroom production (2272,25 g. Statistically the result showed that the concentrations of EM-4 had no significant effect on the mushroom production.

Equivalent charge source model based iterative maximum neighbor weight for sparse EEG source localization.

Science.gov (United States)

Xu, Peng; Tian, Yin; Lei, Xu; Hu, Xiao; Yao, Dezhong

2008-12-01

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.

Understandings of the Concept of Iteration in Design-Based Research

DEFF Research Database (Denmark)

Gundersen, Peter Bukovica

2017-01-01

The paper is the first in a series of papers addressing design in Design-based research. The series looks into the question of how this research approach is connected to design. What happens when educational researchers adopt designerly ways of working? This paper provides an overview of design......-based research and from there on discuss one key characteristic, namely iterations, which are fundamental to educational design research in relation to how designers operate and why. The paper concludes that in general iteration is not a particularly well-described aspect in the reporting of DBR-projects. Half...... and usually after long periods of testing design solutions in practice....

A new subspace based approach to iterative learning control

NARCIS (Netherlands)

Nijsse, G.; Verhaegen, M.; Doelman, N.J.

2001-01-01

This paper1 presents an iterative learning control (ILC) procedure based on an inverse model of the plant under control. Our first contribution is that we formulate the inversion procedure as a Kalman smoothing problem: based on a compact state space model of a possibly non-minimum phase system,