Real-time generation of infrared ocean scene based on GPU

NASA Astrophysics Data System (ADS)

Jiang, Zhaoyi; Wang, Xun; Lin, Yun; Jin, Jianqiu

2007-12-01

Infrared (IR) image synthesis for ocean scene has become more and more important nowadays, especially for remote sensing and military application. Although a number of works present ready-to-use simulations, those techniques cover only a few possible ways of water interacting with the environment. And the detail calculation of ocean temperature is rarely considered by previous investigators. With the advance of programmable features of graphic card, many algorithms previously limited to offline processing have become feasible for real-time usage. In this paper, we propose an efficient algorithm for real-time rendering of infrared ocean scene using the newest features of programmable graphics processors (GPU). It differs from previous works in three aspects: adaptive GPU-based ocean surface tessellation, sophisticated balance equation of thermal balance for ocean surface, and GPU-based rendering for infrared ocean scene. Finally some results of infrared image are shown, which are in good accordance with real images.

Visual Media Reasoning - Terrain-based Geolocation

DTIC Science & Technology

2015-06-01

the drawings, specifications, or other data does not license the holder or any other person or corporation ; or convey any rights or permission to...3.4 Alternative Metric Investigation This section describes a graphics processor unit (GPU) based implementation in the NVIDIA CUDA programming...utilizing 2 concurrent CPU cores, each controlling a single Nvidia C2075 Tesla Fermi CUDA card. Figure 22 shows a comparison of the CPU and the GPU powered

A survey of GPU-based medical image computing techniques

PubMed Central

Shi, Lin; Liu, Wen; Zhang, Heye; Xie, Yongming

2012-01-01

Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine. PMID:23256080

CPU architecture for a fast and energy-saving calculation of convolution neural networks

NASA Astrophysics Data System (ADS)

Knoll, Florian J.; Grelcke, Michael; Czymmek, Vitali; Holtorf, Tim; Hussmann, Stephan

2017-06-01

One of the most difficult problem in the use of artificial neural networks is the computational capacity. Although large search engine companies own specially developed hardware to provide the necessary computing power, for the conventional user only remains the state of the art method, which is the use of a graphic processing unit (GPU) as a computational basis. Although these processors are well suited for large matrix computations, they need massive energy. Therefore a new processor on the basis of a field programmable gate array (FPGA) has been developed and is optimized for the application of deep learning. This processor is presented in this paper. The processor can be adapted for a particular application (in this paper to an organic farming application). The power consumption is only a fraction of a GPU application and should therefore be well suited for energy-saving applications.

High-performance computing on GPUs for resistivity logging of oil and gas wells

NASA Astrophysics Data System (ADS)

Glinskikh, V.; Dudaev, A.; Nechaev, O.; Surodina, I.

2017-10-01

We developed and implemented into software an algorithm for high-performance simulation of electrical logs from oil and gas wells using high-performance heterogeneous computing. The numerical solution of the 2D forward problem is based on the finite-element method and the Cholesky decomposition for solving a system of linear algebraic equations (SLAE). Software implementations of the algorithm used the NVIDIA CUDA technology and computing libraries are made, allowing us to perform decomposition of SLAE and find its solution on central processor unit (CPU) and graphics processor unit (GPU). The calculation time is analyzed depending on the matrix size and number of its non-zero elements. We estimated the computing speed on CPU and GPU, including high-performance heterogeneous CPU-GPU computing. Using the developed algorithm, we simulated resistivity data in realistic models.

A GPU Parallelization of the Absolute Nodal Coordinate Formulation for Applications in Flexible Multibody Dynamics

DTIC Science & Technology

2012-02-17

to be solved. Disclaimer: Reference herein to any specific commercial company , product, process, or service by trade name, trademark...data processing rather than data caching and control flow. To make use of this computational power, NVIDIA introduced a general purpose parallel...GPU implementations were run on an Intel Nehalem Xeon E5520 2.26GHz processor with an NVIDIA Tesla C2070 graphics card for varying numbers of

MATCHED FILTER COMPUTATION ON FPGA, CELL, AND GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

BAKER, ZACHARY K.; GOKHALE, MAYA B.; TRIPP, JUSTIN L.

2007-01-08

The matched filter is an important kernel in the processing of hyperspectral data. The filter enables researchers to sift useful data from instruments that span large frequency bands. In this work, they evaluate the performance of a matched filter algorithm implementation on accelerated co-processor (XD1000), the IBM Cell microprocessor, and the NVIDIA GeForce 6900 GTX GPU graphics card. They provide extensive discussion of the challenges and opportunities afforded by each platform. In particular, they explore the problems of partitioning the filter most efficiently between the host CPU and the co-processor. Using their results, they derive several performance metrics that providemore » the optimal solution for a variety of application situations.« less

Analysis of impact of general-purpose graphics processor units in supersonic flow modeling

NASA Astrophysics Data System (ADS)

Emelyanov, V. N.; Karpenko, A. G.; Kozelkov, A. S.; Teterina, I. V.; Volkov, K. N.; Yalozo, A. V.

2017-06-01

Computational methods are widely used in prediction of complex flowfields associated with off-normal situations in aerospace engineering. Modern graphics processing units (GPU) provide architectures and new programming models that enable to harness their large processing power and to design computational fluid dynamics (CFD) simulations at both high performance and low cost. Possibilities of the use of GPUs for the simulation of external and internal flows on unstructured meshes are discussed. The finite volume method is applied to solve three-dimensional unsteady compressible Euler and Navier-Stokes equations on unstructured meshes with high resolution numerical schemes. CUDA technology is used for programming implementation of parallel computational algorithms. Solutions of some benchmark test cases on GPUs are reported, and the results computed are compared with experimental and computational data. Approaches to optimization of the CFD code related to the use of different types of memory are considered. Speedup of solution on GPUs with respect to the solution on central processor unit (CPU) is compared. Performance measurements show that numerical schemes developed achieve 20-50 speedup on GPU hardware compared to CPU reference implementation. The results obtained provide promising perspective for designing a GPU-based software framework for applications in CFD.

Accelerating Pseudo-Random Number Generator for MCNP on GPU

NASA Astrophysics Data System (ADS)

Gong, Chunye; Liu, Jie; Chi, Lihua; Hu, Qingfeng; Deng, Li; Gong, Zhenghu

2010-09-01

Pseudo-random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N-Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA's GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.

Proton Testing of nVidia GTX 1050 GPU

NASA Technical Reports Server (NTRS)

Wyrwas, E. J.

2017-01-01

Single-Event Effects (SEE) testing was conducted on the nVidia GTX 1050 Graphics Processor Unit (GPU); herein referred to as device under test (DUT). Testing was conducted at Massachusetts General Hospitals (MGH) Francis H. Burr Proton Therapy Center on April 9th, 2017 using 200-MeV protons. This testing trip was purposed to provide a baseline assessment of the radiation susceptibility of the DUT as no previous testing had been conducted on this component.

permGPU: Using graphics processing units in RNA microarray association studies.

PubMed

Shterev, Ivo D; Jung, Sin-Ho; George, Stephen L; Owzar, Kouros

2010-06-16

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. 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. 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.

Real time emotion aware applications: a case study employing emotion evocative pictures and neuro-physiological sensing enhanced by Graphic Processor Units.

PubMed

Konstantinidis, Evdokimos I; Frantzidis, Christos A; Pappas, Costas; Bamidis, Panagiotis D

2012-07-01

In this paper the feasibility of adopting Graphic Processor Units towards real-time emotion aware computing is investigated for boosting the time consuming computations employed in such applications. The proposed methodology was employed in analysis of encephalographic and electrodermal data gathered when participants passively viewed emotional evocative stimuli. The GPU effectiveness when processing electroencephalographic and electrodermal recordings is demonstrated by comparing the execution time of chaos/complexity analysis through nonlinear dynamics (multi-channel correlation dimension/D2) and signal processing algorithms (computation of skin conductance level/SCL) into various popular programming environments. Apart from the beneficial role of parallel programming, the adoption of special design techniques regarding memory management may further enhance the time minimization which approximates a factor of 30 in comparison with ANSI C language (single-core sequential execution). Therefore, the use of GPU parallel capabilities offers a reliable and robust solution for real-time sensing the user's affective state. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

GPU acceleration for digitally reconstructed radiographs using bindless texture objects and CUDA/OpenGL interoperability.

PubMed

Abdellah, Marwan; Eldeib, Ayman; Owis, Mohamed I

2015-01-01

This paper features an advanced implementation of the X-ray rendering algorithm that harnesses the giant computing power of the current commodity graphics processors to accelerate the generation of high resolution digitally reconstructed radiographs (DRRs). The presented pipeline exploits the latest features of NVIDIA Graphics Processing Unit (GPU) architectures, mainly bindless texture objects and dynamic parallelism. The rendering throughput is substantially improved by exploiting the interoperability mechanisms between CUDA and OpenGL. The benchmarks of our optimized rendering pipeline reflect its capability of generating DRRs with resolutions of 2048(2) and 4096(2) at interactive and semi interactive frame-rates using an NVIDIA GeForce 970 GTX device.

GPU Based Software Correlators - Perspectives for VLBI2010

NASA Technical Reports Server (NTRS)

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.

Acceleration of the Smith-Waterman algorithm using single and multiple graphics processors

NASA Astrophysics Data System (ADS)

Khajeh-Saeed, Ali; Poole, Stephen; Blair Perot, J.

2010-06-01

Finding regions of similarity between two very long data streams is a computationally intensive problem referred to as sequence alignment. Alignment algorithms must allow for imperfect sequence matching with different starting locations and some gaps and errors between the two data sequences. Perhaps the most well known application of sequence matching is the testing of DNA or protein sequences against genome databases. The Smith-Waterman algorithm is a method for precisely characterizing how well two sequences can be aligned and for determining the optimal alignment of those two sequences. Like many applications in computational science, the Smith-Waterman algorithm is constrained by the memory access speed and can be accelerated significantly by using graphics processors (GPUs) as the compute engine. In this work we show that effective use of the GPU requires a novel reformulation of the Smith-Waterman algorithm. The performance of this new version of the algorithm is demonstrated using the SSCA#1 (Bioinformatics) benchmark running on one GPU and on up to four GPUs executing in parallel. The results indicate that for large problems a single GPU is up to 45 times faster than a CPU for this application, and the parallel implementation shows linear speed up on up to 4 GPUs.

Fast 2D flood modelling using GPU technology - recent applications and new developments

NASA Astrophysics Data System (ADS)

Crossley, Amanda; Lamb, Rob; Waller, Simon; Dunning, Paul

2010-05-01

In recent years there has been considerable interest amongst scientists and engineers in exploiting the potential of commodity graphics hardware for desktop parallel computing. The Graphics Processing Units (GPUs) that are used in PC graphics cards have now evolved into powerful parallel co-processors that can be used to accelerate the numerical codes used for floodplain inundation modelling. We report in this paper on experience over the past two years in developing and applying two dimensional (2D) flood inundation models using GPUs to achieve significant practical performance benefits. Starting with a solution scheme for the 2D diffusion wave approximation to the 2D Shallow Water Equations (SWEs), we have demonstrated the capability to reduce model run times in ‘real-world' applications using GPU hardware and programming techniques. We then present results from a GPU-based 2D finite volume SWE solver. A series of numerical test cases demonstrate that the model produces outputs that are accurate and consistent with reference results published elsewhere. In comparisons conducted for a real world test case, the GPU-based SWE model was over 100 times faster than the CPU version. We conclude with some discussion of practical experience in using the GPU technology for flood mapping applications, and for research projects investigating use of Monte Carlo simulation methods for the analysis of uncertainty in 2D flood modelling.

Performance and scalability of Fourier domain optical coherence tomography acceleration using graphics processing units.

PubMed

Li, Jian; Bloch, Pavel; Xu, Jing; Sarunic, Marinko V; Shannon, Lesley

2011-05-01

Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.

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

NASA Astrophysics Data System (ADS)

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.

Quantum Chemical Calculations Using Accelerators: Migrating Matrix Operations to the NVIDIA Kepler GPU and the Intel Xeon Phi.

PubMed

Leang, Sarom S; Rendell, Alistair P; Gordon, Mark S

2014-03-11

Increasingly, modern computer systems comprise a multicore general-purpose processor augmented with a number of special purpose devices or accelerators connected via an external interface such as a PCI bus. The NVIDIA Kepler Graphical Processing Unit (GPU) and the Intel Phi are two examples of such accelerators. Accelerators offer peak performances that can be well above those of the host processor. How to exploit this heterogeneous environment for legacy application codes is not, however, straightforward. This paper considers how matrix operations in typical quantum chemical calculations can be migrated to the GPU and Phi systems. Double precision general matrix multiply operations are endemic in electronic structure calculations, especially methods that include electron correlation, such as density functional theory, second order perturbation theory, and coupled cluster theory. The use of approaches that automatically determine whether to use the host or an accelerator, based on problem size, is explored, with computations that are occurring on the accelerator and/or the host. For data-transfers over PCI-e, the GPU provides the best overall performance for data sizes up to 4096 MB with consistent upload and download rates between 5-5.6 GB/s and 5.4-6.3 GB/s, respectively. The GPU outperforms the Phi for both square and nonsquare matrix multiplications.

Execution of a parallel edge-based Navier-Stokes solver on commodity graphics processor units

NASA Astrophysics Data System (ADS)

Corral, Roque; Gisbert, Fernando; Pueblas, Jesus

2017-02-01

The implementation of an edge-based three-dimensional Reynolds Average Navier-Stokes solver for unstructured grids able to run on multiple graphics processing units (GPUs) is presented. Loops over edges, which are the most time-consuming part of the solver, have been written to exploit the massively parallel capabilities of GPUs. Non-blocking communications between parallel processes and between the GPU and the central processor unit (CPU) have been used to enhance code scalability. The code is written using a mixture of C++ and OpenCL, to allow the execution of the source code on GPUs. The Message Passage Interface (MPI) library is used to allow the parallel execution of the solver on multiple GPUs. A comparative study of the solver parallel performance is carried out using a cluster of CPUs and another of GPUs. It is shown that a single GPU is up to 64 times faster than a single CPU core. The parallel scalability of the solver is mainly degraded due to the loss of computing efficiency of the GPU when the size of the case decreases. However, for large enough grid sizes, the scalability is strongly improved. A cluster featuring commodity GPUs and a high bandwidth network is ten times less costly and consumes 33% less energy than a CPU-based cluster with an equivalent computational power.

Graphics processing unit accelerated intensity-based optical coherence tomography angiography using differential frames with real-time motion correction.

PubMed

Watanabe, Yuuki; Takahashi, Yuhei; Numazawa, Hiroshi

2014-02-01

We demonstrate intensity-based optical coherence tomography (OCT) angiography using the squared difference of two sequential frames with bulk-tissue-motion (BTM) correction. This motion correction was performed by minimization of the sum of the pixel values using axial- and lateral-pixel-shifted structural OCT images. We extract the BTM-corrected image from a total of 25 calculated OCT angiographic images. Image processing was accelerated by a graphics processing unit (GPU) with many stream processors to optimize the parallel processing procedure. The GPU processing rate was faster than that of a line scan camera (46.9 kHz). Our OCT system provides the means of displaying structural OCT images and BTM-corrected OCT angiographic images in real time.

Parallel hyperbolic PDE simulation on clusters: Cell versus GPU

NASA Astrophysics Data System (ADS)

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:http://cpc.cs.qub.ac.uk/summaries/AEGY_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v3 No. of lines in distributed program, including test data, etc.: 59 168 No. of bytes in distributed program, including test data, etc.: 453 409 Distribution format: tar.gz Programming language: C, CUDA Computer: Parallel Computing Clusters. Individual compute nodes may consist of x86 CPU, Cell processor, or x86 CPU with attached NVIDIA GPU accelerator. Operating system: Linux Has the code been vectorised or parallelized?: Yes. Tested on 1-128 x86 CPU cores, 1-32 Cell Processors, and 1-32 NVIDIA GPUs. RAM: Tested on Problems requiring up to 4 GB per compute node. Classification: 12 External routines: MPI, CUDA, IBM Cell SDK Nature of problem: MPI-parallel simulation of Shallow Water equations using high-resolution 2D hyperbolic equation solver on regular Cartesian grids for x86 CPU, Cell Processor, and NVIDIA GPU using CUDA. Solution method: SWsolver provides 3 implementations of a high-resolution 2D Shallow Water equation solver on regular Cartesian grids, for CPU, Cell Processor, and NVIDIA GPU. Each implementation uses MPI to divide work across a parallel computing cluster. Additional comments: Sub-program numdiff is used for the test run.

Multilevel Summation of Electrostatic Potentials Using Graphics Processing Units*

PubMed Central

Hardy, David J.; Stone, John E.; Schulten, Klaus

2009-01-01

Physical and engineering practicalities involved in microprocessor design have resulted in flat performance growth for traditional single-core microprocessors. The urgent need for continuing increases in the performance of scientific applications requires the use of many-core processors and accelerators such as graphics processing units (GPUs). This paper discusses GPU acceleration of the multilevel summation method for computing electrostatic potentials and forces for a system of charged atoms, which is a problem of paramount importance in biomolecular modeling applications. We present and test a new GPU algorithm for the long-range part of the potentials that computes a cutoff pair potential between lattice points, essentially convolving a fixed 3-D lattice of “weights” over all sub-cubes of a much larger lattice. The implementation exploits the different memory subsystems provided on the GPU to stream optimally sized data sets through the multiprocessors. We demonstrate for the full multilevel summation calculation speedups of up to 26 using a single GPU and 46 using multiple GPUs, enabling the computation of a high-resolution map of the electrostatic potential for a system of 1.5 million atoms in under 12 seconds. PMID:20161132

GPUs, a New Tool of Acceleration in CFD: Efficiency and Reliability on Smoothed Particle Hydrodynamics Methods

PubMed Central

Crespo, Alejandro C.; Dominguez, Jose M.; Barreiro, Anxo; Gómez-Gesteira, Moncho; Rogers, Benedict D.

2011-01-01

Smoothed Particle Hydrodynamics (SPH) is a numerical method commonly used in Computational Fluid Dynamics (CFD) to simulate complex free-surface flows. Simulations with this mesh-free particle method far exceed the capacity of a single processor. In this paper, as part of a dual-functioning code for either central processing units (CPUs) or Graphics Processor Units (GPUs), a parallelisation using GPUs is presented. The GPU parallelisation technique uses the Compute Unified Device Architecture (CUDA) of nVidia devices. Simulations with more than one million particles on a single GPU card exhibit speedups of up to two orders of magnitude over using a single-core CPU. It is demonstrated that the code achieves different speedups with different CUDA-enabled GPUs. The numerical behaviour of the SPH code is validated with a standard benchmark test case of dam break flow impacting on an obstacle where good agreement with the experimental results is observed. Both the achieved speed-ups and the quantitative agreement with experiments suggest that CUDA-based GPU programming can be used in SPH methods with efficiency and reliability. PMID:21695185

Discovering epistasis in large scale genetic association studies by exploiting graphics cards.

PubMed

Chen, Gary K; Guo, Yunfei

2013-12-03

Despite the enormous investments made in collecting DNA samples and generating germline variation data across thousands of individuals in modern genome-wide association studies (GWAS), progress has been frustratingly slow in explaining much of the heritability in common disease. Today's paradigm of testing independent hypotheses on each single nucleotide polymorphism (SNP) marker is unlikely to adequately reflect the complex biological processes in disease risk. Alternatively, modeling risk as an ensemble of SNPs that act in concert in a pathway, and/or interact non-additively on log risk for example, may be a more sensible way to approach gene mapping in modern studies. Implementing such analyzes genome-wide can quickly become intractable due to the fact that even modest size SNP panels on modern genotype arrays (500k markers) pose a combinatorial nightmare, require tens of billions of models to be tested for evidence of interaction. In this article, we provide an in-depth analysis of programs that have been developed to explicitly overcome these enormous computational barriers through the use of processors on graphics cards known as Graphics Processing Units (GPU). We include tutorials on GPU technology, which will convey why they are growing in appeal with today's numerical scientists. One obvious advantage is the impressive density of microprocessor cores that are available on only a single GPU. Whereas high end servers feature up to 24 Intel or AMD CPU cores, the latest GPU offerings from nVidia feature over 2600 cores. Each compute node may be outfitted with up to 4 GPU devices. Success on GPUs varies across problems. However, epistasis screens fare well due to the high degree of parallelism exposed in these problems. Papers that we review routinely report GPU speedups of over two orders of magnitude (>100x) over standard CPU implementations.

Discovering epistasis in large scale genetic association studies by exploiting graphics cards

PubMed Central

Chen, Gary K.; Guo, Yunfei

2013-01-01

Despite the enormous investments made in collecting DNA samples and generating germline variation data across thousands of individuals in modern genome-wide association studies (GWAS), progress has been frustratingly slow in explaining much of the heritability in common disease. Today's paradigm of testing independent hypotheses on each single nucleotide polymorphism (SNP) marker is unlikely to adequately reflect the complex biological processes in disease risk. Alternatively, modeling risk as an ensemble of SNPs that act in concert in a pathway, and/or interact non-additively on log risk for example, may be a more sensible way to approach gene mapping in modern studies. Implementing such analyzes genome-wide can quickly become intractable due to the fact that even modest size SNP panels on modern genotype arrays (500k markers) pose a combinatorial nightmare, require tens of billions of models to be tested for evidence of interaction. In this article, we provide an in-depth analysis of programs that have been developed to explicitly overcome these enormous computational barriers through the use of processors on graphics cards known as Graphics Processing Units (GPU). We include tutorials on GPU technology, which will convey why they are growing in appeal with today's numerical scientists. One obvious advantage is the impressive density of microprocessor cores that are available on only a single GPU. Whereas high end servers feature up to 24 Intel or AMD CPU cores, the latest GPU offerings from nVidia feature over 2600 cores. Each compute node may be outfitted with up to 4 GPU devices. Success on GPUs varies across problems. However, epistasis screens fare well due to the high degree of parallelism exposed in these problems. Papers that we review routinely report GPU speedups of over two orders of magnitude (>100x) over standard CPU implementations. PMID:24348518

Equalizer: a scalable parallel rendering framework.

PubMed

Eilemann, Stefan; Makhinya, Maxim; Pajarola, Renato

2009-01-01

Continuing improvements in CPU and GPU performances as well as increasing multi-core processor and cluster-based parallelism demand for flexible and scalable parallel rendering solutions that can exploit multipipe hardware accelerated graphics. In fact, to achieve interactive visualization, scalable rendering systems are essential to cope with the rapid growth of data sets. However, parallel rendering systems are non-trivial to develop and often only application specific implementations have been proposed. The task of developing a scalable parallel rendering framework is even more difficult if it should be generic to support various types of data and visualization applications, and at the same time work efficiently on a cluster with distributed graphics cards. In this paper we introduce a novel system called Equalizer, a toolkit for scalable parallel rendering based on OpenGL which provides an application programming interface (API) to develop scalable graphics applications for a wide range of systems ranging from large distributed visualization clusters and multi-processor multipipe graphics systems to single-processor single-pipe desktop machines. We describe the system architecture, the basic API, discuss its advantages over previous approaches, present example configurations and usage scenarios as well as scalability results.

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System

PubMed Central

Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram

2014-01-01

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second. PMID:24891848

High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System.

PubMed

Saikia, Manob Jyoti; Kanhirodan, Rajan; Mohan Vasu, Ram

2014-01-01

We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second.

Massively Parallel Signal Processing using the Graphics Processing Unit for Real-Time Brain-Computer Interface Feature Extraction.

PubMed

Wilson, J Adam; Williams, Justin C

2009-01-01

The clock speeds of modern computer processors have nearly plateaued in the past 5 years. Consequently, neural prosthetic systems that rely on processing large quantities of data in a short period of time face a bottleneck, in that it may not be possible to process all of the data recorded from an electrode array with high channel counts and bandwidth, such as electrocorticographic grids or other implantable systems. Therefore, in this study a method of using the processing capabilities of a graphics card [graphics processing unit (GPU)] was developed for real-time neural signal processing of a brain-computer interface (BCI). The NVIDIA CUDA system was used to offload processing to the GPU, which is capable of running many operations in parallel, potentially greatly increasing the speed of existing algorithms. The BCI system records many channels of data, which are processed and translated into a control signal, such as the movement of a computer cursor. This signal processing chain involves computing a matrix-matrix multiplication (i.e., a spatial filter), followed by calculating the power spectral density on every channel using an auto-regressive method, and finally classifying appropriate features for control. In this study, the first two computationally intensive steps were implemented on the GPU, and the speed was compared to both the current implementation and a central processing unit-based implementation that uses multi-threading. Significant performance gains were obtained with GPU processing: the current implementation processed 1000 channels of 250 ms in 933 ms, while the new GPU method took only 27 ms, an improvement of nearly 35 times.

A Programming Framework for Scientific Applications on CPU-GPU Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Owens, John

2013-03-24

At a high level, my research interests center around designing, programming, and evaluating computer systems that use new approaches to solve interesting problems. The rapid change of technology allows a variety of different architectural approaches to computationally difficult problems, and a constantly shifting set of constraints and trends makes the solutions to these problems both challenging and interesting. One of the most important recent trends in computing has been a move to commodity parallel architectures. This sea change is motivated by the industry’s inability to continue to profitably increase performance on a single processor and instead to move to multiplemore » parallel processors. In the period of review, my most significant work has been leading a research group looking at the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver superior performance on a broad range of problems than their CPU counterparts, but effectively mapping complex applications to a parallel programming model with an emerging programming environment is a significant and important research problem.« less

Apparatus and method for implementing power saving techniques when processing floating point values

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Young Moon; Park, Sang Phill

An apparatus and method are described for reducing power when reading and writing graphics data. For example, one embodiment of an apparatus comprises: a graphics processor unit (GPU) to process graphics data including floating point data; a set of registers, at least one of the registers of the set partitioned to store the floating point data; and encode/decode logic to reduce a number of binary 1 values being read from the at least one register by causing a specified set of bit positions within the floating point data to be read out as 0s rather than 1s.

A Real-Time Capable Software-Defined Receiver Using GPU for Adaptive Anti-Jam GPS Sensors

PubMed Central

Seo, Jiwon; Chen, Yu-Hsuan; De Lorenzo, David S.; Lo, Sherman; Enge, Per; Akos, Dennis; Lee, Jiyun

2011-01-01

Due to their weak received signal power, Global Positioning System (GPS) signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs). However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR) with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU) coupled with a new generation Graphics Processing Unit (GPU) having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities. PMID:22164116

A real-time capable software-defined receiver using GPU for adaptive anti-jam GPS sensors.

PubMed

Seo, Jiwon; Chen, Yu-Hsuan; De Lorenzo, David S; Lo, Sherman; Enge, Per; Akos, Dennis; Lee, Jiyun

2011-01-01

Due to their weak received signal power, Global Positioning System (GPS) signals are vulnerable to radio frequency interference. Adaptive beam and null steering of the gain pattern of a GPS antenna array can significantly increase the resistance of GPS sensors to signal interference and jamming. Since adaptive array processing requires intensive computational power, beamsteering GPS receivers were usually implemented using hardware such as field-programmable gate arrays (FPGAs). However, a software implementation using general-purpose processors is much more desirable because of its flexibility and cost effectiveness. This paper presents a GPS software-defined radio (SDR) with adaptive beamsteering capability for anti-jam applications. The GPS SDR design is based on an optimized desktop parallel processing architecture using a quad-core Central Processing Unit (CPU) coupled with a new generation Graphics Processing Unit (GPU) having massively parallel processors. This GPS SDR demonstrates sufficient computational capability to support a four-element antenna array and future GPS L5 signal processing in real time. After providing the details of our design and optimization schemes for future GPU-based GPS SDR developments, the jamming resistance of our GPS SDR under synthetic wideband jamming is presented. Since the GPS SDR uses commercial-off-the-shelf hardware and processors, it can be easily adopted in civil GPS applications requiring anti-jam capabilities.

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

NASA Astrophysics Data System (ADS)

Schultz, A.

2010-12-01

3D forward solvers lie at the core of inverse formulations used to image the variation of electrical conductivity within the Earth's interior. This property is associated with variations in temperature, composition, phase, presence of volatiles, and in specific settings, the presence of groundwater, geothermal resources, oil/gas or minerals. The high cost of 3D solutions has been a stumbling block to wider adoption of 3D methods. Parallel algorithms for modeling frequency domain 3D EM problems have not achieved wide scale adoption, with emphasis on fairly coarse grained parallelism using MPI and similar approaches. The communications bandwidth as well as the latency required to send and receive network communication packets is a limiting factor in implementing fine grained parallel strategies, inhibiting wide adoption of these algorithms. Leading Graphics Processor Unit (GPU) companies now produce GPUs with hundreds of GPU processor cores per die. The footprint, in silicon, of the GPU's restricted instruction set is much smaller than the general purpose instruction set required of a CPU. Consequently, the density of processor cores on a GPU can be much greater than on a CPU. GPUs also have local memory, registers and high speed communication with host CPUs, usually through PCIe type interconnects. The extremely low cost and high computational power of GPUs provides the EM geophysics community with an opportunity to achieve fine grained (i.e. massive) parallelization of codes on low cost hardware. The current generation of GPUs (e.g. NVidia Fermi) provides 3 billion transistors per chip die, with nearly 500 processor cores and up to 6 GB of fast (DDR5) GPU memory. This latest generation of GPU supports fast hardware double precision (64 bit) floating point operations of the type required for frequency domain EM forward solutions. Each Fermi GPU board can sustain nearly 1 TFLOP in double precision, and multiple boards can be installed in the host computer system. We 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.

SU-E-J-91: FFT Based Medical Image Registration Using a Graphics Processing Unit (GPU).

PubMed

Luce, J; Hoggarth, M; Lin, J; Block, A; Roeske, J

2012-06-01

To evaluate the efficiency gains obtained from using a Graphics Processing Unit (GPU) to perform a Fourier Transform (FT) based image registration. Fourier-based image registration involves obtaining the FT of the component images, and analyzing them in Fourier space to determine the translations and rotations of one image set relative to another. An important property of FT registration is that by enlarging the images (adding additional pixels), one can obtain translations and rotations with sub-pixel resolution. The expense, however, is an increased computational time. GPUs may decrease the computational time associated with FT image registration by taking advantage of their parallel architecture to perform matrix computations much more efficiently than a Central Processor Unit (CPU). In order to evaluate the computational gains produced by a GPU, images with known translational shifts were utilized. A program was written in the Interactive Data Language (IDL; Exelis, Boulder, CO) to performCPU-based calculations. Subsequently, the program was modified using GPU bindings (Tech-X, Boulder, CO) to perform GPU-based computation on the same system. Multiple image sizes were used, ranging from 256×256 to 2304×2304. The time required to complete the full algorithm by the CPU and GPU were benchmarked and the speed increase was defined as the ratio of the CPU-to-GPU computational time. The ratio of the CPU-to- GPU time was greater than 1.0 for all images, which indicates the GPU is performing the algorithm faster than the CPU. The smallest improvement, a 1.21 ratio, was found with the smallest image size of 256×256, and the largest speedup, a 4.25 ratio, was observed with the largest image size of 2304×2304. GPU programming resulted in a significant decrease in computational time associated with a FT image registration algorithm. The inclusion of the GPU may provide near real-time, sub-pixel registration capability. © 2012 American Association of Physicists in Medicine.

Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications

NASA Astrophysics Data System (ADS)

Jimenez, Edward S.; Goodman, Eric L.; Park, Ryeojin; Orr, Laurel J.; Thompson, Kyle R.

2014-09-01

This paper will investigate energy-efficiency for various real-world industrial computed-tomography reconstruction algorithms, both CPU- and GPU-based implementations. This work shows that the energy required for a given reconstruction is based on performance and problem size. There are many ways to describe performance and energy efficiency, thus this work will investigate multiple metrics including performance-per-watt, energy-delay product, and energy consumption. This work found that irregular GPU-based approaches1 realized tremendous savings in energy consumption when compared to CPU implementations while also significantly improving the performance-per- watt and energy-delay product metrics. Additional energy savings and other metric improvement was realized on the GPU-based reconstructions by improving storage I/O by implementing a parallel MIMD-like modularization of the compute and I/O tasks.

Symplectic multi-particle tracking on GPUs

NASA Astrophysics Data System (ADS)

Liu, Zhicong; Qiang, Ji

2018-05-01

A symplectic multi-particle tracking model is implemented on the Graphic Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) language. The symplectic tracking model can preserve phase space structure and reduce non-physical effects in long term simulation, which is important for beam property evaluation in particle accelerators. Though this model is computationally expensive, it is very suitable for parallelization and can be accelerated significantly by using GPUs. In this paper, we optimized the implementation of the symplectic tracking model on both single GPU and multiple GPUs. Using a single GPU processor, the code achieves a factor of 2-10 speedup for a range of problem sizes compared with the time on a single state-of-the-art Central Processing Unit (CPU) node with similar power consumption and semiconductor technology. It also shows good scalability on a multi-GPU cluster at Oak Ridge Leadership Computing Facility. In an application to beam dynamics simulation, the GPU implementation helps save more than a factor of two total computing time in comparison to the CPU implementation.

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

PubMed

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

2007-10-07

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.

Methods for compressible fluid simulation on GPUs using high-order finite differences

NASA Astrophysics Data System (ADS)

Pekkilä, Johannes; Väisälä, Miikka S.; Käpylä, Maarit J.; Käpylä, Petri J.; Anjum, Omer

2017-08-01

We focus on implementing and optimizing a sixth-order finite-difference solver for simulating compressible fluids on a GPU using third-order Runge-Kutta integration. Since graphics processing units perform well in data-parallel tasks, this makes them an attractive platform for fluid simulation. However, high-order stencil computation is memory-intensive with respect to both main memory and the caches of the GPU. We present two approaches for simulating compressible fluids using 55-point and 19-point stencils. We seek to reduce the requirements for memory bandwidth and cache size in our methods by using cache blocking and decomposing a latency-bound kernel into several bandwidth-bound kernels. Our fastest implementation is bandwidth-bound and integrates 343 million grid points per second on a Tesla K40t GPU, achieving a 3 . 6 × speedup over a comparable hydrodynamics solver benchmarked on two Intel Xeon E5-2690v3 processors. Our alternative GPU implementation is latency-bound and achieves the rate of 168 million updates per second.

Spectral-element simulation of two-dimensional elastic wave propagation in fully heterogeneous media on a GPU cluster

NASA Astrophysics Data System (ADS)

Rudianto, Indra; Sudarmaji

2018-04-01

We present an implementation of the spectral-element method for simulation of two-dimensional elastic wave propagation in fully heterogeneous media. We have incorporated most of realistic geological features in the model, including surface topography, curved layer interfaces, and 2-D wave-speed heterogeneity. To accommodate such complexity, we use an unstructured quadrilateral meshing technique. Simulation was performed on a GPU cluster, which consists of 24 core processors Intel Xeon CPU and 4 NVIDIA Quadro graphics cards using CUDA and MPI implementation. We speed up the computation by a factor of about 5 compared to MPI only, and by a factor of about 40 compared to Serial implementation.

Bin-Hash Indexing: A Parallel Method for Fast Query Processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bethel, Edward W; Gosink, Luke J.; Wu, Kesheng

2008-06-27

This paper presents a new parallel indexing data structure for answering queries. The index, called Bin-Hash, offers extremely high levels of concurrency, and is therefore well-suited for the emerging commodity of parallel processors, such as multi-cores, cell processors, and general purpose graphics processing units (GPU). The Bin-Hash approach first bins the base data, and then partitions and separately stores the values in each bin as a perfect spatial hash table. To answer a query, we first determine whether or not a record satisfies the query conditions based on the bin boundaries. For the bins with records that can not bemore » resolved, we examine the spatial hash tables. The procedures for examining the bin numbers and the spatial hash tables offer the maximum possible level of concurrency; all records are able to be evaluated by our procedure independently in parallel. Additionally, our Bin-Hash procedures access much smaller amounts of data than similar parallel methods, such as the projection index. This smaller data footprint is critical for certain parallel processors, like GPUs, where memory resources are limited. To demonstrate the effectiveness of Bin-Hash, we implement it on a GPU using the data-parallel programming language CUDA. The concurrency offered by the Bin-Hash index allows us to fully utilize the GPU's massive parallelism in our work; over 12,000 records can be simultaneously evaluated at any one time. We show that our new query processing method is an order of magnitude faster than current state-of-the-art CPU-based indexing technologies. Additionally, we compare our performance to existing GPU-based projection index strategies.« less

A fast CT reconstruction scheme for a general multi-core PC.

PubMed

Zeng, Kai; Bai, Erwei; Wang, Ge

2007-01-01

Expensive computational cost is a severe limitation in CT reconstruction for clinical applications that need real-time feedback. A primary example is bolus-chasing computed tomography (CT) angiography (BCA) that we have been developing for the past several years. To accelerate the reconstruction process using the filtered backprojection (FBP) method, specialized hardware or graphics cards can be used. However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. In this paper, an acceleration scheme is proposed based on a multi-core PC. In the proposed scheme, several techniques are integrated, including utilization of geometric symmetry, optimization of data structures, single-instruction multiple-data (SIMD) processing, multithreaded computation, and an Intel C++ compilier. Our scheme maintains the original precision and involves no data exchange between the GPU and CPU. The merits of our scheme are demonstrated in numerical experiments against the traditional implementation. Our scheme achieves a speedup of about 40, which can be further improved by several folds using the latest quad-core processors.

A Fast CT Reconstruction Scheme for a General Multi-Core PC

PubMed Central

Zeng, Kai; Bai, Erwei; Wang, Ge

2007-01-01

Expensive computational cost is a severe limitation in CT reconstruction for clinical applications that need real-time feedback. A primary example is bolus-chasing computed tomography (CT) angiography (BCA) that we have been developing for the past several years. To accelerate the reconstruction process using the filtered backprojection (FBP) method, specialized hardware or graphics cards can be used. However, specialized hardware is expensive and not flexible. The graphics processing unit (GPU) in a current graphic card can only reconstruct images in a reduced precision and is not easy to program. In this paper, an acceleration scheme is proposed based on a multi-core PC. In the proposed scheme, several techniques are integrated, including utilization of geometric symmetry, optimization of data structures, single-instruction multiple-data (SIMD) processing, multithreaded computation, and an Intel C++ compilier. Our scheme maintains the original precision and involves no data exchange between the GPU and CPU. The merits of our scheme are demonstrated in numerical experiments against the traditional implementation. Our scheme achieves a speedup of about 40, which can be further improved by several folds using the latest quad-core processors. PMID:18256731

Numerical simulation of air hypersonic flows with equilibrium chemical reactions

NASA Astrophysics Data System (ADS)

Emelyanov, Vladislav; Karpenko, Anton; Volkov, Konstantin

2018-05-01

The finite volume method is applied to solve unsteady three-dimensional compressible Navier-Stokes equations on unstructured meshes. High-temperature gas effects altering the aerodynamics of vehicles are taken into account. Possibilities of the use of graphics processor units (GPUs) for the simulation of hypersonic flows are demonstrated. Solutions of some test cases on GPUs are reported, and a comparison between computational results of equilibrium chemically reacting and perfect air flowfields is performed. Speedup of solution on GPUs with respect to the solution on central processor units (CPUs) is compared. The results obtained provide promising perspective for designing a GPU-based software framework for practical applications.

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores.

PubMed

Chikkagoudar, Satish; Wang, Kai; Li, Mingyao

2011-05-26

Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.

GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores

PubMed Central

2011-01-01

Background Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Findings Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1) the interaction of SNPs within it in parallel, and 2) the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. Conclusions GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/. PMID:21615923

Graphics Processing Unit Acceleration of Gyrokinetic Turbulence Simulations

NASA Astrophysics Data System (ADS)

Hause, Benjamin; Parker, Scott; Chen, Yang

2013-10-01

We find a substantial increase in on-node performance using Graphics Processing Unit (GPU) acceleration in gyrokinetic delta-f particle-in-cell simulation. Optimization is performed on a two-dimensional slab gyrokinetic particle simulation using the Portland Group Fortran compiler with the OpenACC compiler directives and Fortran CUDA. Mixed implementation of both Open-ACC and CUDA is demonstrated. CUDA is required for optimizing the particle deposition algorithm. We have implemented the GPU acceleration on a third generation Core I7 gaming PC with two NVIDIA GTX 680 GPUs. We find comparable, or better, acceleration relative to the NERSC DIRAC cluster with the NVIDIA Tesla C2050 computing processor. The Tesla C 2050 is about 2.6 times more expensive than the GTX 580 gaming GPU. We also see enormous speedups (10 or more) on the Titan supercomputer at Oak Ridge with Kepler K20 GPUs. Results show speed-ups comparable or better than that of OpenMP models utilizing multiple cores. The use of hybrid OpenACC, CUDA Fortran, and MPI models across many nodes will also be discussed. Optimization strategies will be presented. We will discuss progress on optimizing the comprehensive three dimensional general geometry GEM code.

Identifying opportune landing sites in degraded visual environments with terrain and cultural databases

NASA Astrophysics Data System (ADS)

Moody, Marc; Fisher, Robert; Little, J. Kristin

2014-06-01

Boeing has developed a degraded visual environment navigational aid that is flying on the Boeing AH-6 light attack helicopter. The navigational aid is a two dimensional software digital map underlay generated by the Boeing™ Geospatial Embedded Mapping Software (GEMS) and fully integrated with the operational flight program. The page format on the aircraft's multi function displays (MFD) is termed the Approach page. The existing work utilizes Digital Terrain Elevation Data (DTED) and OpenGL ES 2.0 graphics capabilities to compute the pertinent graphics underlay entirely on the graphics processor unit (GPU) within the AH-6 mission computer. The next release will incorporate cultural databases containing Digital Vertical Obstructions (DVO) to warn the crew of towers, buildings, and power lines when choosing an opportune landing site. Future IRAD will include Light Detection and Ranging (LIDAR) point cloud generating sensors to provide 2D and 3D synthetic vision on the final approach to the landing zone. Collision detection with respect to terrain, cultural, and point cloud datasets may be used to further augment the crew warning system. The techniques for creating the digital map underlay leverage the GPU almost entirely, making this solution viable on most embedded mission computing systems with an OpenGL ES 2.0 capable GPU. This paper focuses on the AH-6 crew interface process for determining a landing zone and flying the aircraft to it.

GPU-Accelerated Molecular Modeling Coming Of Age

PubMed Central

Stone, John E.; Hardy, David J.; Ufimtsev, Ivan S.

2010-01-01

Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks. PMID:20675161

GPU-accelerated molecular modeling coming of age.

PubMed

Stone, John E; Hardy, David J; Ufimtsev, Ivan S; Schulten, Klaus

2010-09-01

Graphics processing units (GPUs) have traditionally been used in molecular modeling solely for visualization of molecular structures and animation of trajectories resulting from molecular dynamics simulations. Modern GPUs have evolved into fully programmable, massively parallel co-processors that can now be exploited to accelerate many scientific computations, typically providing about one order of magnitude speedup over CPU code and in special cases providing speedups of two orders of magnitude. This paper surveys the development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling. Hardware acceleration with commodity GPUs is expected to benefit the overall computational biology community by bringing teraflops performance to desktop workstations and in some cases potentially changing what were formerly batch-mode computational jobs into interactive tasks. (c) 2010 Elsevier Inc. All rights reserved.

High-performance image reconstruction in fluorescence tomography on desktop computers and graphics hardware.

PubMed

Freiberger, Manuel; Egger, Herbert; Liebmann, Manfred; Scharfetter, Hermann

2011-11-01

Image reconstruction in fluorescence optical tomography is a three-dimensional nonlinear ill-posed problem governed by a system of partial differential equations. In this paper we demonstrate that a combination of state of the art numerical algorithms and a careful hardware optimized implementation allows to solve this large-scale inverse problem in a few seconds on standard desktop PCs with modern graphics hardware. In particular, we present methods to solve not only the forward but also the non-linear inverse problem by massively parallel programming on graphics processors. A comparison of optimized CPU and GPU implementations shows that the reconstruction can be accelerated by factors of about 15 through the use of the graphics hardware without compromising the accuracy in the reconstructed images.

Performance analysis of a parallel Monte Carlo code for simulating solar radiative transfer in cloudy atmospheres using CUDA-enabled NVIDIA GPU

NASA Astrophysics Data System (ADS)

Russkova, Tatiana V.

2017-11-01

One tool to improve the performance of Monte Carlo methods for numerical simulation of light transport in the Earth's atmosphere is the parallel technology. A new algorithm oriented to parallel execution on the CUDA-enabled NVIDIA graphics processor is discussed. The efficiency of parallelization is analyzed on the basis of calculating the upward and downward fluxes of solar radiation in both a vertically homogeneous and inhomogeneous models of the atmosphere. The results of testing the new code under various atmospheric conditions including continuous singlelayered and multilayered clouds, and selective molecular absorption are presented. The results of testing the code using video cards with different compute capability are analyzed. It is shown that the changeover of computing from conventional PCs to the architecture of graphics processors gives more than a hundredfold increase in performance and fully reveals the capabilities of the technology used.

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications.

PubMed

Lei, Guoqing; Dou, Yong; Wan, Wen; Xia, Fei; Li, Rongchun; Ma, Meng; Zou, Dan

2012-01-01

Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications.

Efficient Implementation of MrBayes on Multi-GPU

PubMed Central

Zhou, Jianfu; Liu, Xiaoguang; Wang, Gang

2013-01-01

MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)3), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)3 Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)3 (aMCMCMC) for MrBayes (MC)3 on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new “node-by-node” task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)3 achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)3 is dramatically faster than all the previous (MC)3 algorithms and scales well to large GPU clusters. PMID:23493260

Efficient implementation of MrBayes on multi-GPU.

PubMed

Bao, Jie; Xia, Hongju; Zhou, Jianfu; Liu, Xiaoguang; Wang, Gang

2013-06-01

MrBayes, using Metropolis-coupled Markov chain Monte Carlo (MCMCMC or (MC)(3)), is a popular program for Bayesian inference. As a leading method of using DNA data to infer phylogeny, the (MC)(3) Bayesian algorithm and its improved and parallel versions are now not fast enough for biologists to analyze massive real-world DNA data. Recently, graphics processor unit (GPU) has shown its power as a coprocessor (or rather, an accelerator) in many fields. This article describes an efficient implementation a(MC)(3) (aMCMCMC) for MrBayes (MC)(3) on compute unified device architecture. By dynamically adjusting the task granularity to adapt to input data size and hardware configuration, it makes full use of GPU cores with different data sets. An adaptive method is also developed to split and combine DNA sequences to make full use of a large number of GPU cards. Furthermore, a new "node-by-node" task scheduling strategy is developed to improve concurrency, and several optimizing methods are used to reduce extra overhead. Experimental results show that a(MC)(3) achieves up to 63× speedup over serial MrBayes on a single machine with one GPU card, and up to 170× speedup with four GPU cards, and up to 478× speedup with a 32-node GPU cluster. a(MC)(3) is dramatically faster than all the previous (MC)(3) algorithms and scales well to large GPU clusters.

Architecting the Finite Element Method Pipeline for the GPU.

PubMed

Fu, Zhisong; Lewis, T James; Kirby, Robert M; Whitaker, Ross T

2014-02-01

The finite element method (FEM) is a widely employed numerical technique for approximating the solution of partial differential equations (PDEs) in various science and engineering applications. Many of these applications benefit from fast execution of the FEM pipeline. One way to accelerate the FEM pipeline is by exploiting advances in modern computational hardware, such as the many-core streaming processors like the graphical processing unit (GPU). In this paper, we present the algorithms and data-structures necessary to move the entire FEM pipeline to the GPU. First we propose an efficient GPU-based algorithm to generate local element information and to assemble the global linear system associated with the FEM discretization of an elliptic PDE. To solve the corresponding linear system efficiently on the GPU, we implement a conjugate gradient method preconditioned with a geometry-informed algebraic multi-grid (AMG) method preconditioner. We propose a new fine-grained parallelism strategy, a corresponding multigrid cycling stage and efficient data mapping to the many-core architecture of GPU. Comparison of our on-GPU assembly versus a traditional serial implementation on the CPU achieves up to an 87 × speedup. Focusing on the linear system solver alone, we achieve a speedup of up to 51 × versus use of a comparable state-of-the-art serial CPU linear system solver. Furthermore, the method compares favorably with other GPU-based, sparse, linear solvers.

Orthorectification by Using Gpgpu Method

NASA Astrophysics Data System (ADS)

Sahin, H.; Kulur, S.

2012-07-01

Thanks to the nature of the graphics processing, the newly released products offer highly parallel processing units with high-memory bandwidth and computational power of more than teraflops per second. The modern GPUs are not only powerful graphic engines but also they are high level parallel programmable processors with very fast computing capabilities and high-memory bandwidth speed compared to central processing units (CPU). Data-parallel computations can be shortly described as mapping data elements to parallel processing threads. The rapid development of GPUs programmability and capabilities attracted the attentions of researchers dealing with complex problems which need high level calculations. This interest has revealed the concepts of "General Purpose Computation on Graphics Processing Units (GPGPU)" and "stream processing". The graphic processors are powerful hardware which is really cheap and affordable. So the graphic processors became an alternative to computer processors. The graphic chips which were standard application hardware have been transformed into modern, powerful and programmable processors to meet the overall needs. Especially in recent years, the phenomenon of the usage of graphics processing units in general purpose computation has led the researchers and developers to this point. The biggest problem is that the graphics processing units use different programming models unlike current programming methods. Therefore, an efficient GPU programming requires re-coding of the current program algorithm by considering the limitations and the structure of the graphics hardware. Currently, multi-core processors can not be programmed by using traditional programming methods. Event procedure programming method can not be used for programming the multi-core processors. GPUs are especially effective in finding solution for repetition of the computing steps for many data elements when high accuracy is needed. Thus, it provides the computing process more quickly and accurately. Compared to the GPUs, CPUs which perform just one computing in a time according to the flow control are slower in performance. This structure can be evaluated for various applications of computer technology. In this study covers how general purpose parallel programming and computational power of the GPUs can be used in photogrammetric applications especially direct georeferencing. The direct georeferencing algorithm is coded by using GPGPU method and CUDA (Compute Unified Device Architecture) programming language. Results provided by this method were compared with the traditional CPU programming. In the other application the projective rectification is coded by using GPGPU method and CUDA programming language. Sample images of various sizes, as compared to the results of the program were evaluated. GPGPU method can be used especially in repetition of same computations on highly dense data, thus finding the solution quickly.

GPU-Q-J, a fast method for calculating root mean square deviation (RMSD) after optimal superposition

PubMed Central

2011-01-01

Background Calculation of the root mean square deviation (RMSD) between the atomic coordinates of two optimally superposed structures is a basic component of structural comparison techniques. We describe a quaternion based method, GPU-Q-J, that is stable with single precision calculations and suitable for graphics processor units (GPUs). The application was implemented on an ATI 4770 graphics card in C/C++ and Brook+ in Linux where it was 260 to 760 times faster than existing unoptimized CPU methods. Source code is available from the Compbio website http://software.compbio.washington.edu/misc/downloads/st_gpu_fit/ or from the author LHH. Findings The Nutritious Rice for the World Project (NRW) on World Community Grid predicted de novo, the structures of over 62,000 small proteins and protein domains returning a total of 10 billion candidate structures. Clustering ensembles of structures on this scale requires calculation of large similarity matrices consisting of RMSDs between each pair of structures in the set. As a real-world test, we calculated the matrices for 6 different ensembles from NRW. The GPU method was 260 times faster that the fastest existing CPU based method and over 500 times faster than the method that had been previously used. Conclusions GPU-Q-J is a significant advance over previous CPU methods. It relieves a major bottleneck in the clustering of large numbers of structures for NRW. It also has applications in structure comparison methods that involve multiple superposition and RMSD determination steps, particularly when such methods are applied on a proteome and genome wide scale. PMID:21453553

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

PubMed

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.

Supermassive Black Hole Binaries in High Performance Massively Parallel Direct N-body Simulations on Large GPU Clusters

NASA Astrophysics Data System (ADS)

Spurzem, R.; Berczik, P.; Zhong, S.; Nitadori, K.; Hamada, T.; Berentzen, I.; Veles, A.

2012-07-01

Astrophysical Computer Simulations of Dense Star Clusters in Galactic Nuclei with Supermassive Black Holes are presented using new cost-efficient supercomputers in China accelerated by graphical processing cards (GPU). We use large high-accuracy direct N-body simulations with Hermite scheme and block-time steps, parallelised across a large number of nodes on the large scale and across many GPU thread processors on each node on the small scale. A sustained performance of more than 350 Tflop/s for a science run on using simultaneously 1600 Fermi C2050 GPUs is reached; a detailed performance model is presented and studies for the largest GPU clusters in China with up to Petaflop/s performance and 7000 Fermi GPU cards. In our case study we look at two supermassive black holes with equal and unequal masses embedded in a dense stellar cluster in a galactic nucleus. The hardening processes due to interactions between black holes and stars, effects of rotation in the stellar system and relativistic forces between the black holes are simultaneously taken into account. The simulation stops at the complete relativistic merger of the black holes.

Optimizing ion channel models using a parallel genetic algorithm on graphical processors.

PubMed

Ben-Shalom, Roy; Aviv, Amit; Razon, Benjamin; Korngreen, Alon

2012-01-01

We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs. Copyright © 2012 Elsevier B.V. All rights reserved.

Parallel Agent-Based Simulations on Clusters of GPUs and Multi-Core Processors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aaby, Brandon G; Perumalla, Kalyan S; Seal, Sudip K

2010-01-01

An effective latency-hiding mechanism is presented in the parallelization of agent-based model simulations (ABMS) with millions of agents. The mechanism is designed to accommodate the hierarchical organization as well as heterogeneity of current state-of-the-art parallel computing platforms. We use it to explore the computation vs. communication trade-off continuum available with the deep computational and memory hierarchies of extant platforms and present a novel analytical model of the tradeoff. We describe our implementation and report preliminary performance results on two distinct parallel platforms suitable for ABMS: CUDA threads on multiple, networked graphical processing units (GPUs), and pthreads on multi-core processors. Messagemore » Passing Interface (MPI) is used for inter-GPU as well as inter-socket communication on a cluster of multiple GPUs and multi-core processors. Results indicate the benefits of our latency-hiding scheme, delivering as much as over 100-fold improvement in runtime for certain benchmark ABMS application scenarios with several million agents. This speed improvement is obtained on our system that is already two to three orders of magnitude faster on one GPU than an equivalent CPU-based execution in a popular simulator in Java. Thus, the overall execution of our current work is over four orders of magnitude faster when executed on multiple GPUs.« less

GPULife

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kelly, Priscilla N.

2016-08-12

The code runs the Game of Life among several processors. Each processor uses CUDA to set up the grid's buffer on the GPU, and that buffer is fed to other GPU languages to apply the rules of the game of life. Only the halo is copied off the buffer and exchanged using MPI. This code looks at the interoperability of GPU languages among current platforms.

Acceleration of spiking neural network based pattern recognition on NVIDIA graphics processors.

PubMed

Han, Bing; Taha, Tarek M

2010-04-01

There is currently a strong push in the research community to develop biological scale implementations of neuron based vision models. Systems at this scale are computationally demanding and generally utilize more accurate neuron models, such as the Izhikevich and the Hodgkin-Huxley models, in favor of the more popular integrate and fire model. We examine the feasibility of using graphics processing units (GPUs) to accelerate a spiking neural network based character recognition network to enable such large scale systems. Two versions of the network utilizing the Izhikevich and Hodgkin-Huxley models are implemented. Three NVIDIA general-purpose (GP) GPU platforms are examined, including the GeForce 9800 GX2, the Tesla C1060, and the Tesla S1070. Our results show that the GPGPUs can provide significant speedup over conventional processors. In particular, the fastest GPGPU utilized, the Tesla S1070, provided a speedup of 5.6 and 84.4 over highly optimized implementations on the fastest central processing unit (CPU) tested, a quadcore 2.67 GHz Xeon processor, for the Izhikevich and the Hodgkin-Huxley models, respectively. The CPU implementation utilized all four cores and the vector data parallelism offered by the processor. The results indicate that GPUs are well suited for this application domain.

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

PubMed Central

2012-01-01

Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

Stochastic first passage time accelerated with CUDA

NASA Astrophysics Data System (ADS)

Pierro, Vincenzo; Troiano, Luigi; Mejuto, Elena; Filatrella, Giovanni

2018-05-01

The numerical integration of stochastic trajectories to estimate the time to pass a threshold is an interesting physical quantity, for instance in Josephson junctions and atomic force microscopy, where the full trajectory is not accessible. We propose an algorithm suitable for efficient implementation on graphical processing unit in CUDA environment. The proposed approach for well balanced loads achieves almost perfect scaling with the number of available threads and processors, and allows an acceleration of about 400× with a GPU GTX980 respect to standard multicore CPU. This method allows with off the shell GPU to challenge problems that are otherwise prohibitive, as thermal activation in slowly tilted potentials. In particular, we demonstrate that it is possible to simulate the switching currents distributions of Josephson junctions in the timescale of actual experiments.

Track finding in ATLAS using GPUs

NASA Astrophysics Data System (ADS)

Mattmann, J.; Schmitt, C.

2012-12-01

The reconstruction and simulation of collision events is a major task in modern HEP experiments involving several ten thousands of standard CPUs. On the other hand the graphics processors (GPUs) have become much more powerful and are by far outperforming the standard CPUs in terms of floating point operations due to their massive parallel approach. The usage of these GPUs could therefore significantly reduce the overall reconstruction time per event or allow for the usage of more sophisticated algorithms. In this paper the track finding in the ATLAS experiment will be used as an example on how the GPUs can be used in this context: the implementation on the GPU requires a change in the algorithmic flow to allow the code to work in the rather limited environment on the GPU in terms of memory, cache, and transfer speed from and to the GPU and to make use of the massive parallel computation. Both, the specific implementation of parts of the ATLAS track reconstruction chain and the performance improvements obtained will be discussed.

GPU accelerated dynamic functional connectivity analysis for functional MRI data.

PubMed

Akgün, Devrim; Sakoğlu, Ünal; Esquivel, Johnny; Adinoff, Bryon; Mete, Mutlu

2015-07-01

Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Novel hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization estimation method for population pharmacokinetic data analysis.

PubMed

Ng, C M

2013-10-01

The development of a population PK/PD model, an essential component for model-based drug development, is both time- and labor-intensive. A graphical-processing unit (GPU) computing technology has been proposed and used to accelerate many scientific computations. The objective of this study was to develop a hybrid GPU-CPU implementation of parallelized Monte Carlo parametric expectation maximization (MCPEM) estimation algorithm for population PK data analysis. A hybrid GPU-CPU implementation of the MCPEM algorithm (MCPEMGPU) and identical algorithm that is designed for the single CPU (MCPEMCPU) were developed using MATLAB in a single computer equipped with dual Xeon 6-Core E5690 CPU and a NVIDIA Tesla C2070 GPU parallel computing card that contained 448 stream processors. Two different PK models with rich/sparse sampling design schemes were used to simulate population data in assessing the performance of MCPEMCPU and MCPEMGPU. Results were analyzed by comparing the parameter estimation and model computation times. Speedup factor was used to assess the relative benefit of parallelized MCPEMGPU over MCPEMCPU in shortening model computation time. The MCPEMGPU consistently achieved shorter computation time than the MCPEMCPU and can offer more than 48-fold speedup using a single GPU card. The novel hybrid GPU-CPU implementation of parallelized MCPEM algorithm developed in this study holds a great promise in serving as the core for the next-generation of modeling software for population PK/PD analysis.

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

DTIC Science & Technology

2017-08-01

access to the GPU for general purpose processing .5 CUDA is designed to work easily with multiple programming languages , including Fortran. CUDA is a...Using Graphics Processing Unit (GPU) Computing by Leelinda P Dawson Approved for public release; distribution unlimited...The Performance Improvement of the Lagrangian Particle Dispersion Model (LPDM) Using Graphics Processing Unit (GPU) Computing by Leelinda

Efficient Analysis of Simulations of the Sun's Magnetic Field

NASA Astrophysics Data System (ADS)

Scarborough, C. W.; Martínez-Sykora, J.

2014-12-01

Dynamics in the solar atmosphere, including solar flares, coronal mass ejections, micro-flares and different types of jets, are powered by the evolution of the sun's intense magnetic field. 3D Radiative Magnetohydrodnamics (MHD) computer simulations have furthered our understanding of the processes involved: When non aligned magnetic field lines reconnect, the alteration of the magnetic topology causes stored magnetic energy to be converted into thermal and kinetic energy. Detailed analysis of this evolution entails tracing magnetic field lines, an operation which is not time-efficient on a single processor. By utilizing a graphics card (GPU) to trace lines in parallel, conducting such analysis is made feasible. We applied our GPU implementation to the most advanced 3D Radiative-MHD simulations (Bifrost, Gudicksen et al. 2011) of the solar atmosphere in order to better understand the evolution of the modeled field lines.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aliaga, José I., E-mail: aliaga@uji.es; Alonso, Pedro; Badía, José M.

We introduce a new iterative Krylov subspace-based eigensolver for the simulation of macromolecular motions on desktop multithreaded platforms equipped with multicore processors and, possibly, a graphics accelerator (GPU). The method consists of two stages, with the original problem first reduced into a simpler band-structured form by means of a high-performance compute-intensive procedure. This is followed by a memory-intensive but low-cost Krylov iteration, which is off-loaded to be computed on the GPU by means of an efficient data-parallel kernel. The experimental results reveal the performance of the new eigensolver. Concretely, when applied to the simulation of macromolecules with a few thousandsmore » degrees of freedom and the number of eigenpairs to be computed is small to moderate, the new solver outperforms other methods implemented as part of high-performance numerical linear algebra packages for multithreaded architectures.« less

Generating Billion-Edge Scale-Free Networks in Seconds: Performance Study of a Novel GPU-based Preferential Attachment Model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S.; Alam, Maksudul

A novel parallel algorithm is presented for generating random scale-free networks using the preferential-attachment model. The algorithm, named cuPPA, is custom-designed for single instruction multiple data (SIMD) style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. In one of the best cases, when executed on an NVidiamore » GeForce 1080 GPU, cuPPA generates a scale free network of a billion edges in less than 2 seconds.« less

High-Performance High-Order Simulation of Wave and Plasma Phenomena

NASA Astrophysics Data System (ADS)

Klockner, Andreas

This thesis presents results aiming to enhance and broaden the applicability of the discontinuous Galerkin ("DG") method in a variety of ways. DG was chosen as a foundation for this work because it yields high-order finite element discretizations with very favorable numerical properties for the treatment of hyperbolic conservation laws. In a first part, I examine progress that can be made on implementation aspects of DG. In adapting the method to mass-market massively parallel computation hardware in the form of graphics processors ("GPUs"), I obtain an increase in computation performance per unit of cost by more than an order of magnitude over conventional processor architectures. Key to this advance is a recipe that adapts DG to a variety of hardware through automated self-tuning. I discuss new parallel programming tools supporting GPU run-time code generation which are instrumental in the DG self-tuning process and contribute to its reaching application floating point throughput greater than 200 GFlops/s on a single GPU and greater than 3 TFlops/s on a 16-GPU cluster in simulations of electromagnetics problems in three dimensions. I further briefly discuss the solver infrastructure that makes this possible. In the second part of the thesis, I introduce a number of new numerical methods whose motivation is partly rooted in the opportunity created by GPU-DG: First, I construct and examine a novel GPU-capable shock detector, which, when used to control an artificial viscosity, helps stabilize DG computations in gas dynamics and a number of other fields. Second, I describe my pursuit of a method that allows the simulation of rarefied plasmas using a DG discretization of the electromagnetic field. Finally, I introduce new explicit multi-rate time integrators for ordinary differential equations with multiple time scales, with a focus on applicability to DG discretizations of time-dependent problems.

Singular value decomposition utilizing parallel algorithms on graphical processors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotas, Charlotte W; Barhen, Jacob

2011-01-01

One of the current challenges in underwater acoustic array signal processing is the detection of quiet targets in the presence of noise. In order to enable robust detection, one of the key processing steps requires data and replica whitening. This, in turn, involves the eigen-decomposition of the sample spectral matrix, Cx = 1/K xKX(k)XH(k) where X(k) denotes a single frequency snapshot with an element for each element of the array. By employing the singular value decomposition (SVD) method, the eigenvectors and eigenvalues can be determined directly from the data without computing the sample covariance matrix, reducing the computational requirements formore » a given level of accuracy (van Trees, Optimum Array Processing). (Recall that the SVD of a complex matrix A involves determining V, , and U such that A = U VH where U and V are orthonormal and is a positive, real, diagonal matrix containing the singular values of A. U and V are the eigenvectors of AAH and AHA, respectively, while the singular values are the square roots of the eigenvalues of AAH.) Because it is desirable to be able to compute these quantities in real time, an efficient technique for computing the SVD is vital. In addition, emerging multicore processors like graphical processing units (GPUs) are bringing parallel processing capabilities to an ever increasing number of users. Since the computational tasks involved in array signal processing are well suited for parallelization, it is expected that these computations will be implemented using GPUs as soon as users have the necessary computational tools available to them. Thus, it is important to have an SVD algorithm that is suitable for these processors. This work explores the effectiveness of two different parallel SVD implementations on an NVIDIA Tesla C2050 GPU (14 multiprocessors, 32 cores per multiprocessor, 1.15 GHz clock - peed). The first algorithm is based on a two-step algorithm which bidiagonalizes the matrix using Householder transformations, and then diagonalizes the intermediate bidiagonal matrix through implicit QR shifts. This is similar to that implemented for real matrices by Lahabar and Narayanan ("Singular Value Decomposition on GPU using CUDA", IEEE International Parallel Distributed Processing Symposium 2009). The implementation is done in a hybrid manner, with the bidiagonalization stage done using the GPU while the diagonalization stage is done using the CPU, with the GPU used to update the U and V matrices. The second algorithm is based on a one-sided Jacobi scheme utilizing a sequence of pair-wise column orthogonalizations such that A is replaced by AV until the resulting matrix is sufficiently orthogonal (that is, equal to U ). V is obtained from the sequence of orthogonalizations, while can be found from the square root of the diagonal elements of AH A and, once is known, U can be found from column scaling the resulting matrix. These implementations utilize CUDA Fortran and NVIDIA's CUB LAS library. The primary goal of this study is to quantify the comparative performance of these two techniques against themselves and other standard implementations (for example, MATLAB). Considering that there is significant overhead associated with transferring data to the GPU and with synchronization between the GPU and the host CPU, it is also important to understand when it is worthwhile to use the GPU in terms of the matrix size and number of concurrent SVDs to be calculated.« less

PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.

PubMed

Örtenberg, A; Magnusson, M; Sandborg, M; Alm Carlsson, G; Malusek, A

2016-06-01

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 parallelisation 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 parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation 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. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences

NASA Astrophysics Data System (ADS)

Kose, Ryoichi; Kose, Katsumi

2017-08-01

A magnetic resonance imaging (MRI) simulator, which reproduces MRI experiments using computers, has been developed using two graphic-processor-unit (GPU) boards (GTX 1080). The MRI simulator was developed to run according to pulse sequences used in experiments. Experiments and simulations were performed to demonstrate the usefulness of the MRI simulator for three types of pulse sequences, namely, three-dimensional (3D) gradient-echo, 3D radio-frequency spoiled gradient-echo, and gradient-echo multislice with practical matrix sizes. The results demonstrated that the calculation speed using two GPU boards was typically about 7 TFLOPS and about 14 times faster than the calculation speed using CPUs (two 18-core Xeons). We also found that MR images acquired by experiment could be reproduced using an appropriate number of subvoxels, and that 3D isotropic and two-dimensional multislice imaging experiments for practical matrix sizes could be simulated using the MRI simulator. Therefore, we concluded that such powerful MRI simulators are expected to become an indispensable tool for MRI research and development.

A fully parallel in time and space algorithm for simulating the electrical activity of a neural tissue.

PubMed

Bedez, Mathieu; Belhachmi, Zakaria; Haeberlé, Olivier; Greget, Renaud; Moussaoui, Saliha; Bouteiller, Jean-Marie; Bischoff, Serge

2016-01-15

The resolution of a model describing the electrical activity of neural tissue and its propagation within this tissue is highly consuming in term of computing time and requires strong computing power to achieve good results. In this study, we present a method to solve a model describing the electrical propagation in neuronal tissue, using parareal algorithm, coupling with parallelization space using CUDA in graphical processing unit (GPU). We applied the method of resolution to different dimensions of the geometry of our model (1-D, 2-D and 3-D). The GPU results are compared with simulations from a multi-core processor cluster, using message-passing interface (MPI), where the spatial scale was parallelized in order to reach a comparable calculation time than that of the presented method using GPU. A gain of a factor 100 in term of computational time between sequential results and those obtained using the GPU has been obtained, in the case of 3-D geometry. Given the structure of the GPU, this factor increases according to the fineness of the geometry used in the computation. To the best of our knowledge, it is the first time such a method is used, even in the case of neuroscience. Parallelization time coupled with GPU parallelization space allows for drastically reducing computational time with a fine resolution of the model describing the propagation of the electrical signal in a neuronal tissue. Copyright © 2015 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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 and both second- and fourth-order differencing in space. The integrators are written to run on NVIDIA GPUs and are interfaced with MATLAB including built-in visualization and analysis tools. Restrictions: The main restriction for the GPU integrators is the amount of RAM on the GPU as the code is currently only designed for running on a single GPU. Unusual features: Ability to visualize real-time simulations through the interaction of MATLAB and the compiled GPU integrators. Additional comments: Setup guide and Installation guide provided. Program has a dedicated web site at www.nlsemagic.com. Running time: A three-dimensional run with a grid dimension of 87×87×203 for 3360 time steps (100 non-dimensional time units) takes about one and a half minutes on a GeForce GTX 580 GPU card.

Modelling Nonlinear Dynamic Textures using Hybrid DWT-DCT and Kernel PCA with GPU

NASA Astrophysics Data System (ADS)

Ghadekar, Premanand Pralhad; Chopade, Nilkanth Bhikaji

2016-12-01

Most of the real-world dynamic textures are nonlinear, non-stationary, and irregular. Nonlinear motion also has some repetition of motion, but it exhibits high variation, stochasticity, and randomness. Hybrid DWT-DCT and Kernel Principal Component Analysis (KPCA) with YCbCr/YIQ colour coding using the Dynamic Texture Unit (DTU) approach is proposed to model a nonlinear dynamic texture, which provides better results than state-of-art methods in terms of PSNR, compression ratio, model coefficients, and model size. Dynamic texture is decomposed into DTUs as they help to extract temporal self-similarity. Hybrid DWT-DCT is used to extract spatial redundancy. YCbCr/YIQ colour encoding is performed to capture chromatic correlation. KPCA is applied to capture nonlinear motion. Further, the proposed algorithm is implemented on Graphics Processing Unit (GPU), which comprise of hundreds of small processors to decrease time complexity and to achieve parallelism.

Automatic detection and classification of obstacles with applications in autonomous mobile robots

NASA Astrophysics Data System (ADS)

Ponomaryov, Volodymyr I.; Rosas-Miranda, Dario I.

2016-04-01

Hardware implementation of an automatic detection and classification of objects that can represent an obstacle for an autonomous mobile robot using stereo vision algorithms is presented. We propose and evaluate a new method to detect and classify objects for a mobile robot in outdoor conditions. This method is divided in two parts, the first one is the object detection step based on the distance from the objects to the camera and a BLOB analysis. The second part is the classification step that is based on visuals primitives and a SVM classifier. The proposed method is performed in GPU in order to reduce the processing time values. This is performed with help of hardware based on multi-core processors and GPU platform, using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.

SIMD Optimization of Linear Expressions for Programmable Graphics Hardware

PubMed Central

Bajaj, Chandrajit; Ihm, Insung; Min, Jungki; Oh, Jinsang

2009-01-01

The increased programmability of graphics hardware allows efficient graphical processing unit (GPU) implementations of a wide range of general computations on commodity PCs. An important factor in such implementations is how to fully exploit the SIMD computing capacities offered by modern graphics processors. Linear expressions in the form of ȳ = Ax̄ + b̄, where A is a matrix, and x̄, ȳ and b̄ are vectors, constitute one of the most basic operations in many scientific computations. In this paper, we propose a SIMD code optimization technique that enables efficient shader codes to be generated for evaluating linear expressions. It is shown that performance can be improved considerably by efficiently packing arithmetic operations into four-wide SIMD instructions through reordering of the operations in linear expressions. We demonstrate that the presented technique can be used effectively for programming both vertex and pixel shaders for a variety of mathematical applications, including integrating differential equations and solving a sparse linear system of equations using iterative methods. PMID:19946569

Rapid indirect trajectory optimization on highly parallel computing architectures

NASA Astrophysics Data System (ADS)

Antony, Thomas

Trajectory optimization is a field which can benefit greatly from the advantages offered by parallel computing. The current state-of-the-art in trajectory optimization focuses on the use of direct optimization methods, such as the pseudo-spectral method. These methods are favored due to their ease of implementation and large convergence regions while indirect methods have largely been ignored in the literature in the past decade except for specific applications in astrodynamics. It has been shown that the shortcomings conventionally associated with indirect methods can be overcome by the use of a continuation method in which complex trajectory solutions are obtained by solving a sequence of progressively difficult optimization problems. High performance computing hardware is trending towards more parallel architectures as opposed to powerful single-core processors. Graphics Processing Units (GPU), which were originally developed for 3D graphics rendering have gained popularity in the past decade as high-performance, programmable parallel processors. The Compute Unified Device Architecture (CUDA) framework, a parallel computing architecture and programming model developed by NVIDIA, is one of the most widely used platforms in GPU computing. GPUs have been applied to a wide range of fields that require the solution of complex, computationally demanding problems. A GPU-accelerated indirect trajectory optimization methodology which uses the multiple shooting method and continuation is developed using the CUDA platform. The various algorithmic optimizations used to exploit the parallelism inherent in the indirect shooting method are described. The resulting rapid optimal control framework enables the construction of high quality optimal trajectories that satisfy problem-specific constraints and fully satisfy the necessary conditions of optimality. The benefits of the framework are highlighted by construction of maximum terminal velocity trajectories for a hypothetical long range weapon system. The techniques used to construct an initial guess from an analytic near-ballistic trajectory and the methods used to formulate the necessary conditions of optimality in a manner that is transparent to the designer are discussed. Various hypothetical mission scenarios that enforce different combinations of initial, terminal, interior point and path constraints demonstrate the rapid construction of complex trajectories without requiring any a-priori insight into the structure of the solutions. Trajectory problems of this kind were previously considered impractical to solve using indirect methods. The performance of the GPU-accelerated solver is found to be 2x--4x faster than MATLAB's bvp4c, even while running on GPU hardware that is five years behind the state-of-the-art.

Accelerating Electrostatic Surface Potential Calculation with Multiscale Approximation on Graphics Processing Units

PubMed Central

Anandakrishnan, Ramu; Scogland, Tom R. W.; Fenley, Andrew T.; Gordon, John C.; Feng, Wu-chun; Onufriev, Alexey V.

2010-01-01

Tools that compute and visualize biomolecular electrostatic surface potential have been used extensively for studying biomolecular function. However, determining the surface potential for large biomolecules on a typical desktop computer can take days or longer using currently available tools and methods. Two commonly used techniques to speed up these types of electrostatic computations are approximations based on multi-scale coarse-graining and parallelization across multiple processors. This paper demonstrates that for the computation of electrostatic surface potential, these two techniques can be combined to deliver significantly greater speed-up than either one separately, something that is in general not always possible. Specifically, the electrostatic potential computation, using an analytical linearized Poisson Boltzmann (ALPB) method, is approximated using the hierarchical charge partitioning (HCP) multiscale method, and parallelized on an ATI Radeon 4870 graphical processing unit (GPU). The implementation delivers a combined 934-fold speed-up for a 476,040 atom viral capsid, compared to an equivalent non-parallel implementation on an Intel E6550 CPU without the approximation. This speed-up is significantly greater than the 42-fold speed-up for the HCP approximation alone or the 182-fold speed-up for the GPU alone. PMID:20452792

Graphics Processors in HEP Low-Level Trigger Systems

NASA Astrophysics Data System (ADS)

Ammendola, Roberto; Biagioni, Andrea; Chiozzi, Stefano; Cotta Ramusino, Angelo; Cretaro, Paolo; Di Lorenzo, Stefano; Fantechi, Riccardo; Fiorini, Massimiliano; Frezza, Ottorino; Lamanna, Gianluca; Lo Cicero, Francesca; Lonardo, Alessandro; Martinelli, Michele; Neri, Ilaria; Paolucci, Pier Stanislao; Pastorelli, Elena; Piandani, Roberto; Pontisso, Luca; Rossetti, Davide; Simula, Francesco; Sozzi, Marco; Vicini, Piero

2016-11-01

Usage of Graphics Processing Units (GPUs) in the so called general-purpose computing is emerging as an effective approach in several fields of science, although so far applications have been employing GPUs typically for offline computations. Taking into account the steady performance increase of GPU architectures in terms of computing power and I/O capacity, the real-time applications of these devices can thrive in high-energy physics data acquisition and trigger systems. We will examine the use of online parallel computing on GPUs for the synchronous low-level trigger, focusing on tests performed on the trigger system of the CERN NA62 experiment. To successfully integrate GPUs in such an online environment, latencies of all components need analysing, networking being the most critical. To keep it under control, we envisioned NaNet, an FPGA-based PCIe Network Interface Card (NIC) enabling GPUDirect connection. Furthermore, it is assessed how specific trigger algorithms can be parallelized and thus benefit from a GPU implementation, in terms of increased execution speed. Such improvements are particularly relevant for the foreseen Large Hadron Collider (LHC) luminosity upgrade where highly selective algorithms will be essential to maintain sustainable trigger rates with very high pileup.

Graphics processing unit (GPU)-based computation of heat conduction in thermally anisotropic solids

NASA Astrophysics Data System (ADS)

Nahas, C. A.; Balasubramaniam, Krishnan; Rajagopal, Prabhu

2013-01-01

Numerical modeling of anisotropic media is a computationally intensive task since it brings additional complexity to the field problem in such a way that the physical properties are different in different directions. Largely used in the aerospace industry because of their lightweight nature, composite materials are a very good example of thermally anisotropic media. With advancements in video gaming technology, parallel processors are much cheaper today and accessibility to higher-end graphical processing devices has increased dramatically over the past couple of years. Since these massively parallel GPUs are very good in handling floating point arithmetic, they provide a new platform for engineers and scientists to accelerate their numerical models using commodity hardware. In this paper we implement a parallel finite difference model of thermal diffusion through anisotropic media using the NVIDIA CUDA (Compute Unified device Architecture). We use the NVIDIA GeForce GTX 560 Ti as our primary computing device which consists of 384 CUDA cores clocked at 1645 MHz with a standard desktop pc as the host platform. We compare the results from standard CPU implementation for its accuracy and speed and draw implications for simulation using the GPU paradigm.

High-Speed Particle-in-Cell Simulation Parallelized with Graphic Processing Units for Low Temperature Plasmas for Material Processing

NASA Astrophysics Data System (ADS)

Hur, Min Young; Verboncoeur, John; Lee, Hae June

2014-10-01

Particle-in-cell (PIC) simulations have high fidelity in the plasma device requiring transient kinetic modeling compared with fluid simulations. It uses less approximation on the plasma kinetics but requires many particles and grids to observe the semantic results. It means that the simulation spends lots of simulation time in proportion to the number of particles. Therefore, PIC simulation needs high performance computing. In this research, a graphic processing unit (GPU) is adopted for high performance computing of PIC simulation for low temperature discharge plasmas. GPUs have many-core processors and high memory bandwidth compared with a central processing unit (CPU). NVIDIA GeForce GPUs were used for the test with hundreds of cores which show cost-effective performance. PIC code algorithm is divided into two modules which are a field solver and a particle mover. The particle mover module is divided into four routines which are named move, boundary, Monte Carlo collision (MCC), and deposit. Overall, the GPU code solves particle motions as well as electrostatic potential in two-dimensional geometry almost 30 times faster than a single CPU code. This work was supported by the Korea Institute of Science Technology Information.

Rubus: A compiler for seamless and extensible parallelism.

PubMed

Adnan, Muhammad; Aslam, Faisal; Nawaz, Zubair; Sarwar, Syed Mansoor

2017-01-01

Nowadays, a typical processor may have multiple processing cores on a single chip. Furthermore, a special purpose processing unit called Graphic Processing Unit (GPU), originally designed for 2D/3D games, is now available for general purpose use in computers and mobile devices. However, the traditional programming languages which were designed to work with machines having single core CPUs, cannot utilize the parallelism available on multi-core processors efficiently. Therefore, to exploit the extraordinary processing power of multi-core processors, researchers are working on new tools and techniques to facilitate parallel programming. To this end, languages like CUDA and OpenCL have been introduced, which can be used to write code with parallelism. The main shortcoming of these languages is that programmer needs to specify all the complex details manually in order to parallelize the code across multiple cores. Therefore, the code written in these languages is difficult to understand, debug and maintain. Furthermore, to parallelize legacy code can require rewriting a significant portion of code in CUDA or OpenCL, which can consume significant time and resources. Thus, the amount of parallelism achieved is proportional to the skills of the programmer and the time spent in code optimizations. This paper proposes a new open source compiler, Rubus, to achieve seamless parallelism. The Rubus compiler relieves the programmer from manually specifying the low-level details. It analyses and transforms a sequential program into a parallel program automatically, without any user intervention. This achieves massive speedup and better utilization of the underlying hardware without a programmer's expertise in parallel programming. For five different benchmarks, on average a speedup of 34.54 times has been achieved by Rubus as compared to Java on a basic GPU having only 96 cores. Whereas, for a matrix multiplication benchmark the average execution speedup of 84 times has been achieved by Rubus on the same GPU. Moreover, Rubus achieves this performance without drastically increasing the memory footprint of a program.

Rubus: A compiler for seamless and extensible parallelism

PubMed Central

Adnan, Muhammad; Aslam, Faisal; Sarwar, Syed Mansoor

2017-01-01

Nowadays, a typical processor may have multiple processing cores on a single chip. Furthermore, a special purpose processing unit called Graphic Processing Unit (GPU), originally designed for 2D/3D games, is now available for general purpose use in computers and mobile devices. However, the traditional programming languages which were designed to work with machines having single core CPUs, cannot utilize the parallelism available on multi-core processors efficiently. Therefore, to exploit the extraordinary processing power of multi-core processors, researchers are working on new tools and techniques to facilitate parallel programming. To this end, languages like CUDA and OpenCL have been introduced, which can be used to write code with parallelism. The main shortcoming of these languages is that programmer needs to specify all the complex details manually in order to parallelize the code across multiple cores. Therefore, the code written in these languages is difficult to understand, debug and maintain. Furthermore, to parallelize legacy code can require rewriting a significant portion of code in CUDA or OpenCL, which can consume significant time and resources. Thus, the amount of parallelism achieved is proportional to the skills of the programmer and the time spent in code optimizations. This paper proposes a new open source compiler, Rubus, to achieve seamless parallelism. The Rubus compiler relieves the programmer from manually specifying the low-level details. It analyses and transforms a sequential program into a parallel program automatically, without any user intervention. This achieves massive speedup and better utilization of the underlying hardware without a programmer’s expertise in parallel programming. For five different benchmarks, on average a speedup of 34.54 times has been achieved by Rubus as compared to Java on a basic GPU having only 96 cores. Whereas, for a matrix multiplication benchmark the average execution speedup of 84 times has been achieved by Rubus on the same GPU. Moreover, Rubus achieves this performance without drastically increasing the memory footprint of a program. PMID:29211758

GPU-Accelerated Voxelwise Hepatic Perfusion Quantification

PubMed Central

Wang, H; Cao, Y

2012-01-01

Voxelwise quantification of hepatic perfusion parameters from dynamic contrast enhanced (DCE) imaging greatly contributes to assessment of liver function in response to radiation therapy. However, the efficiency of the estimation of hepatic perfusion parameters voxel-by-voxel in the whole liver using a dual-input single-compartment model requires substantial improvement for routine clinical applications. In this paper, we utilize the parallel computation power of a graphics processing unit (GPU) to accelerate the computation, while maintaining the same accuracy as the conventional method. Using CUDA-GPU, the hepatic perfusion computations over multiple voxels are run across the GPU blocks concurrently but independently. At each voxel, non-linear least squares fitting the time series of the liver DCE data to the compartmental model is distributed to multiple threads in a block, and the computations of different time points are performed simultaneously and synchronically. An efficient fast Fourier transform in a block is also developed for the convolution computation in the model. The GPU computations of the voxel-by-voxel hepatic perfusion images are compared with ones by the CPU using the simulated DCE data and the experimental DCE MR images from patients. The computation speed is improved by 30 times using a NVIDIA Tesla C2050 GPU compared to a 2.67 GHz Intel Xeon CPU processor. To obtain liver perfusion maps with 626400 voxels in a patient’s liver, it takes 0.9 min with the GPU-accelerated voxelwise computation, compared to 110 min with the CPU, while both methods result in perfusion parameters differences less than 10−6. The method will be useful for generating liver perfusion images in clinical settings. PMID:22892645

Improving the performance of heterogeneous multi-core processors by modifying the cache coherence protocol

NASA Astrophysics Data System (ADS)

Fang, Juan; Hao, Xiaoting; Fan, Qingwen; Chang, Zeqing; Song, Shuying

2017-05-01

In the Heterogeneous multi-core architecture, CPU and GPU processor are integrated on the same chip, which poses a new challenge to the last-level cache management. In this architecture, the CPU application and the GPU application execute concurrently, accessing the last-level cache. CPU and GPU have different memory access characteristics, so that they have differences in the sensitivity of last-level cache (LLC) capacity. For many CPU applications, a reduced share of the LLC could lead to significant performance degradation. On the contrary, GPU applications can tolerate increase in memory access latency when there is sufficient thread-level parallelism. Taking into account the GPU program memory latency tolerance characteristics, this paper presents a method that let GPU applications can access to memory directly, leaving lots of LLC space for CPU applications, in improving the performance of CPU applications and does not affect the performance of GPU applications. When the CPU application is cache sensitive, and the GPU application is insensitive to the cache, the overall performance of the system is improved significantly.

Accelerating Wright–Fisher Forward Simulations on the Graphics Processing Unit

PubMed Central

Lawrie, David S.

2017-01-01

Forward Wright–Fisher simulations are powerful in their ability to model complex demography and selection scenarios, but suffer from slow execution on the Central Processor Unit (CPU), thus limiting their usefulness. However, the single-locus Wright–Fisher forward algorithm is exceedingly parallelizable, with many steps that are so-called “embarrassingly parallel,” consisting of a vast number of individual computations that are all independent of each other and thus capable of being performed concurrently. The rise of modern Graphics Processing Units (GPUs) and programming languages designed to leverage the inherent parallel nature of these processors have allowed researchers to dramatically speed up many programs that have such high arithmetic intensity and intrinsic concurrency. The presented GPU Optimized Wright–Fisher simulation, or “GO Fish” for short, can be used to simulate arbitrary selection and demographic scenarios while running over 250-fold faster than its serial counterpart on the CPU. Even modest GPU hardware can achieve an impressive speedup of over two orders of magnitude. With simulations so accelerated, one can not only do quick parametric bootstrapping of previously estimated parameters, but also use simulated results to calculate the likelihoods and summary statistics of demographic and selection models against real polymorphism data, all without restricting the demographic and selection scenarios that can be modeled or requiring approximations to the single-locus forward algorithm for efficiency. Further, as many of the parallel programming techniques used in this simulation can be applied to other computationally intensive algorithms important in population genetics, GO Fish serves as an exciting template for future research into accelerating computation in evolution. GO Fish is part of the Parallel PopGen Package available at: http://dl42.github.io/ParallelPopGen/. PMID:28768689

CUDAICA: GPU Optimization of Infomax-ICA EEG Analysis

PubMed Central

Raimondo, Federico; Kamienkowski, Juan E.; Sigman, Mariano; Fernandez Slezak, Diego

2012-01-01

In recent years, Independent Component Analysis (ICA) has become a standard to identify relevant dimensions of the data in neuroscience. ICA is a very reliable method to analyze data but it is, computationally, very costly. The use of ICA for online analysis of the data, used in brain computing interfaces, results are almost completely prohibitive. We show an increase with almost no cost (a rapid video card) of speed of ICA by about 25 fold. The EEG data, which is a repetition of many independent signals in multiple channels, is very suitable for processing using the vector processors included in the graphical units. We profiled the implementation of this algorithm and detected two main types of operations responsible of the processing bottleneck and taking almost 80% of computing time: vector-matrix and matrix-matrix multiplications. By replacing function calls to basic linear algebra functions to the standard CUBLAS routines provided by GPU manufacturers, it does not increase performance due to CUDA kernel launch overhead. Instead, we developed a GPU-based solution that, comparing with the original BLAS and CUBLAS versions, obtains a 25x increase of performance for the ICA calculation. PMID:22811699

Impact of memory bottleneck on the performance of graphics processing units

NASA Astrophysics Data System (ADS)

Son, Dong Oh; Choi, Hong Jun; Kim, Jong Myon; Kim, Cheol Hong

2015-12-01

Recent graphics processing units (GPUs) can process general-purpose applications as well as graphics applications with the help of various user-friendly application programming interfaces (APIs) supported by GPU vendors. Unfortunately, utilizing the hardware resource in the GPU efficiently is a challenging problem, since the GPU architecture is totally different to the traditional CPU architecture. To solve this problem, many studies have focused on the techniques for improving the system performance using GPUs. In this work, we analyze the GPU performance varying GPU parameters such as the number of cores and clock frequency. According to our simulations, the GPU performance can be improved by 125.8% and 16.2% on average as the number of cores and clock frequency increase, respectively. However, the performance is saturated when memory bottleneck problems incur due to huge data requests to the memory. The performance of GPUs can be improved as the memory bottleneck is reduced by changing GPU parameters dynamically.

Bayer image parallel decoding based on GPU

NASA Astrophysics Data System (ADS)

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.

Accelerating electrostatic surface potential calculation with multi-scale approximation on graphics processing units.

PubMed

Anandakrishnan, Ramu; Scogland, Tom R W; Fenley, Andrew T; Gordon, John C; Feng, Wu-chun; Onufriev, Alexey V

2010-06-01

Tools that compute and visualize biomolecular electrostatic surface potential have been used extensively for studying biomolecular function. However, determining the surface potential for large biomolecules on a typical desktop computer can take days or longer using currently available tools and methods. Two commonly used techniques to speed-up these types of electrostatic computations are approximations based on multi-scale coarse-graining and parallelization across multiple processors. This paper demonstrates that for the computation of electrostatic surface potential, these two techniques can be combined to deliver significantly greater speed-up than either one separately, something that is in general not always possible. Specifically, the electrostatic potential computation, using an analytical linearized Poisson-Boltzmann (ALPB) method, is approximated using the hierarchical charge partitioning (HCP) multi-scale method, and parallelized on an ATI Radeon 4870 graphical processing unit (GPU). The implementation delivers a combined 934-fold speed-up for a 476,040 atom viral capsid, compared to an equivalent non-parallel implementation on an Intel E6550 CPU without the approximation. This speed-up is significantly greater than the 42-fold speed-up for the HCP approximation alone or the 182-fold speed-up for the GPU alone. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Spins Dynamics in a Dissipative Environment: Hierarchal Equations of Motion Approach Using a Graphics Processing Unit (GPU).

PubMed

Tsuchimoto, Masashi; Tanimura, Yoshitaka

2015-08-11

A system with many energy states coupled to a harmonic oscillator bath is considered. To study quantum non-Markovian system-bath dynamics numerically rigorously and nonperturbatively, we developed a computer code for the reduced hierarchy equations of motion (HEOM) for a graphics processor unit (GPU) that can treat the system as large as 4096 energy states. The code employs a Padé spectrum decomposition (PSD) for a construction of HEOM and the exponential integrators. Dynamics of a quantum spin glass system are studied by calculating the free induction decay signal for the cases of 3 × 2 to 3 × 4 triangular lattices with antiferromagnetic interactions. We found that spins relax faster at lower temperature due to transitions through a quantum coherent state, as represented by the off-diagonal elements of the reduced density matrix, while it has been known that the spins relax slower due to suppression of thermal activation in a classical case. The decay of the spins are qualitatively similar regardless of the lattice sizes. The pathway of spin relaxation is analyzed under a sudden temperature drop condition. The Compute Unified Device Architecture (CUDA) based source code used in the present calculations is provided as Supporting Information .

BarraCUDA - a fast short read sequence aligner using graphics processing units

PubMed Central

2012-01-01

Background With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy efficient alternative to traditional high-performance computing (HPC) clusters. In this article, we describe the implementation of BarraCUDA, a GPGPU sequence alignment software that is based on BWA, to accelerate the alignment of sequencing reads generated by these instruments to a reference DNA sequence. Findings Using the NVIDIA Compute Unified Device Architecture (CUDA) software development environment, we ported the most computational-intensive alignment component of BWA to GPU to take advantage of the massive parallelism. As a result, BarraCUDA offers a magnitude of performance boost in alignment throughput when compared to a CPU core while delivering the same level of alignment fidelity. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the alignment throughput. Conclusions BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology. BarraCUDA is currently available from http://seqbarracuda.sf.net PMID:22244497

GPU acceleration of particle-in-cell methods

NASA Astrophysics Data System (ADS)

Cowan, Benjamin; Cary, John; Meiser, Dominic

2015-11-01

Graphics processing units (GPUs) have become key components in many supercomputing systems, as they can provide more computations relative to their cost and power consumption than conventional processors. However, to take full advantage of this capability, they require a strict programming model which involves single-instruction multiple-data execution as well as significant constraints on memory accesses. To bring the full power of GPUs to bear on plasma physics problems, we must adapt the computational methods to this new programming model. We have developed a GPU implementation of the particle-in-cell (PIC) method, one of the mainstays of plasma physics simulation. This framework is highly general and enables advanced PIC features such as high order particles and absorbing boundary conditions. The main elements of the PIC loop, including field interpolation and particle deposition, are designed to optimize memory access. We describe the performance of these algorithms and discuss some of the methods used. Work supported by DARPA contract W31P4Q-15-C-0061 (SBIR).

A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy

DOE PAGES

Pryor, Alan; Ophus, Colin; Miao, Jianwei

2017-10-25

Simulation of atomic-resolution image formation in scanning transmission electron microscopy can require significant computation times using traditional methods. A recently developed method, termed plane-wave reciprocal-space interpolated scattering matrix (PRISM), demonstrates potential for significant acceleration of such simulations with negligible loss of accuracy. In this paper, we present a software package called Prismatic for parallelized simulation of image formation in scanning transmission electron microscopy (STEM) using both the PRISM and multislice methods. By distributing the workload between multiple CUDA-enabled GPUs and multicore processors, accelerations as high as 1000 × for PRISM and 15 × for multislice are achieved relative to traditionalmore » multislice implementations using a single 4-GPU machine. We demonstrate a potentially important application of Prismatic, using it to compute images for atomic electron tomography at sufficient speeds to include in the reconstruction pipeline. Prismatic is freely available both as an open-source CUDA/C++ package with a graphical user interface and as a Python package, PyPrismatic.« less

Graphical processors for HEP trigger systems

NASA Astrophysics Data System (ADS)

Ammendola, R.; Biagioni, A.; Chiozzi, S.; Cotta Ramusino, A.; 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.

2017-02-01

General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to employ GPUs as accelerators in offline computations. With the steady decrease of GPU latencies and the increase in link and memory throughputs, time is ripe for real-time applications using GPUs in high-energy physics data acquisition and trigger systems. We will discuss the use of online parallel computing on GPUs for synchronous low level trigger systems, focusing on tests performed on the trigger of the CERN NA62 experiment. Latencies of all components need analysing, networking being the most critical. To keep it under control, we envisioned NaNet, an FPGA-based PCIe Network Interface Card (NIC) enabling GPUDirect connection. Moreover, we discuss how specific trigger algorithms can be parallelised and thus benefit from a GPU implementation, in terms of increased execution speed. Such improvements are particularly relevant for the foreseen LHC luminosity upgrade where highly selective algorithms will be crucial to maintain sustainable trigger rates with very high pileup.

A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy.

PubMed

Pryor, Alan; Ophus, Colin; Miao, Jianwei

2017-01-01

Simulation of atomic-resolution image formation in scanning transmission electron microscopy can require significant computation times using traditional methods. A recently developed method, termed plane-wave reciprocal-space interpolated scattering matrix (PRISM), demonstrates potential for significant acceleration of such simulations with negligible loss of accuracy. Here, we present a software package called Prismatic for parallelized simulation of image formation in scanning transmission electron microscopy (STEM) using both the PRISM and multislice methods. By distributing the workload between multiple CUDA-enabled GPUs and multicore processors, accelerations as high as 1000 × for PRISM and 15 × for multislice are achieved relative to traditional multislice implementations using a single 4-GPU machine. We demonstrate a potentially important application of Prismatic , using it to compute images for atomic electron tomography at sufficient speeds to include in the reconstruction pipeline. Prismatic is freely available both as an open-source CUDA/C++ package with a graphical user interface and as a Python package, PyPrismatic .

A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pryor, Alan; Ophus, Colin; Miao, Jianwei

Simulation of atomic-resolution image formation in scanning transmission electron microscopy can require significant computation times using traditional methods. A recently developed method, termed plane-wave reciprocal-space interpolated scattering matrix (PRISM), demonstrates potential for significant acceleration of such simulations with negligible loss of accuracy. In this paper, we present a software package called Prismatic for parallelized simulation of image formation in scanning transmission electron microscopy (STEM) using both the PRISM and multislice methods. By distributing the workload between multiple CUDA-enabled GPUs and multicore processors, accelerations as high as 1000 × for PRISM and 15 × for multislice are achieved relative to traditionalmore » multislice implementations using a single 4-GPU machine. We demonstrate a potentially important application of Prismatic, using it to compute images for atomic electron tomography at sufficient speeds to include in the reconstruction pipeline. Prismatic is freely available both as an open-source CUDA/C++ package with a graphical user interface and as a Python package, PyPrismatic.« less

Particle-In-Cell simulations of high pressure plasmas using graphics processing units

NASA Astrophysics Data System (ADS)

Gebhardt, Markus; Atteln, Frank; Brinkmann, Ralf Peter; Mussenbrock, Thomas; Mertmann, Philipp; Awakowicz, Peter

2009-10-01

Particle-In-Cell (PIC) simulations are widely used to understand the fundamental phenomena in low-temperature plasmas. Particularly plasmas at very low gas pressures are studied using PIC methods. The inherent drawback of these methods is that they are very time consuming -- certain stability conditions has to be satisfied. This holds even more for the PIC simulation of high pressure plasmas due to the very high collision rates. The simulations take up to very much time to run on standard computers and require the help of computer clusters or super computers. Recent advances in the field of graphics processing units (GPUs) provides every personal computer with a highly parallel multi processor architecture for very little money. This architecture is freely programmable and can be used to implement a wide class of problems. In this paper we present the concepts of a fully parallel PIC simulation of high pressure plasmas using the benefits of GPU programming.

Accelerated Adaptive MGS Phase Retrieval

NASA Technical Reports Server (NTRS)

Lam, Raymond K.; Ohara, Catherine M.; Green, Joseph J.; Bikkannavar, Siddarayappa A.; Basinger, Scott A.; Redding, David C.; Shi, Fang

2011-01-01

The Modified Gerchberg-Saxton (MGS) algorithm is an image-based wavefront-sensing method that can turn any science instrument focal plane into a wavefront sensor. MGS characterizes optical systems by estimating the wavefront errors in the exit pupil using only intensity images of a star or other point source of light. This innovative implementation of MGS significantly accelerates the MGS phase retrieval algorithm by using stream-processing hardware on conventional graphics cards. Stream processing is a relatively new, yet powerful, paradigm to allow parallel processing of certain applications that apply single instructions to multiple data (SIMD). These stream processors are designed specifically to support large-scale parallel computing on a single graphics chip. Computationally intensive algorithms, such as the Fast Fourier Transform (FFT), are particularly well suited for this computing environment. This high-speed version of MGS exploits commercially available hardware to accomplish the same objective in a fraction of the original time. The exploit involves performing matrix calculations in nVidia graphic cards. The graphical processor unit (GPU) is hardware that is specialized for computationally intensive, highly parallel computation. From the software perspective, a parallel programming model is used, called CUDA, to transparently scale multicore parallelism in hardware. This technology gives computationally intensive applications access to the processing power of the nVidia GPUs through a C/C++ programming interface. The AAMGS (Accelerated Adaptive MGS) software takes advantage of these advanced technologies, to accelerate the optical phase error characterization. With a single PC that contains four nVidia GTX-280 graphic cards, the new implementation can process four images simultaneously to produce a JWST (James Webb Space Telescope) wavefront measurement 60 times faster than the previous code.

Performance analysis of parallel gravitational N-body codes on large GPU clusters

NASA Astrophysics Data System (ADS)

Huang, Si-Yi; Spurzem, Rainer; Berczik, Peter

2016-01-01

We compare the performance of two very different parallel gravitational N-body codes for astrophysical simulations on large Graphics Processing Unit (GPU) clusters, both of which are pioneers in their own fields as well as on certain mutual scales - NBODY6++ and Bonsai. We carry out benchmarks of the two codes by analyzing their performance, accuracy and efficiency through the modeling of structure decomposition and timing measurements. We find that both codes are heavily optimized to leverage the computational potential of GPUs as their performance has approached half of the maximum single precision performance of the underlying GPU cards. With such performance we predict that a speed-up of 200 - 300 can be achieved when up to 1k processors and GPUs are employed simultaneously. We discuss the quantitative information about comparisons of the two codes, finding that in the same cases Bonsai adopts larger time steps as well as larger relative energy errors than NBODY6++, typically ranging from 10 - 50 times larger, depending on the chosen parameters of the codes. Although the two codes are built for different astrophysical applications, in specified conditions they may overlap in performance at certain physical scales, thus allowing the user to choose either one by fine-tuning parameters accordingly.

Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs

PubMed Central

Hernández, Moisés; Guerrero, Ginés D.; Cecilia, José M.; García, José M.; Inuggi, Alberto; Jbabdi, Saad; Behrens, Timothy E. J.; Sotiropoulos, Stamatios N.

2013-01-01

With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation. PMID:23658616

Fast parallel tandem mass spectral library searching using GPU hardware acceleration.

PubMed

Baumgardner, Lydia Ashleigh; Shanmugam, Avinash Kumar; Lam, Henry; Eng, Jimmy K; Martin, Daniel B

2011-06-03

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.

Web-based, GPU-accelerated, Monte Carlo simulation and visualization of indirect radiation imaging detector performance.

PubMed

Dong, Han; Sharma, Diksha; Badano, Aldo

2014-12-01

Monte Carlo simulations play a vital role in the understanding of the fundamental limitations, design, and optimization of existing and emerging medical imaging systems. Efforts in this area have resulted in the development of a wide variety of open-source software packages. One such package, hybridmantis, uses a novel hybrid concept to model indirect scintillator detectors by balancing the computational load using dual CPU and graphics processing unit (GPU) processors, obtaining computational efficiency with reasonable accuracy. In this work, the authors describe two open-source visualization interfaces, webmantis and visualmantis to facilitate the setup of computational experiments via hybridmantis. The visualization tools visualmantis and webmantis enable the user to control simulation properties through a user interface. In the case of webmantis, control via a web browser allows access through mobile devices such as smartphones or tablets. webmantis acts as a server back-end and communicates with an NVIDIA GPU computing cluster that can support multiuser environments where users can execute different experiments in parallel. The output consists of point response and pulse-height spectrum, and optical transport statistics generated by hybridmantis. The users can download the output images and statistics through a zip file for future reference. In addition, webmantis provides a visualization window that displays a few selected optical photon path as they get transported through the detector columns and allows the user to trace the history of the optical photons. The visualization tools visualmantis and webmantis provide features such as on the fly generation of pulse-height spectra and response functions for microcolumnar x-ray imagers while allowing users to save simulation parameters and results from prior experiments. The graphical interfaces simplify the simulation setup and allow the user to go directly from specifying input parameters to receiving visual feedback for the model predictions.

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

PubMed

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.

A fast ultrasonic simulation tool based on massively parallel implementations

NASA Astrophysics Data System (ADS)

Lambert, Jason; Rougeron, Gilles; Lacassagne, Lionel; Chatillon, Sylvain

2014-02-01

This paper presents a CIVA optimized ultrasonic inspection simulation tool, which takes benefit of the power of massively parallel architectures: graphical processing units (GPU) and multi-core general purpose processors (GPP). This tool is based on the classical approach used in CIVA: the interaction model is based on Kirchoff, and the ultrasonic field around the defect is computed by the pencil method. The model has been adapted and parallelized for both architectures. At this stage, the configurations addressed by the tool are : multi and mono-element probes, planar specimens made of simple isotropic materials, planar rectangular defects or side drilled holes of small diameter. Validations on the model accuracy and performances measurements are presented.

On the effective implementation of a boundary element code on graphics processing units unsing an out-of-core LU algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

D'Azevedo, Ed F; Nintcheu Fata, Sylvain

2012-01-01

A collocation boundary element code for solving the three-dimensional Laplace equation, publicly available from \\url{http://www.intetec.org}, has been adapted to run on an Nvidia Tesla general purpose graphics processing unit (GPU). Global matrix assembly and LU factorization of the resulting dense matrix were performed on the GPU. Out-of-core techniques were used to solve problems larger than available GPU memory. The code achieved over eight times speedup in matrix assembly and about 56~Gflops/sec in the LU factorization using only 512~Mbytes of GPU memory. Details of the GPU implementation and comparisons with the standard sequential algorithm are included to illustrate the performance ofmore » the GPU code.« less

Line-by-line spectroscopic simulations on graphics processing units

NASA Astrophysics Data System (ADS)

Collange, Sylvain; Daumas, Marc; Defour, David

2008-01-01

We report here on software that performs line-by-line spectroscopic simulations on gases. Elaborate models (such as narrow band and correlated-K) are accurate and efficient for bands where various components are not simultaneously and significantly active. Line-by-line is probably the most accurate model in the infrared for blends of gases that contain high proportions of H 2O and CO 2 as this was the case for our prototype simulation. Our implementation on graphics processing units sustains a speedup close to 330 on computation-intensive tasks and 12 on memory intensive tasks compared to implementations on one core of high-end processors. This speedup is due to data parallelism, efficient memory access for specific patterns and some dedicated hardware operators only available in graphics processing units. It is obtained leaving most of processor resources available and it would scale linearly with the number of graphics processing units in parallel machines. Line-by-line simulation coupled with simulation of fluid dynamics was long believed to be economically intractable but our work shows that it could be done with some affordable additional resources compared to what is necessary to perform simulations on fluid dynamics alone. Program summaryProgram title: GPU4RE Catalogue identifier: ADZY_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADZY_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.: 62 776 No. of bytes in distributed program, including test data, etc.: 1 513 247 Distribution format: tar.gz Programming language: C++ Computer: x86 PC Operating system: Linux, Microsoft Windows. Compilation requires either gcc/g++ under Linux or Visual C++ 2003/2005 and Cygwin under Windows. It has been tested using gcc 4.1.2 under Ubuntu Linux 7.04 and using Visual C++ 2005 with Cygwin 1.5.24 under Windows XP. RAM: 1 gigabyte Classification: 21.2 External routines: OpenGL ( http://www.opengl.org) Nature of problem: Simulating radiative transfer on high-temperature high-pressure gases. Solution method: Line-by-line Monte-Carlo ray-tracing. Unusual features: Parallel computations are moved to the GPU. Additional comments: nVidia GeForce 7000 or ATI Radeon X1000 series graphics processing unit is required. Running time: A few minutes.

Parallel processor-based raster graphics system architecture

DOEpatents

Littlefield, Richard J.

1990-01-01

An apparatus for generating raster graphics images from the graphics command stream includes a plurality of graphics processors connected in parallel, each adapted to receive any part of the graphics command stream for processing the command stream part into pixel data. The apparatus also includes a frame buffer for mapping the pixel data to pixel locations and an interconnection network for interconnecting the graphics processors to the frame buffer. Through the interconnection network, each graphics processor may access any part of the frame buffer concurrently with another graphics processor accessing any other part of the frame buffer. The plurality of graphics processors can thereby transmit concurrently pixel data to pixel locations in the frame buffer.

MILC Code Performance on High End CPU and GPU Supercomputer Clusters

NASA Astrophysics Data System (ADS)

DeTar, Carleton; Gottlieb, Steven; Li, Ruizi; Toussaint, Doug

2018-03-01

With recent developments in parallel supercomputing architecture, many core, multi-core, and GPU processors are now commonplace, resulting in more levels of parallelism, memory hierarchy, and programming complexity. It has been necessary to adapt the MILC code to these new processors starting with NVIDIA GPUs, and more recently, the Intel Xeon Phi processors. We report on our efforts to port and optimize our code for the Intel Knights Landing architecture. We consider performance of the MILC code with MPI and OpenMP, and optimizations with QOPQDP and QPhiX. For the latter approach, we concentrate on the staggered conjugate gradient and gauge force. We also consider performance on recent NVIDIA GPUs using the QUDA library.

High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers

PubMed Central

Linderman, Michael D.; Athalye, Vivek; Meng, Teresa H.; Asadi, Narges Bani; Bruggner, Robert; Nolan, Garry P.

2017-01-01

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20–50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations. PMID:28819655

Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic

DTIC Science & Technology

2015-03-01

Defense Department’s use. vi THIS PAGE INTENTIONALLY LEFT BLANK vii TABLE OF CONTENTS I.  INTRODUCTION...22  D.  GENERATING DIGESTS ............................................................................23  1.  Reference...the-shelf GPU Graphical Processing Unit GPGPU General -Purpose Graphic Processing Unit HBSS Host-Based Security System HIPS Host Intrusion

GPU MrBayes V3.1: MrBayes on Graphics Processing Units for Protein Sequence Data.

PubMed

Pang, Shuai; Stones, Rebecca J; Ren, Ming-Ming; Liu, Xiao-Guang; Wang, Gang; Xia, Hong-ju; Wu, Hao-Yang; Liu, Yang; Xie, Qiang

2015-09-01

We present a modified GPU (graphics processing unit) version of MrBayes, called ta(MC)(3) (GPU MrBayes V3.1), for Bayesian phylogenetic inference on protein data sets. Our main contributions are 1) utilizing 64-bit variables, thereby enabling ta(MC)(3) to process larger data sets than MrBayes; and 2) to use Kahan summation to improve accuracy, convergence rates, and consequently runtime. Versus the current fastest software, we achieve a speedup of up to around 2.5 (and up to around 90 vs. serial MrBayes), and more on multi-GPU hardware. GPU MrBayes V3.1 is available from http://sourceforge.net/projects/mrbayes-gpu/. © The Author 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The density matrix renormalization group algorithm on kilo-processor architectures: Implementation and trade-offs

NASA Astrophysics Data System (ADS)

Nemes, Csaba; Barcza, Gergely; Nagy, Zoltán; Legeza, Örs; Szolgay, Péter

2014-06-01

In the numerical analysis of strongly correlated quantum lattice models one of the leading algorithms developed to balance the size of the effective Hilbert space and the accuracy of the simulation is the density matrix renormalization group (DMRG) algorithm, in which the run-time is dominated by the iterative diagonalization of the Hamilton operator. As the most time-dominant step of the diagonalization can be expressed as a list of dense matrix operations, the DMRG is an appealing candidate to fully utilize the computing power residing in novel kilo-processor architectures. In the paper a smart hybrid CPU-GPU implementation is presented, which exploits the power of both CPU and GPU and tolerates problems exceeding the GPU memory size. Furthermore, a new CUDA kernel has been designed for asymmetric matrix-vector multiplication to accelerate the rest of the diagonalization. Besides the evaluation of the GPU implementation, the practical limits of an FPGA implementation are also discussed.

Fast parallel tandem mass spectral library searching using GPU hardware acceleration

PubMed Central

Baumgardner, Lydia Ashleigh; Shanmugam, Avinash Kumar; Lam, Henry; Eng, Jimmy K.; Martin, Daniel B.

2011-01-01

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching) is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment. PMID:21545112

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

NASA Astrophysics Data System (ADS)

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.

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

PubMed Central

Manavski, Svetlin A; Valle, Giorgio

2008-01-01

Background Searching for similarities in protein and DNA databases has become a routine procedure in Molecular Biology. The Smith-Waterman algorithm has been available for more than 25 years. It is based on a dynamic programming approach that explores all the possible alignments between two sequences; as a result it returns the optimal local alignment. Unfortunately, the computational cost is very high, requiring a number of operations proportional to the product of the length of two sequences. Furthermore, the exponential growth of protein and DNA databases makes the Smith-Waterman algorithm unrealistic for searching similarities in large sets of sequences. For these reasons heuristic approaches such as those implemented in FASTA and BLAST tend to be preferred, allowing faster execution times at the cost of reduced sensitivity. The main motivation of our work is to exploit the huge computational power of commonly available graphic cards, to develop high performance solutions for sequence alignment. Results In this paper we present what we believe is the fastest solution of the exact Smith-Waterman algorithm running on commodity hardware. It is implemented in the recently released CUDA programming environment by NVidia. CUDA allows direct access to the hardware primitives of the last-generation Graphics Processing Units (GPU) G80. Speeds of more than 3.5 GCUPS (Giga Cell Updates Per Second) are achieved on a workstation running two GeForce 8800 GTX. Exhaustive tests have been done to compare our implementation to SSEARCH and BLAST, running on a 3 GHz Intel Pentium IV processor. Our solution was also compared to a recently published GPU implementation and to a Single Instruction Multiple Data (SIMD) solution. These tests show that our implementation performs from 2 to 30 times faster than any other previous attempt available on commodity hardware. Conclusions The results show that graphic cards are now sufficiently advanced to be used as efficient hardware accelerators for sequence alignment. Their performance is better than any alternative available on commodity hardware platforms. The solution presented in this paper allows large scale alignments to be performed at low cost, using the exact Smith-Waterman algorithm instead of the largely adopted heuristic approaches. PMID:18387198

GASPRNG: GPU accelerated scalable parallel random number generator library

NASA Astrophysics Data System (ADS)

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 workstation with NVIDIA GPU (Tested on Fermi GTX480, Tesla C1060, Tesla M2070). Operating system: Linux with CUDA version 4.0 or later. Should also run on MacOS, Windows, or UNIX. Has the code been vectorized or parallelized?: Yes. Parallelized using MPI directives. RAM: 512 MB˜ 732 MB (main memory on host CPU, depending on the data type of random numbers.) / 512 MB (GPU global memory) Classification: 4.13, 6.5. Nature of problem: Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations are able to consume limitless random numbers for the computation as long as resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computational cost. The GASPRNG library presented here accelerates the generators of independent streams of random numbers using graphical processing units (GPUs). Solution method: Multiple copies of random number generators in GPUs allow a computational science application to consume large numbers of random numbers from independent, parallel streams. GASPRNG is a random number generators library to allow a computational science application to employ multiple copies of random number generators to boost performance. Users can interface GASPRNG with software code executing on microprocessors and/or GPUs. Running time: The tests provided take a few minutes to run.

Computational performance of a smoothed particle hydrodynamics simulation for shared-memory parallel computing

NASA Astrophysics Data System (ADS)

Nishiura, Daisuke; Furuichi, Mikito; Sakaguchi, Hide

2015-09-01

The computational performance of a smoothed particle hydrodynamics (SPH) simulation is investigated for three types of current shared-memory parallel computer devices: many integrated core (MIC) processors, graphics processing units (GPUs), and multi-core CPUs. We are especially interested in efficient shared-memory allocation methods for each chipset, because the efficient data access patterns differ between compute unified device architecture (CUDA) programming for GPUs and OpenMP programming for MIC processors and multi-core CPUs. We first introduce several parallel implementation techniques for the SPH code, and then examine these on our target computer architectures to determine the most effective algorithms for each processor unit. In addition, we evaluate the effective computing performance and power efficiency of the SPH simulation on each architecture, as these are critical metrics for overall performance in a multi-device environment. In our benchmark test, the GPU is found to produce the best arithmetic performance as a standalone device unit, and gives the most efficient power consumption. The multi-core CPU obtains the most effective computing performance. The computational speed of the MIC processor on Xeon Phi approached that of two Xeon CPUs. This indicates that using MICs is an attractive choice for existing SPH codes on multi-core CPUs parallelized by OpenMP, as it gains computational acceleration without the need for significant changes to the source code.

Experiences modeling ocean circulation problems on a 30 node commodity cluster with 3840 GPU processor cores.

NASA Astrophysics Data System (ADS)

Hill, C.

2008-12-01

Low cost graphic cards today use many, relatively simple, compute cores to deliver support for memory bandwidth of more than 100GB/s and theoretical floating point performance of more than 500 GFlop/s. Right now this performance is, however, only accessible to highly parallel algorithm implementations that, (i) can use a hundred or more, 32-bit floating point, concurrently executing cores, (ii) can work with graphics memory that resides on the graphics card side of the graphics bus and (iii) can be partially expressed in a language that can be compiled by a graphics programming tool. In this talk we describe our experiences implementing a complete, but relatively simple, time dependent shallow-water equations simulation targeting a cluster of 30 computers each hosting one graphics card. The implementation takes into account the considerations (i), (ii) and (iii) listed previously. We code our algorithm as a series of numerical kernels. Each kernel is designed to be executed by multiple threads of a single process. Kernels are passed memory blocks to compute over which can be persistent blocks of memory on a graphics card. Each kernel is individually implemented using the NVidia CUDA language but driven from a higher level supervisory code that is almost identical to a standard model driver. The supervisory code controls the overall simulation timestepping, but is written to minimize data transfer between main memory and graphics memory (a massive performance bottle-neck on current systems). Using the recipe outlined we can boost the performance of our cluster by nearly an order of magnitude, relative to the same algorithm executing only on the cluster CPU's. Achieving this performance boost requires that many threads are available to each graphics processor for execution within each numerical kernel and that the simulations working set of data can fit into the graphics card memory. As we describe, this puts interesting upper and lower bounds on the problem sizes for which this technology is currently most useful. However, many interesting problems fit within this envelope. Looking forward, we extrapolate our experience to estimate full-scale ocean model performance and applicability. Finally we describe preliminary hybrid mixed 32-bit and 64-bit experiments with graphics cards that support 64-bit arithmetic, albeit at a lower performance.

GPU Acceleration of DSP for Communication Receivers.

PubMed

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.

Dataflow-Based Implementation of Layered Sensing Applications on High-Performance Embedded Processors

DTIC Science & Technology

2013-03-01

time (milliseconds) GFlops Comparison to GPU peak performance (%) Cascade Gaussian Filtering 13 45.19 6.3 Difference of Gaussian 0.512 152...values for the GPU-targeted actor implementations in terms of Giga Floating Point Operations Per Second ( GFLOPS ). Our GFLOPS calculation for an actor...kernels. The results for GFLOPS are provided in Table . The actors were implemented on an NVIDIA GTX260 GPU, which provides 715 GFLOPS as peak

Accelerating image recognition on mobile devices using GPGPU

NASA Astrophysics Data System (ADS)

Bordallo López, Miguel; Nykänen, Henri; Hannuksela, Jari; Silvén, Olli; Vehviläinen, Markku

2011-01-01

The future multi-modal user interfaces of battery-powered mobile devices are expected to require computationally costly image analysis techniques. The use of Graphic Processing Units for computing is very well suited for parallel processing and the addition of programmable stages and high precision arithmetic provide for opportunities to implement energy-efficient complete algorithms. At the moment the first mobile graphics accelerators with programmable pipelines are available, enabling the GPGPU implementation of several image processing algorithms. In this context, we consider a face tracking approach that uses efficient gray-scale invariant texture features and boosting. The solution is based on the Local Binary Pattern (LBP) features and makes use of the GPU on the pre-processing and feature extraction phase. We have implemented a series of image processing techniques in the shader language of OpenGL ES 2.0, compiled them for a mobile graphics processing unit and performed tests on a mobile application processor platform (OMAP3530). In our contribution, we describe the challenges of designing on a mobile platform, present the performance achieved and provide measurement results for the actual power consumption in comparison to using the CPU (ARM) on the same platform.

Distributed GPU Computing in GIScience

NASA Astrophysics Data System (ADS)

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 Transactions on, 9(3), 378-394. 2. Li, J., Jiang, Y., Yang, C., Huang, Q., & Rice, M. (2013). Visualizing 3D/4D Environmental Data Using Many-core Graphics Processing Units (GPUs) and Multi-core Central Processing Units (CPUs). Computers & Geosciences, 59(9), 78-89. 3. Owens, J. D., Houston, M., Luebke, D., Green, S., Stone, J. E., & Phillips, J. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879-899.

Development of an embedded atmospheric turbulence mitigation engine

NASA Astrophysics Data System (ADS)

Paolini, Aaron; Bonnett, James; Kozacik, Stephen; Kelmelis, Eric

2017-05-01

Methods to reconstruct pictures from imagery degraded by atmospheric turbulence have been under development for decades. The techniques were initially developed for observing astronomical phenomena from the Earth's surface, but have more recently been modified for ground and air surveillance scenarios. Such applications can impose significant constraints on deployment options because they both increase the computational complexity of the algorithms themselves and often dictate a requirement for low size, weight, and power (SWaP) form factors. Consequently, embedded implementations must be developed that can perform the necessary computations on low-SWaP platforms. Fortunately, there is an emerging class of embedded processors driven by the mobile and ubiquitous computing industries. We have leveraged these processors to develop embedded versions of the core atmospheric correction engine found in our ATCOM software. In this paper, we will present our experience adapting our algorithms for embedded systems on a chip (SoCs), namely the NVIDIA Tegra that couples general-purpose ARM cores with their graphics processing unit (GPU) technology and the Xilinx Zynq which pairs similar ARM cores with their field-programmable gate array (FPGA) fabric.

Exploiting graphics processing units for computational biology and bioinformatics.

PubMed

Payne, Joshua L; Sinnott-Armstrong, Nicholas A; Moore, Jason H

2010-09-01

Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs), the standard workhorses of scientific computing. With the recent release of generalpurpose GPUs and NVIDIA's GPU programming language, CUDA, graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. The goal of this article is to concisely present an introduction to GPU hardware and programming, aimed at the computational biologist or bioinformaticist. To this end, we discuss the primary differences between GPU and CPU architecture, introduce the basics of the CUDA programming language, and discuss important CUDA programming practices, such as the proper use of coalesced reads, data types, and memory hierarchies. We highlight each of these topics in the context of computing the all-pairs distance between instances in a dataset, a common procedure in numerous disciplines of scientific computing. We conclude with a runtime analysis of the GPU and CPU implementations of the all-pairs distance calculation. We show our final GPU implementation to outperform the CPU implementation by a factor of 1700.

Simultaneous reconstruction of multiple depth images without off-focus points in integral imaging using a graphics processing unit.

PubMed

Yi, Faliu; Lee, Jieun; Moon, Inkyu

2014-05-01

The reconstruction of multiple depth images with a ray back-propagation algorithm in three-dimensional (3D) computational integral imaging is computationally burdensome. Further, a reconstructed depth image consists of a focus and an off-focus area. Focus areas are 3D points on the surface of an object that are located at the reconstructed depth, while off-focus areas include 3D points in free-space that do not belong to any object surface in 3D space. Generally, without being removed, the presence of an off-focus area would adversely affect the high-level analysis of a 3D object, including its classification, recognition, and tracking. Here, we use a graphics processing unit (GPU) that supports parallel processing with multiple processors to simultaneously reconstruct multiple depth images using a lookup table containing the shifted values along the x and y directions for each elemental image in a given depth range. Moreover, each 3D point on a depth image can be measured by analyzing its statistical variance with its corresponding samples, which are captured by the two-dimensional (2D) elemental images. These statistical variances can be used to classify depth image pixels as either focus or off-focus points. At this stage, the measurement of focus and off-focus points in multiple depth images is also implemented in parallel on a GPU. Our proposed method is conducted based on the assumption that there is no occlusion of the 3D object during the capture stage of the integral imaging process. Experimental results have demonstrated that this method is capable of removing off-focus points in the reconstructed depth image. The results also showed that using a GPU to remove the off-focus points could greatly improve the overall computational speed compared with using a CPU.

Web-based, GPU-accelerated, Monte Carlo simulation and visualization of indirect radiation imaging detector performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong, Han; Sharma, Diksha; Badano, Aldo, E-mail: aldo.badano@fda.hhs.gov

2014-12-15

Purpose: Monte Carlo simulations play a vital role in the understanding of the fundamental limitations, design, and optimization of existing and emerging medical imaging systems. Efforts in this area have resulted in the development of a wide variety of open-source software packages. One such package, hybridMANTIS, uses a novel hybrid concept to model indirect scintillator detectors by balancing the computational load using dual CPU and graphics processing unit (GPU) processors, obtaining computational efficiency with reasonable accuracy. In this work, the authors describe two open-source visualization interfaces, webMANTIS and visualMANTIS to facilitate the setup of computational experiments via hybridMANTIS. Methods: Themore » visualization tools visualMANTIS and webMANTIS enable the user to control simulation properties through a user interface. In the case of webMANTIS, control via a web browser allows access through mobile devices such as smartphones or tablets. webMANTIS acts as a server back-end and communicates with an NVIDIA GPU computing cluster that can support multiuser environments where users can execute different experiments in parallel. Results: The output consists of point response and pulse-height spectrum, and optical transport statistics generated by hybridMANTIS. The users can download the output images and statistics through a zip file for future reference. In addition, webMANTIS provides a visualization window that displays a few selected optical photon path as they get transported through the detector columns and allows the user to trace the history of the optical photons. Conclusions: The visualization tools visualMANTIS and webMANTIS provide features such as on the fly generation of pulse-height spectra and response functions for microcolumnar x-ray imagers while allowing users to save simulation parameters and results from prior experiments. The graphical interfaces simplify the simulation setup and allow the user to go directly from specifying input parameters to receiving visual feedback for the model predictions.« less

Light scattering microscopy measurements of single nuclei compared with GPU-accelerated FDTD simulations

NASA Astrophysics Data System (ADS)

Stark, Julian; Rothe, Thomas; Kieß, Steffen; Simon, Sven; Kienle, Alwin

2016-04-01

Single cell nuclei were investigated using two-dimensional angularly and spectrally resolved scattering microscopy. We show that even for a qualitative comparison of experimental and theoretical data, the standard Mie model of a homogeneous sphere proves to be insufficient. Hence, an accelerated finite-difference time-domain method using a graphics processor unit and domain decomposition was implemented to analyze the experimental scattering patterns. The measured cell nuclei were modeled as single spheres with randomly distributed spherical inclusions of different size and refractive index representing the nucleoli and clumps of chromatin. Taking into account the nuclear heterogeneity of a large number of inclusions yields a qualitative agreement between experimental and theoretical spectra and illustrates the impact of the nuclear micro- and nanostructure on the scattering patterns.

Use of GPUs in Trigger Systems

NASA Astrophysics Data System (ADS)

Lamanna, Gianluca

In recent years the interest for using graphics processor (GPU) in general purpose high performance computing is constantly rising. In this paper we discuss the possible use of GPUs to construct a fast and effective real time trigger system, both in software and hardware levels. In particular, we study the integration of such a system in the NA62 trigger. The first application of GPUs for rings pattern recognition in the RICH will be presented. The results obtained show that there are not showstoppers in trigger systems with relatively low latency. Thanks to the use of off-the-shelf technology, in continous development for purposes related to video game and image processing market, the architecture described would be easily exported to other experiments, to build a versatile and fully customizable online selection.

Light scattering microscopy measurements of single nuclei compared with GPU-accelerated FDTD simulations.

PubMed

Stark, Julian; Rothe, Thomas; Kieß, Steffen; Simon, Sven; Kienle, Alwin

2016-04-07

Single cell nuclei were investigated using two-dimensional angularly and spectrally resolved scattering microscopy. We show that even for a qualitative comparison of experimental and theoretical data, the standard Mie model of a homogeneous sphere proves to be insufficient. Hence, an accelerated finite-difference time-domain method using a graphics processor unit and domain decomposition was implemented to analyze the experimental scattering patterns. The measured cell nuclei were modeled as single spheres with randomly distributed spherical inclusions of different size and refractive index representing the nucleoli and clumps of chromatin. Taking into account the nuclear heterogeneity of a large number of inclusions yields a qualitative agreement between experimental and theoretical spectra and illustrates the impact of the nuclear micro- and nanostructure on the scattering patterns.

Prefiltering Model for Homology Detection Algorithms on GPU.

PubMed

Retamosa, Germán; de Pedro, Luis; González, Ivan; Tamames, Javier

2016-01-01

Homology detection has evolved over the time from heavy algorithms based on dynamic programming approaches to lightweight alternatives based on different heuristic models. However, the main problem with these algorithms is that they use complex statistical models, which makes it difficult to achieve a relevant speedup and find exact matches with the original results. Thus, their acceleration is essential. The aim of this article was to prefilter a sequence database. To make this work, we have implemented a groundbreaking heuristic model based on NVIDIA's graphics processing units (GPUs) and multicore processors. Depending on the sensitivity settings, this makes it possible to quickly reduce the sequence database by factors between 50% and 95%, while rejecting no significant sequences. Furthermore, this prefiltering application can be used together with multiple homology detection algorithms as a part of a next-generation sequencing system. Extensive performance and accuracy tests have been carried out in the Spanish National Centre for Biotechnology (NCB). The results show that GPU hardware can accelerate the execution times of former homology detection applications, such as National Centre for Biotechnology Information (NCBI), Basic Local Alignment Search Tool for Proteins (BLASTP), up to a factor of 4.

Bridging FPGA and GPU technologies for AO real-time control

NASA Astrophysics Data System (ADS)

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.

High performance computing for deformable image registration: towards a new paradigm in adaptive radiotherapy.

PubMed

Samant, Sanjiv S; Xia, Junyi; Muyan-Ozcelik, Pinar; Owens, John D

2008-08-01

The advent of readily available temporal imaging or time series volumetric (4D) imaging has become an indispensable component of treatment planning and adaptive radiotherapy (ART) at many radiotherapy centers. Deformable image registration (DIR) is also used in other areas of medical imaging, including motion corrected image reconstruction. Due to long computation time, clinical applications of DIR in radiation therapy and elsewhere have been limited and consequently relegated to offline analysis. With the recent advances in hardware and software, graphics processing unit (GPU) based computing is an emerging technology for general purpose computation, including DIR, and is suitable for highly parallelized computing. However, traditional general purpose computation on the GPU is limited because the constraints of the available programming platforms. As well, compared to CPU programming, the GPU currently has reduced dedicated processor memory, which can limit the useful working data set for parallelized processing. We present an implementation of the demons algorithm using the NVIDIA 8800 GTX GPU and the new CUDA programming language. The GPU performance will be compared with single threading and multithreading CPU implementations on an Intel dual core 2.4 GHz CPU using the C programming language. CUDA provides a C-like language programming interface, and allows for direct access to the highly parallel compute units in the GPU. Comparisons for volumetric clinical lung images acquired using 4DCT were carried out. Computation time for 100 iterations in the range of 1.8-13.5 s was observed for the GPU with image size ranging from 2.0 x 10(6) to 14.2 x 10(6) pixels. The GPU registration was 55-61 times faster than the CPU for the single threading implementation, and 34-39 times faster for the multithreading implementation. For CPU based computing, the computational time generally has a linear dependence on image size for medical imaging data. Computational efficiency is characterized in terms of time per megapixels per iteration (TPMI) with units of seconds per megapixels per iteration (or spmi). For the demons algorithm, our CPU implementation yielded largely invariant values of TPMI. The mean TPMIs were 0.527 spmi and 0.335 spmi for the single threading and multithreading cases, respectively, with <2% variation over the considered image data range. For GPU computing, we achieved TPMI =0.00916 spmi with 3.7% variation, indicating optimized memory handling under CUDA. The paradigm of GPU based real-time DIR opens up a host of clinical applications for medical imaging.

GPU COMPUTING FOR PARTICLE TRACKING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nishimura, Hiroshi; Song, Kai; Muriki, Krishna

2011-03-25

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 calculationmore » 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.« less

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

NASA Astrophysics Data System (ADS)

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 successfully running it on a variety of different computing devices including an NVidia GPU card, two AMD GPU cards and an Intel CPU processor. Computational efficiency among these platforms was compared.

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

PubMed

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 successfully running it on a variety of different computing devices including an NVidia GPU card, two AMD GPU cards and an Intel CPU processor. Computational efficiency among these platforms was compared.

An accelerated framework for the classification of biological targets from solid-state micropore data.

PubMed

Hanif, Madiha; Hafeez, Abdul; Suleman, Yusuf; Mustafa Rafique, M; Butt, Ali R; Iqbal, Samir M

2016-10-01

Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

DOE SBIR Phase-1 Report on Hybrid CPU-GPU Parallel Development of the Eulerian-Lagrangian Barracuda Multiphase Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dr. Dale M. Snider

2011-02-28

This report gives the result from the Phase-1 work on demonstrating greater than 10x speedup of the Barracuda computer program using parallel methods and GPU processors (General-Purpose Graphics Processing Unit or Graphics Processing Unit). Phase-1 demonstrated a 12x speedup on a typical Barracuda function using the GPU processor. The problem test case used about 5 million particles and 250,000 Eulerian grid cells. The relative speedup, compared to a single CPU, increases with increased number of particles giving greater than 12x speedup. Phase-1 work provided a path for reformatting data structure modifications to give good parallel performance while keeping a friendlymore » environment for new physics development and code maintenance. The implementation of data structure changes will be in Phase-2. Phase-1 laid the ground work for the complete parallelization of Barracuda in Phase-2, with the caveat that implemented computer practices for parallel programming done in Phase-1 gives immediate speedup in the current Barracuda serial running code. The Phase-1 tasks were completed successfully laying the frame work for Phase-2. The detailed results of Phase-1 are within this document. In general, the speedup of one function would be expected to be higher than the speedup of the entire code because of I/O functions and communication between the algorithms. However, because one of the most difficult Barracuda algorithms was parallelized in Phase-1 and because advanced parallelization methods and proposed parallelization optimization techniques identified in Phase-1 will be used in Phase-2, an overall Barracuda code speedup (relative to a single CPU) is expected to be greater than 10x. This means that a job which takes 30 days to complete will be done in 3 days. Tasks completed in Phase-1 are: Task 1: Profile the entire Barracuda code and select which subroutines are to be parallelized (See Section Choosing a Function to Accelerate) Task 2: Select a GPU consultant company and jointly parallelize subroutines (CPFD chose the small business EMPhotonics for the Phase-1 the technical partner. See Section Technical Objective and Approach) Task 3: Integrate parallel subroutines into Barracuda (See Section Results from Phase-1 and its subsections) Task 4: Testing, refinement, and optimization of parallel methodology (See Section Results from Phase-1 and Section Result Comparison Program) Task 5: Integrate Phase-1 parallel subroutines into Barracuda and release (See Section Results from Phase-1 and its subsections) Task 6: Roadmap of Phase-2 (See Section Plan for Phase-2) With the completion of Phase 1 we have the base understanding to completely parallelize Barracuda. An overview of the work to move Barracuda to a parallelized code is given in Plan for Phase-2.« less

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava

2017-01-01

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particlemore » tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.« less

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

NASA Astrophysics Data System (ADS)

Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; Masciovecchio, Mario; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

2017-08-01

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU).

PubMed

Yang, Owen; Choi, Bernard

2013-01-01

To interpret fiber-based and camera-based measurements of remitted light from biological tissues, researchers typically use analytical models, such as the diffusion approximation to light transport theory, or stochastic models, such as Monte Carlo modeling. To achieve rapid (ideally real-time) measurement of tissue optical properties, especially in clinical situations, there is a critical need to accelerate Monte Carlo simulation runs. In this manuscript, we report on our approach using the Graphics Processing Unit (GPU) to accelerate rescaling of single Monte Carlo runs to calculate rapidly diffuse reflectance values for different sets of tissue optical properties. We selected MATLAB to enable non-specialists in C and CUDA-based programming to use the generated open-source code. We developed a software package with four abstraction layers. To calculate a set of diffuse reflectance values from a simulated tissue with homogeneous optical properties, our rescaling GPU-based approach achieves a reduction in computation time of several orders of magnitude as compared to other GPU-based approaches. Specifically, our GPU-based approach generated a diffuse reflectance value in 0.08ms. The transfer time from CPU to GPU memory currently is a limiting factor with GPU-based calculations. However, for calculation of multiple diffuse reflectance values, our GPU-based approach still can lead to processing that is ~3400 times faster than other GPU-based approaches.

TH-A-19A-04: Latent Uncertainties and Performance of a GPU-Implemented Pre-Calculated Track Monte Carlo Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Renaud, M; Seuntjens, J; Roberge, D

Purpose: Assessing the performance and uncertainty of a pre-calculated Monte Carlo (PMC) algorithm for proton and electron transport running on graphics processing units (GPU). While PMC methods have been described in the past, an explicit quantification of the latent uncertainty arising from recycling a limited number of tracks in the pre-generated track bank is missing from the literature. With a proper uncertainty analysis, an optimal pre-generated track bank size can be selected for a desired dose calculation uncertainty. Methods: Particle tracks were pre-generated for electrons and protons using EGSnrc and GEANT4, respectively. The PMC algorithm for track transport was implementedmore » on the CUDA programming framework. GPU-PMC dose distributions were compared to benchmark dose distributions simulated using general-purpose MC codes in the same conditions. A latent uncertainty analysis was performed by comparing GPUPMC dose values to a “ground truth” benchmark while varying the track bank size and primary particle histories. Results: GPU-PMC dose distributions and benchmark doses were within 1% of each other in voxels with dose greater than 50% of Dmax. In proton calculations, a submillimeter distance-to-agreement error was observed at the Bragg Peak. Latent uncertainty followed a Poisson distribution with the number of tracks per energy (TPE) and a track bank of 20,000 TPE produced a latent uncertainty of approximately 1%. Efficiency analysis showed a 937× and 508× gain over a single processor core running DOSXYZnrc for 16 MeV electrons in water and bone, respectively. Conclusion: The GPU-PMC method can calculate dose distributions for electrons and protons to a statistical uncertainty below 1%. The track bank size necessary to achieve an optimal efficiency can be tuned based on the desired uncertainty. Coupled with a model to calculate dose contributions from uncharged particles, GPU-PMC is a candidate for inverse planning of modulated electron radiotherapy and scanned proton beams. This work was supported in part by FRSQ-MSSS (Grant No. 22090), NSERC RG (Grant No. 432290) and CIHR MOP (Grant No. MOP-211360)« less

Graphics Processing Units for HEP trigger systems

NASA Astrophysics Data System (ADS)

Ammendola, R.; Bauce, M.; Biagioni, A.; Chiozzi, S.; Cotta Ramusino, A.; Fantechi, R.; Fiorini, M.; Giagu, S.; Gianoli, A.; Lamanna, G.; Lonardo, A.; Messina, A.; Neri, I.; Paolucci, P. S.; Piandani, R.; Pontisso, L.; Rescigno, M.; Simula, F.; Sozzi, M.; Vicini, P.

2016-07-01

General-purpose computing on GPUs (Graphics Processing Units) is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerator in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughput, the use of such devices for real-time applications in high-energy physics data acquisition and trigger systems is becoming ripe. We will discuss the use of online parallel computing on GPU for synchronous low level trigger, focusing on CERN NA62 experiment trigger system. The use of GPU in higher level trigger system is also briefly considered.

GPU-BSM: A GPU-Based Tool to Map Bisulfite-Treated Reads

PubMed Central

Manconi, Andrea; Orro, Alessandro; Manca, Emanuele; Armano, Giuliano; Milanesi, Luciano

2014-01-01

Cytosine DNA methylation is an epigenetic mark implicated in several biological processes. Bisulfite treatment of DNA is acknowledged as the gold standard technique to study methylation. This technique introduces changes in the genomic DNA by converting cytosines to uracils while 5-methylcytosines remain nonreactive. During PCR amplification 5-methylcytosines are amplified as cytosine, whereas uracils and thymines as thymine. To detect the methylation levels, reads treated with the bisulfite must be aligned against a reference genome. Mapping these reads to a reference genome represents a significant computational challenge mainly due to the increased search space and the loss of information introduced by the treatment. To deal with this computational challenge we devised GPU-BSM, a tool based on modern Graphics Processing Units. Graphics Processing Units are hardware accelerators that are increasingly being used successfully to accelerate general-purpose scientific applications. GPU-BSM is a tool able to map bisulfite-treated reads from whole genome bisulfite sequencing and reduced representation bisulfite sequencing, and to estimate methylation levels, with the goal of detecting methylation. Due to the massive parallelization obtained by exploiting graphics cards, GPU-BSM aligns bisulfite-treated reads faster than other cutting-edge solutions, while outperforming most of them in terms of unique mapped reads. PMID:24842718

Real-time processing for full-range Fourier-domain optical-coherence tomography with zero-filling interpolation using multiple graphic processing units.

PubMed

Watanabe, Yuuki; Maeno, Seiya; Aoshima, Kenji; Hasegawa, Haruyuki; Koseki, Hitoshi

2010-09-01

The real-time display of full-range, 2048?axial pixelx1024?lateral pixel, Fourier-domain optical-coherence tomography (FD-OCT) images is demonstrated. The required speed was achieved by using dual graphic processing units (GPUs) with many stream processors to realize highly parallel processing. We used a zero-filling technique, including a forward Fourier transform, a zero padding to increase the axial data-array size to 8192, an inverse-Fourier transform back to the spectral domain, a linear interpolation from wavelength to wavenumber, a lateral Hilbert transform to obtain the complex spectrum, a Fourier transform to obtain the axial profiles, and a log scaling. The data-transfer time of the frame grabber was 15.73?ms, and the processing time, which includes the data transfer between the GPU memory and the host computer, was 14.75?ms, for a total time shorter than the 36.70?ms frame-interval time using a line-scan CCD camera operated at 27.9?kHz. That is, our OCT system achieved a processed-image display rate of 27.23 frames/s.

GPU-computing in econophysics and statistical physics

NASA Astrophysics Data System (ADS)

Preis, T.

2011-03-01

A recent trend in computer science and related fields is general purpose computing on graphics processing units (GPUs), which can yield impressive performance. With multiple cores connected by high memory bandwidth, today's GPUs offer resources for non-graphics parallel processing. This article provides a brief introduction into the field of GPU computing and includes examples. In particular computationally expensive analyses employed in financial market context are coded on a graphics card architecture which leads to a significant reduction of computing time. In order to demonstrate the wide range of possible applications, a standard model in statistical physics - the Ising model - is ported to a graphics card architecture as well, resulting in large speedup values.

Simultaneous Range-Velocity Processing and SNR Analysis of AFIT’s Random Noise Radar

DTIC Science & Technology

2012-03-22

reducing the overall processing time. Two computers, equipped with NVIDIA ® GPUs, were used to process the col- 45 lected data. The specifications for each...gather the results back to the CPU. Another company , AccelerEyes®, has developed a product called Jacket® that claims to be better than the parallel...Number of Processing Cores 4 8 Processor Speed 3.33 GHz 3.07 GHz Installed Memory 48 GB 48 GB GPU Make NVIDIA NVIDIA GPU Model Tesla 1060 Tesla C2070 GPU

Accelerating Molecular Dynamic Simulation on Graphics Processing Units

PubMed Central

Friedrichs, Mark S.; Eastman, Peter; Vaidyanathan, Vishal; Houston, Mike; Legrand, Scott; Beberg, Adam L.; Ensign, Daniel L.; Bruns, Christopher M.; Pande, Vijay S.

2009-01-01

We describe a complete implementation of all-atom protein molecular dynamics running entirely on a graphics processing unit (GPU), including all standard force field terms, integration, constraints, and implicit solvent. We discuss the design of our algorithms and important optimizations needed to fully take advantage of a GPU. We evaluate its performance, and show that it can be more than 700 times faster than a conventional implementation running on a single CPU core. PMID:19191337

SU-G-TeP1-15: Toward a Novel GPU Accelerated Deterministic Solution to the Linear Boltzmann Transport Equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, R; Fallone, B; Cross Cancer Institute, Edmonton, AB

Purpose: To develop a Graphic Processor Unit (GPU) accelerated deterministic solution to the Linear Boltzmann Transport Equation (LBTE) for accurate dose calculations in radiotherapy (RT). A deterministic solution yields the potential for major speed improvements due to the sparse matrix-vector and vector-vector multiplications and would thus be of benefit to RT. Methods: In order to leverage the massively parallel architecture of GPUs, the first order LBTE was reformulated as a second order self-adjoint equation using the Least Squares Finite Element Method (LSFEM). This produces a symmetric positive-definite matrix which is efficiently solved using a parallelized conjugate gradient (CG) solver. Themore » LSFEM formalism is applied in space, discrete ordinates is applied in angle, and the Multigroup method is applied in energy. The final linear system of equations produced is tightly coupled in space and angle. Our code written in CUDA-C was benchmarked on an Nvidia GeForce TITAN-X GPU against an Intel i7-6700K CPU. A spatial mesh of 30,950 tetrahedral elements was used with an S4 angular approximation. Results: To avoid repeating a full computationally intensive finite element matrix assembly at each Multigroup energy, a novel mapping algorithm was developed which minimized the operations required at each energy. Additionally, a parallelized memory mapping for the kronecker product between the sparse spatial and angular matrices, including Dirichlet boundary conditions, was created. Atomicity is preserved by graph-coloring overlapping nodes into separate kernel launches. The one-time mapping calculations for matrix assembly, kronecker product, and boundary condition application took 452±1ms on GPU. Matrix assembly for 16 energy groups took 556±3s on CPU, and 358±2ms on GPU using the mappings developed. The CG solver took 93±1s on CPU, and 468±2ms on GPU. Conclusion: Three computationally intensive subroutines in deterministically solving the LBTE have been formulated on GPU, resulting in two orders of magnitude speedup. Funding support from Natural Sciences and Engineering Research Council and Alberta Innovates Health Solutions. Dr. Fallone is a co-founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi-planar linac MR for commercialization).« less

GPU computing in medical physics: a review.

PubMed

Pratx, Guillem; Xing, Lei

2011-05-01

The graphics processing unit (GPU) has emerged as a competitive platform for computing massively parallel problems. Many computing applications in medical physics can be formulated as data-parallel tasks that exploit the capabilities of the GPU for reducing processing times. The authors review the basic principles of GPU computing as well as the main performance optimization techniques, and survey existing applications in three areas of medical physics, namely image reconstruction, dose calculation and treatment plan optimization, and image processing.

A High Performance VLSI Computer Architecture For Computer Graphics

NASA Astrophysics Data System (ADS)

Chin, Chi-Yuan; Lin, Wen-Tai

1988-10-01

A VLSI computer architecture, consisting of multiple processors, is presented in this paper to satisfy the modern computer graphics demands, e.g. high resolution, realistic animation, real-time display etc.. All processors share a global memory which are partitioned into multiple banks. Through a crossbar network, data from one memory bank can be broadcasted to many processors. Processors are physically interconnected through a hyper-crossbar network (a crossbar-like network). By programming the network, the topology of communication links among processors can be reconfigurated to satisfy specific dataflows of different applications. Each processor consists of a controller, arithmetic operators, local memory, a local crossbar network, and I/O ports to communicate with other processors, memory banks, and a system controller. Operations in each processor are characterized into two modes, i.e. object domain and space domain, to fully utilize the data-independency characteristics of graphics processing. Special graphics features such as 3D-to-2D conversion, shadow generation, texturing, and reflection, can be easily handled. With the current high density interconnection (MI) technology, it is feasible to implement a 64-processor system to achieve 2.5 billion operations per second, a performance needed in most advanced graphics applications.

Ice-sheet modelling accelerated by graphics cards

NASA Astrophysics Data System (ADS)

Brædstrup, Christian Fredborg; Damsgaard, Anders; Egholm, David Lundbek

2014-11-01

Studies of glaciers and ice sheets have increased the demand for high performance numerical ice flow models over the past decades. When exploring the highly non-linear dynamics of fast flowing glaciers and ice streams, or when coupling multiple flow processes for ice, water, and sediment, researchers are often forced to use super-computing clusters. As an alternative to conventional high-performance computing hardware, the Graphical Processing Unit (GPU) is capable of massively parallel computing while retaining a compact design and low cost. In this study, we present a strategy for accelerating a higher-order ice flow model using a GPU. By applying the newest GPU hardware, we achieve up to 180× speedup compared to a similar but serial CPU implementation. Our results suggest that GPU acceleration is a competitive option for ice-flow modelling when compared to CPU-optimised algorithms parallelised by the OpenMP or Message Passing Interface (MPI) protocols.

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

NASA Astrophysics Data System (ADS)

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.

Advantages of GPU technology in DFT calculations of intercalated graphene

NASA Astrophysics Data System (ADS)

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 acceleration of several times compared to standard CPU calculations.

High-throughput sequence alignment using Graphics Processing Units

PubMed Central

Schatz, Michael C; Trapnell, Cole; Delcher, Arthur L; Varshney, Amitabh

2007-01-01

Background The recent availability of new, less expensive high-throughput DNA sequencing technologies has yielded a dramatic increase in the volume of sequence data that must be analyzed. These data are being generated for several purposes, including genotyping, genome resequencing, metagenomics, and de novo genome assembly projects. Sequence alignment programs such as MUMmer have proven essential for analysis of these data, but researchers will need ever faster, high-throughput alignment tools running on inexpensive hardware to keep up with new sequence technologies. Results This paper describes MUMmerGPU, an open-source high-throughput parallel pairwise local sequence alignment program that runs on commodity Graphics Processing Units (GPUs) in common workstations. MUMmerGPU uses the new Compute Unified Device Architecture (CUDA) from nVidia to align multiple query sequences against a single reference sequence stored as a suffix tree. By processing the queries in parallel on the highly parallel graphics card, MUMmerGPU achieves more than a 10-fold speedup over a serial CPU version of the sequence alignment kernel, and outperforms the exact alignment component of MUMmer on a high end CPU by 3.5-fold in total application time when aligning reads from recent sequencing projects using Solexa/Illumina, 454, and Sanger sequencing technologies. Conclusion MUMmerGPU is a low cost, ultra-fast sequence alignment program designed to handle the increasing volume of data produced by new, high-throughput sequencing technologies. MUMmerGPU demonstrates that even memory-intensive applications can run significantly faster on the relatively low-cost GPU than on the CPU. PMID:18070356

Implementing Molecular Dynamics on Hybrid High Performance Computers - Particle-Particle Particle-Mesh

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, W Michael; Kohlmeyer, Axel; Plimpton, Steven J

The use of accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high-performance computers, machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. In this paper, we present a continuation of previous work implementing algorithms for using accelerators into the LAMMPS molecular dynamics software for distributed memory parallel hybrid machines. In our previous work, we focused on acceleration for short-range models with anmore » approach intended to harness the processing power of both the accelerator and (multi-core) CPUs. To augment the existing implementations, we present an efficient implementation of long-range electrostatic force calculation for molecular dynamics. Specifically, we present an implementation of the particle-particle particle-mesh method based on the work by Harvey and De Fabritiis. We present benchmark results on the Keeneland InfiniBand GPU cluster. We provide a performance comparison of the same kernels compiled with both CUDA and OpenCL. We discuss limitations to parallel efficiency and future directions for improving performance on hybrid or heterogeneous computers.« less

GPU acceleration of Runge Kutta-Fehlberg and its comparison with Dormand-Prince method

NASA Astrophysics Data System (ADS)

Seen, Wo Mei; Gobithaasan, R. U.; Miura, Kenjiro T.

2014-07-01

There is a significant reduction of processing time and speedup of performance in computer graphics with the emergence of Graphic Processing Units (GPUs). GPUs have been developed to surpass Central Processing Unit (CPU) in terms of performance and processing speed. This evolution has opened up a new area in computing and researches where highly parallel GPU has been used for non-graphical algorithms. Physical or phenomenal simulations and modelling can be accelerated through General Purpose Graphic Processing Units (GPGPU) and Compute Unified Device Architecture (CUDA) implementations. These phenomena can be represented with mathematical models in the form of Ordinary Differential Equations (ODEs) which encompasses the gist of change rate between independent and dependent variables. ODEs are numerically integrated over time in order to simulate these behaviours. The classical Runge-Kutta (RK) scheme is the common method used to numerically solve ODEs. The Runge Kutta Fehlberg (RKF) scheme has been specially developed to provide an estimate of the principal local truncation error at each step, known as embedding estimate technique. This paper delves into the implementation of RKF scheme for GPU devices and compares its result with Dorman Prince method. A pseudo code is developed to show the implementation in detail. Hence, practitioners will be able to understand the data allocation in GPU, formation of RKF kernels and the flow of data to/from GPU-CPU upon RKF kernel evaluation. The pseudo code is then written in C Language and two ODE models are executed to show the achievable speedup as compared to CPU implementation. The accuracy and efficiency of the proposed implementation method is discussed in the final section of this paper.

GPU applications for data processing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vladymyrov, Mykhailo, E-mail: mykhailo.vladymyrov@cern.ch; Aleksandrov, Andrey; INFN sezione di Napoli, I-80125 Napoli

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.

Gfargo: Fargo for Gpu

NASA Astrophysics Data System (ADS)

Masset, Frédéric

2015-09-01

GFARGO is a GPU version of FARGO. It is written in C and C for CUDA and runs only on NVIDIA’s graphics cards. Though it corresponds to the standard, isothermal version of FARGO, not all functionnalities of the CPU version have been translated to CUDA. The code is available in single and double precision versions, the latter compatible with FERMI architectures. GFARGO can run on a graphics card connected to the display, allowing the user to see in real time how the fields evolve.

Next-generation acceleration and code optimization for light transport in turbid media using GPUs

PubMed Central

Alerstam, Erik; Lo, William Chun Yip; Han, Tianyi David; Rose, Jonathan; Andersson-Engels, Stefan; Lilge, Lothar

2010-01-01

A highly optimized Monte Carlo (MC) code package for simulating light transport is developed on the latest graphics processing unit (GPU) built for general-purpose computing from NVIDIA - the Fermi GPU. In biomedical optics, the MC method is the gold standard approach for simulating light transport in biological tissue, both due to its accuracy and its flexibility in modelling realistic, heterogeneous tissue geometry in 3-D. However, the widespread use of MC simulations in inverse problems, such as treatment planning for PDT, is limited by their long computation time. Despite its parallel nature, optimizing MC code on the GPU has been shown to be a challenge, particularly when the sharing of simulation result matrices among many parallel threads demands the frequent use of atomic instructions to access the slow GPU global memory. This paper proposes an optimization scheme that utilizes the fast shared memory to resolve the performance bottleneck caused by atomic access, and discusses numerous other optimization techniques needed to harness the full potential of the GPU. Using these techniques, a widely accepted MC code package in biophotonics, called MCML, was successfully accelerated on a Fermi GPU by approximately 600x compared to a state-of-the-art Intel Core i7 CPU. A skin model consisting of 7 layers was used as the standard simulation geometry. To demonstrate the possibility of GPU cluster computing, the same GPU code was executed on four GPUs, showing a linear improvement in performance with an increasing number of GPUs. The GPU-based MCML code package, named GPU-MCML, is compatible with a wide range of graphics cards and is released as an open-source software in two versions: an optimized version tuned for high performance and a simplified version for beginners (http://code.google.com/p/gpumcml). PMID:21258498

The Metropolis Monte Carlo method with CUDA enabled Graphic Processing Units

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hall, Clifford; School of Physics, Astronomy, and Computational Sciences, George Mason University, 4400 University Dr., Fairfax, VA 22030; Ji, Weixiao

2014-02-01

We present a CPU–GPU system for runtime acceleration of large molecular simulations using GPU computation and memory swaps. The memory architecture of the GPU can be used both as container for simulation data stored on the graphics card and as floating-point code target, providing an effective means for the manipulation of atomistic or molecular data on the GPU. To fully take advantage of this mechanism, efficient GPU realizations of algorithms used to perform atomistic and molecular simulations are essential. Our system implements a versatile molecular engine, including inter-molecule interactions and orientational variables for performing the Metropolis Monte Carlo (MMC) algorithm,more » which is one type of Markov chain Monte Carlo. By combining memory objects with floating-point code fragments we have implemented an MMC parallel engine that entirely avoids the communication time of molecular data at runtime. Our runtime acceleration system is a forerunner of a new class of CPU–GPU algorithms exploiting memory concepts combined with threading for avoiding bus bandwidth and communication. The testbed molecular system used here is a condensed phase system of oligopyrrole chains. A benchmark shows a size scaling speedup of 60 for systems with 210,000 pyrrole monomers. Our implementation can easily be combined with MPI to connect in parallel several CPU–GPU duets. -- Highlights: •We parallelize the Metropolis Monte Carlo (MMC) algorithm on one CPU—GPU duet. •The Adaptive Tempering Monte Carlo employs MMC and profits from this CPU—GPU implementation. •Our benchmark shows a size scaling-up speedup of 62 for systems with 225,000 particles. •The testbed involves a polymeric system of oligopyrroles in the condensed phase. •The CPU—GPU parallelization includes dipole—dipole and Mie—Jones classic potentials.« less

Efficient implementation of constant pH molecular dynamics on modern graphics processors.

PubMed

Arthur, Evan J; Brooks, Charles L

2016-09-15

The treatment of pH sensitive ionization states for titratable residues in proteins is often omitted from molecular dynamics (MD) simulations. While static charge models can answer many questions regarding protein conformational equilibrium and protein-ligand interactions, pH-sensitive phenomena such as acid-activated chaperones and amyloidogenic protein aggregation are inaccessible to such models. Constant pH molecular dynamics (CPHMD) coupled with the Generalized Born with a Simple sWitching function (GBSW) implicit solvent model provide an accurate framework for simulating pH sensitive processes in biological systems. Although this combination has demonstrated success in predicting pKa values of protein structures, and in exploring dynamics of ionizable side-chains, its speed has been an impediment to routine application. The recent availability of low-cost graphics processing unit (GPU) chipsets with thousands of processing cores, together with the implementation of the accurate GBSW implicit solvent model on those chipsets (Arthur and Brooks, J. Comput. Chem. 2016, 37, 927), provide an opportunity to improve the speed of CPHMD and ionization modeling greatly. Here, we present a first implementation of GPU-enabled CPHMD within the CHARMM-OpenMM simulation package interface. Depending on the system size and nonbonded force cutoff parameters, we find speed increases of between one and three orders of magnitude. Additionally, the algorithm scales better with system size than the CPU-based algorithm, thus allowing for larger systems to be modeled in a cost effective manner. We anticipate that the improved performance of this methodology will open the door for broad-spread application of CPHMD in its modeling pH-mediated biological processes. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Laser Speckle Imaging of Cerebral Blood Flow

NASA Astrophysics Data System (ADS)

Luo, Qingming; Jiang, Chao; Li, Pengcheng; Cheng, Haiying; Wang, Zhen; Wang, Zheng; Tuchin, Valery V.

Monitoring the spatio-temporal characteristics of cerebral blood flow (CBF) is crucial for studying the normal and pathophysiologic conditions of brain metabolism. By illuminating the cortex with laser light and imaging the resulting speckle pattern, relative CBF images with tens of microns spatial and millisecond temporal resolution can be obtained. In this chapter, a laser speckle imaging (LSI) method for monitoring dynamic, high-resolution CBF is introduced. To improve the spatial resolution of current LSI, a modified LSI method is proposed. To accelerate the speed of data processing, three LSI data processing frameworks based on graphics processing unit (GPU), digital signal processor (DSP), and field-programmable gate array (FPGA) are also presented. Applications for detecting the changes in local CBF induced by sensory stimulation and thermal stimulation, the influence of a chemical agent on CBF, and the influence of acute hyperglycemia following cortical spreading depression on CBF are given.

GPU real-time processing in NA62 trigger system

NASA Astrophysics Data System (ADS)

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

2017-01-01

A commercial Graphics Processing Unit (GPU) is used to build a fast Level 0 (L0) trigger system tested parasitically with the TDAQ (Trigger and Data Acquisition systems) of the NA62 experiment at CERN. In particular, the parallel computing power of the GPU is exploited to perform real-time fitting in the Ring Imaging CHerenkov (RICH) detector. Direct GPU communication using a FPGA-based board has been used to reduce the data transmission latency. The performance of the system for multi-ring reconstrunction obtained during the NA62 physics run will be presented.

Data Parallel Bin-Based Indexing for Answering Queries on Multi-Core Architectures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gosink, Luke; Wu, Kesheng; Bethel, E. Wes

2009-06-02

The multi-core trend in CPUs and general purpose graphics processing units (GPUs) offers new opportunities for the database community. The increase of cores at exponential rates is likely to affect virtually every server and client in the coming decade, and presents database management systems with a huge, compelling disruption that will radically change how processing is done. This paper presents a new parallel indexing data structure for answering queries that takes full advantage of the increasing thread-level parallelism emerging in multi-core architectures. In our approach, our Data Parallel Bin-based Index Strategy (DP-BIS) first bins the base data, and then partitionsmore » and stores the values in each bin as a separate, bin-based data cluster. In answering a query, the procedures for examining the bin numbers and the bin-based data clusters offer the maximum possible level of concurrency; each record is evaluated by a single thread and all threads are processed simultaneously in parallel. We implement and demonstrate the effectiveness of DP-BIS on two multi-core architectures: a multi-core CPU and a GPU. The concurrency afforded by DP-BIS allows us to fully utilize the thread-level parallelism provided by each architecture--for example, our GPU-based DP-BIS implementation simultaneously evaluates over 12,000 records with an equivalent number of concurrently executing threads. In comparing DP-BIS's performance across these architectures, we show that the GPU-based DP-BIS implementation requires significantly less computation time to answer a query than the CPU-based implementation. We also demonstrate in our analysis that DP-BIS provides better overall performance than the commonly utilized CPU and GPU-based projection index. Finally, due to data encoding, we show that DP-BIS accesses significantly smaller amounts of data than index strategies that operate solely on a column's base data; this smaller data footprint is critical for parallel processors that possess limited memory resources (e.g., GPUs).« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan Chan Tseung, Hok Seum, E-mail: wanchantseung.hok@mayo.edu; Ma, Jiasen; Kreofsky, Cole R.

Purpose: Our aim is to demonstrate the feasibility of fast Monte Carlo (MC)–based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. Methods and Materials: Recently, a fast and accurate graphics processor unit (GPU)–based MC simulation of proton transport was developed and used as the dose-calculation engine in a GPU-accelerated intensity modulated proton therapy (IMPT) optimizer. Besides dose, the MC can simultaneously score the dose-averaged linear energy transfer (LET{sub d}), which makes biological dose (BD) optimization possible. To convert from LET{sub d} to BD, a simple linear relation was assumed. By use of thismore » novel optimizer, inverse biological planning was applied to 4 patients, including 2 small and 1 large thyroid tumor targets, as well as 1 glioma case. To create these plans, constraints were placed to maintain the physical dose (PD) within 1.25 times the prescription while maximizing target BD. For comparison, conventional intensity modulated radiation therapy (IMRT) and IMPT plans were also created using Eclipse (Varian Medical Systems) in each case. The same critical-structure PD constraints were used for the IMRT, IMPT, and biologically optimized plans. The BD distributions for the IMPT plans were obtained through MC recalculations. Results: Compared with standard IMPT, the biologically optimal plans for patients with small tumor targets displayed a BD escalation that was around twice the PD increase. Dose sparing to critical structures was improved compared with both IMRT and IMPT. No significant BD increase could be achieved for the large thyroid tumor case and when the presence of critical structures mitigated the contribution of additional fields. The calculation of the biologically optimized plans can be completed in a clinically viable time (<30 minutes) on a small 24-GPU system. Conclusions: By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small–tumor target patients. This optimizer will be used in an upcoming feasibility trial on LET{sub d} painting for radioresistant tumors.« less

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

PubMed

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.

Particle-in-cell simulations with charge-conserving current deposition on graphic processing units

NASA Astrophysics Data System (ADS)

Ren, Chuang; Kong, Xianglong; Huang, Michael; Decyk, Viktor; Mori, Warren

2011-10-01

Recently using CUDA, we have developed an electromagnetic Particle-in-Cell (PIC) code with charge-conserving current deposition for Nvidia graphic processing units (GPU's) (Kong et al., Journal of Computational Physics 230, 1676 (2011). On a Tesla M2050 (Fermi) card, the GPU PIC code can achieve a one-particle-step process time of 1.2 - 3.2 ns in 2D and 2.3 - 7.2 ns in 3D, depending on plasma temperatures. In this talk we will discuss novel algorithms for GPU-PIC including charge-conserving current deposition scheme with few branching and parallel particle sorting. These algorithms have made efficient use of the GPU shared memory. We will also discuss how to replace the computation kernels of existing parallel CPU codes while keeping their parallel structures. This work was supported by U.S. Department of Energy under Grant Nos. DE-FG02-06ER54879 and DE-FC02-04ER54789 and by NSF under Grant Nos. PHY-0903797 and CCF-0747324.

Efficient Acceleration of the Pair-HMMs Forward Algorithm for GATK HaplotypeCaller on Graphics Processing Units.

PubMed

Ren, Shanshan; Bertels, Koen; Al-Ars, Zaid

2018-01-01

GATK HaplotypeCaller (HC) is a popular variant caller, which is widely used to identify variants in complex genomes. However, due to its high variants detection accuracy, it suffers from long execution time. In GATK HC, the pair-HMMs forward algorithm accounts for a large percentage of the total execution time. This article proposes to accelerate the pair-HMMs forward algorithm on graphics processing units (GPUs) to improve the performance of GATK HC. This article presents several GPU-based implementations of the pair-HMMs forward algorithm. It also analyzes the performance bottlenecks of the implementations on an NVIDIA Tesla K40 card with various data sets. Based on these results and the characteristics of GATK HC, we are able to identify the GPU-based implementations with the highest performance for the various analyzed data sets. Experimental results show that the GPU-based implementations of the pair-HMMs forward algorithm achieve a speedup of up to 5.47× over existing GPU-based implementations.

Numerical study of the vortex tube reconnection using vortex particle method on many graphics cards

NASA Astrophysics Data System (ADS)

Kudela, Henryk; Kosior, Andrzej

2014-08-01

Vortex Particle Methods are one of the most convenient ways of tracking the vorticity evolution. In the article we presented numerical recreation of the real life experiment concerning head-on collision of two vortex rings. In the experiment the evolution and reconnection of the vortex structures is tracked with passive markers (paint particles) which in viscous fluid does not follow the evolution of vorticity field. In numerical computations we showed the difference between vorticity evolution and movement of passive markers. The agreement with the experiment was very good. Due to problems with very long time of computations on a single processor the Vortex-in-Cell method was implemented on the multicore architecture of the graphics cards (GPUs). Vortex Particle Methods are very well suited for parallel computations. As there are myriads of particles in the flow and for each of them the same equations of motion have to be solved the SIMD architecture used in GPUs seems to be perfect. The main disadvantage in this case is the small amount of the RAM memory. To overcome this problem we created a multiGPU implementation of the VIC method. Some remarks on parallel computing are given in the article.

GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering.

PubMed

Suzuki, Shuji; Kakuta, Masanori; Ishida, Takashi; Akiyama, Yutaka

2016-01-01

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads.

Mendel-GPU: haplotyping and genotype imputation on graphics processing units

PubMed Central

Chen, Gary K.; Wang, Kai; Stram, Alex H.; Sobel, Eric M.; Lange, Kenneth

2012-01-01

Motivation: In modern sequencing studies, one can improve the confidence of genotype calls by phasing haplotypes using information from an external reference panel of fully typed unrelated individuals. However, the computational demands are so high that they prohibit researchers with limited computational resources from haplotyping large-scale sequence data. Results: Our graphics processing unit based software delivers haplotyping and imputation accuracies comparable to competing programs at a fraction of the computational cost and peak memory demand. Availability: Mendel-GPU, our OpenCL software, runs on Linux platforms and is portable across AMD and nVidia GPUs. Users can download both code and documentation at http://code.google.com/p/mendel-gpu/. Contact: gary.k.chen@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22954633

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards

PubMed Central

Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G.

2012-01-01

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids. The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable. In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation. We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards. PMID:22347787

Compute-unified device architecture implementation of a block-matching algorithm for multiple graphical processing unit cards.

PubMed

Massanes, Francesc; Cadennes, Marie; Brankov, Jovan G

2011-07-01

In this paper we describe and evaluate a fast implementation of a classical block matching motion estimation algorithm for multiple Graphical Processing Units (GPUs) using the Compute Unified Device Architecture (CUDA) computing engine. The implemented block matching algorithm (BMA) uses summed absolute difference (SAD) error criterion and full grid search (FS) for finding optimal block displacement. In this evaluation we compared the execution time of a GPU and CPU implementation for images of various sizes, using integer and non-integer search grids.The results show that use of a GPU card can shorten computation time by a factor of 200 times for integer and 1000 times for a non-integer search grid. The additional speedup for non-integer search grid comes from the fact that GPU has built-in hardware for image interpolation. Further, when using multiple GPU cards, the presented evaluation shows the importance of the data splitting method across multiple cards, but an almost linear speedup with a number of cards is achievable.In addition we compared execution time of the proposed FS GPU implementation with two existing, highly optimized non-full grid search CPU based motion estimations methods, namely implementation of the Pyramidal Lucas Kanade Optical flow algorithm in OpenCV and Simplified Unsymmetrical multi-Hexagon search in H.264/AVC standard. In these comparisons, FS GPU implementation still showed modest improvement even though the computational complexity of FS GPU implementation is substantially higher than non-FS CPU implementation.We also demonstrated that for an image sequence of 720×480 pixels in resolution, commonly used in video surveillance, the proposed GPU implementation is sufficiently fast for real-time motion estimation at 30 frames-per-second using two NVIDIA C1060 Tesla GPU cards.

Study on efficiency of time computation in x-ray imaging simulation base on Monte Carlo algorithm using graphics processing unit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Setiani, Tia Dwi, E-mail: tiadwisetiani@gmail.com; Suprijadi; Nuclear Physics and Biophysics Reaserch Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung Jalan Ganesha 10 Bandung, 40132

Monte Carlo (MC) is one of the powerful techniques for simulation in x-ray imaging. MC method can simulate the radiation transport within matter with high accuracy and provides a natural way to simulate radiation transport in complex systems. One of the codes based on MC algorithm that are widely used for radiographic images simulation is MC-GPU, a codes developed by Andrea Basal. This study was aimed to investigate the time computation of x-ray imaging simulation in GPU (Graphics Processing Unit) compared to a standard CPU (Central Processing Unit). Furthermore, the effect of physical parameters to the quality of radiographic imagesmore » and the comparison of image quality resulted from simulation in the GPU and CPU are evaluated in this paper. The simulations were run in CPU which was simulated in serial condition, and in two GPU with 384 cores and 2304 cores. In simulation using GPU, each cores calculates one photon, so, a large number of photon were calculated simultaneously. Results show that the time simulations on GPU were significantly accelerated compared to CPU. The simulations on the 2304 core of GPU were performed about 64 -114 times faster than on CPU, while the simulation on the 384 core of GPU were performed about 20 – 31 times faster than in a single core of CPU. Another result shows that optimum quality of images from the simulation was gained at the history start from 10{sup 8} and the energy from 60 Kev to 90 Kev. Analyzed by statistical approach, the quality of GPU and CPU images are relatively the same.« less

Inversion of Heavy Current Electroheat Problems on a Graphics Processing Unit (GPU)

DTIC Science & Technology

2013-11-10

Unit (GPU) Victor U. Karthik 1 , Sivamayam Sivasuthan 1, Arunasalam Rahunanthan 2 , Ravi S. Thyagarajan 3 and Paramsothy Jayakumar 3 , Lalita...Victor Karthik; Sivamayam Sivasuthan; Arunasalam Rahunanthan; Ravi Thyagarajan; Paramsothy Jayakumar 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

PubMed

Katsigiannis, Stamos; Zacharia, Eleni; Maroulis, Dimitris

2017-05-01

Complementary DNA (cDNA) microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a user-friendly interface that requires minimum input in order to run.

Real-time unmanned aircraft systems surveillance video mosaicking using GPU

NASA Astrophysics Data System (ADS)

Camargo, Aldo; Anderson, Kyle; Wang, Yi; Schultz, Richard R.; Fevig, Ronald A.

2010-04-01

Digital video mosaicking from Unmanned Aircraft Systems (UAS) is being used for many military and civilian applications, including surveillance, target recognition, border protection, forest fire monitoring, traffic control on highways, monitoring of transmission lines, among others. Additionally, NASA is using digital video mosaicking to explore the moon and planets such as Mars. In order to compute a "good" mosaic from video captured by a UAS, the algorithm must deal with motion blur, frame-to-frame jitter associated with an imperfectly stabilized platform, perspective changes as the camera tilts in flight, as well as a number of other factors. The most suitable algorithms use SIFT (Scale-Invariant Feature Transform) to detect the features consistent between video frames. Utilizing these features, the next step is to estimate the homography between two consecutives video frames, perform warping to properly register the image data, and finally blend the video frames resulting in a seamless video mosaick. All this processing takes a great deal of resources of resources from the CPU, so it is almost impossible to compute a real time video mosaic on a single processor. Modern graphics processing units (GPUs) offer computational performance that far exceeds current CPU technology, allowing for real-time operation. This paper presents the development of a GPU-accelerated digital video mosaicking implementation and compares it with CPU performance. Our tests are based on two sets of real video captured by a small UAS aircraft; one video comes from Infrared (IR) and Electro-Optical (EO) cameras. Our results show that we can obtain a speed-up of more than 50 times using GPU technology, so real-time operation at a video capture of 30 frames per second is feasible.

GPU-based optimal control for RWM feedback in tokamaks

DOE PAGES

Clement, Mitchell; Hanson, Jeremy; Bialek, Jim; ...

2017-08-23

The design and implementation of a Graphics Processing Unit (GPU) based Resistive Wall Mode (RWM) controller to perform feedback control on the RWM using Linear Quadratic Gaussian (LQG) control is reported herein. Also, the control algorithm is based on a simplified DIII-D VALEN model. By using NVIDIA’s GPUDirect RDMA framework, the digitizer and output module are able to write and read directly to and from GPU memory, eliminating memory transfers between host and GPU. In conclusion, the system and algorithm was able to reduce plasma response excited by externally applied fields by 32% during development experiments.

GPU-based optimal control for RWM feedback in tokamaks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clement, Mitchell; Hanson, Jeremy; Bialek, Jim

The design and implementation of a Graphics Processing Unit (GPU) based Resistive Wall Mode (RWM) controller to perform feedback control on the RWM using Linear Quadratic Gaussian (LQG) control is reported herein. Also, the control algorithm is based on a simplified DIII-D VALEN model. By using NVIDIA’s GPUDirect RDMA framework, the digitizer and output module are able to write and read directly to and from GPU memory, eliminating memory transfers between host and GPU. In conclusion, the system and algorithm was able to reduce plasma response excited by externally applied fields by 32% during development experiments.

Efficient Parallel Levenberg-Marquardt Model Fitting towards Real-Time Automated Parametric Imaging Microscopy

PubMed Central

Zhu, Xiang; Zhang, Dianwen

2013-01-01

We present a fast, accurate and robust parallel Levenberg-Marquardt minimization optimizer, GPU-LMFit, which is implemented on graphics processing unit for high performance scalable parallel model fitting processing. GPU-LMFit can provide a dramatic speed-up in massive model fitting analyses to enable real-time automated pixel-wise parametric imaging microscopy. We demonstrate the performance of GPU-LMFit for the applications in superresolution localization microscopy and fluorescence lifetime imaging microscopy. PMID:24130785

Higher-order ice-sheet modelling accelerated by multigrid on graphics cards

NASA Astrophysics Data System (ADS)

Brædstrup, Christian; Egholm, David

2013-04-01

Higher-order ice flow modelling is a very computer intensive process owing primarily to the nonlinear influence of the horizontal stress coupling. When applied for simulating long-term glacial landscape evolution, the ice-sheet models must consider very long time series, while both high temporal and spatial resolution is needed to resolve small effects. The use of higher-order and full stokes models have therefore seen very limited usage in this field. However, 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 working on ice flow models. Our current research focuses on utilising the GPU as a tool in ice-sheet and glacier modelling. To this extent we have implemented the Integrated Second-Order Shallow Ice Approximation (iSOSIA) equations on the device using the finite difference method. To accelerate the computations, the GPU solver uses a non-linear Red-Black Gauss-Seidel iterator coupled with a Full Approximation Scheme (FAS) multigrid setup to further aid convergence. The GPU finite difference implementation provides the inherent parallelization that scales from hundreds to several thousands of cores on newer cards. We demonstrate the efficiency of the GPU multigrid solver using benchmark experiments.

Application of the graphics processor unit to simulate a near field diffraction

NASA Astrophysics Data System (ADS)

Zinchik, Alexander A.; Topalov, Oleg K.; Muzychenko, Yana B.

2017-06-01

For many years, computer modeling program used for lecture demonstrations. Most of the existing commercial software, such as Virtual Lab, LightTrans GmbH company are quite expensive and have a surplus capabilities for educational tasks. The complexity of the diffraction demonstrations in the near zone, due to the large amount of calculations required to obtain the two-dimensional distribution of the amplitude and phase. At this day, there are no demonstrations, allowing to show the resulting distribution of amplitude and phase without much time delay. Even when using Fast Fourier Transform (FFT) algorithms diffraction calculation speed in the near zone for the input complex amplitude distributions with size more than 2000 × 2000 pixels is tens of seconds. Our program selects the appropriate propagation operator from a prescribed set of operators including Spectrum of Plane Waves propagation and Rayleigh-Sommerfeld propagation (using convolution). After implementation, we make a comparison between the calculation time for the near field diffraction: calculations made on GPU and CPU, showing that using GPU for calculations diffraction pattern in near zone does increase the overall speed of algorithm for an image of size 2048×2048 sampling points and more. The modules are implemented as separate dynamic-link libraries and can be used for lecture demonstrations, workshops, selfstudy and students in solving various problems such as the phase retrieval task.

High-performance computing in image registration

NASA Astrophysics Data System (ADS)

Zanin, Michele; Remondino, Fabio; Dalla Mura, Mauro

2012-10-01

Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance computing techniques in order to deliver timely responses e.g. for rapid decisions or real-time actions. Thus, parallel or distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical Processing Unit (GPU) programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations. Examples and comparisons with standard CPU processing are also reported and commented.

Heterogeneous real-time computing in radio astronomy

NASA Astrophysics Data System (ADS)

Ford, John M.; Demorest, Paul; Ransom, Scott

2010-07-01

Modern computer architectures suited for general purpose computing are often not the best choice for either I/O-bound or compute-bound problems. Sometimes the best choice is not to choose a single architecture, but to take advantage of the best characteristics of different computer architectures to solve your problems. This paper examines the tradeoffs between using computer systems based on the ubiquitous X86 Central Processing Units (CPU's), Field Programmable Gate Array (FPGA) based signal processors, and Graphical Processing Units (GPU's). We will show how a heterogeneous system can be produced that blends the best of each of these technologies into a real-time signal processing system. FPGA's tightly coupled to analog-to-digital converters connect the instrument to the telescope and supply the first level of computing to the system. These FPGA's are coupled to other FPGA's to continue to provide highly efficient processing power. Data is then packaged up and shipped over fast networks to a cluster of general purpose computers equipped with GPU's, which are used for floating-point intensive computation. Finally, the data is handled by the CPU and written to disk, or further processed. Each of the elements in the system has been chosen for its specific characteristics and the role it can play in creating a system that does the most for the least, in terms of power, space, and money.

Design and optimization of a portable LQCD Monte Carlo code using OpenACC

NASA Astrophysics Data System (ADS)

Bonati, Claudio; Coscetti, Simone; D'Elia, Massimo; Mesiti, Michele; Negro, Francesco; Calore, Enrico; Schifano, Sebastiano Fabio; Silvi, Giorgio; Tripiccione, Raffaele

The present panorama of HPC architectures is extremely heterogeneous, ranging from traditional multi-core CPU processors, supporting a wide class of applications but delivering moderate computing performance, to many-core Graphics Processor Units (GPUs), exploiting aggressive data-parallelism and delivering higher performances for streaming computing applications. In this scenario, code portability (and performance portability) become necessary for easy maintainability of applications; this is very relevant in scientific computing where code changes are very frequent, making it tedious and prone to error to keep different code versions aligned. In this work, we present the design and optimization of a state-of-the-art production-level LQCD Monte Carlo application, using the directive-based OpenACC programming model. OpenACC abstracts parallel programming to a descriptive level, relieving programmers from specifying how codes should be mapped onto the target architecture. We describe the implementation of a code fully written in OpenAcc, and show that we are able to target several different architectures, including state-of-the-art traditional CPUs and GPUs, with the same code. We also measure performance, evaluating the computing efficiency of our OpenACC code on several architectures, comparing with GPU-specific implementations and showing that a good level of performance-portability can be reached.

Application of graphics processing units to search pipelines for gravitational waves from coalescing binaries of compact objects

NASA Astrophysics Data System (ADS)

Chung, Shin Kee; Wen, Linqing; Blair, David; Cannon, Kipp; Datta, Amitava

2010-07-01

We report a novel application of a graphics processing unit (GPU) for the purpose of accelerating the search pipelines for gravitational waves from coalescing binaries of compact objects. A speed-up of 16-fold in total has been achieved with an NVIDIA GeForce 8800 Ultra GPU card compared with one core of a 2.5 GHz Intel Q9300 central processing unit (CPU). We show that substantial improvements are possible and discuss the reduction in CPU count required for the detection of inspiral sources afforded by the use of GPUs.

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

PubMed

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

2011-11-21

We implemented the simplified Monte Carlo (SMC) method on graphics processing unit (GPU) architecture under the computer-unified device architecture platform developed by NVIDIA. The GPU-based SMC was clinically applied for four patients with head and neck, lung, or prostate cancer. The results were compared to those obtained by a traditional CPU-based SMC with respect to the computation time and discrepancy. In the CPU- and GPU-based SMC calculations, the estimated mean statistical errors of the calculated doses in the planning target volume region were within 0.5% rms. The dose distributions calculated by the GPU- and CPU-based SMCs were similar, within statistical errors. The GPU-based SMC showed 12.30-16.00 times faster performance than the CPU-based SMC. The computation time per beam arrangement using the GPU-based SMC for the clinical cases ranged 9-67 s. The results demonstrate the successful application of the GPU-based SMC to a clinical proton treatment planning.

GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering

PubMed Central

Suzuki, Shuji; Kakuta, Masanori; Ishida, Takashi; Akiyama, Yutaka

2016-01-01

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads. PMID:27482905

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

NASA Astrophysics Data System (ADS)

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.

HASEonGPU-An adaptive, load-balanced MPI/GPU-code for calculating the amplified spontaneous emission in high power laser media

NASA Astrophysics Data System (ADS)

Eckert, C. H. J.; Zenker, E.; Bussmann, M.; Albach, D.

2016-10-01

We present an adaptive Monte Carlo algorithm for computing the amplified spontaneous emission (ASE) flux in laser gain media pumped by pulsed lasers. With the design of high power lasers in mind, which require large size gain media, we have developed the open source code HASEonGPU that is capable of utilizing multiple graphic processing units (GPUs). With HASEonGPU, time to solution is reduced to minutes on a medium size GPU cluster of 64 NVIDIA Tesla K20m GPUs and excellent speedup is achieved when scaling to multiple GPUs. Comparison of simulation results to measurements of ASE in Y b 3 + : Y AG ceramics show perfect agreement.

Computational Particle Dynamic Simulations on Multicore Processors (CPDMu) Final Report Phase I

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schmalz, Mark S

2011-07-24

Statement of Problem - Department of Energy has many legacy codes for simulation of computational particle dynamics and computational fluid dynamics applications that are designed to run on sequential processors and are not easily parallelized. Emerging high-performance computing architectures employ massively parallel multicore architectures (e.g., graphics processing units) to increase throughput. Parallelization of legacy simulation codes is a high priority, to achieve compatibility, efficiency, accuracy, and extensibility. General Statement of Solution - A legacy simulation application designed for implementation on mainly-sequential processors has been represented as a graph G. Mathematical transformations, applied to G, produce a graph representation {und G}more » for a high-performance architecture. Key computational and data movement kernels of the application were analyzed/optimized for parallel execution using the mapping G {yields} {und G}, which can be performed semi-automatically. This approach is widely applicable to many types of high-performance computing systems, such as graphics processing units or clusters comprised of nodes that contain one or more such units. Phase I Accomplishments - Phase I research decomposed/profiled computational particle dynamics simulation code for rocket fuel combustion into low and high computational cost regions (respectively, mainly sequential and mainly parallel kernels), with analysis of space and time complexity. Using the research team's expertise in algorithm-to-architecture mappings, the high-cost kernels were transformed, parallelized, and implemented on Nvidia Fermi GPUs. Measured speedups (GPU with respect to single-core CPU) were approximately 20-32X for realistic model parameters, without final optimization. Error analysis showed no loss of computational accuracy. Commercial Applications and Other Benefits - The proposed research will constitute a breakthrough in solution of problems related to efficient parallel computation of particle and fluid dynamics simulations. These problems occur throughout DOE, military and commercial sectors: the potential payoff is high. We plan to license or sell the solution to contractors for military and domestic applications such as disaster simulation (aerodynamic and hydrodynamic), Government agencies (hydrological and environmental simulations), and medical applications (e.g., in tomographic image reconstruction). Keywords - High-performance Computing, Graphic Processing Unit, Fluid/Particle Simulation. Summary for Members of Congress - Department of Energy has many simulation codes that must compute faster, to be effective. The Phase I research parallelized particle/fluid simulations for rocket combustion, for high-performance computing systems.« less

GPU-accelerated low-latency real-time searches for gravitational waves from compact binary coalescence

NASA Astrophysics Data System (ADS)

Liu, Yuan; Du, Zhihui; Chung, Shin Kee; Hooper, Shaun; Blair, David; Wen, Linqing

2012-12-01

We present a graphics processing unit (GPU)-accelerated time-domain low-latency algorithm to search for gravitational waves (GWs) from coalescing binaries of compact objects based on the summed parallel infinite impulse response (SPIIR) filtering technique. The aim is to facilitate fast detection of GWs with a minimum delay to allow prompt electromagnetic follow-up observations. To maximize the GPU acceleration, we apply an efficient batched parallel computing model that significantly reduces the number of synchronizations in SPIIR and optimizes the usage of the memory and hardware resource. Our code is tested on the CUDA ‘Fermi’ architecture in a GTX 480 graphics card and its performance is compared with a single core of Intel Core i7 920 (2.67 GHz). A 58-fold speedup is achieved while giving results in close agreement with the CPU implementation. Our result indicates that it is possible to conduct a full search for GWs from compact binary coalescence in real time with only one desktop computer equipped with a Fermi GPU card for the initial LIGO detectors which in the past required more than 100 CPUs.

Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit.

PubMed

Badal, Andreu; Badano, Aldo

2009-11-01

It is a known fact that Monte Carlo simulations of radiation transport are computationally intensive and may require long computing times. The authors introduce a new paradigm for the acceleration of Monte Carlo simulations: The use of a graphics processing unit (GPU) as the main computing device instead of a central processing unit (CPU). A GPU-based Monte Carlo code that simulates photon transport in a voxelized geometry with the accurate physics models from PENELOPE has been developed using the CUDATM programming model (NVIDIA Corporation, Santa Clara, CA). An outline of the new code and a sample x-ray imaging simulation with an anthropomorphic phantom are presented. A remarkable 27-fold speed up factor was obtained using a GPU compared to a single core CPU. The reported results show that GPUs are currently a good alternative to CPUs for the simulation of radiation transport. Since the performance of GPUs is currently increasing at a faster pace than that of CPUs, the advantages of GPU-based software are likely to be more pronounced in the future.

Practical Implementation of Prestack Kirchhoff Time Migration on a General Purpose Graphics Processing Unit

NASA Astrophysics Data System (ADS)

Liu, Guofeng; Li, Chun

2016-08-01

In this study, we present a practical implementation of prestack Kirchhoff time migration (PSTM) on a general purpose graphic processing unit. First, we consider the three main optimizations of the PSTM GPU code, i.e., designing a configuration based on a reasonable execution, using the texture memory for velocity interpolation, and the application of an intrinsic function in device code. This approach can achieve a speedup of nearly 45 times on a NVIDIA GTX 680 GPU compared with CPU code when a larger imaging space is used, where the PSTM output is a common reflection point that is gathered as I[ nx][ ny][ nh][ nt] in matrix format. However, this method requires more memory space so the limited imaging space cannot fully exploit the GPU sources. To overcome this problem, we designed a PSTM scheme with multi-GPUs for imaging different seismic data on different GPUs using an offset value. This process can achieve the peak speedup of GPU PSTM code and it greatly increases the efficiency of the calculations, but without changing the imaging result.

Transportable GPU (General Processor Units) chip set technology for standard computer architectures

NASA Astrophysics Data System (ADS)

Fosdick, R. E.; Denison, H. C.

1982-11-01

The USAFR-developed GPU Chip Set has been utilized by Tracor to implement both USAF and Navy Standard 16-Bit Airborne Computer Architectures. Both configurations are currently being delivered into DOD full-scale development programs. Leadless Hermetic Chip Carrier packaging has facilitated implementation of both architectures on single 41/2 x 5 substrates. The CMOS and CMOS/SOS implementations of the GPU Chip Set have allowed both CPU implementations to use less than 3 watts of power each. Recent efforts by Tracor for USAF have included the definition of a next-generation GPU Chip Set that will retain the application-proven architecture of the current chip set while offering the added cost advantages of transportability across ISO-CMOS and CMOS/SOS processes and across numerous semiconductor manufacturers using a newly-defined set of common design rules. The Enhanced GPU Chip Set will increase speed by an approximate factor of 3 while significantly reducing chip counts and costs of standard CPU implementations.

Parallelized multi–graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy

PubMed Central

Tankam, Patrice; Santhanam, Anand P.; Lee, Kye-Sung; Won, Jungeun; Canavesi, Cristina; Rolland, Jannick P.

2014-01-01

Abstract. Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000×1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1×1×0.6  mm3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing. PMID:24695868

Parallelized multi-graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy.

PubMed

Tankam, Patrice; Santhanam, Anand P; Lee, Kye-Sung; Won, Jungeun; Canavesi, Cristina; Rolland, Jannick P

2014-07-01

Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing. A parallelized control mechanism was developed to individually assign computation tasks to each of the GPUs. For each GPU, the optimal number of amplitude-scans (A-scans) to be processed in parallel was selected to maximize GPU memory usage and core throughput. We investigated five computing architectures for computational speed-up in processing 1000×1000 A-scans. The proposed parallelized multi-GPU computing framework enables processing at a computational speed faster than the GD-OCM image acquisition, thereby facilitating high-speed GD-OCM imaging in a clinical setting. Using two parallelized GPUs, the image processing of a 1×1×0.6  mm3 skin sample was performed in about 13 s, and the performance was benchmarked at 6.5 s with four GPUs. This work thus demonstrates that 3-D GD-OCM data may be displayed in real-time to the examiner using parallelized GPU processing.

gpuPOM: a GPU-based Princeton Ocean Model

NASA Astrophysics Data System (ADS)

Xu, S.; Huang, X.; Zhang, Y.; Fu, H.; Oey, L.-Y.; Xu, F.; Yang, G.

2014-11-01

Rapid advances in the performance of the graphics processing unit (GPU) have made the GPU a compelling solution for a series of scientific applications. However, most existing GPU acceleration works for climate models are doing partial code porting for certain hot spots, and can only achieve limited speedup for the entire model. In this work, we take the mpiPOM (a parallel version of the Princeton Ocean Model) as our starting point, design and implement a GPU-based Princeton Ocean Model. By carefully considering the architectural features of the state-of-the-art GPU devices, we rewrite the full mpiPOM model from the original Fortran version into a new Compute Unified Device Architecture C (CUDA-C) version. We take several accelerating methods to further improve the performance of gpuPOM, including optimizing memory access in a single GPU, overlapping communication and boundary operations among multiple GPUs, and overlapping input/output (I/O) between the hybrid Central Processing Unit (CPU) and the GPU. Our experimental results indicate that the performance of the gpuPOM on a workstation containing 4 GPUs is comparable to a powerful cluster with 408 CPU cores and it reduces the energy consumption by 6.8 times.

Particle-in-cell simulations on graphic processing units

NASA Astrophysics Data System (ADS)

Ren, C.; Zhou, X.; Li, J.; Huang, M. C.; Zhao, Y.

2014-10-01

We will show our recent progress in using GPU's to accelerate the PIC code OSIRIS [Fonseca et al. LNCS 2331, 342 (2002)]. The OISRIS parallel structure is retained and the computation-intensive kernels are shipped to GPU's. Algorithms for the kernels are adapted for the GPU, including high-order charge-conserving current deposition schemes with few branching and parallel particle sorting [Kong et al., JCP 230, 1676 (2011)]. These algorithms make efficient use of the GPU shared memory. This work was supported by U.S. Department of Energy under Grant No. DE-FC02-04ER54789 and by NSF under Grant No. PHY-1314734.

Near-realtime simulations of biolelectric activity in small mammalian hearts using graphical processing units

PubMed Central

Vigmond, Edward J.; Boyle, Patrick M.; Leon, L. Joshua; Plank, Gernot

2014-01-01

Simulations of cardiac bioelectric phenomena remain a significant challenge despite continual advancements in computational machinery. Spanning large temporal and spatial ranges demands millions of nodes to accurately depict geometry, and a comparable number of timesteps to capture dynamics. This study explores a new hardware computing paradigm, the graphics processing unit (GPU), to accelerate cardiac models, and analyzes results in the context of simulating a small mammalian heart in real time. The ODEs associated with membrane ionic flow were computed on traditional CPU and compared to GPU performance, for one to four parallel processing units. The scalability of solving the PDE responsible for tissue coupling was examined on a cluster using up to 128 cores. Results indicate that the GPU implementation was between 9 and 17 times faster than the CPU implementation and scaled similarly. Solving the PDE was still 160 times slower than real time. PMID:19964295

A Survey Of Techniques for Managing and Leveraging Caches in GPUs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mittal, Sparsh

2014-09-01

Initially introduced as special-purpose accelerators for graphics applications, graphics processing units (GPUs) have now emerged as general purpose computing platforms for a wide range of applications. To address the requirements of these applications, modern GPUs include sizable hardware-managed caches. However, several factors, such as unique architecture of GPU, rise of CPU–GPU heterogeneous computing, etc., demand effective management of caches to achieve high performance and energy efficiency. Recently, several techniques have been proposed for this purpose. In this paper, we survey several architectural and system-level techniques proposed for managing and leveraging GPU caches. We also discuss the importance and challenges ofmore » cache management in GPUs. The aim of this paper is to provide the readers insights into cache management techniques for GPUs and motivate them to propose even better techniques for leveraging the full potential of caches in the GPUs of tomorrow.« less

Classification of hyperspectral imagery using MapReduce on a NVIDIA graphics processing unit (Conference Presentation)

NASA Astrophysics Data System (ADS)

Ramirez, Andres; Rahnemoonfar, Maryam

2017-04-01

A hyperspectral image provides multidimensional figure rich in data consisting of hundreds of spectral dimensions. Analyzing the spectral and spatial information of such image with linear and non-linear algorithms will result in high computational time. In order to overcome this problem, this research presents a system using a MapReduce-Graphics Processing Unit (GPU) model that can help analyzing a hyperspectral image through the usage of parallel hardware and a parallel programming model, which will be simpler to handle compared to other low-level parallel programming models. Additionally, Hadoop was used as an open-source version of the MapReduce parallel programming model. This research compared classification accuracy results and timing results between the Hadoop and GPU system and tested it against the following test cases: the CPU and GPU test case, a CPU test case and a test case where no dimensional reduction was applied.

Specialized Computer Systems for Environment Visualization

NASA Astrophysics Data System (ADS)

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

2018-06-01

The need for real time image generation of landscapes arises in various fields as part of tasks solved by virtual and augmented reality systems, as well as geographic information systems. Such systems provide opportunities for collecting, storing, analyzing and graphically visualizing geographic data. Algorithmic and hardware software tools for increasing the realism and efficiency of the environment visualization in 3D visualization systems are proposed. This paper discusses a modified path tracing algorithm with a two-level hierarchy of bounding volumes and finding intersections with Axis-Aligned Bounding Box. The proposed algorithm eliminates the branching and hence makes the algorithm more suitable to be implemented on the multi-threaded CPU and GPU. A modified ROAM algorithm is used to solve the qualitative visualization of reliefs' problems and landscapes. The algorithm is implemented on parallel systems—cluster and Compute Unified Device Architecture-networks. Results show that the implementation on MPI clusters is more efficient than Graphics Processing Unit/Graphics Processing Clusters and allows real-time synthesis. The organization and algorithms of the parallel GPU system for the 3D pseudo stereo image/video synthesis are proposed. With realizing possibility analysis on a parallel GPU-architecture of each stage, 3D pseudo stereo synthesis is performed. An experimental prototype of a specialized hardware-software system 3D pseudo stereo imaging and video was developed on the CPU/GPU. The experimental results show that the proposed adaptation of 3D pseudo stereo imaging to the architecture of GPU-systems is efficient. Also it accelerates the computational procedures of 3D pseudo-stereo synthesis for the anaglyph and anamorphic formats of the 3D stereo frame without performing optimization procedures. The acceleration is on average 11 and 54 times for test GPUs.

SU-E-T-423: Fast Photon Convolution Calculation with a 3D-Ideal Kernel On the GPU

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriya, S; Sato, M; Tachibana, H

Purpose: The calculation time is a trade-off for improving the accuracy of convolution dose calculation with fine calculation spacing of the KERMA kernel. We investigated to accelerate the convolution calculation using an ideal kernel on the Graphic Processing Units (GPU). Methods: The calculation was performed on the AMD graphics hardware of Dual FirePro D700 and our algorithm was implemented using the Aparapi that convert Java bytecode to OpenCL. The process of dose calculation was separated with the TERMA and KERMA steps. The dose deposited at the coordinate (x, y, z) was determined in the process. In the dose calculation runningmore » on the central processing unit (CPU) of Intel Xeon E5, the calculation loops were performed for all calculation points. On the GPU computation, all of the calculation processes for the points were sent to the GPU and the multi-thread computation was done. In this study, the dose calculation was performed in a water equivalent homogeneous phantom with 150{sup 3} voxels (2 mm calculation grid) and the calculation speed on the GPU to that on the CPU and the accuracy of PDD were compared. Results: The calculation time for the GPU and the CPU were 3.3 sec and 4.4 hour, respectively. The calculation speed for the GPU was 4800 times faster than that for the CPU. The PDD curve for the GPU was perfectly matched to that for the CPU. Conclusion: The convolution calculation with the ideal kernel on the GPU was clinically acceptable for time and may be more accurate in an inhomogeneous region. Intensity modulated arc therapy needs dose calculations for different gantry angles at many control points. Thus, it would be more practical that the kernel uses a coarse spacing technique if the calculation is faster while keeping the similar accuracy to a current treatment planning system.« less

Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems

PubMed Central

Teodoro, George; Kurc, Tahsin M.; Pan, Tony; Cooper, Lee A.D.; Kong, Jun; Widener, Patrick; Saltz, Joel H.

2014-01-01

The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches. PMID:25419545

Graphics Processor Units (GPUs)

NASA Technical Reports Server (NTRS)

Wyrwas, Edward J.

2017-01-01

This presentation will include information about Graphics Processor Units (GPUs) technology, NASA Electronic Parts and Packaging (NEPP) tasks, The test setup, test parameter considerations, lessons learned, collaborations, a roadmap, NEPP partners, results to date, and future plans.

A sample implementation for parallelizing Divide-and-Conquer algorithms on the GPU.

PubMed

Mei, Gang; Zhang, Jiayin; Xu, Nengxiong; Zhao, Kunyang

2018-01-01

The strategy of Divide-and-Conquer (D&C) is one of the frequently used programming patterns to design efficient algorithms in computer science, which has been parallelized on shared memory systems and distributed memory systems. Tzeng and Owens specifically developed a generic paradigm for parallelizing D&C algorithms on modern Graphics Processing Units (GPUs). In this paper, by following the generic paradigm proposed by Tzeng and Owens, we provide a new and publicly available GPU implementation of the famous D&C algorithm, QuickHull, to give a sample and guide for parallelizing D&C algorithms on the GPU. The experimental results demonstrate the practicality of our sample GPU implementation. Our research objective in this paper is to present a sample GPU implementation of a classical D&C algorithm to help interested readers to develop their own efficient GPU implementations with fewer efforts.

GPU-based High-Performance Computing for Radiation Therapy

PubMed Central

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

2014-01-01

Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. 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 a few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of studies have been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this article, 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. PMID:24486639

GPU accelerated implementation of NCI calculations using promolecular density.

PubMed

Rubez, Gaëtan; Etancelin, Jean-Matthieu; Vigouroux, Xavier; Krajecki, Michael; Boisson, Jean-Charles; Hénon, Eric

2017-05-30

The NCI approach is a modern tool to reveal chemical noncovalent interactions. It is particularly attractive to describe ligand-protein binding. A custom implementation for NCI using promolecular density is presented. It is designed to leverage the computational power of NVIDIA graphics processing unit (GPU) accelerators through the CUDA programming model. The code performances of three versions are examined on a test set of 144 systems. NCI calculations are particularly well suited to the GPU architecture, which reduces drastically the computational time. On a single compute node, the dual-GPU version leads to a 39-fold improvement for the biggest instance compared to the optimal OpenMP parallel run (C code, icc compiler) with 16 CPU cores. Energy consumption measurements carried out on both CPU and GPU NCI tests show that the GPU approach provides substantial energy savings. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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

PubMed

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.

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

PubMed

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.

GPU-accelerated phase-field simulation of dendritic solidification in a binary alloy

NASA Astrophysics Data System (ADS)

Yamanaka, Akinori; Aoki, Takayuki; Ogawa, Satoi; Takaki, Tomohiro

2011-03-01

The phase-field simulation for dendritic solidification of a binary alloy has been accelerated by using a graphic processing unit (GPU). To perform the phase-field simulation of the alloy solidification on GPU, a program code was developed with computer unified device architecture (CUDA). In this paper, the implementation technique of the phase-field model on GPU is presented. Also, we evaluated the acceleration performance of the three-dimensional solidification simulation by using a single NVIDIA TESLA C1060 GPU and the developed program code. The results showed that the GPU calculation for 5763 computational grids achieved the performance of 170 GFLOPS by utilizing the shared memory as a software-managed cache. Furthermore, it can be demonstrated that the computation with the GPU is 100 times faster than that with a single CPU core. From the obtained results, we confirmed the feasibility of realizing a real-time full three-dimensional phase-field simulation of microstructure evolution on a personal desktop computer.

Weather information network including graphical display

NASA Technical Reports Server (NTRS)

Leger, Daniel R. (Inventor); Burdon, David (Inventor); Son, Robert S. (Inventor); Martin, Kevin D. (Inventor); Harrison, John (Inventor); Hughes, Keith R. (Inventor)

2006-01-01

An apparatus for providing weather information onboard an aircraft includes a processor unit and a graphical user interface. The processor unit processes weather information after it is received onboard the aircraft from a ground-based source, and the graphical user interface provides a graphical presentation of the weather information to a user onboard the aircraft. Preferably, the graphical user interface includes one or more user-selectable options for graphically displaying at least one of convection information, turbulence information, icing information, weather satellite information, SIGMET information, significant weather prognosis information, and winds aloft information.

Porting a Hall MHD Code to a Graphic Processing Unit

NASA Technical Reports Server (NTRS)

Dorelli, John C.

2011-01-01

We present our experience porting a Hall MHD code to a Graphics Processing Unit (GPU). The code is a 2nd order accurate MUSCL-Hancock scheme which makes use of an HLL Riemann solver to compute numerical fluxes and second-order finite differences to compute the Hall contribution to the electric field. The divergence of the magnetic field is controlled with Dedner?s hyperbolic divergence cleaning method. Preliminary benchmark tests indicate a speedup (relative to a single Nehalem core) of 58x for a double precision calculation. We discuss scaling issues which arise when distributing work across multiple GPUs in a CPU-GPU cluster.

Graphics processing unit based computation for NDE applications

NASA Astrophysics Data System (ADS)

Nahas, C. A.; Rajagopal, Prabhu; Balasubramaniam, Krishnan; Krishnamurthy, C. V.

2012-05-01

Advances in parallel processing in recent years are helping to improve the cost of numerical simulation. Breakthroughs in Graphical Processing Unit (GPU) based computation now offer the prospect of further drastic improvements. The introduction of 'compute unified device architecture' (CUDA) by NVIDIA (the global technology company based in Santa Clara, California, USA) has made programming GPUs for general purpose computing accessible to the average programmer. Here we use CUDA to develop parallel finite difference schemes as applicable to two problems of interest to NDE community, namely heat diffusion and elastic wave propagation. The implementations are for two-dimensions. Performance improvement of the GPU implementation against serial CPU implementation is then discussed.

CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

PubMed Central

Liu, Yongchao; Maskell, Douglas L; Schmidt, Bertil

2009-01-01

Background The Smith-Waterman algorithm is one of the most widely used tools for searching biological sequence databases due to its high sensitivity. Unfortunately, the Smith-Waterman algorithm is computationally demanding, which is further compounded by the exponential growth of sequence databases. The recent emergence of many-core architectures, and their associated programming interfaces, provides an opportunity to accelerate sequence database searches using commonly available and inexpensive hardware. Findings Our CUDASW++ implementation (benchmarked on a single-GPU NVIDIA GeForce GTX 280 graphics card and a dual-GPU GeForce GTX 295 graphics card) provides a significant performance improvement compared to other publicly available implementations, such as SWPS3, CBESW, SW-CUDA, and NCBI-BLAST. CUDASW++ supports query sequences of length up to 59K and for query sequences ranging in length from 144 to 5,478 in Swiss-Prot release 56.6, the single-GPU version achieves an average performance of 9.509 GCUPS with a lowest performance of 9.039 GCUPS and a highest performance of 9.660 GCUPS, and the dual-GPU version achieves an average performance of 14.484 GCUPS with a lowest performance of 10.660 GCUPS and a highest performance of 16.087 GCUPS. Conclusion CUDASW++ is publicly available open-source software. It provides a significant performance improvement for Smith-Waterman-based protein sequence database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs. PMID:19416548

GPU based framework for geospatial analyses

NASA Astrophysics Data System (ADS)

Cosmin Sandric, Ionut; Ionita, Cristian; Dardala, Marian; Furtuna, Titus

2017-04-01

Parallel processing on multiple CPU cores is already used at large scale in geocomputing, but parallel processing on graphics cards is just at the beginning. Being able to use an simple laptop with a dedicated graphics card for advanced and very fast geocomputation is an advantage that each scientist wants to have. The necessity to have high speed computation in geosciences has increased in the last 10 years, mostly due to the increase in the available datasets. These datasets are becoming more and more detailed and hence they require more space to store and more time to process. Distributed computation on multicore CPU's and GPU's plays an important role by processing one by one small parts from these big datasets. These way of computations allows to speed up the process, because instead of using just one process for each dataset, the user can use all the cores from a CPU or up to hundreds of cores from GPU The framework provide to the end user a standalone tools for morphometry analyses at multiscale level. An important part of the framework is dedicated to uncertainty propagation in geospatial analyses. The uncertainty may come from the data collection or may be induced by the model or may have an infinite sources. These uncertainties plays important roles when a spatial delineation of the phenomena is modelled. Uncertainty propagation is implemented inside the GPU framework using Monte Carlo simulations. The GPU framework with the standalone tools proved to be a reliable tool for modelling complex natural phenomena The framework is based on NVidia Cuda technology and is written in C++ programming language. The code source will be available on github at https://github.com/sandricionut/GeoRsGPU Acknowledgement: GPU framework for geospatial analysis, Young Researchers Grant (ICUB-University of Bucharest) 2016, director Ionut Sandric

GPU accelerated simulations of three-dimensional flow of power-law fluids in a driven cube

NASA Astrophysics Data System (ADS)

Jin, K.; Vanka, S. P.; Agarwal, R. K.; Thomas, B. G.

2017-01-01

Newtonian fluid flow in two- and three-dimensional cavities with a moving wall has been studied extensively in a number of previous works. However, relatively a fewer number of studies have considered the motion of non-Newtonian fluids such as shear thinning and shear thickening power law fluids. In this paper, we have simulated the three-dimensional, non-Newtonian flow of a power law fluid in a cubic cavity driven by shear from the top wall. We have used an in-house developed fractional step code, implemented on a Graphics Processor Unit. Three Reynolds numbers have been studied with power law index set to 0.5, 1.0 and 1.5. The flow patterns, viscosity distributions and velocity profiles are presented for Reynolds numbers of 100, 400 and 1000. All three Reynolds numbers are found to yield steady state flows. Tabulated values of velocity are given for the nine cases studied, including the Newtonian cases.

Benchmarking GPU and CPU codes for Heisenberg spin glass over-relaxation

NASA Astrophysics Data System (ADS)

Bernaschi, M.; Parisi, G.; Parisi, L.

2011-06-01

We present a set of possible implementations for Graphics Processing Units (GPU) of the Over-relaxation technique applied to the 3D Heisenberg spin glass model. The results show that a carefully tuned code can achieve more than 100 GFlops/s of sustained performance and update a single spin in about 0.6 nanoseconds. A multi-hit technique that exploits the GPU shared memory further reduces this time. Such results are compared with those obtained by means of a highly-tuned vector-parallel code on latest generation multi-core CPUs.

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

NASA Astrophysics Data System (ADS)

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.

GPU-accelerated computation of electron transfer.

PubMed

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.

GPU Accelerated Prognostics

NASA Technical Reports Server (NTRS)

Gorospe, George E., Jr.; Daigle, Matthew J.; Sankararaman, Shankar; Kulkarni, Chetan S.; Ng, Eley

2017-01-01

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

Hardware based redundant multi-threading inside a GPU for improved reliability

DOEpatents

Sridharan, Vilas; Gurumurthi, Sudhanva

2015-05-05

A system and method for verifying computation output using computer hardware are provided. Instances of computation are generated and processed on hardware-based processors. As instances of computation are processed, each instance of computation receives a load accessible to other instances of computation. Instances of output are generated by processing the instances of computation. The instances of output are verified against each other in a hardware based processor to ensure accuracy of the output.

Graphics Processing Unit (GPU) implementation of image processing algorithms to improve system performance of the Control, Acquisition, Processing, and Image Display System (CAPIDS) of the Micro-Angiographic Fluoroscope (MAF).

PubMed

Vasan, S N Swetadri; Ionita, Ciprian N; Titus, A H; Cartwright, A N; Bednarek, D R; Rudin, S

2012-02-23

We present the image processing upgrades implemented on a Graphics Processing Unit (GPU) in the Control, Acquisition, Processing, and Image Display System (CAPIDS) for the custom Micro-Angiographic Fluoroscope (MAF) detector. Most of the image processing currently implemented in the CAPIDS system is pixel independent; that is, the operation on each pixel is the same and the operation on one does not depend upon the result from the operation on the other, allowing the entire image to be processed in parallel. GPU hardware was developed for this kind of massive parallel processing implementation. Thus for an algorithm which has a high amount of parallelism, a GPU implementation is much faster than a CPU implementation. The image processing algorithm upgrades implemented on the CAPIDS system include flat field correction, temporal filtering, image subtraction, roadmap mask generation and display window and leveling. A comparison between the previous and the upgraded version of CAPIDS has been presented, to demonstrate how the improvement is achieved. By performing the image processing on a GPU, significant improvements (with respect to timing or frame rate) have been achieved, including stable operation of the system at 30 fps during a fluoroscopy run, a DSA run, a roadmap procedure and automatic image windowing and leveling during each frame.

System Level RBDO for Military Ground Vehicles using High Performance Computing

DTIC Science & Technology

2008-01-01

platform. Only the analyses that required more than 24 processors were conducted on the Onyx 350 due to the limited number of processors on the...optimization constraints varied. The queues set the number of processors and number of finite element code licenses available to the analyses. sgi ONYX ...3900: unix 24 MIPS R16000 PROCESSORS 4 IR2 GRAPHICS PIPES 4 IR3 GRAPHICS PIPES 24 GBYTES MEMORY 36 GBYTES LOCAL DISK SPACE sgi ONYX 350: unix 32 MIPS

Molecular Monte Carlo Simulations Using Graphics Processing Units: To Waste Recycle or Not?

PubMed

Kim, Jihan; Rodgers, Jocelyn M; Athènes, Manuel; Smit, Berend

2011-10-11

In the waste recycling Monte Carlo (WRMC) algorithm, (1) multiple trial states may be simultaneously generated and utilized during Monte Carlo moves to improve the statistical accuracy of the simulations, suggesting that such an algorithm may be well posed for implementation in parallel on graphics processing units (GPUs). In this paper, we implement two waste recycling Monte Carlo algorithms in CUDA (Compute Unified Device Architecture) using uniformly distributed random trial states and trial states based on displacement random-walk steps, and we test the methods on a methane-zeolite MFI framework system to evaluate their utility. We discuss the specific implementation details of the waste recycling GPU algorithm and compare the methods to other parallel algorithms optimized for the framework system. We analyze the relationship between the statistical accuracy of our simulations and the CUDA block size to determine the efficient allocation of the GPU hardware resources. We make comparisons between the GPU and the serial CPU Monte Carlo implementations to assess speedup over conventional microprocessors. Finally, we apply our optimized GPU algorithms to the important problem of determining free energy landscapes, in this case for molecular motion through the zeolite LTA.

Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Badal, Andreu; Badano, Aldo

Purpose: It is a known fact that Monte Carlo simulations of radiation transport are computationally intensive and may require long computing times. The authors introduce a new paradigm for the acceleration of Monte Carlo simulations: The use of a graphics processing unit (GPU) as the main computing device instead of a central processing unit (CPU). Methods: A GPU-based Monte Carlo code that simulates photon transport in a voxelized geometry with the accurate physics models from PENELOPE has been developed using the CUDA programming model (NVIDIA Corporation, Santa Clara, CA). Results: An outline of the new code and a sample x-raymore » imaging simulation with an anthropomorphic phantom are presented. A remarkable 27-fold speed up factor was obtained using a GPU compared to a single core CPU. Conclusions: The reported results show that GPUs are currently a good alternative to CPUs for the simulation of radiation transport. Since the performance of GPUs is currently increasing at a faster pace than that of CPUs, the advantages of GPU-based software are likely to be more pronounced in the future.« less

Parallel design of JPEG-LS encoder on graphics processing units

NASA Astrophysics Data System (ADS)

Duan, Hao; Fang, Yong; Huang, Bormin

2012-01-01

With recent technical advances in graphic processing units (GPUs), GPUs have outperformed CPUs in terms of compute capability and memory bandwidth. Many successful GPU applications to high performance computing have been reported. JPEG-LS is an ISO/IEC standard for lossless image compression which utilizes adaptive context modeling and run-length coding to improve compression ratio. However, adaptive context modeling causes data dependency among adjacent pixels and the run-length coding has to be performed in a sequential way. Hence, using JPEG-LS to compress large-volume hyperspectral image data is quite time-consuming. We implement an efficient parallel JPEG-LS encoder for lossless hyperspectral compression on a NVIDIA GPU using the computer unified device architecture (CUDA) programming technology. We use the block parallel strategy, as well as such CUDA techniques as coalesced global memory access, parallel prefix sum, and asynchronous data transfer. We also show the relation between GPU speedup and AVIRIS block size, as well as the relation between compression ratio and AVIRIS block size. When AVIRIS images are divided into blocks, each with 64×64 pixels, we gain the best GPU performance with 26.3x speedup over its original CPU code.

Accelerating epistasis analysis in human genetics with consumer graphics hardware.

PubMed

Sinnott-Armstrong, Nicholas A; Greene, Casey S; Cancare, Fabio; Moore, Jason H

2009-07-24

Human geneticists are now capable of measuring more than one million DNA sequence variations from across the human genome. The new challenge is to develop computationally feasible methods capable of analyzing these data for associations with common human disease, particularly in the context of epistasis. Epistasis describes the situation where multiple genes interact in a complex non-linear manner to determine an individual's disease risk and is thought to be ubiquitous for common diseases. Multifactor Dimensionality Reduction (MDR) is an algorithm capable of detecting epistasis. An exhaustive analysis with MDR is often computationally expensive, particularly for high order interactions. This challenge has previously been met with parallel computation and expensive hardware. The option we examine here exploits commodity hardware designed for computer graphics. In modern computers Graphics Processing Units (GPUs) have more memory bandwidth and computational capability than Central Processing Units (CPUs) and are well suited to this problem. Advances in the video game industry have led to an economy of scale creating a situation where these powerful components are readily available at very low cost. Here we implement and evaluate the performance of the MDR algorithm on GPUs. Of primary interest are the time required for an epistasis analysis and the price to performance ratio of available solutions. We found that using MDR on GPUs consistently increased performance per machine over both a feature rich Java software package and a C++ cluster implementation. The performance of a GPU workstation running a GPU implementation reduces computation time by a factor of 160 compared to an 8-core workstation running the Java implementation on CPUs. This GPU workstation performs similarly to 150 cores running an optimized C++ implementation on a Beowulf cluster. Furthermore this GPU system provides extremely cost effective performance while leaving the CPU available for other tasks. The GPU workstation containing three GPUs costs $2000 while obtaining similar performance on a Beowulf cluster requires 150 CPU cores which, including the added infrastructure and support cost of the cluster system, cost approximately $82,500. Graphics hardware based computing provides a cost effective means to perform genetic analysis of epistasis using MDR on large datasets without the infrastructure of a computing cluster.

GPUs: An Emerging Platform for General-Purpose Computation

DTIC Science & Technology

2007-08-01

programming; real-time cinematic quality graphics Peak stream (26) License required (limited time no- cost evaluation program) Commercially...folding.stanford.edu (accessed 30 March 2007). 2. Fan, Z.; Qiu, F.; Kaufman, A.; Yoakum-Stover, S. GPU Cluster for High Performance Computing. ACM/IEEE...accessed 30 March 2007). 8. Goodnight, N.; Wang, R.; Humphreys, G. Computation on Programmable Graphics Hardware. IEEE Computer Graphics and

Multi-GPU and multi-CPU accelerated FDTD scheme for vibroacoustic applications

NASA Astrophysics Data System (ADS)

Francés, J.; Otero, B.; Bleda, S.; Gallego, S.; Neipp, C.; Márquez, A.; Beléndez, A.

2015-06-01

The Finite-Difference Time-Domain (FDTD) method is applied to the analysis of vibroacoustic problems and to study the propagation of longitudinal and transversal waves in a stratified media. The potential of the scheme and the relevance of each acceleration strategy for massively computations in FDTD are demonstrated in this work. In this paper, we propose two new specific implementations of the bi-dimensional scheme of the FDTD method using multi-CPU and multi-GPU, respectively. In the first implementation, an open source message passing interface (OMPI) has been included in order to massively exploit the resources of a biprocessor station with two Intel Xeon processors. Moreover, regarding CPU code version, the streaming SIMD extensions (SSE) and also the advanced vectorial extensions (AVX) have been included with shared memory approaches that take advantage of the multi-core platforms. On the other hand, the second implementation called the multi-GPU code version is based on Peer-to-Peer communications available in CUDA on two GPUs (NVIDIA GTX 670). Subsequently, this paper presents an accurate analysis of the influence of the different code versions including shared memory approaches, vector instructions and multi-processors (both CPU and GPU) and compares them in order to delimit the degree of improvement of using distributed solutions based on multi-CPU and multi-GPU. The performance of both approaches was analysed and it has been demonstrated that the addition of shared memory schemes to CPU computing improves substantially the performance of vector instructions enlarging the simulation sizes that use efficiently the cache memory of CPUs. In this case GPU computing is slightly twice times faster than the fine tuned CPU version in both cases one and two nodes. However, for massively computations explicit vector instructions do not worth it since the memory bandwidth is the limiting factor and the performance tends to be the same than the sequential version with auto-vectorisation and also shared memory approach. In this scenario GPU computing is the best option since it provides a homogeneous behaviour. More specifically, the speedup of GPU computing achieves an upper limit of 12 for both one and two GPUs, whereas the performance reaches peak values of 80 GFlops and 146 GFlops for the performance for one GPU and two GPUs respectively. Finally, the method is applied to an earth crust profile in order to demonstrate the potential of our approach and the necessity of applying acceleration strategies in these type of applications.

Implementation of Multipattern String Matching Accelerated with GPU for Intrusion Detection System

NASA Astrophysics Data System (ADS)

Nehemia, Rangga; Lim, Charles; Galinium, Maulahikmah; Rinaldi Widianto, Ahmad

2017-04-01

As Internet-related security threats continue to increase in terms of volume and sophistication, existing Intrusion Detection System is also being challenged to cope with the current Internet development. Multi Pattern String Matching algorithm accelerated with Graphical Processing Unit is being utilized to improve the packet scanning performance of the IDS. This paper implements a Multi Pattern String Matching algorithm, also called Parallel Failureless Aho Corasick accelerated with GPU to improve the performance of IDS. OpenCL library is used to allow the IDS to support various GPU, including popular GPU such as NVIDIA and AMD, used in our research. The experiment result shows that the application of Multi Pattern String Matching using GPU accelerated platform provides a speed up, by up to 141% in term of throughput compared to the previous research.

An efficient spectral crystal plasticity solver for GPU architectures

NASA Astrophysics Data System (ADS)

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.

Modeling Cooperative Threads to Project GPU Performance for Adaptive Parallelism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Jiayuan; Uram, Thomas; Morozov, Vitali A.

Most accelerators, such as graphics processing units (GPUs) and vector processors, are particularly suitable for accelerating massively parallel workloads. On the other hand, conventional workloads are developed for multi-core parallelism, which often scale to only a few dozen OpenMP threads. When hardware threads significantly outnumber the degree of parallelism in the outer loop, programmers are challenged with efficient hardware utilization. A common solution is to further exploit the parallelism hidden deep in the code structure. Such parallelism is less structured: parallel and sequential loops may be imperfectly nested within each other, neigh boring inner loops may exhibit different concurrency patternsmore » (e.g. Reduction vs. Forall), yet have to be parallelized in the same parallel section. Many input-dependent transformations have to be explored. A programmer often employs a larger group of hardware threads to cooperatively walk through a smaller outer loop partition and adaptively exploit any encountered parallelism. This process is time-consuming and error-prone, yet the risk of gaining little or no performance remains high for such workloads. To reduce risk and guide implementation, we propose a technique to model workloads with limited parallelism that can automatically explore and evaluate transformations involving cooperative threads. Eventually, our framework projects the best achievable performance and the most promising transformations without implementing GPU code or using physical hardware. We envision our technique to be integrated into future compilers or optimization frameworks for autotuning.« less

Real-time track-less Cherenkov ring fitting trigger system based on Graphics Processing Units

NASA Astrophysics Data System (ADS)

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

2017-12-01

The parallel computing power of commercial Graphics Processing Units (GPUs) is exploited to perform real-time ring fitting at the lowest trigger level using information coming from the Ring Imaging Cherenkov (RICH) detector of the NA62 experiment at CERN. To this purpose, direct GPU communication with a custom FPGA-based board has been used to reduce the data transmission latency. The GPU-based trigger system is currently integrated in the experimental setup of the RICH detector of the NA62 experiment, in order to reconstruct ring-shaped hit patterns. The ring-fitting algorithm running on GPU is fed with raw RICH data only, with no information coming from other detectors, and is able to provide more complex trigger primitives with respect to the simple photodetector hit multiplicity, resulting in a higher selection efficiency. The performance of the system for multi-ring Cherenkov online reconstruction obtained during the NA62 physics run is presented.

High-Throughput Characterization of Porous Materials Using Graphics Processing Units

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Jihan; Martin, Richard L.; Rübel, Oliver

We have developed a high-throughput graphics processing units (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CHmore » $$_{4}$$ and CO$$_{2}$$) and material's framework atoms. Using a parallel flood fill CPU algorithm, inaccessible regions inside the framework structures are identified and blocked based on their energy profiles. Finally, we compute the Henry coefficients and heats of adsorption through statistical Widom insertion Monte Carlo moves in the domain restricted to the accessible space. The code offers significant speedup over a single core CPU code and allows us to characterize a set of porous materials at least an order of magnitude larger than ones considered in earlier studies. For structures selected from such a prescreening algorithm, full adsorption isotherms can be calculated by conducting multiple grand canonical Monte Carlo simulations concurrently within the GPU.« less

Communication: A reduced scaling J-engine based reformulation of SOS-MP2 using graphics processing units.

PubMed

Maurer, S A; Kussmann, J; Ochsenfeld, C

2014-08-07

We present a low-prefactor, cubically scaling scaled-opposite-spin second-order Møller-Plesset perturbation theory (SOS-MP2) method which is highly suitable for massively parallel architectures like graphics processing units (GPU). The scaling is reduced from O(N⁵) to O(N³) by a reformulation of the MP2-expression in the atomic orbital basis via Laplace transformation and the resolution-of-the-identity (RI) approximation of the integrals in combination with efficient sparse algebra for the 3-center integral transformation. In contrast to previous works that employ GPUs for post Hartree-Fock calculations, we do not simply employ GPU-based linear algebra libraries to accelerate the conventional algorithm. Instead, our reformulation allows to replace the rate-determining contraction step with a modified J-engine algorithm, that has been proven to be highly efficient on GPUs. Thus, our SOS-MP2 scheme enables us to treat large molecular systems in an accurate and efficient manner on a single GPU-server.

High performance hybrid functional Petri net simulations of biological pathway models on CUDA.

PubMed

Chalkidis, Georgios; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Hybrid functional Petri nets are a wide-spread tool for representing and simulating biological models. Due to their potential of providing virtual drug testing environments, biological simulations have a growing impact on pharmaceutical research. Continuous research advancements in biology and medicine lead to exponentially increasing simulation times, thus raising the demand for performance accelerations by efficient and inexpensive parallel computation solutions. Recent developments in the field of general-purpose computation on graphics processing units (GPGPU) enabled the scientific community to port a variety of compute intensive algorithms onto the graphics processing unit (GPU). This work presents the first scheme for mapping biological hybrid functional Petri net models, which can handle both discrete and continuous entities, onto compute unified device architecture (CUDA) enabled GPUs. GPU accelerated simulations are observed to run up to 18 times faster than sequential implementations. Simulating the cell boundary formation by Delta-Notch signaling on a CUDA enabled GPU results in a speedup of approximately 7x for a model containing 1,600 cells.

Discovery of Deep Structure from Unlabeled Data

DTIC Science & Technology

2014-11-01

GPU processors . To evaluate the unsupervised learning component of the algorithms (which has become of less importance in the era of “big data...representations to those in biological visual, auditory, and somatosensory cortex ; and ran numerous control experiments investigating the impact of

Digital image processing using parallel computing based on CUDA technology

NASA Astrophysics Data System (ADS)

Skirnevskiy, I. P.; Pustovit, A. V.; Abdrashitova, M. O.

2017-01-01

This article describes expediency of using a graphics processing unit (GPU) in big data processing in the context of digital images processing. It provides a short description of a parallel computing technology and its usage in different areas, definition of the image noise and a brief overview of some noise removal algorithms. It also describes some basic requirements that should be met by certain noise removal algorithm in the projection to computer tomography. It provides comparison of the performance with and without using GPU as well as with different percentage of using CPU and GPU.

GAPD: a GPU-accelerated atom-based polychromatic diffraction simulation code.

PubMed

E, J C; Wang, L; Chen, S; Zhang, Y Y; Luo, S N

2018-03-01

GAPD, a graphics-processing-unit (GPU)-accelerated atom-based polychromatic diffraction simulation code for direct, kinematics-based, simulations of X-ray/electron diffraction of large-scale atomic systems with mono-/polychromatic beams and arbitrary plane detector geometries, is presented. This code implements GPU parallel computation via both real- and reciprocal-space decompositions. With GAPD, direct simulations are performed of the reciprocal lattice node of ultralarge systems (∼5 billion atoms) and diffraction patterns of single-crystal and polycrystalline configurations with mono- and polychromatic X-ray beams (including synchrotron undulator sources), and validation, benchmark and application cases are presented.

Improving Quantum Gate Simulation using a GPU

NASA Astrophysics Data System (ADS)

Gutierrez, Eladio; Romero, Sergio; Trenas, Maria A.; Zapata, Emilio L.

2008-11-01

Due to the increasing computing power of the graphics processing units (GPU), they are becoming more and more popular when solving general purpose algorithms. As the simulation of quantum computers results on a problem with exponential complexity, it is advisable to perform a parallel computation, such as the one provided by the SIMD multiprocessors present in recent GPUs. In this paper, we focus on an important quantum algorithm, the quantum Fourier transform (QTF), in order to evaluate different parallelization strategies on a novel GPU architecture. Our implementation makes use of the new CUDA software/hardware architecture developed recently by NVIDIA.

GPU accelerated manifold correction method for spinning compact binaries

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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.

Parallelized CCHE2D flow model with CUDA Fortran on Graphics Process Units

USDA-ARS?s Scientific Manuscript database

This paper presents the CCHE2D implicit flow model parallelized using CUDA Fortran programming technique on Graphics Processing Units (GPUs). A parallelized implicit Alternating Direction Implicit (ADI) solver using Parallel Cyclic Reduction (PCR) algorithm on GPU is developed and tested. This solve...

Recent developments of NASTRAN pre- amd post-processors: Response spectrum analysis (RESPAN) and interactive graphics (GIFTS)

NASA Technical Reports Server (NTRS)

Hirt, E. F.; Fox, G. L.

1982-01-01

Two specific NASTRAN preprocessors and postprocessors are examined. A postprocessor for dynamic analysis and a graphical interactive package for model generation and review of resuls are presented. A computer program that provides response spectrum analysis capability based on data from NASTRAN finite element model is described and the GIFTS system, a graphic processor to augment NASTRAN is introduced.

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

NASA Astrophysics Data System (ADS)

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.

Storage strategies of eddy-current FE-BI model for GPU implementation

NASA Astrophysics Data System (ADS)

Bardel, Charles; Lei, Naiguang; Udpa, Lalita

2013-01-01

In the past few years graphical processing units (GPUs) have shown tremendous improvements in computational throughput over standard CPU architecture. However, this comes at the cost of restructuring the algorithms to meet the strengths and drawbacks of this GPU architecture. A major drawback is the state of limited memory, and hence storage of FE stiffness matrices on the GPU is important. In contrast to storage on CPU the GPU storage format has significant influence on the overall performance. This paper presents an investigation of a storage strategy in the implementation of a two-dimensional finite element-boundary integral (FE-BI) model for Eddy current NDE applications, on GPU architecture. Specifically, the high dimensional matrices are manipulated by examining the matrix structure and optimally splitting into structurally independent component matrices for efficient storage and retrieval of each component. Results obtained using the proposed approach are compared to those of conventional CPU implementation for validating the method.

Semiempirical Quantum Chemical Calculations Accelerated on a Hybrid Multicore CPU-GPU Computing Platform.

PubMed

Wu, Xin; Koslowski, Axel; Thiel, Walter

2012-07-10

In this work, we demonstrate that semiempirical quantum chemical calculations can be accelerated significantly by leveraging the graphics processing unit (GPU) as a coprocessor on a hybrid multicore CPU-GPU computing platform. Semiempirical calculations using the MNDO, AM1, PM3, OM1, OM2, and OM3 model Hamiltonians were systematically profiled for three types of test systems (fullerenes, water clusters, and solvated crambin) to identify the most time-consuming sections of the code. The corresponding routines were ported to the GPU and optimized employing both existing library functions and a GPU kernel that carries out a sequence of noniterative Jacobi transformations during pseudodiagonalization. The overall computation times for single-point energy calculations and geometry optimizations of large molecules were reduced by one order of magnitude for all methods, as compared to runs on a single CPU core.

GPU-Powered Coherent Beamforming

NASA Astrophysics Data System (ADS)

Magro, A.; Adami, K. Zarb; Hickish, J.

2015-03-01

Graphics processing units (GPU)-based beamforming is a relatively unexplored area in radio astronomy, possibly due to the assumption that any such system will be severely limited by the PCIe bandwidth required to transfer data to the GPU. We have developed a CUDA-based GPU implementation of a coherent beamformer, specifically designed and optimized for deployment at the BEST-2 array which can generate an arbitrary number of synthesized beams for a wide range of parameters. It achieves ˜1.3 TFLOPs on an NVIDIA Tesla K20, approximately 10x faster than an optimized, multithreaded CPU implementation. This kernel has been integrated into two real-time, GPU-based time-domain software pipelines deployed at the BEST-2 array in Medicina: a standalone beamforming pipeline and a transient detection pipeline. We present performance benchmarks for the beamforming kernel as well as the transient detection pipeline with beamforming capabilities as well as results of test observation.

From experiment to design -- Fault characterization and detection in parallel computer systems using computational accelerators

NASA Astrophysics Data System (ADS)

Yim, Keun Soo

This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of program states that included dynamically allocated memory (to be spatially comprehensive). In GPUs, we used fault injection studies to demonstrate the importance of detecting silent data corruption (SDC) errors that are mainly due to the lack of fine-grained protections and the massive use of fault-insensitive data. This dissertation also presents transparent fault tolerance frameworks and techniques that are directly applicable to hybrid computers built using only commercial off-the-shelf hardware components. This dissertation shows that by developing understanding of the failure characteristics and error propagation paths of target programs, we were able to create fault tolerance frameworks and techniques that can quickly detect and recover from hardware faults with low performance and hardware overheads.

Graphics processing unit accelerated phase field dislocation dynamics: Application to bi-metallic interfaces

DOE PAGES

Eghtesad, Adnan; Germaschewski, Kai; Beyerlein, Irene J.; ...

2017-10-14

We present the first high-performance computing implementation of the meso-scale phase field dislocation dynamics (PFDD) model on a graphics processing unit (GPU)-based platform. The implementation takes advantage of the portable OpenACC standard directive pragmas along with Nvidia's compute unified device architecture (CUDA) fast Fourier transform (FFT) library called CUFFT to execute the FFT computations within the PFDD formulation on the same GPU platform. The overall implementation is termed ACCPFDD-CUFFT. The package is entirely performance portable due to the use of OPENACC-CUDA inter-operability, in which calls to CUDA functions are replaced with the OPENACC data regions for a host central processingmore » unit (CPU) and device (GPU). A comprehensive benchmark study has been conducted, which compares a number of FFT routines, the Numerical Recipes FFT (FOURN), Fastest Fourier Transform in the West (FFTW), and the CUFFT. The last one exploits the advantages of the GPU hardware for FFT calculations. The novel ACCPFDD-CUFFT implementation is verified using the analytical solutions for the stress field around an infinite edge dislocation and subsequently applied to simulate the interaction and motion of dislocations through a bi-phase copper-nickel (Cu–Ni) interface. It is demonstrated that the ACCPFDD-CUFFT implementation on a single TESLA K80 GPU offers a 27.6X speedup relative to the serial version and a 5X speedup relative to the 22-multicore Intel Xeon CPU E5-2699 v4 @ 2.20 GHz version of the code.« less

Graphics processing unit accelerated phase field dislocation dynamics: Application to bi-metallic interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eghtesad, Adnan; Germaschewski, Kai; Beyerlein, Irene J.

We present the first high-performance computing implementation of the meso-scale phase field dislocation dynamics (PFDD) model on a graphics processing unit (GPU)-based platform. The implementation takes advantage of the portable OpenACC standard directive pragmas along with Nvidia's compute unified device architecture (CUDA) fast Fourier transform (FFT) library called CUFFT to execute the FFT computations within the PFDD formulation on the same GPU platform. The overall implementation is termed ACCPFDD-CUFFT. The package is entirely performance portable due to the use of OPENACC-CUDA inter-operability, in which calls to CUDA functions are replaced with the OPENACC data regions for a host central processingmore » unit (CPU) and device (GPU). A comprehensive benchmark study has been conducted, which compares a number of FFT routines, the Numerical Recipes FFT (FOURN), Fastest Fourier Transform in the West (FFTW), and the CUFFT. The last one exploits the advantages of the GPU hardware for FFT calculations. The novel ACCPFDD-CUFFT implementation is verified using the analytical solutions for the stress field around an infinite edge dislocation and subsequently applied to simulate the interaction and motion of dislocations through a bi-phase copper-nickel (Cu–Ni) interface. It is demonstrated that the ACCPFDD-CUFFT implementation on a single TESLA K80 GPU offers a 27.6X speedup relative to the serial version and a 5X speedup relative to the 22-multicore Intel Xeon CPU E5-2699 v4 @ 2.20 GHz version of the code.« less

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization

NASA Astrophysics Data System (ADS)

Hagan, Aaron; Sawant, Amit; Folkerts, Michael; Modiri, Arezoo

2018-01-01

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of 26% in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

Multi-GPU configuration of 4D intensity modulated radiation therapy inverse planning using global optimization.

PubMed

Hagan, Aaron; Sawant, Amit; Folkerts, Michael; Modiri, Arezoo

2018-01-16

We report on the design, implementation and characterization of a multi-graphic processing unit (GPU) computational platform for higher-order optimization in radiotherapy treatment planning. In collaboration with a commercial vendor (Varian Medical Systems, Palo Alto, CA), a research prototype GPU-enabled Eclipse (V13.6) workstation was configured. The hardware consisted of dual 8-core Xeon processors, 256 GB RAM and four NVIDIA Tesla K80 general purpose GPUs. We demonstrate the utility of this platform for large radiotherapy optimization problems through the development and characterization of a parallelized particle swarm optimization (PSO) four dimensional (4D) intensity modulated radiation therapy (IMRT) technique. The PSO engine was coupled to the Eclipse treatment planning system via a vendor-provided scripting interface. Specific challenges addressed in this implementation were (i) data management and (ii) non-uniform memory access (NUMA). For the former, we alternated between parameters over which the computation process was parallelized. For the latter, we reduced the amount of data required to be transferred over the NUMA bridge. The datasets examined in this study were approximately 300 GB in size, including 4D computed tomography images, anatomical structure contours and dose deposition matrices. For evaluation, we created a 4D-IMRT treatment plan for one lung cancer patient and analyzed computation speed while varying several parameters (number of respiratory phases, GPUs, PSO particles, and data matrix sizes). The optimized 4D-IMRT plan enhanced sparing of organs at risk by an average reduction of [Formula: see text] in maximum dose, compared to the clinical optimized IMRT plan, where the internal target volume was used. We validated our computation time analyses in two additional cases. The computation speed in our implementation did not monotonically increase with the number of GPUs. The optimal number of GPUs (five, in our study) is directly related to the hardware specifications. The optimization process took 35 min using 50 PSO particles, 25 iterations and 5 GPUs.

N-body simulation for self-gravitating collisional systems with a new SIMD instruction set extension to the x86 architecture, Advanced Vector eXtensions

NASA Astrophysics Data System (ADS)

Tanikawa, Ataru; Yoshikawa, Kohji; Okamoto, Takashi; Nitadori, Keigo

2012-02-01

We present a high-performance N-body code for self-gravitating collisional systems accelerated with the aid of a new SIMD instruction set extension of the x86 architecture: Advanced Vector eXtensions (AVX), an enhanced version of the Streaming SIMD Extensions (SSE). With one processor core of Intel Core i7-2600 processor (8 MB cache and 3.40 GHz) based on Sandy Bridge micro-architecture, we implemented a fourth-order Hermite scheme with individual timestep scheme ( Makino and Aarseth, 1992), and achieved the performance of ˜20 giga floating point number operations per second (GFLOPS) for double-precision accuracy, which is two times and five times higher than that of the previously developed code implemented with the SSE instructions ( Nitadori et al., 2006b), and that of a code implemented without any explicit use of SIMD instructions with the same processor core, respectively. We have parallelized the code by using so-called NINJA scheme ( Nitadori et al., 2006a), and achieved ˜90 GFLOPS for a system containing more than N = 8192 particles with 8 MPI processes on four cores. We expect to achieve about 10 tera FLOPS (TFLOPS) for a self-gravitating collisional system with N ˜ 10 5 on massively parallel systems with at most 800 cores with Sandy Bridge micro-architecture. This performance will be comparable to that of Graphic Processing Unit (GPU) cluster systems, such as the one with about 200 Tesla C1070 GPUs ( Spurzem et al., 2010). This paper offers an alternative to collisional N-body simulations with GRAPEs and GPUs.

Protein alignment algorithms with an efficient backtracking routine on multiple GPUs.

PubMed

Blazewicz, Jacek; Frohmberg, Wojciech; Kierzynka, Michal; Pesch, Erwin; Wojciechowski, Pawel

2011-05-20

Pairwise sequence alignment methods are widely used in biological research. The increasing number of sequences is perceived as one of the upcoming challenges for sequence alignment methods in the nearest future. To overcome this challenge several GPU (Graphics Processing Unit) computing approaches have been proposed lately. These solutions show a great potential of a GPU platform but in most cases address the problem of sequence database scanning and computing only the alignment score whereas the alignment itself is omitted. Thus, the need arose to implement the global and semiglobal Needleman-Wunsch, and Smith-Waterman algorithms with a backtracking procedure which is needed to construct the alignment. In this paper we present the solution that performs the alignment of every given sequence pair, which is a required step for progressive multiple sequence alignment methods, as well as for DNA recognition at the DNA assembly stage. Performed tests show that the implementation, with performance up to 6.3 GCUPS on a single GPU for affine gap penalties, is very efficient in comparison to other CPU and GPU-based solutions. Moreover, multiple GPUs support with load balancing makes the application very scalable. The article shows that the backtracking procedure of the sequence alignment algorithms may be designed to fit in with the GPU architecture. Therefore, our algorithm, apart from scores, is able to compute pairwise alignments. This opens a wide range of new possibilities, allowing other methods from the area of molecular biology to take advantage of the new computational architecture. Performed tests show that the efficiency of the implementation is excellent. Moreover, the speed of our GPU-based algorithms can be almost linearly increased when using more than one graphics card.

Computing the Density Matrix in Electronic Structure Theory on Graphics Processing Units.

PubMed

Cawkwell, M J; Sanville, E J; Mniszewski, S M; Niklasson, Anders M N

2012-11-13

The self-consistent solution of a Schrödinger-like equation for the density matrix is a critical and computationally demanding step in quantum-based models of interatomic bonding. This step was tackled historically via the diagonalization of the Hamiltonian. We have investigated the performance and accuracy of the second-order spectral projection (SP2) algorithm for the computation of the density matrix via a recursive expansion of the Fermi operator in a series of generalized matrix-matrix multiplications. We demonstrate that owing to its simplicity, the SP2 algorithm [Niklasson, A. M. N. Phys. Rev. B2002, 66, 155115] is exceptionally well suited to implementation on graphics processing units (GPUs). The performance in double and single precision arithmetic of a hybrid GPU/central processing unit (CPU) and full GPU implementation of the SP2 algorithm exceed those of a CPU-only implementation of the SP2 algorithm and traditional matrix diagonalization when the dimensions of the matrices exceed about 2000 × 2000. Padding schemes for arrays allocated in the GPU memory that optimize the performance of the CUBLAS implementations of the level 3 BLAS DGEMM and SGEMM subroutines for generalized matrix-matrix multiplications are described in detail. The analysis of the relative performance of the hybrid CPU/GPU and full GPU implementations indicate that the transfer of arrays between the GPU and CPU constitutes only a small fraction of the total computation time. The errors measured in the self-consistent density matrices computed using the SP2 algorithm are generally smaller than those measured in matrices computed via diagonalization. Furthermore, the errors in the density matrices computed using the SP2 algorithm do not exhibit any dependence of system size, whereas the errors increase linearly with the number of orbitals when diagonalization is employed.

Acceleration of Linear Finite-Difference Poisson-Boltzmann Methods on Graphics Processing Units.

PubMed

Qi, Ruxi; Botello-Smith, Wesley M; Luo, Ray

2017-07-11

Electrostatic interactions play crucial roles in biophysical processes such as protein folding and molecular recognition. Poisson-Boltzmann equation (PBE)-based models have emerged as widely used in modeling these important processes. Though great efforts have been put into developing efficient PBE numerical models, challenges still remain due to the high dimensionality of typical biomolecular systems. In this study, we implemented and analyzed commonly used linear PBE solvers for the ever-improving graphics processing units (GPU) for biomolecular simulations, including both standard and preconditioned conjugate gradient (CG) solvers with several alternative preconditioners. Our implementation utilizes the standard Nvidia CUDA libraries cuSPARSE, cuBLAS, and CUSP. Extensive tests show that good numerical accuracy can be achieved given that the single precision is often used for numerical applications on GPU platforms. The optimal GPU performance was observed with the Jacobi-preconditioned CG solver, with a significant speedup over standard CG solver on CPU in our diversified test cases. Our analysis further shows that different matrix storage formats also considerably affect the efficiency of different linear PBE solvers on GPU, with the diagonal format best suited for our standard finite-difference linear systems. Further efficiency may be possible with matrix-free operations and integrated grid stencil setup specifically tailored for the banded matrices in PBE-specific linear systems.

Simulation of ring polymer melts with GPU acceleration

NASA Astrophysics Data System (ADS)

Schram, R. D.; Barkema, G. T.

2018-06-01

We implemented the elastic lattice polymer model on the GPU (Graphics Processing Unit), and show that the GPU is very efficient for polymer simulations of dense polymer melts. The implementation is able to perform up to 4.1 ṡ109 Monte Carlo moves per second. Compared to our standard CPU implementation, we find an effective speed-up of a factor 92. Using this GPU implementation we studied the equilibrium properties and the dynamics of non-concatenated ring polymers in a melt of such polymers, using Rouse modes. With increasing polymer length, we found a very slow transition to compactness with a growth exponent ν ≈ 1 / 3. Numerically we find that the longest internal time scale of the polymer scales as N3.1, with N the molecular weight of the ring polymer.

Tensor Algebra Library for NVidia Graphics Processing Units

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liakh, Dmitry

This is a general purpose math library implementing basic tensor algebra operations on NVidia GPU accelerators. This software is a tensor algebra library that can perform basic tensor algebra operations, including tensor contractions, tensor products, tensor additions, etc., on NVidia GPU accelerators, asynchronously with respect to the CPU host. It supports a simultaneous use of multiple NVidia GPUs. Each asynchronous API function returns a handle which can later be used for querying the completion of the corresponding tensor algebra operation on a specific GPU. The tensors participating in a particular tensor operation are assumed to be stored in local RAMmore » of a node or GPU RAM. The main research area where this library can be utilized is the quantum many-body theory (e.g., in electronic structure theory).« less

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

NASA Astrophysics Data System (ADS)

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 algorithms based on existing parallelization strategies.

Evaluating Multi-core Architectures through Accelerating the Three-Dimensional Lax–Wendroff Correction

DOE Office of Scientific and Technical Information (OSTI.GOV)

You, Yang; Fu, Haohuan; Song, Shuaiwen

2014-07-18

Wave propagation forward modeling is a widely used computational method in oil and gas exploration. The iterative stencil loops in such problems have broad applications in scientific computing. However, executing such loops can be highly time time-consuming, which greatly limits application’s performance and power efficiency. In this paper, we accelerate the forward modeling technique on the latest multi-core and many-core architectures such as Intel Sandy Bridge CPUs, NVIDIA Fermi C2070 GPU, NVIDIA Kepler K20x GPU, and the Intel Xeon Phi Co-processor. For the GPU platforms, we propose two parallel strategies to explore the performance optimization opportunities for our stencil kernels.more » For Sandy Bridge CPUs and MIC, we also employ various optimization techniques in order to achieve the best.« less

Introduction of Parallel GPGPU Acceleration Algorithms for the Solution of Radiative Transfer

NASA Technical Reports Server (NTRS)

Godoy, William F.; Liu, Xu

2011-01-01

General-purpose computing on graphics processing units (GPGPU) is a recent technique that allows the parallel graphics processing unit (GPU) to accelerate calculations performed sequentially by the central processing unit (CPU). To introduce GPGPU to radiative transfer, the Gauss-Seidel solution of the well-known expressions for 1-D and 3-D homogeneous, isotropic media is selected as a test case. Different algorithms are introduced to balance memory and GPU-CPU communication, critical aspects of GPGPU. Results show that speed-ups of one to two orders of magnitude are obtained when compared to sequential solutions. The underlying value of GPGPU is its potential extension in radiative solvers (e.g., Monte Carlo, discrete ordinates) at a minimal learning curve.

Efficiency of the energy transfer in the FMO complex using hierarchical equations on Graphics Processing Units

NASA Astrophysics Data System (ADS)

Kramer, Tobias; Kreisbeck, Christoph; Rodriguez, Mirta; Hein, Birgit

2011-03-01

We study the efficiency of the energy transfer in the Fenna-Matthews-Olson complex solving the non-Markovian hierarchical equations (HE) proposed by Ishizaki and Fleming in 2009, which include properly the reorganization process. We compare it to the Markovian approach and find that the Markovian dynamics overestimates the thermalization rate, yielding higher efficiencies than the HE. Using the high-performance of graphics processing units (GPU) we cover a large range of reorganization energies and temperatures and find that initial quantum beatings are important for the energy distribution, but of limited influence to the efficiency. Our efficient GPU implementation of the HE allows us to calculate nonlinear spectra of the FMO complex. References see www.quantumdynamics.de

Gravitational tree-code on graphics processing units: implementation in CUDA

NASA Astrophysics Data System (ADS)

Gaburov, Evghenii; Bédorf, Jeroen; Portegies Zwart, Simon

2010-05-01

We present a new very fast tree-code which runs on massively parallel Graphical Processing Units (GPU) with NVIDIA CUDA architecture. The tree-construction and calculation of multipole moments is carried out on the host CPU, while the force calculation which consists of tree walks and evaluation of interaction list is carried out on the GPU. In this way we achieve a sustained performance of about 100GFLOP/s and data transfer rates of about 50GB/s. It takes about a second to compute forces on a million particles with an opening angle of θ ≈ 0.5. The code has a convenient user interface and is freely available for use. http://castle.strw.leidenuniv.nl/software/octgrav.html

Scan line graphics generation on the massively parallel processor

NASA Technical Reports Server (NTRS)

Dorband, John E.

1988-01-01

Described here is how researchers implemented a scan line graphics generation algorithm on the Massively Parallel Processor (MPP). Pixels are computed in parallel and their results are applied to the Z buffer in large groups. To perform pixel value calculations, facilitate load balancing across the processors and apply the results to the Z buffer efficiently in parallel requires special virtual routing (sort computation) techniques developed by the author especially for use on single-instruction multiple-data (SIMD) architectures.

The development of an engineering computer graphics laboratory

NASA Technical Reports Server (NTRS)

Anderson, D. C.; Garrett, R. E.

1975-01-01

Hardware and software systems developed to further research and education in interactive computer graphics were described, as well as several of the ongoing application-oriented projects, educational graphics programs, and graduate research projects. The software system consists of a FORTRAN 4 subroutine package, in conjunction with a PDP 11/40 minicomputer as the primary computation processor and the Imlac PDS-1 as an intelligent display processor. The package comprises a comprehensive set of graphics routines for dynamic, structured two-dimensional display manipulation, and numerous routines to handle a variety of input devices at the Imlac.

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

NASA Astrophysics Data System (ADS)

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, we use the GPU to accelerate our gravity and seismic inversion. Taking the gravity inversion as an example, its kernels are gravity forward simulation and correlation imaging, after the parallelization in GPU, in 3D case,the inversion module, the original five CPU loops are reduced to three,the forward module the original five CPU loops are reduced to two. 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).

Fast quantum Monte Carlo on a GPU

NASA Astrophysics Data System (ADS)

Lutsyshyn, Y.

2015-02-01

We present a scheme for the parallelization of quantum Monte Carlo method on graphical processing units, focusing on variational Monte Carlo simulation of bosonic systems. We use asynchronous execution schemes with shared memory persistence, and obtain an excellent utilization of the accelerator. The CUDA code is provided along with a package that simulates liquid helium-4. The program was benchmarked on several models of Nvidia GPU, including Fermi GTX560 and M2090, and the Kepler architecture K20 GPU. Special optimization was developed for the Kepler cards, including placement of data structures in the register space of the Kepler GPUs. Kepler-specific optimization is discussed.

Graphics Processing Units (GPU) and the Goddard Earth Observing System atmospheric model (GEOS-5): Implementation and Potential Applications

NASA Technical Reports Server (NTRS)

Putnam, William M.

2011-01-01

Earth system models like the Goddard Earth Observing System model (GEOS-5) have been pushing the limits of large clusters of multi-core microprocessors, producing breath-taking fidelity in resolving cloud systems at a global scale. GPU computing presents an opportunity for improving the efficiency of these leading edge models. A GPU implementation of GEOS-5 will facilitate the use of cloud-system resolving resolutions in data assimilation and weather prediction, at resolutions near 3.5 km, improving our ability to extract detailed information from high-resolution satellite observations and ultimately produce better weather and climate predictions

A survey of GPU-based acceleration techniques in MRI reconstructions

PubMed Central

Wang, Haifeng; Peng, Hanchuan; Chang, Yuchou

2018-01-01

Image reconstruction in magnetic resonance imaging (MRI) clinical applications has become increasingly more complicated. However, diagnostic and treatment require very fast computational procedure. Modern competitive platforms of graphics processing unit (GPU) have been used to make high-performance parallel computations available, and attractive to common consumers for computing massively parallel reconstruction problems at commodity price. GPUs have also become more and more important for reconstruction computations, especially when deep learning starts to be applied into MRI reconstruction. The motivation of this survey is to review the image reconstruction schemes of GPU computing for MRI applications and provide a summary reference for researchers in MRI community. PMID:29675361

A survey of GPU-based acceleration techniques in MRI reconstructions.

PubMed

Wang, Haifeng; Peng, Hanchuan; Chang, Yuchou; Liang, Dong

2018-03-01

Image reconstruction in magnetic resonance imaging (MRI) clinical applications has become increasingly more complicated. However, diagnostic and treatment require very fast computational procedure. Modern competitive platforms of graphics processing unit (GPU) have been used to make high-performance parallel computations available, and attractive to common consumers for computing massively parallel reconstruction problems at commodity price. GPUs have also become more and more important for reconstruction computations, especially when deep learning starts to be applied into MRI reconstruction. The motivation of this survey is to review the image reconstruction schemes of GPU computing for MRI applications and provide a summary reference for researchers in MRI community.

Real time 3D structural and Doppler OCT imaging on graphics processing units

NASA Astrophysics Data System (ADS)

Sylwestrzak, Marcin; Szlag, Daniel; Szkulmowski, Maciej; Gorczyńska, Iwona; Bukowska, Danuta; Wojtkowski, Maciej; Targowski, Piotr

2013-03-01

In this report the application of graphics processing unit (GPU) programming for real-time 3D Fourier domain Optical Coherence Tomography (FdOCT) imaging with implementation of Doppler algorithms for visualization of the flows in capillary vessels is presented. Generally, the time of the data processing of the FdOCT data on the main processor of the computer (CPU) constitute a main limitation for real-time imaging. Employing additional algorithms, such as Doppler OCT analysis, makes this processing even more time consuming. Lately developed GPUs, which offers a very high computational power, give a solution to this problem. Taking advantages of them for massively parallel data processing, allow for real-time imaging in FdOCT. The presented software for structural and Doppler OCT allow for the whole processing with visualization of 2D data consisting of 2000 A-scans generated from 2048 pixels spectra with frame rate about 120 fps. The 3D imaging in the same mode of the volume data build of 220 × 100 A-scans is performed at a rate of about 8 frames per second. In this paper a software architecture, organization of the threads and optimization applied is shown. For illustration the screen shots recorded during real time imaging of the phantom (homogeneous water solution of Intralipid in glass capillary) and the human eye in-vivo is presented.

Accelerated Monte Carlo Simulation on the Chemical Stage in Water Radiolysis using GPU

PubMed Central

Tian, Zhen; Jiang, Steve B.; Jia, Xun

2018-01-01

The accurate simulation of water radiolysis is an important step to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, the simulation of water radiolysis is highly time consuming, taking hours or even days to be completed by a conventional CPU processor. This time limitation hinders cell-level simulations for a number of research studies. We recently initiated efforts to develop gMicroMC, a GPU-based fast microscopic MC simulation package for water radiolysis. The first step of this project focused on accelerating the simulation of the chemical stage, the most time consuming stage in the entire water radiolysis process. A GPU-friendly parallelization strategy was designed to address the highly correlated many-body simulation problem caused by the mutual competitive chemical reactions between the radiolytic molecules. Two cases were tested, using a 750 keV electron and a 5 MeV proton incident in pure water, respectively. The time-dependent yields of all the radiolytic species during the chemical stage were used to evaluate the accuracy of the simulation. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, successfully completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2. PMID:28323637

Accelerated Monte Carlo simulation on the chemical stage in water radiolysis using GPU

NASA Astrophysics Data System (ADS)

Tian, Zhen; Jiang, Steve B.; Jia, Xun

2017-04-01

The accurate simulation of water radiolysis is an important step to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, the simulation of water radiolysis is highly time consuming, taking hours or even days to be completed by a conventional CPU processor. This time limitation hinders cell-level simulations for a number of research studies. We recently initiated efforts to develop gMicroMC, a GPU-based fast microscopic MC simulation package for water radiolysis. The first step of this project focused on accelerating the simulation of the chemical stage, the most time consuming stage in the entire water radiolysis process. A GPU-friendly parallelization strategy was designed to address the highly correlated many-body simulation problem caused by the mutual competitive chemical reactions between the radiolytic molecules. Two cases were tested, using a 750 keV electron and a 5 MeV proton incident in pure water, respectively. The time-dependent yields of all the radiolytic species during the chemical stage were used to evaluate the accuracy of the simulation. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, successfully completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2.

Accelerated Monte Carlo simulation on the chemical stage in water radiolysis using GPU.

PubMed

Tian, Zhen; Jiang, Steve B; Jia, Xun

2017-04-21

The accurate simulation of water radiolysis is an important step to understand the mechanisms of radiobiology and quantitatively test some hypotheses regarding radiobiological effects. However, the simulation of water radiolysis is highly time consuming, taking hours or even days to be completed by a conventional CPU processor. This time limitation hinders cell-level simulations for a number of research studies. We recently initiated efforts to develop gMicroMC, a GPU-based fast microscopic MC simulation package for water radiolysis. The first step of this project focused on accelerating the simulation of the chemical stage, the most time consuming stage in the entire water radiolysis process. A GPU-friendly parallelization strategy was designed to address the highly correlated many-body simulation problem caused by the mutual competitive chemical reactions between the radiolytic molecules. Two cases were tested, using a 750 keV electron and a 5 MeV proton incident in pure water, respectively. The time-dependent yields of all the radiolytic species during the chemical stage were used to evaluate the accuracy of the simulation. The relative differences between our simulation and the Geant4-DNA simulation were on average 5.3% and 4.4% for the two cases. Our package, executed on an Nvidia Titan black GPU card, successfully completed the chemical stage simulation of the two cases within 599.2 s and 489.0 s. As compared with Geant4-DNA that was executed on an Intel i7-5500U CPU processor and needed 28.6 h and 26.8 h for the two cases using a single CPU core, our package achieved a speed-up factor of 171.1-197.2.

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units.

PubMed

Igarashi, Jun; Shouno, Osamu; Fukai, Tomoki; Tsujino, Hiroshi

2011-11-01

Real-time simulation of a biologically realistic spiking neural network is necessary for evaluation of its capacity to interact with real environments. However, the real-time simulation of such a neural network is difficult due to its high computational costs that arise from two factors: (1) vast network size and (2) the complicated dynamics of biologically realistic neurons. In order to address these problems, mainly the latter, we chose to use general purpose computing on graphics processing units (GPGPUs) for simulation of such a neural network, taking advantage of the powerful computational capability of a graphics processing unit (GPU). As a target for real-time simulation, we used a model of the basal ganglia that has been developed according to electrophysiological and anatomical knowledge. The model consists of heterogeneous populations of 370 spiking model neurons, including computationally heavy conductance-based models, connected by 11,002 synapses. Simulation of the model has not yet been performed in real-time using a general computing server. By parallelization of the model on the NVIDIA Geforce GTX 280 GPU in data-parallel and task-parallel fashion, faster-than-real-time simulation was robustly realized with only one-third of the GPU's total computational resources. Furthermore, we used the GPU's full computational resources to perform faster-than-real-time simulation of three instances of the basal ganglia model; these instances consisted of 1100 neurons and 33,006 synapses and were synchronized at each calculation step. Finally, we developed software for simultaneous visualization of faster-than-real-time simulation output. These results suggest the potential power of GPGPU techniques in real-time simulation of realistic neural networks. Copyright © 2011 Elsevier Ltd. All rights reserved.

GPU Accelerated Ultrasonic Tomography Using Propagation and Back Propagation Method

DTIC Science & Technology

2015-09-28

the medical imaging field using GPUs has been done for many years. In [1], Copeland et al. used 2D images , obtained by X - ray projections, to...Index Terms— Medical Imaging , Ultrasonic Tomography, GPU, CUDA, Parallel Computing I. INTRODUCTION GRAPHIC Processing Units (GPUs) are computation... Imaging Algorithm The process of reconstructing images from ultrasonic infor- mation starts with the following acoustical wave equation: ∂2 ∂t2 u ( x

GPU-accelerated non-uniform fast Fourier transform-based compressive sensing spectral domain optical coherence tomography.

PubMed

Xu, Daguang; Huang, Yong; Kang, Jin U

2014-06-16

We implemented the graphics processing unit (GPU) accelerated compressive sensing (CS) non-uniform in k-space spectral domain optical coherence tomography (SD OCT). Kaiser-Bessel (KB) function and Gaussian function are used independently as the convolution kernel in the gridding-based non-uniform fast Fourier transform (NUFFT) algorithm with different oversampling ratios and kernel widths. Our implementation is compared with the GPU-accelerated modified non-uniform discrete Fourier transform (MNUDFT) matrix-based CS SD OCT and the GPU-accelerated fast Fourier transform (FFT)-based CS SD OCT. It was found that our implementation has comparable performance to the GPU-accelerated MNUDFT-based CS SD OCT in terms of image quality while providing more than 5 times speed enhancement. When compared to the GPU-accelerated FFT based-CS SD OCT, it shows smaller background noise and less side lobes while eliminating the need for the cumbersome k-space grid filling and the k-linear calibration procedure. Finally, we demonstrated that by using a conventional desktop computer architecture having three GPUs, real-time B-mode imaging can be obtained in excess of 30 fps for the GPU-accelerated NUFFT based CS SD OCT with frame size 2048(axial) × 1,000(lateral).

Optimizing Tensor Contraction Expressions for Hybrid CPU-GPU Execution

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Wenjing; Krishnamoorthy, Sriram; Villa, Oreste

2013-03-01

Tensor contractions are generalized multidimensional matrix multiplication operations that widely occur in quantum chemistry. Efficient execution of tensor contractions on Graphics Processing Units (GPUs) requires several challenges to be addressed, including index permutation and small dimension-sizes reducing thread block utilization. Moreover, to apply the same optimizations to various expressions, we need a code generation tool. In this paper, we present our approach to automatically generate CUDA code to execute tensor contractions on GPUs, including management of data movement between CPU and GPU. To evaluate our tool, GPU-enabled code is generated for the most expensive contractions in CCSD(T), a key coupledmore » cluster method, and incorporated into NWChem, a popular computational chemistry suite. For this method, we demonstrate speedup over a factor of 8.4 using one GPU (instead of one core per node) and over 2.6 when utilizing the entire system using hybrid CPU+GPU solution with 2 GPUs and 5 cores (instead of 7 cores per node). Finally, we analyze the implementation behavior on future GPU systems.« less

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

PubMed Central

Chen, Yaw-Chung

2015-01-01

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. PMID:26437335

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

PubMed

Lee, Chun-Liang; Lin, Yi-Shan; Chen, Yaw-Chung

2015-01-01

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.

Work stealing for GPU-accelerated parallel programs in a global address space framework: WORK STEALING ON GPU-ACCELERATED SYSTEMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arafat, Humayun; Dinan, James; Krishnamoorthy, Sriram

Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parallel programs executed on hybrid distributed-memory CPU-graphics processing unit (GPU) systems in a global-address space framework. We take into account the unique nature of the accelerator model employed by GPUs, the significant performance difference between GPU and CPU execution as a functionmore » of problem size, and the distinct CPU and GPU memory domains. We consider various alternatives in designing a distributed work stealing algorithm for CPU-GPU systems, while taking into account the impact of task distribution and data movement overheads. These strategies are evaluated using microbenchmarks that capture various execution configurations as well as the state-of-the-art CCSD(T) application module from the computational chemistry domain.« less

Communication: A reduced scaling J-engine based reformulation of SOS-MP2 using graphics processing units

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maurer, S. A.; Kussmann, J.; Ochsenfeld, C., E-mail: Christian.Ochsenfeld@cup.uni-muenchen.de

2014-08-07

We present a low-prefactor, cubically scaling scaled-opposite-spin second-order Møller-Plesset perturbation theory (SOS-MP2) method which is highly suitable for massively parallel architectures like graphics processing units (GPU). The scaling is reduced from O(N{sup 5}) to O(N{sup 3}) by a reformulation of the MP2-expression in the atomic orbital basis via Laplace transformation and the resolution-of-the-identity (RI) approximation of the integrals in combination with efficient sparse algebra for the 3-center integral transformation. In contrast to previous works that employ GPUs for post Hartree-Fock calculations, we do not simply employ GPU-based linear algebra libraries to accelerate the conventional algorithm. Instead, our reformulation allows tomore » replace the rate-determining contraction step with a modified J-engine algorithm, that has been proven to be highly efficient on GPUs. Thus, our SOS-MP2 scheme enables us to treat large molecular systems in an accurate and efficient manner on a single GPU-server.« less

Accelerating 3D Hall MHD Magnetosphere Simulations with Graphics Processing Units

NASA Astrophysics Data System (ADS)

Bard, C.; Dorelli, J.

2017-12-01

The resolution required to simulate planetary magnetospheres with Hall magnetohydrodynamics result in program sizes approaching several hundred million grid cells. These would take years to run on a single computational core and require hundreds or thousands of computational cores to complete in a reasonable time. However, this requires access to the largest supercomputers. Graphics processing units (GPUs) provide a viable alternative: one GPU can do the work of roughly 100 cores, bringing Hall MHD simulations of Ganymede within reach of modest GPU clusters ( 8 GPUs). We report our progress in developing a GPU-accelerated, three-dimensional Hall magnetohydrodynamic code and present Hall MHD simulation results for both Ganymede (run on 8 GPUs) and Mercury (56 GPUs). We benchmark our Ganymede simulation with previous results for the Galileo G8 flyby, namely that adding the Hall term to ideal MHD simulations changes the global convection pattern within the magnetosphere. Additionally, we present new results for the G1 flyby as well as initial results from Hall MHD simulations of Mercury and compare them with the corresponding ideal MHD runs.

Fast, multi-channel real-time processing of signals with microsecond latency using graphics processing units.

PubMed

Rath, N; Kato, S; Levesque, J P; Mauel, M E; Navratil, G A; Peng, Q

2014-04-01

Fast, digital signal processing (DSP) has many applications. Typical hardware options for performing DSP are field-programmable gate arrays (FPGAs), application-specific integrated DSP chips, or general purpose personal computer systems. This paper presents a novel DSP platform that has been developed for feedback control on the HBT-EP tokamak device. The system runs all signal processing exclusively on a Graphics Processing Unit (GPU) to achieve real-time performance with latencies below 8 μs. Signals are transferred into and out of the GPU using PCI Express peer-to-peer direct-memory-access transfers without involvement of the central processing unit or host memory. Tests were performed on the feedback control system of the HBT-EP tokamak using forty 16-bit floating point inputs and outputs each and a sampling rate of up to 250 kHz. Signals were digitized by a D-TACQ ACQ196 module, processing done on an NVIDIA GTX 580 GPU programmed in CUDA, and analog output was generated by D-TACQ AO32CPCI modules.

Real-time electroholography using a multiple-graphics processing unit cluster system with a single spatial light modulator and the InfiniBand network

NASA Astrophysics Data System (ADS)

Niwase, Hiroaki; Takada, Naoki; Araki, Hiromitsu; Maeda, Yuki; Fujiwara, Masato; Nakayama, Hirotaka; Kakue, Takashi; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2016-09-01

Parallel calculations of large-pixel-count computer-generated holograms (CGHs) are suitable for multiple-graphics processing unit (multi-GPU) cluster systems. However, it is not easy for a multi-GPU cluster system to accomplish fast CGH calculations when CGH transfers between PCs are required. In these cases, the CGH transfer between the PCs becomes a bottleneck. Usually, this problem occurs only in multi-GPU cluster systems with a single spatial light modulator. To overcome this problem, we propose a simple method using the InfiniBand network. The computational speed of the proposed method using 13 GPUs (NVIDIA GeForce GTX TITAN X) was more than 3000 times faster than that of a CPU (Intel Core i7 4770) when the number of three-dimensional (3-D) object points exceeded 20,480. In practice, we achieved ˜40 tera floating point operations per second (TFLOPS) when the number of 3-D object points exceeded 40,960. Our proposed method was able to reconstruct a real-time movie of a 3-D object comprising 95,949 points.

GPU-Accelerated Stony-Brook University 5-class Microphysics Scheme in WRF

NASA Astrophysics Data System (ADS)

Mielikainen, J.; Huang, B.; Huang, A.

2011-12-01

The Weather Research and Forecasting (WRF) model is a next-generation mesoscale numerical weather prediction system. Microphysics plays an important role in weather and climate prediction. Several bulk water microphysics schemes are available within the WRF, with different numbers of simulated hydrometeor classes and methods for estimating their size fall speeds, distributions and densities. Stony-Brook University scheme (SBU-YLIN) is a 5-class scheme with riming intensity predicted to account for mixed-phase processes. In the past few years, co-processing on Graphics Processing Units (GPUs) has been a disruptive technology in High Performance Computing (HPC). GPUs use the ever increasing transistor count for adding more processor cores. Therefore, GPUs are well suited for massively data parallel processing with high floating point arithmetic intensity. Thus, it is imperative to update legacy scientific applications to take advantage of this unprecedented increase in computing power. CUDA is an extension to the C programming language offering programming GPU's directly. It is designed so that its constructs allow for natural expression of data-level parallelism. A CUDA program is organized into two parts: a serial program running on the CPU and a CUDA kernel running on the GPU. The CUDA code consists of three computational phases: transmission of data into the global memory of the GPU, execution of the CUDA kernel, and transmission of results from the GPU into the memory of CPU. CUDA takes a bottom-up point of view of parallelism is which thread is an atomic unit of parallelism. Individual threads are part of groups called warps, within which every thread executes exactly the same sequence of instructions. To test SBU-YLIN, we used a CONtinental United States (CONUS) benchmark data set for 12 km resolution domain for October 24, 2001. A WRF domain is a geographic region of interest discretized into a 2-dimensional grid parallel to the ground. Each grid point has multiple levels, which correspond to various vertical heights in the atmosphere. The size of the CONUS 12 km domain is 433 x 308 horizontal grid points with 35 vertical levels. First, the entire SBU-YLIN Fortran code was rewritten in C in preparation of GPU accelerated version. After that, C code was verified against Fortran code for identical outputs. Default compiler options from WRF were used for gfortran and gcc compilers. The processing time for the original Fortran code is 12274 ms and 12893 ms for C version. The processing times for GPU implementation of SBU-YLIN microphysics scheme with I/O are 57.7 ms and 37.2 ms for 1 and 2 GPUs, respectively. The corresponding speedups are 213x and 330x compared to a Fortran implementation. Without I/O the speedup is 896x on 1 GPU. Obviously, ignoring I/O time speedup scales linearly with GPUs. Thus, 2 GPUs have a speedup of 1788x without I/O. Microphysics computation is just a small part of the whole WRF model. After having completely implemented WRF on GPU, the inputs for SBU-YLIN do not have to be transferred from CPU. Instead they are results of previous WRF modules. Therefore, the role of I/O is greatly diminished once all of WRF have been converted to run on GPUs. In the near future, we expect to have a WRF running completely on GPUs for a superior performance.

A computational approach to real-time image processing for serial time-encoded amplified microscopy

NASA Astrophysics Data System (ADS)

Oikawa, Minoru; Hiyama, Daisuke; Hirayama, Ryuji; Hasegawa, Satoki; Endo, Yutaka; Sugie, Takahisa; Tsumura, Norimichi; Kuroshima, Mai; Maki, Masanori; Okada, Genki; Lei, Cheng; Ozeki, Yasuyuki; Goda, Keisuke; Shimobaba, Tomoyoshi

2016-03-01

High-speed imaging is an indispensable technique, particularly for identifying or analyzing fast-moving objects. The serial time-encoded amplified microscopy (STEAM) technique was proposed to enable us to capture images with a frame rate 1,000 times faster than using conventional methods such as CCD (charge-coupled device) cameras. The application of this high-speed STEAM imaging technique to a real-time system, such as flow cytometry for a cell-sorting system, requires successively processing a large number of captured images with high throughput in real time. We are now developing a high-speed flow cytometer system including a STEAM camera. In this paper, we describe our approach to processing these large amounts of image data in real time. We use an analog-to-digital converter that has up to 7.0G samples/s and 8-bit resolution for capturing the output voltage signal that involves grayscale images from the STEAM camera. Therefore the direct data output from the STEAM camera generates 7.0G byte/s continuously. We provided a field-programmable gate array (FPGA) device as a digital signal pre-processor for image reconstruction and finding objects in a microfluidic channel with high data rates in real time. We also utilized graphics processing unit (GPU) devices for accelerating the calculation speed of identification of the reconstructed images. We built our prototype system, which including a STEAM camera, a FPGA device and a GPU device, and evaluated its performance in real-time identification of small particles (beads), as virtual biological cells, owing through a microfluidic channel.

Creation of parallel algorithms for the solution of problems of gas dynamics on multi-core computers and GPU

NASA Astrophysics Data System (ADS)

Rybakin, B.; Bogatencov, P.; Secrieru, G.; Iliuha, N.

2013-10-01

The paper deals with a parallel algorithm for calculations on multiprocessor computers and GPU accelerators. The calculations of shock waves interaction with low-density bubble results and the problem of the gas flow with the forces of gravity are presented. This algorithm combines a possibility to capture a high resolution of shock waves, the second-order accuracy for TVD schemes, and a possibility to observe a low-level diffusion of the advection scheme. Many complex problems of continuum mechanics are numerically solved on structured or unstructured grids. To improve the accuracy of the calculations is necessary to choose a sufficiently small grid (with a small cell size). This leads to the drawback of a substantial increase of computation time. Therefore, for the calculations of complex problems it is reasonable to use the method of Adaptive Mesh Refinement. That is, the grid refinement is performed only in the areas of interest of the structure, where, e.g., the shock waves are generated, or a complex geometry or other such features exist. Thus, the computing time is greatly reduced. In addition, the execution of the application on the resulting sequence of nested, decreasing nets can be parallelized. Proposed algorithm is based on the AMR method. Utilization of AMR method can significantly improve the resolution of the difference grid in areas of high interest, and from other side to accelerate the processes of the multi-dimensional problems calculating. Parallel algorithms of the analyzed difference models realized for the purpose of calculations on graphic processors using the CUDA technology [1].

Finite Element Optimization for Nondestructive Evaluation on a Graphics Processing Unit for Ground Vehicle Hull Inspection

DTIC Science & Technology

2013-08-22

4 cores, where the code may simultaneously run on the multiple cores or the graphics processing unit (or GPU – to be more specific on an NVIDIA ...allowed to get accurate crack shapes. DISCLAIMER Reference herein to any specific commercial company , product, process, or service by trade name

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

PubMed Central

de Paula, Lauro C. M.; Soares, Anderson S.; de Lima, Telma W.; Delbem, Alexandre C. B.; Coelho, Clarimar J.; Filho, Arlindo R. G.

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation. PMID:25493625

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

PubMed

de Paula, Lauro C M; Soares, Anderson S; de Lima, Telma W; Delbem, Alexandre C B; Coelho, Clarimar J; Filho, Arlindo R G

2014-01-01

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Accelerating electron tomography reconstruction algorithm ICON with GPU.

PubMed

Chen, Yu; Wang, Zihao; Zhang, Jingrong; Li, Lun; Wan, Xiaohua; Sun, Fei; Zhang, Fa

2017-01-01

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the "missing wedge" problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON's dependence on computing resource.

Protein-protein docking on hardware accelerators: comparison of GPU and MIC architectures

PubMed Central

2015-01-01

Background The hardware accelerators will provide solutions to computationally complex problems in bioinformatics fields. However, the effect of acceleration depends on the nature of the application, thus selection of an appropriate accelerator requires some consideration. Results In the present study, we compared the effects of acceleration using graphics processing unit (GPU) and many integrated core (MIC) on the speed of fast Fourier transform (FFT)-based protein-protein docking calculation. The GPU implementation performed the protein-protein docking calculations approximately five times faster than the MIC offload mode implementation. The MIC native mode implementation has the advantage in the implementation costs. However, the performance was worse with larger protein pairs because of memory limitations. Conclusion The results suggest that GPU is more suitable than MIC for accelerating FFT-based protein-protein docking applications. PMID:25707855

Validation of columnar CsI x-ray detector responses obtained with hybridMANTIS, a CPU-GPU Monte Carlo code for coupled x-ray, electron, and optical transport.

PubMed

Sharma, Diksha; Badano, Aldo

2013-03-01

hybridMANTIS is a Monte Carlo package for modeling indirect x-ray imagers using columnar geometry based on a hybrid concept that maximizes the utilization of available CPU and graphics processing unit processors in a workstation. The authors compare hybridMANTIS x-ray response simulations to previously published MANTIS and experimental data for four cesium iodide scintillator screens. These screens have a variety of reflective and absorptive surfaces with different thicknesses. The authors analyze hybridMANTIS results in terms of modulation transfer function and calculate the root mean square difference and Swank factors from simulated and experimental results. The comparison suggests that hybridMANTIS better matches the experimental data as compared to MANTIS, especially at high spatial frequencies and for the thicker screens. hybridMANTIS simulations are much faster than MANTIS with speed-ups up to 5260. hybridMANTIS is a useful tool for improved description and optimization of image acquisition stages in medical imaging systems and for modeling the forward problem in iterative reconstruction algorithms.

Large calculation of the flow over a hypersonic vehicle using a GPU

NASA Astrophysics Data System (ADS)

Elsen, Erich; LeGresley, Patrick; Darve, Eric

2008-12-01

Graphics processing units are capable of impressive computing performance up to 518 Gflops peak performance. Various groups have been using these processors for general purpose computing; most efforts have focussed on demonstrating relatively basic calculations, e.g. numerical linear algebra, or physical simulations for visualization purposes with limited accuracy. This paper describes the simulation of a hypersonic vehicle configuration with detailed geometry and accurate boundary conditions using the compressible Euler equations. To the authors' knowledge, this is the most sophisticated calculation of this kind in terms of complexity of the geometry, the physical model, the numerical methods employed, and the accuracy of the solution. The Navier-Stokes Stanford University Solver (NSSUS) was used for this purpose. NSSUS is a multi-block structured code with a provably stable and accurate numerical discretization which uses a vertex-based finite-difference method. A multi-grid scheme is used to accelerate the solution of the system. Based on a comparison of the Intel Core 2 Duo and NVIDIA 8800GTX, speed-ups of over 40× were demonstrated for simple test geometries and 20× for complex geometries.

Optimizing a mobile robot control system using GPU acceleration

NASA Astrophysics Data System (ADS)

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.

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

PubMed

Chou, Cheng-Ying; Dong, Yun; Hung, Yukai; Kao, Yu-Jiun; Wang, Weichung; Kao, Chien-Min; Chen, Chin-Tu

2012-01-01

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.

GPU computing with Kaczmarz’s and other iterative algorithms for linear systems

PubMed Central

Elble, Joseph M.; Sahinidis, Nikolaos V.; Vouzis, Panagiotis

2009-01-01

The graphics processing unit (GPU) is used to solve large linear systems derived from partial differential equations. The differential equations studied are strongly convection-dominated, of various sizes, and common to many fields, including computational fluid dynamics, heat transfer, and structural mechanics. The paper presents comparisons between GPU and CPU implementations of several well-known iterative methods, including Kaczmarz’s, Cimmino’s, component averaging, conjugate gradient normal residual (CGNR), symmetric successive overrelaxation-preconditioned conjugate gradient, and conjugate-gradient-accelerated component-averaged row projections (CARP-CG). Computations are preformed with dense as well as general banded systems. The results demonstrate that our GPU implementation outperforms CPU implementations of these algorithms, as well as previously studied parallel implementations on Linux clusters and shared memory systems. While the CGNR method had begun to fall out of favor for solving such problems, for the problems studied in this paper, the CGNR method implemented on the GPU performed better than the other methods, including a cluster implementation of the CARP-CG method. PMID:20526446

Efficient Execution of Microscopy Image Analysis on CPU, GPU, and MIC Equipped Cluster Systems.

PubMed

Andrade, G; Ferreira, R; Teodoro, George; Rocha, Leonardo; Saltz, Joel H; Kurc, Tahsin

2014-10-01

High performance computing is experiencing a major paradigm shift with the introduction of accelerators, such as graphics processing units (GPUs) and Intel Xeon Phi (MIC). These processors have made available a tremendous computing power at low cost, and are transforming machines into hybrid systems equipped with CPUs and accelerators. Although these systems can deliver a very high peak performance, making full use of its resources in real-world applications is a complex problem. Most current applications deployed to these machines are still being executed in a single processor, leaving other devices underutilized. In this paper we explore a scenario in which applications are composed of hierarchical data flow tasks which are allocated to nodes of a distributed memory machine in coarse-grain, but each of them may be composed of several finer-grain tasks which can be allocated to different devices within the node. We propose and implement novel performance aware scheduling techniques that can be used to allocate tasks to devices. We evaluate our techniques using a pathology image analysis application used to investigate brain cancer morphology, and our experimental evaluation shows that the proposed scheduling strategies significantly outperforms other efficient scheduling techniques, such as Heterogeneous Earliest Finish Time - HEFT, in cooperative executions using CPUs, GPUs, and MICs. We also experimentally show that our strategies are less sensitive to inaccuracy in the scheduling input data and that the performance gains are maintained as the application scales.

Efficient Execution of Microscopy Image Analysis on CPU, GPU, and MIC Equipped Cluster Systems

PubMed Central

Andrade, G.; Ferreira, R.; Teodoro, George; Rocha, Leonardo; Saltz, Joel H.; Kurc, Tahsin

2015-01-01

High performance computing is experiencing a major paradigm shift with the introduction of accelerators, such as graphics processing units (GPUs) and Intel Xeon Phi (MIC). These processors have made available a tremendous computing power at low cost, and are transforming machines into hybrid systems equipped with CPUs and accelerators. Although these systems can deliver a very high peak performance, making full use of its resources in real-world applications is a complex problem. Most current applications deployed to these machines are still being executed in a single processor, leaving other devices underutilized. In this paper we explore a scenario in which applications are composed of hierarchical data flow tasks which are allocated to nodes of a distributed memory machine in coarse-grain, but each of them may be composed of several finer-grain tasks which can be allocated to different devices within the node. We propose and implement novel performance aware scheduling techniques that can be used to allocate tasks to devices. We evaluate our techniques using a pathology image analysis application used to investigate brain cancer morphology, and our experimental evaluation shows that the proposed scheduling strategies significantly outperforms other efficient scheduling techniques, such as Heterogeneous Earliest Finish Time - HEFT, in cooperative executions using CPUs, GPUs, and MICs. We also experimentally show that our strategies are less sensitive to inaccuracy in the scheduling input data and that the performance gains are maintained as the application scales. PMID:26640423

Evaluating the effects of dam breach methodologies on Consequence Estimation through Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Kalyanapu, A. J.; Thames, B. A.

2013-12-01

Dam breach modeling often includes application of models that are sophisticated, yet computationally intensive to compute flood propagation at high temporal and spatial resolutions. This results in a significant need for computational capacity that requires development of newer flood models using multi-processor and graphics processing techniques. Recently, a comprehensive benchmark exercise titled the 12th Benchmark Workshop on Numerical Analysis of Dams, is organized by the International Commission on Large Dams (ICOLD) to evaluate the performance of these various tools used for dam break risk assessment. The ICOLD workshop is focused on estimating the consequences of failure of a hypothetical dam near a hypothetical populated area with complex demographics, and economic activity. The current study uses this hypothetical case study and focuses on evaluating the effects of dam breach methodologies on consequence estimation and analysis. The current study uses ICOLD hypothetical data including the topography, dam geometric and construction information, land use/land cover data along with socio-economic and demographic data. The objective of this study is to evaluate impacts of using four different dam breach methods on the consequence estimates used in the risk assessments. The four methodologies used are: i) Froehlich (1995), ii) MacDonald and Langridge-Monopolis 1984 (MLM), iii) Von Thun and Gillete 1990 (VTG), and iv) Froehlich (2008). To achieve this objective, three different modeling components were used. First, using the HEC-RAS v.4.1, dam breach discharge hydrographs are developed. These hydrographs are then provided as flow inputs into a two dimensional flood model named Flood2D-GPU, which leverages the computer's graphics card for much improved computational capabilities of the model input. Lastly, outputs from Flood2D-GPU, including inundated areas, depth grids, velocity grids, and flood wave arrival time grids, are input into HEC-FIA, which provides the consequence assessment for the solution to the problem statement. For the four breach methodologies, a sensitivity analysis of four breach parameters, breach side slope (SS), breach width (Wb), breach invert elevation (Elb), and time of failure (tf), is conducted. Up to, 68 simulations are computed to produce breach hydrographs in HEC-RAS for input into Flood2D-GPU. The Flood2D-GPU simulation results were then post-processed in HEC-FIA to evaluate: Total Population at Risk (PAR), 14-yr and Under PAR (PAR14-), 65-yr and Over PAR (PAR65+), Loss of Life (LOL) and Direct Economic Impact (DEI). The MLM approach resulted in wide variability in simulated minimum and maximum values of PAR, PAR 65+ and LOL estimates. For PAR14- and DEI, Froehlich (1995) resulted in lower values while MLM resulted in higher estimates. This preliminary study demonstrated the relative performance of four commonly used dam breach methodologies and their impacts on consequence estimation.

Accelerating numerical solution of stochastic differential equations with CUDA

NASA Astrophysics Data System (ADS)

Januszewski, M.; Kostur, M.

2010-01-01

Numerical integration of stochastic differential equations is commonly used in many branches of science. In this paper we present how to accelerate this kind of numerical calculations with popular NVIDIA Graphics Processing Units using the CUDA programming environment. We address general aspects of numerical programming on stream processors and illustrate them by two examples: the noisy phase dynamics in a Josephson junction and the noisy Kuramoto model. In presented cases the measured speedup can be as high as 675× compared to a typical CPU, which corresponds to several billion integration steps per second. This means that calculations which took weeks can now be completed in less than one hour. This brings stochastic simulation to a completely new level, opening for research a whole new range of problems which can now be solved interactively. Program summaryProgram title: SDE Catalogue identifier: AEFG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEFG_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Gnu GPL v3 No. of lines in distributed program, including test data, etc.: 978 No. of bytes in distributed program, including test data, etc.: 5905 Distribution format: tar.gz Programming language: CUDA C Computer: any system with a CUDA-compatible GPU Operating system: Linux RAM: 64 MB of GPU memory Classification: 4.3 External routines: The program requires the NVIDIA CUDA Toolkit Version 2.0 or newer and the GNU Scientific Library v1.0 or newer. Optionally gnuplot is recommended for quick visualization of the results. Nature of problem: Direct numerical integration of stochastic differential equations is a computationally intensive problem, due to the necessity of calculating multiple independent realizations of the system. We exploit the inherent parallelism of this problem and perform the calculations on GPUs using the CUDA programming environment. The GPU's ability to execute hundreds of threads simultaneously makes it possible to speed up the computation by over two orders of magnitude, compared to a typical modern CPU. Solution method: The stochastic Runge-Kutta method of the second order is applied to integrate the equation of motion. Ensemble-averaged quantities of interest are obtained through averaging over multiple independent realizations of the system. Unusual features: The numerical solution of the stochastic differential equations in question is performed on a GPU using the CUDA environment. Running time: < 1 minute

Architectures for single-chip image computing

NASA Astrophysics Data System (ADS)

Gove, Robert J.

1992-04-01

This paper will focus on the architectures of VLSI programmable processing components for image computing applications. TI, the maker of industry-leading RISC, DSP, and graphics components, has developed an architecture for a new-generation of image processors capable of implementing a plurality of image, graphics, video, and audio computing functions. We will show that the use of a single-chip heterogeneous MIMD parallel architecture best suits this class of processors--those which will dominate the desktop multimedia, document imaging, computer graphics, and visualization systems of this decade.

Exact diagonalization of quantum lattice models on coprocessors

NASA Astrophysics Data System (ADS)

Siro, T.; Harju, A.

2016-10-01

We implement the Lanczos algorithm on an Intel Xeon Phi coprocessor and compare its performance to a multi-core Intel Xeon CPU and an NVIDIA graphics processor. The Xeon and the Xeon Phi are parallelized with OpenMP and the graphics processor is programmed with CUDA. The performance is evaluated by measuring the execution time of a single step in the Lanczos algorithm. We study two quantum lattice models with different particle numbers, and conclude that for small systems, the multi-core CPU is the fastest platform, while for large systems, the graphics processor is the clear winner, reaching speedups of up to 7.6 compared to the CPU. The Xeon Phi outperforms the CPU with sufficiently large particle number, reaching a speedup of 2.5.

Blind detection of giant pulses: GPU implementation

NASA Astrophysics Data System (ADS)

Ait-Allal, Dalal; Weber, Rodolphe; Dumez-Viou, Cédric; Cognard, Ismael; Theureau, Gilles

2012-01-01

Radio astronomical pulsar observations require specific instrumentation and dedicated signal processing to cope with the dispersion caused by the interstellar medium. Moreover, the quality of observations can be limited by radio frequency interference (RFI) generated by Telecommunications activity. This article presents the innovative pulsar instrumentation based on graphical processing units (GPU) which has been designed at the Nançay Radio Astronomical Observatory. In addition, for giant pulsar search, we propose a new approach which combines a hardware-efficient search method and some RFI mitigation capabilities. Although this approach is less sensitive than the classical approach, its advantage is that no a priori information on the pulsar parameters is required. The validation of a GPU implementation is under way.

Convolution of large 3D images on GPU and its decomposition

NASA Astrophysics Data System (ADS)

Karas, Pavel; Svoboda, David

2011-12-01

In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.

The gputools package enables GPU computing in R.

PubMed

Buckner, Joshua; Wilson, Justin; Seligman, Mark; Athey, Brian; Watson, Stanley; Meng, Fan

2010-01-01

By default, the R statistical environment does not make use of parallelism. Researchers may resort to expensive solutions such as cluster hardware for large analysis tasks. Graphics processing units (GPUs) provide an inexpensive and computationally powerful alternative. Using R and the CUDA toolkit from Nvidia, we have implemented several functions commonly used in microarray gene expression analysis for GPU-equipped computers. R users can take advantage of the better performance provided by an Nvidia GPU. The package is available from CRAN, the R project's repository of packages, at http://cran.r-project.org/web/packages/gputools More information about our gputools R package is available at http://brainarray.mbni.med.umich.edu/brainarray/Rgpgpu

Hardware Acceleration of Sparse Cognitive Algorithms

DTIC Science & Technology

2016-05-01

Processor in Memory (PiM) extensions and a 65 nm ASIC version. They were compared against a 28 nm GPU baseline using the KTH video action recognition...30 Table 17. Memory Requirement of Proposed ASIC...for improvement of performance per unit of power for customized implementations of the Sparsey and Numenta Hierarchical Temporal Memory (HTM

Investigation of Large Scale Cortical Models on Clustered Multi-Core Processors

DTIC Science & Technology

2013-02-01

with the bias node ( gray ) denoted as ww and the weights associated with the remaining first layer nodes (black) denoted as W. In forming the overall...Implementation of RBF network on GPU Platform 3.5.1 The Cholesky decomposition algorithm We need to invert the matrix multiplication GTG to

Graphics processing unit-assisted lossless decompression

DOEpatents

Loughry, Thomas A.

2016-04-12

Systems and methods for decompressing compressed data that has been compressed by way of a lossless compression algorithm are described herein. In a general embodiment, a graphics processing unit (GPU) is programmed to receive compressed data packets and decompress such packets in parallel. The compressed data packets are compressed representations of an image, and the lossless compression algorithm is a Rice compression algorithm.

Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

PubMed

Shi, Yulin; Veidenbaum, Alexander V; Nicolau, Alex; Xu, Xiangmin

2015-01-15

Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. Copyright © 2014 Elsevier B.V. All rights reserved.

Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

PubMed Central

Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

2014-01-01

Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

DOE PAGES

Bridges, Robert A.; Imam, Neena; Mintz, Tiffany M.

2016-09-01

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to powermore » use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.« less

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

NASA Astrophysics Data System (ADS)

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 prompt gamma ray imaging from boron neutron capture therapy.

PubMed

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

2015-01-01

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. 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. 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). 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.

Fast distributed large-pixel-count hologram computation using a GPU cluster.

PubMed

Pan, Yuechao; Xu, Xuewu; Liang, Xinan

2013-09-10

Large-pixel-count holograms are one essential part for big size holographic three-dimensional (3D) display, but the generation of such holograms is computationally demanding. In order to address this issue, we have built a graphics processing unit (GPU) cluster with 32.5 Tflop/s computing power and implemented distributed hologram computation on it with speed improvement techniques, such as shared memory on GPU, GPU level adaptive load balancing, and node level load distribution. Using these speed improvement techniques on the GPU cluster, we have achieved 71.4 times computation speed increase for 186M-pixel holograms. Furthermore, we have used the approaches of diffraction limits and subdivision of holograms to overcome the GPU memory limit in computing large-pixel-count holograms. 745M-pixel and 1.80G-pixel holograms were computed in 343 and 3326 s, respectively, for more than 2 million object points with RGB colors. Color 3D objects with 1.02M points were successfully reconstructed from 186M-pixel hologram computed in 8.82 s with all the above three speed improvement techniques. It is shown that distributed hologram computation using a GPU cluster is a promising approach to increase the computation speed of large-pixel-count holograms for large size holographic display.

Understanding GPU Power. A Survey of Profiling, Modeling, and Simulation Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bridges, Robert A.; Imam, Neena; Mintz, Tiffany M.

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high throughput applications.Though GPUs consume large amounts of power, their use for high throughput applications facilitate state-of-the-art energy efficiency and performance. Consequently, continued development relies on understanding their power consumption. Our work is a survey of GPU power modeling and profiling methods with increased detail on noteworthy efforts. Moreover, as direct measurement of GPU power is necessary for model evaluation and parameter initiation, internal and external power sensors are discussed. Hardware counters, which are low-level tallies of hardware events, share strong correlation to powermore » use and performance. Statistical correlation between power and performance counters has yielded worthwhile GPU power models, yet the complexity inherent to GPU architectures presents new hurdles for power modeling. Developments and challenges of counter-based GPU power modeling is discussed. Often building on the counter-based models, research efforts for GPU power simulation, which make power predictions from input code and hardware knowledge, provide opportunities for optimization in programming or architectural design. Noteworthy strides in power simulations for GPUs are included along with their performance or functional simulator counterparts when appropriate. Lastly, possible directions for future research are discussed.« less

Gpu Implementation of a Viscous Flow Solver on Unstructured Grids

NASA Astrophysics Data System (ADS)

Xu, Tianhao; Chen, Long

2016-06-01

Graphics processing units have gained popularities in scientific computing over past several years due to their outstanding parallel computing capability. Computational fluid dynamics applications involve large amounts of calculations, therefore a latest GPU card is preferable of which the peak computing performance and memory bandwidth are much better than a contemporary high-end CPU. We herein focus on the detailed implementation of our GPU targeting Reynolds-averaged Navier-Stokes equations solver based on finite-volume method. The solver employs a vertex-centered scheme on unstructured grids for the sake of being capable of handling complex topologies. Multiple optimizations are carried out to improve the memory accessing performance and kernel utilization. Both steady and unsteady flow simulation cases are carried out using explicit Runge-Kutta scheme. The solver with GPU acceleration in this paper is demonstrated to have competitive advantages over the CPU targeting one.

Medical image processing on the GPU - past, present and future.

PubMed

Eklund, Anders; Dufort, Paul; Forsberg, Daniel; LaConte, Stephen M

2013-12-01

Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges. Copyright © 2013 Elsevier B.V. All rights reserved.

SU-E-T-493: Accelerated Monte Carlo Methods for Photon Dosimetry Using a Dual-GPU System and CUDA.

PubMed

Liu, T; Ding, A; Xu, X

2012-06-01

To develop a Graphics Processing Unit (GPU) based Monte Carlo (MC) code that accelerates dose calculations on a dual-GPU system. We simulated a clinical case of prostate cancer treatment. A voxelized abdomen phantom derived from 120 CT slices was used containing 218×126×60 voxels, and a GE LightSpeed 16-MDCT scanner was modeled. A CPU version of the MC code was first developed in C++ and tested on Intel Xeon X5660 2.8GHz CPU, then it was translated into GPU version using CUDA C 4.1 and run on a dual Tesla m 2 090 GPU system. The code was featured with automatic assignment of simulation task to multiple GPUs, as well as accurate calculation of energy- and material- dependent cross-sections. Double-precision floating point format was used for accuracy. Doses to the rectum, prostate, bladder and femoral heads were calculated. When running on a single GPU, the MC GPU code was found to be ×19 times faster than the CPU code and ×42 times faster than MCNPX. These speedup factors were doubled on the dual-GPU system. The dose Result was benchmarked against MCNPX and a maximum difference of 1% was observed when the relative error is kept below 0.1%. A GPU-based MC code was developed for dose calculations using detailed patient and CT scanner models. Efficiency and accuracy were both guaranteed in this code. Scalability of the code was confirmed on the dual-GPU system. © 2012 American Association of Physicists in Medicine.

TU-AB-BRC-09: Fast Dose-Averaged LET and Biological Dose Calculations for Proton Therapy Using Graphics Cards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wan, H; Tseung, Chan; Beltran, C

Purpose: To demonstrate fast and accurate Monte Carlo (MC) calculations of proton dose-averaged linear energy transfer (LETd) and biological dose (BD) on a Graphics Processing Unit (GPU) card. Methods: A previously validated GPU-based MC simulation of proton transport was used to rapidly generate LETd distributions for proton treatment plans. Since this MC handles proton-nuclei interactions on an event-by-event using a Bertini intranuclear cascade-evaporation model, secondary protons were taken into account. The smaller contributions of secondary neutrons and recoil nuclei were ignored. Recent work has shown that LETd values are sensitive to the scoring method. The GPU-based LETd calculations were verifiedmore » by comparing with a TOPAS custom scorer that uses tabulated stopping powers, following recommendations by other authors. Comparisons were made for prostate and head-and-neck patients. A python script is used to convert the MC-generated LETd distributions to BD using a variety of published linear quadratic models, and to export the BD in DICOM format for subsequent evaluation. Results: Very good agreement is obtained between TOPAS and our GPU MC. Given a complex head-and-neck plan with 1 mm voxel spacing, the physical dose, LETd and BD calculations for 10{sup 8} proton histories can be completed in ∼5 minutes using a NVIDIA Titan X card. The rapid turnover means that MC feedback can be obtained on dosimetric plan accuracy as well as BD hotspot locations, particularly in regards to their proximity to critical structures. In our institution the GPU MC-generated dose, LETd and BD maps are used to assess plan quality for all patients undergoing treatment. Conclusion: Fast and accurate MC-based LETd calculations can be performed on the GPU. The resulting BD maps provide valuable feedback during treatment plan review. Partially funded by Varian Medical Systems.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jia, X; Folkerts, M; Shi, F

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 andmore » 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.« less

Graphics Processing Unit-Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations.

PubMed

Tokuda, Junichi; Plishker, William; Torabi, Meysam; Olubiyi, Olutayo I; Zaki, George; Tatli, Servet; Silverman, Stuart G; Shekher, Raj; Hata, Nobuhiko

2015-06-01

Accuracy and speed are essential for the intraprocedural nonrigid magnetic resonance (MR) to computed tomography (CT) image registration in the assessment of tumor margins during CT-guided liver tumor ablations. Although both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique on the basis of volume subdivision with hardware acceleration using a graphics processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice similarity coefficient [DSC] and 95% Hausdorff distance [HD]) and total processing time including contouring of ROIs and computation were compared using a paired Student t test. Accuracies of the GPU-accelerated registrations and B-spline registrations, respectively, were 88.3 ± 3.7% versus 89.3 ± 4.9% (P = .41) for DSC and 13.1 ± 5.2 versus 11.4 ± 6.3 mm (P = .15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 versus 557 ± 116 seconds (P < .000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (P = .71). The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

DspaceOgre 3D Graphics Visualization Tool

NASA Technical Reports Server (NTRS)

Jain, Abhinandan; Myin, Steven; Pomerantz, Marc I.

2011-01-01

This general-purpose 3D graphics visualization C++ tool is designed for visualization of simulation and analysis data for articulated mechanisms. Examples of such systems are vehicles, robotic arms, biomechanics models, and biomolecular structures. DspaceOgre builds upon the open-source Ogre3D graphics visualization library. It provides additional classes to support the management of complex scenes involving multiple viewpoints and different scene groups, and can be used as a remote graphics server. This software provides improved support for adding programs at the graphics processing unit (GPU) level for improved performance. It also improves upon the messaging interface it exposes for use as a visualization server.

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

PubMed

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.

Thread scheduling for GPU-based OPC simulation on multi-thread

NASA Astrophysics Data System (ADS)

Lee, Heejun; Kim, Sangwook; Hong, Jisuk; Lee, Sooryong; Han, Hwansoo

2018-03-01

As semiconductor product development based on shrinkage continues, the accuracy and difficulty required for the model based optical proximity correction (MBOPC) is increasing. OPC simulation time, which is the most timeconsuming part of MBOPC, is rapidly increasing due to high pattern density in a layout and complex OPC model. To reduce OPC simulation time, we attempt to apply graphic processing unit (GPU) to MBOPC because OPC process is good to be programmed in parallel. We address some issues that may typically happen during GPU-based OPC simulation in multi thread system, such as "out of memory" and "GPU idle time". To overcome these problems, we propose a thread scheduling method, which manages OPC jobs in multiple threads in such a way that simulations jobs from multiple threads are alternatively executed on GPU while correction jobs are executed at the same time in each CPU cores. It was observed that the amount of GPU peak memory usage decreases by up to 35%, and MBOPC runtime also decreases by 4%. In cases where out of memory issues occur in a multi-threaded environment, the thread scheduler was used to improve MBOPC runtime up to 23%.

High Performance GPU-Based Fourier Volume Rendering.

PubMed

Abdellah, Marwan; Eldeib, Ayman; Sharawi, Amr

2015-01-01

Fourier volume rendering (FVR) is a significant visualization technique that has been used widely in digital radiography. As a result of its (N (2)log⁡N) time complexity, it provides a faster alternative to spatial domain volume rendering algorithms that are (N (3)) computationally complex. Relying on the Fourier projection-slice theorem, this technique operates on the spectral representation of a 3D volume instead of processing its spatial representation to generate attenuation-only projections that look like X-ray radiographs. Due to the rapid evolution of its underlying architecture, the graphics processing unit (GPU) became an attractive competent platform that can deliver giant computational raw power compared to the central processing unit (CPU) on a per-dollar-basis. The introduction of the compute unified device architecture (CUDA) technology enables embarrassingly-parallel algorithms to run efficiently on CUDA-capable GPU architectures. In this work, a high performance GPU-accelerated implementation of the FVR pipeline on CUDA-enabled GPUs is presented. This proposed implementation can achieve a speed-up of 117x compared to a single-threaded hybrid implementation that uses the CPU and GPU together by taking advantage of executing the rendering pipeline entirely on recent GPU architectures.

Real-time computation of parameter fitting and image reconstruction using graphical processing units

NASA Astrophysics Data System (ADS)

Locans, Uldis; Adelmann, Andreas; Suter, Andreas; Fischer, Jannis; Lustermann, Werner; Dissertori, Günther; Wang, Qiulin

2017-06-01

In recent years graphical processing units (GPUs) have become a powerful tool in scientific computing. Their potential to speed up highly parallel applications brings the power of high performance computing to a wider range of users. However, programming these devices and integrating their use in existing applications is still a challenging task. In this paper we examined the potential of GPUs for two different applications. The first application, created at Paul Scherrer Institut (PSI), is used for parameter fitting during data analysis of μSR (muon spin rotation, relaxation and resonance) experiments. The second application, developed at ETH, is used for PET (Positron Emission Tomography) image reconstruction and analysis. Applications currently in use were examined to identify parts of the algorithms in need of optimization. Efficient GPU kernels were created in order to allow applications to use a GPU, to speed up the previously identified parts. Benchmarking tests were performed in order to measure the achieved speedup. During this work, we focused on single GPU systems to show that real time data analysis of these problems can be achieved without the need for large computing clusters. The results show that the currently used application for parameter fitting, which uses OpenMP to parallelize calculations over multiple CPU cores, can be accelerated around 40 times through the use of a GPU. The speedup may vary depending on the size and complexity of the problem. For PET image analysis, the obtained speedups of the GPU version were more than × 40 larger compared to a single core CPU implementation. The achieved results show that it is possible to improve the execution time by orders of magnitude.

Optimized Laplacian image sharpening algorithm based on graphic processing unit

NASA Astrophysics Data System (ADS)

Ma, Tinghuai; Li, Lu; Ji, Sai; Wang, Xin; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah

2014-12-01

In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.

Real-Space Density Functional Theory on Graphical Processing Units: Computational Approach and Comparison to Gaussian Basis Set Methods.

PubMed

Andrade, Xavier; Aspuru-Guzik, Alán

2013-10-08

We discuss the application of graphical processing units (GPUs) to accelerate real-space density functional theory (DFT) calculations. To make our implementation efficient, we have developed a scheme to expose the data parallelism available in the DFT approach; this is applied to the different procedures required for a real-space DFT calculation. We present results for current-generation GPUs from AMD and Nvidia, which show that our scheme, implemented in the free code Octopus, can reach a sustained performance of up to 90 GFlops for a single GPU, representing a significant speed-up when compared to the CPU version of the code. Moreover, for some systems, our implementation can outperform a GPU Gaussian basis set code, showing that the real-space approach is a competitive alternative for DFT simulations on GPUs.

Viscoelastic Finite Difference Modeling Using Graphics Processing Units

NASA Astrophysics Data System (ADS)

Fabien-Ouellet, G.; Gloaguen, E.; Giroux, B.

2014-12-01

Full waveform seismic modeling requires a huge amount of computing power that still challenges today's technology. This limits the applicability of powerful processing approaches in seismic exploration like full-waveform inversion. This paper explores the use of Graphics Processing Units (GPU) to compute a time based finite-difference solution to the viscoelastic wave equation. The aim is to investigate whether the adoption of the GPU technology is susceptible to reduce significantly the computing time of simulations. The code presented herein is based on the freely accessible software of Bohlen (2002) in 2D provided under a General Public License (GNU) licence. This implementation is based on a second order centred differences scheme to approximate time differences and staggered grid schemes with centred difference of order 2, 4, 6, 8, and 12 for spatial derivatives. The code is fully parallel and is written using the Message Passing Interface (MPI), and it thus supports simulations of vast seismic models on a cluster of CPUs. To port the code from Bohlen (2002) on GPUs, the OpenCl framework was chosen for its ability to work on both CPUs and GPUs and its adoption by most of GPU manufacturers. In our implementation, OpenCL works in conjunction with MPI, which allows computations on a cluster of GPU for large-scale model simulations. We tested our code for model sizes between 1002 and 60002 elements. Comparison shows a decrease in computation time of more than two orders of magnitude between the GPU implementation run on a AMD Radeon HD 7950 and the CPU implementation run on a 2.26 GHz Intel Xeon Quad-Core. The speed-up varies depending on the order of the finite difference approximation and generally increases for higher orders. Increasing speed-ups are also obtained for increasing model size, which can be explained by kernel overheads and delays introduced by memory transfers to and from the GPU through the PCI-E bus. Those tests indicate that the GPU memory size and the slow memory transfers are the limiting factors of our GPU implementation. Those results show the benefits of using GPUs instead of CPUs for time based finite-difference seismic simulations. The reductions in computation time and in hardware costs are significant and open the door for new approaches in seismic inversion.

Vector generator scan converter

DOEpatents

Moore, James M.; Leighton, James F.

1990-01-01

High printing speeds for graphics data are achieved with a laser printer by transmitting compressed graphics data from a main processor over an I/O (input/output) channel to a vector generator scan converter which reconstructs a full graphics image for input to the laser printer through a raster data input port. The vector generator scan converter includes a microprocessor with associated microcode memory containing a microcode instruction set, a working memory for storing compressed data, vector generator hardward for drawing a full graphic image from vector parameters calculated by the microprocessor, image buffer memory for storing the reconstructed graphics image and an output scanner for reading the graphics image data and inputting the data to the printer. The vector generator scan converter eliminates the bottleneck created by the I/O channel for transmitting graphics data from the main processor to the laser printer, and increases printer speed up to thirty fold.

Vector generator scan converter

DOEpatents

Moore, J.M.; Leighton, J.F.

1988-02-05

High printing speeds for graphics data are achieved with a laser printer by transmitting compressed graphics data from a main processor over an I/O channel to a vector generator scan converter which reconstructs a full graphics image for input to the laser printer through a raster data input port. The vector generator scan converter includes a microprocessor with associated microcode memory containing a microcode instruction set, a working memory for storing compressed data, vector generator hardware for drawing a full graphic image from vector parameters calculated by the microprocessor, image buffer memory for storing the reconstructed graphics image and an output scanner for reading the graphics image data and inputting the data to the printer. The vector generator scan converter eliminates the bottleneck created by the I/O channel for transmitting graphics data from the main processor to the laser printer, and increases printer speed up to thirty fold. 7 figs.

Fast data preprocessing with Graphics Processing Units for inverse problem solving in light-scattering measurements

NASA Astrophysics Data System (ADS)

Derkachov, G.; Jakubczyk, T.; Jakubczyk, D.; Archer, J.; Woźniak, M.

2017-07-01

Utilising Compute Unified Device Architecture (CUDA) platform for Graphics Processing Units (GPUs) enables significant reduction of computation time at a moderate cost, by means of parallel computing. In the paper [Jakubczyk et al., Opto-Electron. Rev., 2016] we reported using GPU for Mie scattering inverse problem solving (up to 800-fold speed-up). Here we report the development of two subroutines utilising GPU at data preprocessing stages for the inversion procedure: (i) A subroutine, based on ray tracing, for finding spherical aberration correction function. (ii) A subroutine performing the conversion of an image to a 1D distribution of light intensity versus azimuth angle (i.e. scattering diagram), fed from a movie-reading CPU subroutine running in parallel. All subroutines are incorporated in PikeReader application, which we make available on GitHub repository. PikeReader returns a sequence of intensity distributions versus a common azimuth angle vector, corresponding to the recorded movie. We obtained an overall ∼ 400 -fold speed-up of calculations at data preprocessing stages using CUDA codes running on GPU in comparison to single thread MATLAB-only code running on CPU.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pu, Hung-Yi; Yun, Kiyun; Yoon, Suk-Jin

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 integrationmore » 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.« less

Kinematic modelling of disc galaxies using graphics processing units

NASA Astrophysics Data System (ADS)

Bekiaris, G.; Glazebrook, K.; Fluke, C. J.; Abraham, R.

2016-01-01

With large-scale integral field spectroscopy (IFS) surveys of thousands of galaxies currently under-way or planned, the astronomical community is in need of methods, techniques and tools that will allow the analysis of huge amounts of data. We focus on the kinematic modelling of disc galaxies and investigate the potential use of massively parallel architectures, such as the graphics processing unit (GPU), as an accelerator for the computationally expensive model-fitting procedure. We review the algorithms involved in model-fitting and evaluate their suitability for GPU implementation. We employ different optimization techniques, including the Levenberg-Marquardt and nested sampling algorithms, but also a naive brute-force approach based on nested grids. We find that the GPU can accelerate the model-fitting procedure up to a factor of ˜100 when compared to a single-threaded CPU, and up to a factor of ˜10 when compared to a multithreaded dual CPU configuration. Our method's accuracy, precision and robustness are assessed by successfully recovering the kinematic properties of simulated data, and also by verifying the kinematic modelling results of galaxies from the GHASP and DYNAMO surveys as found in the literature. The resulting GBKFIT code is available for download from: http://supercomputing.swin.edu.au/gbkfit.

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

NASA Astrophysics Data System (ADS)

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.

Simulating spin models on GPU

NASA Astrophysics Data System (ADS)

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.

Interactive brain shift compensation using GPU based programming

NASA Astrophysics Data System (ADS)

van der Steen, Sander; Noordmans, Herke Jan; Verdaasdonk, Rudolf

2009-02-01

Processing large images files or real-time video streams requires intense computational power. Driven by the gaming industry, the processing power of graphic process units (GPUs) has increased significantly. With the pixel shader model 4.0 the GPU can be used for image processing 10x faster than the CPU. Dedicated software was developed to deform 3D MR and CT image sets for real-time brain shift correction during navigated neurosurgery using landmarks or cortical surface traces defined by the navigation pointer. Feedback was given using orthogonal slices and an interactively raytraced 3D brain image. GPU based programming enables real-time processing of high definition image datasets and various applications can be developed in medicine, optics and image sciences.

GPU-accelerated computational tool for studying the effectiveness of asteroid disruption techniques

NASA Astrophysics Data System (ADS)

Zimmerman, Ben J.; Wie, Bong

2016-10-01

This paper presents the development of a new Graphics Processing Unit (GPU) accelerated computational tool for asteroid disruption techniques. Numerical simulations are completed using the high-order spectral difference (SD) method. Due to the compact nature of the SD method, it is well suited for implementation with the GPU architecture, hence solutions are generated at orders of magnitude faster than the Central Processing Unit (CPU) counterpart. A multiphase model integrated with the SD method is introduced, and several asteroid disruption simulations are conducted, including kinetic-energy impactors, multi-kinetic energy impactor systems, and nuclear options. Results illustrate the benefits of using multi-kinetic energy impactor systems when compared to a single impactor system. In addition, the effectiveness of nuclear options is observed.

GPU accelerated FDTD solver and its application in MRI.

PubMed

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.

Data assimilation using a GPU accelerated path integral Monte Carlo approach

NASA Astrophysics Data System (ADS)

Quinn, John C.; Abarbanel, Henry D. I.

2011-09-01

The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a graphics processing unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 300, compared to an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.

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

NASA Astrophysics Data System (ADS)

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.

Work stealing for GPU-accelerated parallel programs in a global address space framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arafat, Humayun; Dinan, James; Krishnamoorthy, Sriram

Task parallelism is an attractive approach to automatically load balance the computation in a parallel system and adapt to dynamism exhibited by parallel systems. Exploiting task parallelism through work stealing has been extensively studied in shared and distributed-memory contexts. In this paper, we study the design of a system that uses work stealing for dynamic load balancing of task-parallel programs executed on hybrid distributed-memory CPU-graphics processing unit (GPU) systems in a global-address space framework. We take into account the unique nature of the accelerator model employed by GPUs, the significant performance difference between GPU and CPU execution as a functionmore » of problem size, and the distinct CPU and GPU memory domains. We consider various alternatives in designing a distributed work stealing algorithm for CPU-GPU systems, while taking into account the impact of task distribution and data movement overheads. These strategies are evaluated using microbenchmarks that capture various execution configurations as well as the state-of-the-art CCSD(T) application module from the computational chemistry domain« less

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

PubMed

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.

Performance evaluation for volumetric segmentation of multiple sclerosis lesions using MATLAB and computing engine in the graphical processing unit (GPU)

NASA Astrophysics Data System (ADS)

Le, Anh H.; Park, Young W.; Ma, Kevin; Jacobs, Colin; Liu, Brent J.

2010-03-01

Multiple Sclerosis (MS) is a progressive neurological disease affecting myelin pathways in the brain. Multiple lesions in the white matter can cause paralysis and severe motor disabilities of the affected patient. To solve the issue of inconsistency and user-dependency in manual lesion measurement of MRI, we have proposed a 3-D automated lesion quantification algorithm to enable objective and efficient lesion volume tracking. The computer-aided detection (CAD) of MS, written in MATLAB, utilizes K-Nearest Neighbors (KNN) method to compute the probability of lesions on a per-voxel basis. Despite the highly optimized algorithm of imaging processing that is used in CAD development, MS CAD integration and evaluation in clinical workflow is technically challenging due to the requirement of high computation rates and memory bandwidth in the recursive nature of the algorithm. In this paper, we present the development and evaluation of using a computing engine in the graphical processing unit (GPU) with MATLAB for segmentation of MS lesions. The paper investigates the utilization of a high-end GPU for parallel computing of KNN in the MATLAB environment to improve algorithm performance. The integration is accomplished using NVIDIA's CUDA developmental toolkit for MATLAB. The results of this study will validate the practicality and effectiveness of the prototype MS CAD in a clinical setting. The GPU method may allow MS CAD to rapidly integrate in an electronic patient record or any disease-centric health care system.

Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit

PubMed Central

Xu, Liangliang; Xu, Nengxiong

2017-01-01

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points’ spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available. PMID:28989754

Accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit.

PubMed

Mei, Gang; Xu, Liangliang; Xu, Nengxiong

2017-09-01

This paper focuses on designing and implementing parallel adaptive inverse distance weighting (AIDW) interpolation algorithms by using the graphics processing unit (GPU). The AIDW is an improved version of the standard IDW, which can adaptively determine the power parameter according to the data points' spatial distribution pattern and achieve more accurate predictions than those predicted by IDW. In this paper, we first present two versions of the GPU-accelerated AIDW, i.e. the naive version without profiting from the shared memory and the tiled version taking advantage of the shared memory. We also implement the naive version and the tiled version using two data layouts, structure of arrays and array of aligned structures, on both single and double precision. We then evaluate the performance of parallel AIDW by comparing it with its corresponding serial algorithm on three different machines equipped with the GPUs GT730M, M5000 and K40c. The experimental results indicate that: (i) there is no significant difference in the computational efficiency when different data layouts are employed; (ii) the tiled version is always slightly faster than the naive version; and (iii) on single precision the achieved speed-up can be up to 763 (on the GPU M5000), while on double precision the obtained highest speed-up is 197 (on the GPU K40c). To benefit the community, all source code and testing data related to the presented parallel AIDW algorithm are publicly available.

Spatial 3D infrastructure: display-independent software framework, high-speed rendering electronics, and several new displays

NASA Astrophysics Data System (ADS)

Chun, Won-Suk; Napoli, Joshua; Cossairt, Oliver S.; Dorval, Rick K.; Hall, Deirdre M.; Purtell, Thomas J., II; Schooler, James F.; Banker, Yigal; Favalora, Gregg E.

2005-03-01

We present a software and hardware foundation to enable the rapid adoption of 3-D displays. Different 3-D displays - such as multiplanar, multiview, and electroholographic displays - naturally require different rendering methods. The adoption of these displays in the marketplace will be accelerated by a common software framework. The authors designed the SpatialGL API, a new rendering framework that unifies these display methods under one interface. SpatialGL enables complementary visualization assets to coexist through a uniform infrastructure. Also, SpatialGL supports legacy interfaces such as the OpenGL API. The authors" first implementation of SpatialGL uses multiview and multislice rendering algorithms to exploit the performance of modern graphics processing units (GPUs) to enable real-time visualization of 3-D graphics from medical imaging, oil & gas exploration, and homeland security. At the time of writing, SpatialGL runs on COTS workstations (both Windows and Linux) and on Actuality"s high-performance embedded computational engine that couples an NVIDIA GeForce 6800 Ultra GPU, an AMD Athlon 64 processor, and a proprietary, high-speed, programmable volumetric frame buffer that interfaces to a 1024 x 768 x 3 digital projector. Progress is illustrated using an off-the-shelf multiview display, Actuality"s multiplanar Perspecta Spatial 3D System, and an experimental multiview display. The experimental display is a quasi-holographic view-sequential system that generates aerial imagery measuring 30 mm x 25 mm x 25 mm, providing 198 horizontal views.

Architecture for high performance stereoscopic game rendering on Android

NASA Astrophysics Data System (ADS)

Flack, Julien; Sanderson, Hugh; Shetty, Sampath

2014-03-01

Stereoscopic gaming is a popular source of content for consumer 3D display systems. There has been a significant shift in the gaming industry towards casual games for mobile devices running on the Android™ Operating System and driven by ARM™ and other low power processors. Such systems are now being integrated directly into the next generation of 3D TVs potentially removing the requirement for an external games console. Although native stereo support has been integrated into some high profile titles on established platforms like Windows PC and PS3 there is a lack of GPU independent 3D support for the emerging Android platform. We describe a framework for enabling stereoscopic 3D gaming on Android for applications on mobile devices, set top boxes and TVs. A core component of the architecture is a 3D game driver, which is integrated into the Android OpenGL™ ES graphics stack to convert existing 2D graphics applications into stereoscopic 3D in real-time. The architecture includes a method of analyzing 2D games and using rule based Artificial Intelligence (AI) to position separate objects in 3D space. We describe an innovative stereo 3D rendering technique to separate the views in the depth domain and render directly into the display buffer. The advantages of the stereo renderer are demonstrated by characterizing the performance in comparison to more traditional render techniques, including depth based image rendering, both in terms of frame rates and impact on battery consumption.

A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations.

PubMed

Persoon, Lucas C G G; Podesta, Mark; van Elmpt, Wouter J C; Nijsten, Sebastiaan M J J G; Verhaegen, Frank

2011-07-01

A widely accepted method to quantify differences in dose distributions is the gamma (gamma) evaluation. Currently, almost all gamma implementations utilize the central processing unit (CPU). Recently, the graphics processing unit (GPU) has become a powerful platform for specific computing tasks. In this study, we describe the implementation of a 3D gamma evaluation using a GPU to improve calculation time. The gamma evaluation algorithm was implemented on an NVIDIA Tesla C2050 GPU using the compute unified device architecture (CUDA). First, several cubic virtual phantoms were simulated. These phantoms were tested with varying dose cube sizes and set-ups, introducing artificial dose differences. Second, to show applicability in clinical practice, five patient cases have been evaluated using the 3D dose distribution from a treatment planning system as the reference and the delivered dose determined during treatment as the comparison. A calculation time comparison between the CPU and GPU was made with varying thread-block sizes including the option of using texture or global memory. A GPU over CPU speed-up of 66 +/- 12 was achieved for the virtual phantoms. For the patient cases, a speed-up of 57 +/- 15 using the GPU was obtained. A thread-block size of 16 x 16 performed best in all cases. The use of texture memory improved the total calculation time, especially when interpolation was applied. Differences between the CPU and GPU gammas were negligible. The GPU and its features, such as texture memory, decreased the calculation time for gamma evaluations considerably without loss of accuracy.

Fast DRR generation for 2D to 3D registration on GPUs.

PubMed

Tornai, Gábor János; Cserey, György; Pappas, Ion

2012-08-01

The generation of digitally reconstructed radiographs (DRRs) is the most time consuming step on the CPU in intensity based two-dimensional x-ray to three-dimensional (CT or 3D rotational x-ray) medical image registration, which has application in several image guided interventions. This work presents optimized DRR rendering on graphical processor units (GPUs) and compares performance achievable on four commercially available devices. A ray-cast based DRR rendering was implemented for a 512 × 512 × 72 CT volume. The block size parameter was optimized for four different GPUs for a region of interest (ROI) of 400 × 225 pixels with different sampling ratios (1.1%-9.1% and 100%). Performance was statistically evaluated and compared for the four GPUs. The method and the block size dependence were validated on the latest GPU for several parameter settings with a public gold standard dataset (512 × 512 × 825 CT) for registration purposes. Depending on the GPU, the full ROI is rendered in 2.7-5.2 ms. If sampling ratio of 1.1%-9.1% is applied, execution time is in the range of 0.3-7.3 ms. On all GPUs, the mean of the execution time increased linearly with respect to the number of pixels if sampling was used. The presented results outperform other results from the literature. This indicates that automatic 2D to 3D registration, which typically requires a couple of hundred DRR renderings to converge, can be performed quasi on-line, in less than a second or depending on the application and hardware in less than a couple of seconds. Accordingly, a whole new field of applications is opened for image guided interventions, where the registration is continuously performed to match the real-time x-ray.

Towards real-time photon Monte Carlo dose calculation in the cloud

NASA Astrophysics Data System (ADS)

Ziegenhein, Peter; Kozin, Igor N.; Kamerling, Cornelis Ph; Oelfke, Uwe

2017-06-01

Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.

Robust adaptive 3-D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation.

PubMed

Narayanaswamy, Arunachalam; Dwarakapuram, Saritha; Bjornsson, Christopher S; Cutler, Barbara M; Shain, William; Roysam, Badrinath

2010-03-01

This paper presents robust 3-D algorithms to segment vasculature that is imaged by labeling laminae, rather than the lumenal volume. The signal is weak, sparse, noisy, nonuniform, low-contrast, and exhibits gaps and spectral artifacts, so adaptive thresholding and Hessian filtering based methods are not effective. The structure deviates from a tubular geometry, so tracing algorithms are not effective. We propose a four step approach. The first step detects candidate voxels using a robust hypothesis test based on a model that assumes Poisson noise and locally planar geometry. The second step performs an adaptive region growth to extract weakly labeled and fine vessels while rejecting spectral artifacts. To enable interactive visualization and estimation of features such as statistical confidence, local curvature, local thickness, and local normal, we perform the third step. In the third step, we construct an accurate mesh representation using marching tetrahedra, volume-preserving smoothing, and adaptive decimation algorithms. To enable topological analysis and efficient validation, we describe a method to estimate vessel centerlines using a ray casting and vote accumulation algorithm which forms the final step of our algorithm. Our algorithm lends itself to parallel processing, and yielded an 8 x speedup on a graphics processor (GPU). On synthetic data, our meshes had average error per face (EPF) values of (0.1-1.6) voxels per mesh face for peak signal-to-noise ratios from (110-28 dB). Separately, the error from decimating the mesh to less than 1% of its original size, the EPF was less than 1 voxel/face. When validated on real datasets, the average recall and precision values were found to be 94.66% and 94.84%, respectively.

Accelerating DNA analysis applications on GPU clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumeo, Antonino; Villa, Oreste

DNA analysis is an emerging application of high performance bioinformatic. Modern sequencing machinery are able to provide, in few hours, large input streams of data which needs to be matched against exponentially growing databases known fragments. The ability to recognize these patterns effectively and fastly may allow extending the scale and the reach of the investigations performed by biology scientists. Aho-Corasick is an exact, multiple pattern matching algorithm often at the base of this application. High performance systems are a promising platform to accelerate this algorithm, which is computationally intensive but also inherently parallel. Nowadays, high performance systems also includemore » heterogeneous processing elements, such as Graphic Processing Units (GPUs), to further accelerate parallel algorithms. Unfortunately, the Aho-Corasick algorithm exhibits large performance variabilities, depending on the size of the input streams, on the number of patterns to search and on the number of matches, and poses significant challenges on current high performance software and hardware implementations. An adequate mapping of the algorithm on the target architecture, coping with the limit of the underlining hardware, is required to reach the desired high throughputs. Load balancing also plays a crucial role when considering the limited bandwidth among the nodes of these systems. In this paper we present an efficient implementation of the Aho-Corasick algorithm for high performance clusters accelerated with GPUs. We discuss how we partitioned and adapted the algorithm to fit the Tesla C1060 GPU and then present a MPI based implementation for a heterogeneous high performance cluster. We compare this implementation to MPI and MPI with pthreads based implementations for a homogeneous cluster of x86 processors, discussing the stability vs. the performance and the scaling of the solutions, taking into consideration aspects such as the bandwidth among the different nodes.« less

Towards real-time photon Monte Carlo dose calculation in the cloud.

PubMed

Ziegenhein, Peter; Kozin, Igor N; Kamerling, Cornelis Ph; Oelfke, Uwe

2017-06-07

Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.

A parallel algorithm for the initial screening of space debris collisions prediction using the SGP4/SDP4 models and GPU acceleration

NASA Astrophysics Data System (ADS)

Lin, Mingpei; Xu, Ming; Fu, Xiaoyu

2017-05-01

Currently, a tremendous amount of space debris in Earth's orbit imperils operational spacecraft. It is essential to undertake risk assessments of collisions and predict dangerous encounters in space. However, collision predictions for an enormous amount of space debris give rise to large-scale computations. In this paper, a parallel algorithm is established on the Compute Unified Device Architecture (CUDA) platform of NVIDIA Corporation for collision prediction. According to the parallel structure of NVIDIA graphics processors, a block decomposition strategy is adopted in the algorithm. Space debris is divided into batches, and the computation and data transfer operations of adjacent batches overlap. As a consequence, the latency to access shared memory during the entire computing process is significantly reduced, and a higher computing speed is reached. Theoretically, a simulation of collision prediction for space debris of any amount and for any time span can be executed. To verify this algorithm, a simulation example including 1382 pieces of debris, whose operational time scales vary from 1 min to 3 days, is conducted on Tesla C2075 of NVIDIA. The simulation results demonstrate that with the same computational accuracy as that of a CPU, the computing speed of the parallel algorithm on a GPU is 30 times that on a CPU. Based on this algorithm, collision prediction of over 150 Chinese spacecraft for a time span of 3 days can be completed in less than 3 h on a single computer, which meets the timeliness requirement of the initial screening task. Furthermore, the algorithm can be adapted for multiple tasks, including particle filtration, constellation design, and Monte-Carlo simulation of an orbital computation.

GPU-based prompt gamma ray imaging from boron neutron capture therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Do-Kun; Jung, Joo-Young; Suk Suh, Tae, E-mail: suhsanta@catholic.ac.kr

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).more » 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.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, S; Suh, T; Yoon, D

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).more » 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.« less

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

PubMed

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.

Mobile GPU-based implementation of automatic analysis method for long-term ECG.

PubMed

Fan, Xiaomao; Yao, Qihang; Li, Ye; Chen, Runge; Cai, Yunpeng

2018-05-03

Long-term electrocardiogram (ECG) is one of the important diagnostic assistant approaches in capturing intermittent cardiac arrhythmias. Combination of miniaturized wearable holters and healthcare platforms enable people to have their cardiac condition monitored at home. The high computational burden created by concurrent processing of numerous holter data poses a serious challenge to the healthcare platform. An alternative solution is to shift the analysis tasks from healthcare platforms to the mobile computing devices. However, long-term ECG data processing is quite time consuming due to the limited computation power of the mobile central unit processor (CPU). This paper aimed to propose a novel parallel automatic ECG analysis algorithm which exploited the mobile graphics processing unit (GPU) to reduce the response time for processing long-term ECG data. By studying the architecture of the sequential automatic ECG analysis algorithm, we parallelized the time-consuming parts and reorganized the entire pipeline in the parallel algorithm to fully utilize the heterogeneous computing resources of CPU and GPU. The experimental results showed that the average executing time of the proposed algorithm on a clinical long-term ECG dataset (duration 23.0 ± 1.0 h per signal) is 1.215 ± 0.140 s, which achieved an average speedup of 5.81 ± 0.39× without compromising analysis accuracy, comparing with the sequential algorithm. Meanwhile, the battery energy consumption of the automatic ECG analysis algorithm was reduced by 64.16%. Excluding energy consumption from data loading, 79.44% of the energy consumption could be saved, which alleviated the problem of limited battery working hours for mobile devices. The reduction of response time and battery energy consumption in ECG analysis not only bring better quality of experience to holter users, but also make it possible to use mobile devices as ECG terminals for healthcare professions such as physicians and health advisers, enabling them to inspect patient ECG recordings onsite efficiently without the need of a high-quality wide-area network environment.

A GPU accelerated PDF transparency engine

NASA Astrophysics Data System (ADS)

Recker, John; Lin, I.-Jong; Tastl, Ingeborg

2011-01-01

As commercial printing presses become faster, cheaper and more efficient, so too must the Raster Image Processors (RIP) that prepare data for them to print. Digital press RIPs, however, have been challenged to on the one hand meet the ever increasing print performance of the latest digital presses, and on the other hand process increasingly complex documents with transparent layers and embedded ICC profiles. This paper explores the challenges encountered when implementing a GPU accelerated driver for the open source Ghostscript Adobe PostScript and PDF language interpreter targeted at accelerating PDF transparency for high speed commercial presses. It further describes our solution, including an image memory manager for tiling input and output images and documents, a PDF compatible multiple image layer blending engine, and a GPU accelerated ICC v4 compatible color transformation engine. The result, we believe, is the foundation for a scalable, efficient, distributed RIP system that can meet current and future RIP requirements for a wide range of commercial digital presses.

Implementation of metal-friendly EAM/FS-type semi-empirical potentials in HOOMD-blue: A GPU-accelerated molecular dynamics software

NASA Astrophysics Data System (ADS)

Yang, Lin; Zhang, Feng; Wang, Cai-Zhuang; Ho, Kai-Ming; Travesset, Alex

2018-04-01

We present an implementation of EAM and FS interatomic potentials, which are widely used in simulating metallic systems, in HOOMD-blue, a software designed to perform classical molecular dynamics simulations using GPU accelerations. We first discuss the details of our implementation and then report extensive benchmark tests. We demonstrate that single-precision floating point operations efficiently implemented on GPUs can produce sufficient accuracy when compared against double-precision codes, as demonstrated in test simulations of calculations of the glass-transition temperature of Cu64.5Zr35.5, and pair correlation function g (r) of liquid Ni3Al. Our code scales well with the size of the simulating system on NVIDIA Tesla M40 and P100 GPUs. Compared with another popular software LAMMPS running on 32 cores of AMD Opteron 6220 processors, the GPU/CPU performance ratio can reach as high as 4.6. The source code can be accessed through the HOOMD-blue web page for free by any interested user.

Real-time colouring and filtering with graphics shaders

NASA Astrophysics Data System (ADS)

Vohl, D.; Fluke, C. J.; Barnes, D. G.; Hassan, A. H.

2017-11-01

Despite the popularity of the Graphics Processing Unit (GPU) for general purpose computing, one should not forget about the practicality of the GPU for fast scientific visualization. As astronomers have increasing access to three-dimensional (3D) data from instruments and facilities like integral field units and radio interferometers, visualization techniques such as volume rendering offer means to quickly explore spectral cubes as a whole. As most 3D visualization techniques have been developed in fields of research like medical imaging and fluid dynamics, many transfer functions are not optimal for astronomical data. We demonstrate how transfer functions and graphics shaders can be exploited to provide new astronomy-specific explorative colouring methods. We present 12 shaders, including four novel transfer functions specifically designed to produce intuitive and informative 3D visualizations of spectral cube data. We compare their utility to classic colour mapping. The remaining shaders highlight how common computation like filtering, smoothing and line ratio algorithms can be integrated as part of the graphics pipeline. We discuss how this can be achieved by utilizing the parallelism of modern GPUs along with a shading language, letting astronomers apply these new techniques at interactive frame rates. All shaders investigated in this work are included in the open source software shwirl (Vohl 2017).

Optimization of Selected Remote Sensing Algorithms for Embedded NVIDIA Kepler GPU Architecture

NASA Technical Reports Server (NTRS)

Riha, Lubomir; Le Moigne, Jacqueline; El-Ghazawi, Tarek

2015-01-01

This paper evaluates the potential of embedded Graphic Processing Units in the Nvidias Tegra K1 for onboard processing. The performance is compared to a general purpose multi-core CPU and full fledge GPU accelerator. This study uses two algorithms: Wavelet Spectral Dimension Reduction of Hyperspectral Imagery and Automated Cloud-Cover Assessment (ACCA) Algorithm. Tegra K1 achieved 51 for ACCA algorithm and 20 for the dimension reduction algorithm, as compared to the performance of the high-end 8-core server Intel Xeon CPU with 13.5 times higher power consumption.

Graphics Processing Unit Assisted Thermographic Compositing

NASA Technical Reports Server (NTRS)

Ragasa, Scott; McDougal, Matthew; Russell, Sam

2013-01-01

Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often greater, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques.

Computer simulations and real-time control of ELT AO systems using graphical processing units

NASA Astrophysics Data System (ADS)

Wang, Lianqi; Ellerbroek, Brent

2012-07-01

The adaptive optics (AO) simulations at the Thirty Meter Telescope (TMT) have been carried out using the efficient, C based multi-threaded adaptive optics simulator (MAOS, http://github.com/lianqiw/maos). By porting time-critical parts of MAOS to graphical processing units (GPU) using NVIDIA CUDA technology, we achieved a 10 fold speed up for each GTX 580 GPU used compared to a modern quad core CPU. Each time step of full scale end to end simulation for the TMT narrow field infrared AO system (NFIRAOS) takes only 0.11 second in a desktop with two GTX 580s. We also demonstrate that the TMT minimum variance reconstructor can be assembled in matrix vector multiply (MVM) format in 8 seconds with 8 GTX 580 GPUs, meeting the TMT requirement for updating the reconstructor. Analysis show that it is also possible to apply the MVM using 8 GTX 580s within the required latency.

Advanced Architectures for Astrophysical Supercomputing

NASA Astrophysics Data System (ADS)

Barsdell, B. R.; Barnes, D. G.; Fluke, C. J.

2010-12-01

Astronomers have come to rely on the increasing performance of computers to reduce, analyze, simulate and visualize their data. In this environment, faster computation can mean more science outcomes or the opening up of new parameter spaces for investigation. If we are to avoid major issues when implementing codes on advanced architectures, it is important that we have a solid understanding of our algorithms. A recent addition to the high-performance computing scene that highlights this point is the graphics processing unit (GPU). The hardware originally designed for speeding-up graphics rendering in video games is now achieving speed-ups of O(100×) in general-purpose computation - performance that cannot be ignored. We are using a generalized approach, based on the analysis of astronomy algorithms, to identify the optimal problem-types and techniques for taking advantage of both current GPU hardware and future developments in computing architectures.

Real-time liquid-crystal atmosphere turbulence simulator with graphic processing unit.

PubMed

Hu, Lifa; Xuan, Li; Li, Dayu; Cao, Zhaoliang; Mu, Quanquan; Liu, Yonggang; Peng, Zenghui; Lu, Xinghai

2009-04-27

To generate time-evolving atmosphere turbulence in real time, a phase-generating method for our liquid-crystal (LC) atmosphere turbulence simulator (ATS) is derived based on the Fourier series (FS) method. A real matrix expression for generating turbulence phases is given and calculated with a graphic processing unit (GPU), the GeForce 8800 Ultra. A liquid crystal on silicon (LCOS) with 256x256 pixels is used as the turbulence simulator. The total time to generate a turbulence phase is about 7.8 ms for calculation and readout with the GPU. A parallel processing method of calculating and sending a picture to the LCOS is used to improve the simulating speed of our LC ATS. Therefore, the real-time turbulence phase-generation frequency of our LC ATS is up to 128 Hz. To our knowledge, it is the highest speed used to generate a turbulence phase in real time.

Tempest: Accelerated MS/MS Database Search Software for Heterogeneous Computing Platforms.

PubMed

Adamo, Mark E; Gerber, Scott A

2016-09-07

MS/MS database search algorithms derive a set of candidate peptide sequences from in silico digest of a protein sequence database, and compute theoretical fragmentation patterns to match these candidates against observed MS/MS spectra. The original Tempest publication described these operations mapped to a CPU-GPU model, in which the CPU (central processing unit) generates peptide candidates that are asynchronously sent to a discrete GPU (graphics processing unit) to be scored against experimental spectra in parallel. The current version of Tempest expands this model, incorporating OpenCL to offer seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors. Three protocols describe how to configure and run a Tempest search, including discussion of how to leverage Tempest's unique feature set to produce optimal results. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Nanoscale multireference quantum chemistry: full configuration interaction on graphical processing units.

PubMed

Fales, B Scott; Levine, Benjamin G

2015-10-13

Methods based on a full configuration interaction (FCI) expansion in an active space of orbitals are widely used for modeling chemical phenomena such as bond breaking, multiply excited states, and conical intersections in small-to-medium-sized molecules, but these phenomena occur in systems of all sizes. To scale such calculations up to the nanoscale, we have developed an implementation of FCI in which electron repulsion integral transformation and several of the more expensive steps in σ vector formation are performed on graphical processing unit (GPU) hardware. When applied to a 1.7 × 1.4 × 1.4 nm silicon nanoparticle (Si72H64) described with the polarized, all-electron 6-31G** basis set, our implementation can solve for the ground state of the 16-active-electron/16-active-orbital CASCI Hamiltonian (more than 100,000,000 configurations) in 39 min on a single NVidia K40 GPU.

Fast MPEG-CDVS Encoder With GPU-CPU Hybrid Computing.

PubMed

Duan, Ling-Yu; Sun, Wei; Zhang, Xinfeng; Wang, Shiqi; Chen, Jie; Yin, Jianxiong; See, Simon; Huang, Tiejun; Kot, Alex C; Gao, Wen

2018-05-01

The compact descriptors for visual search (CDVS) standard from ISO/IEC moving pictures experts group has succeeded in enabling the interoperability for efficient and effective image retrieval by standardizing the bitstream syntax of compact feature descriptors. However, the intensive computation of a CDVS encoder unfortunately hinders its widely deployment in industry for large-scale visual search. In this paper, we revisit the merits of low complexity design of CDVS core techniques and present a very fast CDVS encoder by leveraging the massive parallel execution resources of graphics processing unit (GPU). We elegantly shift the computation-intensive and parallel-friendly modules to the state-of-the-arts GPU platforms, in which the thread block allocation as well as the memory access mechanism are jointly optimized to eliminate performance loss. In addition, those operations with heavy data dependence are allocated to CPU for resolving the extra but non-necessary computation burden for GPU. Furthermore, we have demonstrated the proposed fast CDVS encoder can work well with those convolution neural network approaches which enables to leverage the advantages of GPU platforms harmoniously, and yield significant performance improvements. Comprehensive experimental results over benchmarks are evaluated, which has shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising for scalable visual search.

A CFD Heterogeneous Parallel Solver Based on Collaborating CPU and GPU

NASA Astrophysics Data System (ADS)

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.

Accelerating large-scale simulation of seismic wave propagation by multi-GPUs and three-dimensional domain decomposition

NASA Astrophysics Data System (ADS)

Okamoto, Taro; Takenaka, Hiroshi; Nakamura, Takeshi; Aoki, Takayuki

2010-12-01

We adopted the GPU (graphics processing unit) to accelerate the large-scale finite-difference simulation of seismic wave propagation. The simulation can benefit from the high-memory bandwidth of GPU because it is a "memory intensive" problem. In a single-GPU case we achieved a performance of about 56 GFlops, which was about 45-fold faster than that achieved by a single core of the host central processing unit (CPU). We confirmed that the optimized use of fast shared memory and registers were essential for performance. In the multi-GPU case with three-dimensional domain decomposition, the non-contiguous memory alignment in the ghost zones was found to impose quite long time in data transfer between GPU and the host node. This problem was solved by using contiguous memory buffers for ghost zones. We achieved a performance of about 2.2 TFlops by using 120 GPUs and 330 GB of total memory: nearly (or more than) 2200 cores of host CPUs would be required to achieve the same performance. The weak scaling was nearly proportional to the number of GPUs. We therefore conclude that GPU computing for large-scale simulation of seismic wave propagation is a promising approach as a faster simulation is possible with reduced computational resources compared to CPUs.

Toward GPGPU accelerated human electromechanical cardiac simulations

PubMed Central

Vigueras, Guillermo; Roy, Ishani; Cookson, Andrew; Lee, Jack; Smith, Nicolas; Nordsletten, David

2014-01-01

In this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit (GPU). Specifically, we port to the GPU a number of components of Heart—a CPU-based finite element code developed for simulating multi-physics problems. On the basis of a criterion of computational cost, we implemented on the GPU the ODE and PDE solution steps for the electrophysiology problem and the Jacobian and residual evaluation for the mechanics problem. Performance of the GPU implementation is then compared with single core CPU (SC) execution as well as multi-core CPU (MC) computations with equivalent theoretical performance. Results show that for a human scale left ventricle mesh, GPU acceleration of the electrophysiology problem provided speedups of 164 × compared with SC and 5.5 times compared with MC for the solution of the ODE model. Speedup of up to 72 × compared with SC and 2.6 × compared with MC was also observed for the PDE solve. Using the same human geometry, the GPU implementation of mechanics residual/Jacobian computation provided speedups of up to 44 × compared with SC and 2.0 × compared with MC. © 2013 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons, Ltd. PMID:24115492

Accelerating EPI distortion correction by utilizing a modern GPU-based parallel computation.

PubMed

Yang, Yao-Hao; Huang, Teng-Yi; Wang, Fu-Nien; Chuang, Tzu-Chao; Chen, Nan-Kuei

2013-04-01

The combination of phase demodulation and field mapping is a practical method to correct echo planar imaging (EPI) geometric distortion. However, since phase dispersion accumulates in each phase-encoding step, the calculation complexity of phase modulation is Ny-fold higher than conventional image reconstructions. Thus, correcting EPI images via phase demodulation is generally a time-consuming task. Parallel computing by employing general-purpose calculations on graphics processing units (GPU) can accelerate scientific computing if the algorithm is parallelized. This study proposes a method that incorporates the GPU-based technique into phase demodulation calculations to reduce computation time. The proposed parallel algorithm was applied to a PROPELLER-EPI diffusion tensor data set. The GPU-based phase demodulation method reduced the EPI distortion correctly, and accelerated the computation. The total reconstruction time of the 16-slice PROPELLER-EPI diffusion tensor images with matrix size of 128 × 128 was reduced from 1,754 seconds to 101 seconds by utilizing the parallelized 4-GPU program. GPU computing is a promising method to accelerate EPI geometric correction. The resulting reduction in computation time of phase demodulation should accelerate postprocessing for studies performed with EPI, and should effectuate the PROPELLER-EPI technique for clinical practice. Copyright © 2011 by the American Society of Neuroimaging.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gallarno, George; Rogers, James H; Maxwell, Don E

The high computational capability of graphics processing units (GPUs) is enabling and driving the scientific discovery process at large-scale. The world s second fastest supercomputer for open science, Titan, has more than 18,000 GPUs that computational scientists use to perform scientific simu- lations and data analysis. Understanding of GPU reliability characteristics, however, is still in its nascent stage since GPUs have only recently been deployed at large-scale. This paper presents a detailed study of GPU errors and their impact on system operations and applications, describing experiences with the 18,688 GPUs on the Titan supercom- puter as well as lessons learnedmore » in the process of efficient operation of GPUs at scale. These experiences are helpful to HPC sites which already have large-scale GPU clusters or plan to deploy GPUs in the future.« less

Noniterative Multireference Coupled Cluster Methods on Heterogeneous CPU-GPU Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhaskaran-Nair, Kiran; Ma, Wenjing; Krishnamoorthy, Sriram

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 algorithmmore » is illustrated on the example of the Brillouin-Wigner (BW) and Mukherjee (Mk) state-specific MRCCSD(T) formulations.« less

A simple GPU-accelerated two-dimensional MUSCL-Hancock solver for ideal magnetohydrodynamics

NASA Astrophysics Data System (ADS)

Bard, Christopher M.; Dorelli, John C.

2014-02-01

We describe our experience using NVIDIA's CUDA (Compute Unified Device Architecture) C programming environment to implement a two-dimensional second-order MUSCL-Hancock ideal magnetohydrodynamics (MHD) solver on a GTX 480 Graphics Processing Unit (GPU). Taking a simple approach in which the MHD variables are stored exclusively in the global memory of the GTX 480 and accessed in a cache-friendly manner (without further optimizing memory access by, for example, staging data in the GPU's faster shared memory), we achieved a maximum speed-up of ≈126 for a 10242 grid relative to the sequential C code running on a single Intel Nehalem (2.8 GHz) core. This speedup is consistent with simple estimates based on the known floating point performance, memory throughput and parallel processing capacity of the GTX 480.

Grace: A cross-platform micromagnetic simulator on graphics processing units

NASA Astrophysics Data System (ADS)

Zhu, Ru

2015-12-01

A micromagnetic simulator running on graphics processing units (GPUs) is presented. Different from GPU implementations of other research groups which are predominantly running on NVidia's CUDA platform, this simulator is developed with C++ Accelerated Massive Parallelism (C++ AMP) and is hardware platform independent. It runs on GPUs from venders including NVidia, AMD and Intel, and achieves significant performance boost as compared to previous central processing unit (CPU) simulators, up to two orders of magnitude. The simulator paved the way for running large size micromagnetic simulations on both high-end workstations with dedicated graphics cards and low-end personal computers with integrated graphics cards, and is freely available to download.

A High Performance Computing Framework for Physics-based Modeling and Simulation of Military Ground Vehicles

DTIC Science & Technology

2011-03-25

number one and Nebulae at number three. Both systems rely on GPU co-processing and use Intel Xeon processors cards and NVIDIA Tesla C2050 GPUs. In...spite of a theoretical peak capability of almost 3 Petaflop/s, Nebulae clocked at 1.271 PFlop/s when running the Linpack benchmark, which puts it

Real-time Scheduling for GPUS with Applications in Advanced Automotive Systems

DTIC Science & Technology

2015-01-01

129 3.7 Architecture of GPU tasklet scheduling infrastructure ...throughput. This disparity is even greater when we consider mobile CPUs, such as those designed by ARM. For instance, the ARM Cortex-A15 series processor as...stub library that replaces the GPGPU runtime within each virtual machine. The stub library communicates API calls to a GPGPU backend user-space daemon

Efficient implementation of the many-body Reactive Bond Order (REBO) potential on GPU

NASA Astrophysics Data System (ADS)

Trędak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.

2016-09-01

The second generation Reactive Bond Order (REBO) empirical potential is commonly used to accurately model a wide range hydrocarbon materials. It is also extensible to other atom types and interactions. REBO potential assumes complex multi-body interaction model, that is difficult to represent efficiently in the SIMD or SIMT programming model. Hence, despite its importance, no efficient GPGPU implementation has been developed for this potential. Here we present a detailed description of a highly efficient GPGPU implementation of molecular dynamics algorithm using REBO potential. The presented algorithm takes advantage of rarely used properties of the SIMT architecture of a modern GPU to solve difficult synchronizations issues that arise in computations of multi-body potential. Techniques developed for this problem may be also used to achieve efficient solutions of different problems. The performance of proposed algorithm is assessed using a range of model systems. It is compared to highly optimized CPU implementation (both single core and OpenMP) available in LAMMPS package. These experiments show up to 6x improvement in forces computation time using single processor of the NVIDIA Tesla K80 compared to high end 16-core Intel Xeon processor.

GPU computing of compressible flow problems by a meshless method with space-filling curves

NASA Astrophysics Data System (ADS)

Ma, Z. H.; Wang, H.; Pu, S. H.

2014-04-01

A graphic processing unit (GPU) implementation of a meshless method for solving compressible flow problems is presented in this paper. Least-square fit is used to discretize the spatial derivatives of Euler equations and an upwind scheme is applied to estimate the flux terms. The compute unified device architecture (CUDA) C programming model is employed to efficiently and flexibly port the meshless solver from CPU to GPU. Considering the data locality of randomly distributed points, space-filling curves are adopted to re-number the points in order to improve the memory performance. Detailed evaluations are firstly carried out to assess the accuracy and conservation property of the underlying numerical method. Then the GPU accelerated flow solver is used to solve external steady flows over aerodynamic configurations. Representative results are validated through extensive comparisons with the experimental, finite volume or other available reference solutions. Performance analysis reveals that the running time cost of simulations is significantly reduced while impressive (more than an order of magnitude) speedups are achieved.

GPU-Based Real-Time Volumetric Ultrasound Image Reconstruction for a Ring Array

PubMed Central

Choe, Jung Woo; Nikoozadeh, Amin; Oralkan, Ömer; Khuri-Yakub, Butrus T.

2014-01-01

Synthetic phased array (SPA) beamforming with Hadamard coding and aperture weighting is an optimal option for real-time volumetric imaging with a ring array, a particularly attractive geometry in intracardiac and intravascular applications. However, the imaging frame rate of this method is limited by the immense computational load required in synthetic beamforming. For fast imaging with a ring array, we developed graphics processing unit (GPU)-based, real-time image reconstruction software that exploits massive data-level parallelism in beamforming operations. The GPU-based software reconstructs and displays three cross-sectional images at 45 frames per second (fps). This frame rate is 4.5 times higher than that for our previously-developed multi-core CPU-based software. In an alternative imaging mode, it shows one B-mode image rotating about the axis and its maximum intensity projection (MIP), processed at a rate of 104 fps. This paper describes the image reconstruction procedure on the GPU platform and presents the experimental images obtained using this software. PMID:23529080

Accelerating a three-dimensional eco-hydrological cellular automaton on GPGPU with OpenCL

NASA Astrophysics Data System (ADS)

Senatore, Alfonso; D'Ambrosio, Donato; De Rango, Alessio; Rongo, Rocco; Spataro, William; Straface, Salvatore; Mendicino, Giuseppe

2016-10-01

This work presents an effective implementation of a numerical model for complete eco-hydrological Cellular Automata modeling on Graphical Processing Units (GPU) with OpenCL (Open Computing Language) for heterogeneous computation (i.e., on CPUs and/or GPUs). Different types of parallel implementations were carried out (e.g., use of fast local memory, loop unrolling, etc), showing increasing performance improvements in terms of speedup, adopting also some original optimizations strategies. Moreover, numerical analysis of results (i.e., comparison of CPU and GPU outcomes in terms of rounding errors) have proven to be satisfactory. Experiments were carried out on a workstation with two CPUs (Intel Xeon E5440 at 2.83GHz), one GPU AMD R9 280X and one GPU nVIDIA Tesla K20c. Results have been extremely positive, but further testing should be performed to assess the functionality of the adopted strategies on other complete models and their ability to fruitfully exploit parallel systems resources.

DEM GPU studies of industrial scale particle simulations for granular flow civil engineering applications

NASA Astrophysics Data System (ADS)

Pizette, Patrick; Govender, Nicolin; Wilke, Daniel N.; Abriak, Nor-Edine

2017-06-01

The use of the Discrete Element Method (DEM) for industrial civil engineering industrial applications is currently limited due to the computational demands when large numbers of particles are considered. The graphics processing unit (GPU) with its highly parallelized hardware architecture shows potential to enable solution of civil engineering problems using discrete granular approaches. We demonstrate in this study the pratical utility of a validated GPU-enabled DEM modeling environment to simulate industrial scale granular problems. As illustration, the flow discharge of storage silos using 8 and 17 million particles is considered. DEM simulations have been performed to investigate the influence of particle size (equivalent size for the 20/40-mesh gravel) and induced shear stress for two hopper shapes. The preliminary results indicate that the shape of the hopper significantly influences the discharge rates for the same material. Specifically, this work shows that GPU-enabled DEM modeling environments can model industrial scale problems on a single portable computer within a day for 30 seconds of process time.

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

PubMed Central

Smith, David S.; Gore, John C.; Yankeelov, Thomas E.; Welch, E. Brian

2012-01-01

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images. PMID:22481908

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

PubMed

Smith, David S; Gore, John C; Yankeelov, Thomas E; Welch, E Brian

2012-01-01

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 4096(2) or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 1024(2) and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

PubMed

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

PubMed Central

Cheung, Kit; Schultz, Simon R.; Luk, Wayne

2016-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542

Deep searches for broadband extended gravitational-wave emission bursts by heterogeneous computing

NASA Astrophysics Data System (ADS)

van Putten, Maurice H. P. M.

2017-09-01

We present a heterogeneous search algorithm for broadband extended gravitational-wave emission, expected from gamma-ray bursts and energetic core-collapse supernovae. It searches the (f,\\dot{f})-plane for long-duration bursts by inner engines slowly exhausting their energy reservoir by matched filtering on a graphics processor unit (GPU) over a template bank of millions of 1 s duration chirps. Parseval's theorem is used to predict the standard deviation σ of the filter output, taking advantage of the near-Gaussian noise in the LIGO S6 data over 350-2000 Hz. Tails exceeding a multiple of σ are communicated back to a central processing unit. This algorithm attains about 65% efficiency overall, normalized to the fast Fourier transform. At about one million correlations per second over data segments of 16 s duration (N=2^{16} samples), better than real-time analysis is achieved on a cluster of about a dozen GPUs. We demonstrate its application to the capture of high-frequency hardware LIGO injections. This algorithm serves as a starting point for deep all-sky searches in both archive data and real-time analysis in current observational runs.

Adapting hierarchical bidirectional inter prediction on a GPU-based platform for 2D and 3D H.264 video coding

NASA Astrophysics Data System (ADS)

Rodríguez-Sánchez, Rafael; Martínez, José Luis; Cock, Jan De; Fernández-Escribano, Gerardo; Pieters, Bart; Sánchez, José L.; Claver, José M.; de Walle, Rik Van

2013-12-01

The H.264/AVC video coding standard introduces some improved tools in order to increase compression efficiency. Moreover, the multi-view extension of H.264/AVC, called H.264/MVC, adopts many of them. Among the new features, variable block-size motion estimation is one which contributes to high coding efficiency. Furthermore, it defines a different prediction structure that includes hierarchical bidirectional pictures, outperforming traditional Group of Pictures patterns in both scenarios: single-view and multi-view. However, these video coding techniques have high computational complexity. Several techniques have been proposed in the literature over the last few years which are aimed at accelerating the inter prediction process, but there are no works focusing on bidirectional prediction or hierarchical prediction. In this article, with the emergence of many-core processors or accelerators, a step forward is taken towards an implementation of an H.264/AVC and H.264/MVC inter prediction algorithm on a graphics processing unit. The results show a negligible rate distortion drop with a time reduction of up to 98% for the complete H.264/AVC encoder.

Validation of columnar CsI x-ray detector responses obtained with hybridMANTIS, a CPU-GPU Monte Carlo code for coupled x-ray, electron, and optical transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sharma, Diksha; Badano, Aldo

2013-03-15

Purpose: hybridMANTIS is a Monte Carlo package for modeling indirect x-ray imagers using columnar geometry based on a hybrid concept that maximizes the utilization of available CPU and graphics processing unit processors in a workstation. Methods: The authors compare hybridMANTIS x-ray response simulations to previously published MANTIS and experimental data for four cesium iodide scintillator screens. These screens have a variety of reflective and absorptive surfaces with different thicknesses. The authors analyze hybridMANTIS results in terms of modulation transfer function and calculate the root mean square difference and Swank factors from simulated and experimental results. Results: The comparison suggests thatmore » hybridMANTIS better matches the experimental data as compared to MANTIS, especially at high spatial frequencies and for the thicker screens. hybridMANTIS simulations are much faster than MANTIS with speed-ups up to 5260. Conclusions: hybridMANTIS is a useful tool for improved description and optimization of image acquisition stages in medical imaging systems and for modeling the forward problem in iterative reconstruction algorithms.« less

Validation of GPU based TomoTherapy dose calculation engine.

PubMed

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) < 1. The worst case observed in the phantom had 0.22% voxels violating the criterion. In patient cases, the worst percentage of voxels violating the criterion was 0.57%. For absolute point dose verification, all cases agreed with measurement to within ±3% with average error magnitude within 1%. All cases passed the acceptance criterion that more than 95% of the pixels have Γ(3%, 3 mm) < 1 in film measurement, and the average passing pixel percentage is 98.5%-99%. The GPU dose 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.

Multidisciplinary Simulation Acceleration using Multiple Shared-Memory Graphical Processing Units

NASA Astrophysics Data System (ADS)

Kemal, Jonathan Yashar

For purposes of optimizing and analyzing turbomachinery and other designs, the unsteady Favre-averaged flow-field differential equations for an ideal compressible gas can be solved in conjunction with the heat conduction equation. We solve all equations using the finite-volume multiple-grid numerical technique, with the dual time-step scheme used for unsteady simulations. Our numerical solver code targets CUDA-capable Graphical Processing Units (GPUs) produced by NVIDIA. Making use of MPI, our solver can run across networked compute notes, where each MPI process can use either a GPU or a Central Processing Unit (CPU) core for primary solver calculations. We use NVIDIA Tesla C2050/C2070 GPUs based on the Fermi architecture, and compare our resulting performance against Intel Zeon X5690 CPUs. Solver routines converted to CUDA typically run about 10 times faster on a GPU for sufficiently dense computational grids. We used a conjugate cylinder computational grid and ran a turbulent steady flow simulation using 4 increasingly dense computational grids. Our densest computational grid is divided into 13 blocks each containing 1033x1033 grid points, for a total of 13.87 million grid points or 1.07 million grid points per domain block. To obtain overall speedups, we compare the execution time of the solver's iteration loop, including all resource intensive GPU-related memory copies. Comparing the performance of 8 GPUs to that of 8 CPUs, we obtain an overall speedup of about 6.0 when using our densest computational grid. This amounts to an 8-GPU simulation running about 39.5 times faster than running than a single-CPU simulation.

Transputer parallel processing at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Ellis, Graham K.

1989-01-01

The transputer parallel processing lab at NASA Lewis Research Center (LeRC) consists of 69 processors (transputers) that can be connected into various networks for use in general purpose concurrent processing applications. The main goal of the lab is to develop concurrent scientific and engineering application programs that will take advantage of the computational speed increases available on a parallel processor over the traditional sequential processor. Current research involves the development of basic programming tools. These tools will help standardize program interfaces to specific hardware by providing a set of common libraries for applications programmers. The thrust of the current effort is in developing a set of tools for graphics rendering/animation. The applications programmer currently has two options for on-screen plotting. One option can be used for static graphics displays and the other can be used for animated motion. The option for static display involves the use of 2-D graphics primitives that can be called from within an application program. These routines perform the standard 2-D geometric graphics operations in real-coordinate space as well as allowing multiple windows on a single screen.

cellGPU: Massively parallel simulations of dynamic vertex models

NASA Astrophysics Data System (ADS)

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

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

NASA Astrophysics Data System (ADS)

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.

Testing and Validating Gadget2 for GPUs

NASA Astrophysics Data System (ADS)

Wibking, Benjamin; Holley-Bockelmann, K.; Berlind, A. A.

2013-01-01

We are currently upgrading a version of Gadget2 (Springel et al., 2005) that is optimized for NVIDIA's CUDA GPU architecture (Frigaard, unpublished) to work with the latest libraries and graphics cards. Preliminary tests of its performance indicate a ~40x speedup in the particle force tree approximation calculation, with overall speedup of 5-10x for cosmological simulations run with GPUs compared to running on the same CPU cores without GPU acceleration. We believe this speedup can be reasonably increased by an additional factor of two with futher optimization, including overlap of computation on CPU and GPU. Tests of single-precision GPU numerical fidelity currently indicate accuracy of the mass function and the spectral power density to within a few percent of extended-precision CPU results with the unmodified form of Gadget. Additionally, we plan to test and optimize the GPU code for Millenium-scale "grand challenge" simulations of >10^9 particles, a scale that has been previously untested with this code, with the aid of the NSF XSEDE flagship GPU-based supercomputing cluster codenamed "Keeneland." Current work involves additional validation of numerical results, extending the numerical precision of the GPU calculations to double precision, and evaluating performance/accuracy tradeoffs. We believe that this project, if successful, will yield substantial computational performance benefits to the N-body research community as the next generation of GPU supercomputing resources becomes available, both increasing the electrical power efficiency of ever-larger computations (making simulations possible a decade from now at scales and resolutions unavailable today) and accelerating the pace of research in the field.

Lossless data compression for improving the performance of a GPU-based beamformer.

PubMed

Lok, U-Wai; Fan, Gang-Wei; Li, Pai-Chi

2015-04-01

The powerful parallel computation ability of a graphics processing unit (GPU) makes it feasible to perform dynamic receive beamforming However, a real time GPU-based beamformer requires high data rate to transfer radio-frequency (RF) data from hardware to software memory, as well as from central processing unit (CPU) to GPU memory. There are data compression methods (e.g. Joint Photographic Experts Group (JPEG)) available for the hardware front end to reduce data size, alleviating the data transfer requirement of the hardware interface. Nevertheless, the required decoding time may even be larger than the transmission time of its original data, in turn degrading the overall performance of the GPU-based beamformer. This article proposes and implements a lossless compression-decompression algorithm, which enables in parallel compression and decompression of data. By this means, the data transfer requirement of hardware interface and the transmission time of CPU to GPU data transfers are reduced, without sacrificing image quality. In simulation results, the compression ratio reached around 1.7. The encoder design of our lossless compression approach requires low hardware resources and reasonable latency in a field programmable gate array. In addition, the transmission time of transferring data from CPU to GPU with the parallel decoding process improved by threefold, as compared with transferring original uncompressed data. These results show that our proposed lossless compression plus parallel decoder approach not only mitigate the transmission bandwidth requirement to transfer data from hardware front end to software system but also reduce the transmission time for CPU to GPU data transfer. © The Author(s) 2014.

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

NASA Astrophysics Data System (ADS)

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.

OCTGRAV: Sparse Octree Gravitational N-body Code on Graphics Processing Units

NASA Astrophysics Data System (ADS)

Gaburov, Evghenii; Bédorf, Jeroen; Portegies Zwart, Simon

2010-10-01

Octgrav is a very fast tree-code which runs on massively parallel Graphical Processing Units (GPU) with NVIDIA CUDA architecture. The algorithms are based on parallel-scan and sort methods. The tree-construction and calculation of multipole moments is carried out on the host CPU, while the force calculation which consists of tree walks and evaluation of interaction list is carried out on the GPU. In this way, a sustained performance of about 100GFLOP/s and data transfer rates of about 50GB/s is achieved. It takes about a second to compute forces on a million particles with an opening angle of heta approx 0.5. To test the performance and feasibility, we implemented the algorithms in CUDA in the form of a gravitational tree-code which completely runs on the GPU. The tree construction and traverse algorithms are portable to many-core devices which have support for CUDA or OpenCL programming languages. The gravitational tree-code outperforms tuned CPU code during the tree-construction and shows a performance improvement of more than a factor 20 overall, resulting in a processing rate of more than 2.8 million particles per second. The code has a convenient user interface and is freely available for use.

GRay: A Massively Parallel GPU-based Code for Ray Tracing in Relativistic Spacetimes

NASA Astrophysics Data System (ADS)

Chan, Chi-kwan; Psaltis, Dimitrios; Özel, Feryal

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.

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

NASA Astrophysics Data System (ADS)

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.

Accelerating large-scale protein structure alignments with graphics processing units

PubMed Central

2012-01-01

Background Large-scale protein structure alignment, an indispensable tool to structural bioinformatics, poses a tremendous challenge on computational resources. To ensure structure alignment accuracy and efficiency, efforts have been made to parallelize traditional alignment algorithms in grid environments. However, these solutions are costly and of limited accessibility. Others trade alignment quality for speedup by using high-level characteristics of structure fragments for structure comparisons. Findings We present ppsAlign, a parallel protein structure Alignment framework designed and optimized to exploit the parallelism of Graphics Processing Units (GPUs). As a general-purpose GPU platform, ppsAlign could take many concurrent methods, such as TM-align and Fr-TM-align, into the parallelized algorithm design. We evaluated ppsAlign on an NVIDIA Tesla C2050 GPU card, and compared it with existing software solutions running on an AMD dual-core CPU. We observed a 36-fold speedup over TM-align, a 65-fold speedup over Fr-TM-align, and a 40-fold speedup over MAMMOTH. Conclusions ppsAlign is a high-performance protein structure alignment tool designed to tackle the computational complexity issues from protein structural data. The solution presented in this paper allows large-scale structure comparisons to be performed using massive parallel computing power of GPU. PMID:22357132

Implementation of GPU accelerated SPECT reconstruction with Monte Carlo-based scatter correction.

PubMed

Bexelius, Tobias; Sohlberg, Antti

2018-06-01

Statistical SPECT reconstruction can be very time-consuming especially when compensations for collimator and detector response, attenuation, and scatter are included in the reconstruction. This work proposes an accelerated SPECT reconstruction algorithm based on graphics processing unit (GPU) processing. Ordered subset expectation maximization (OSEM) algorithm with CT-based attenuation modelling, depth-dependent Gaussian convolution-based collimator-detector response modelling, and Monte Carlo-based scatter compensation was implemented using OpenCL. The OpenCL implementation was compared against the existing multi-threaded OSEM implementation running on a central processing unit (CPU) in terms of scatter-to-primary ratios, standardized uptake values (SUVs), and processing speed using mathematical phantoms and clinical multi-bed bone SPECT/CT studies. The difference in scatter-to-primary ratios, visual appearance, and SUVs between GPU and CPU implementations was minor. On the other hand, at its best, the GPU implementation was noticed to be 24 times faster than the multi-threaded CPU version on a normal 128 × 128 matrix size 3 bed bone SPECT/CT data set when compensations for collimator and detector response, attenuation, and scatter were included. GPU SPECT reconstructions show great promise as an every day clinical reconstruction tool.

Accelerated event-by-event Monte Carlo microdosimetric calculations of electrons and protons tracks on a multi-core CPU and a CUDA-enabled GPU.

PubMed

Kalantzis, Georgios; Tachibana, Hidenobu

2014-01-01

For microdosimetric calculations event-by-event Monte Carlo (MC) methods are considered the most accurate. The main shortcoming of those methods is the extensive requirement for computational time. In this work we present an event-by-event MC code of low projectile energy electron and proton tracks for accelerated microdosimetric MC simulations on a graphic processing unit (GPU). Additionally, a hybrid implementation scheme was realized by employing OpenMP and CUDA in such a way that both GPU and multi-core CPU were utilized simultaneously. The two implementation schemes have been tested and compared with the sequential single threaded MC code on the CPU. Performance comparison was established on the speed-up for a set of benchmarking cases of electron and proton tracks. A maximum speedup of 67.2 was achieved for the GPU-based MC code, while a further improvement of the speedup up to 20% was achieved for the hybrid approach. The results indicate the capability of our CPU-GPU implementation for accelerated MC microdosimetric calculations of both electron and proton tracks without loss of accuracy. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography.

PubMed

Peng, Bo; Wang, Yuqi; Hall, Timothy J; Jiang, Jingfeng

2017-04-01

Our primary objective of this paper was to extend a previously published 2-D coupled subsample tracking algorithm for 3-D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3-D coupled subsample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking phantom and in vivo breast ultrasound data. The performance of this 3-D subsample tracking algorithm was compared with the conventional 3-D quadratic subsample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3-D subsample estimation algorithm can provide high-quality strain data (i.e., high correlation between the predeformation and the motion-compensated postdeformation radio frequency echo data and high contrast-to-noise ratio strain images), as compared with the conventional 3-D quadratic subsample algorithm. Using the GPU implementation of the 3-D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 s per volume [2.5 cm ×2.5 cm ×2.5 cm]).

Streaming parallel GPU acceleration of large-scale filter-based spiking neural networks.

PubMed

Slażyński, Leszek; Bohte, Sander

2012-01-01

The arrival of graphics processing (GPU) cards suitable for massively parallel computing promises affordable large-scale neural network simulation previously only available at supercomputing facilities. While the raw numbers suggest that GPUs may outperform CPUs by at least an order of magnitude, the challenge is to develop fine-grained parallel algorithms to fully exploit the particulars of GPUs. Computation in a neural network is inherently parallel and thus a natural match for GPU architectures: given inputs, the internal state for each neuron can be updated in parallel. We show that for filter-based spiking neurons, like the Spike Response Model, the additive nature of membrane potential dynamics enables additional update parallelism. This also reduces the accumulation of numerical errors when using single precision computation, the native precision of GPUs. We further show that optimizing simulation algorithms and data structures to the GPU's architecture has a large pay-off: for example, matching iterative neural updating to the memory architecture of the GPU speeds up this simulation step by a factor of three to five. With such optimizations, we can simulate in better-than-realtime plausible spiking neural networks of up to 50 000 neurons, processing over 35 million spiking events per second.

Real-time time-division color electroholography using a single GPU and a USB module for synchronizing reference light.

PubMed

Araki, Hiromitsu; Takada, Naoki; Niwase, Hiroaki; Ikawa, Shohei; Fujiwara, Masato; Nakayama, Hirotaka; Kakue, Takashi; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2015-12-01

We propose real-time time-division color electroholography using a single graphics processing unit (GPU) and a simple synchronization system of reference light. To facilitate real-time time-division color electroholography, we developed a light emitting diode (LED) controller with a universal serial bus (USB) module and the drive circuit for reference light. A one-chip RGB LED connected to a personal computer via an LED controller was used as the reference light. A single GPU calculates three computer-generated holograms (CGHs) suitable for red, green, and blue colors in each frame of a three-dimensional (3D) movie. After CGH calculation using a single GPU, the CPU can synchronize the CGH display with the color switching of the one-chip RGB LED via the LED controller. Consequently, we succeeded in real-time time-division color electroholography for a 3D object consisting of around 1000 points per color when an NVIDIA GeForce GTX TITAN was used as the GPU. Furthermore, we implemented the proposed method in various GPUs. The experimental results showed that the proposed method was effective for various GPUs.

Large Scale Document Inversion using a Multi-threaded Computing System

PubMed Central

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

2018-01-01

Current microprocessor architecture is moving towards multi-core/multi-threaded systems. This trend has led to a surge of interest in using multi-threaded computing devices, such as the Graphics Processing Unit (GPU), for general purpose computing. We can utilize the GPU in computation as a massive parallel coprocessor because the GPU consists of multiple cores. The GPU is also an affordable, attractive, and user-programmable commodity. Nowadays a lot of information has been flooded into the digital domain around the world. Huge volume of data, such as digital libraries, social networking services, e-commerce product data, and reviews, etc., is produced or collected every moment with dramatic growth in size. Although the inverted index is a useful data structure that can be used for full text searches or document retrieval, a large number of documents will require a tremendous amount of time to create the index. The performance of document inversion can be improved by multi-thread or multi-core GPU. Our approach is to implement a linear-time, hash-based, single program multiple data (SPMD), document inversion algorithm on the NVIDIA GPU/CUDA programming platform utilizing the huge computational power of the GPU, to develop high performance solutions for document indexing. Our proposed parallel document inversion system shows 2-3 times faster performance than a sequential system on two different test datasets from PubMed abstract and e-commerce product reviews. CCS Concepts •Information systems➝Information retrieval • Computing methodologies➝Massively parallel and high-performance simulations. PMID:29861701

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

PubMed

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

2017-06-01

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

A Simple GPU-Accelerated Two-Dimensional MUSCL-Hancock Solver for Ideal Magnetohydrodynamics

NASA Technical Reports Server (NTRS)

Bard, Christopher; Dorelli, John C.

2013-01-01

We describe our experience using NVIDIA's CUDA (Compute Unified Device Architecture) C programming environment to implement a two-dimensional second-order MUSCL-Hancock ideal magnetohydrodynamics (MHD) solver on a GTX 480 Graphics Processing Unit (GPU). Taking a simple approach in which the MHD variables are stored exclusively in the global memory of the GTX 480 and accessed in a cache-friendly manner (without further optimizing memory access by, for example, staging data in the GPU's faster shared memory), we achieved a maximum speed-up of approx. = 126 for a sq 1024 grid relative to the sequential C code running on a single Intel Nehalem (2.8 GHz) core. This speedup is consistent with simple estimates based on the known floating point performance, memory throughput and parallel processing capacity of the GTX 480.

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

NASA Astrophysics Data System (ADS)

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).

Multi-Kepler GPU vs. multi-Intel MIC for spin systems simulations

NASA Astrophysics Data System (ADS)

Bernaschi, M.; Bisson, M.; Salvadore, F.

2014-10-01

We present and compare the performances of two many-core architectures: the Nvidia Kepler and the Intel MIC both in a single system and in cluster configuration for the simulation of spin systems. As a benchmark we consider the time required to update a single spin of the 3D Heisenberg spin glass model by using the Over-relaxation algorithm. We present data also for a traditional high-end multi-core architecture: the Intel Sandy Bridge. The results show that although on the two Intel architectures it is possible to use basically the same code, the performances of a Intel MIC change dramatically depending on (apparently) minor details. Another issue is that to obtain a reasonable scalability with the Intel Phi coprocessor (Phi is the coprocessor that implements the MIC architecture) in a cluster configuration it is necessary to use the so-called offload mode which reduces the performances of the single system. As to the GPU, the Kepler architecture offers a clear advantage with respect to the previous Fermi architecture maintaining exactly the same source code. Scalability of the multi-GPU implementation remains very good by using the CPU as a communication co-processor of the GPU. All source codes are provided for inspection and for double-checking the results.

GPU Lossless Hyperspectral Data Compression System for Space Applications

NASA Technical Reports Server (NTRS)

Keymeulen, Didier; Aranki, Nazeeh; Hopson, Ben; Kiely, Aaron; Klimesh, Matthew; Benkrid, Khaled

2012-01-01

On-board lossless hyperspectral data compression reduces data volume in order to meet NASA and DoD limited downlink capabilities. At JPL, a novel, adaptive and predictive technique for lossless compression of hyperspectral data, named the Fast Lossless (FL) algorithm, was recently developed. This technique uses an adaptive filtering method and achieves state-of-the-art performance in both compression effectiveness and low complexity. Because of its outstanding performance and suitability for real-time onboard hardware implementation, the FL compressor is being formalized as the emerging CCSDS Standard for Lossless Multispectral & Hyperspectral image compression. The FL compressor is well-suited for parallel hardware implementation. A GPU hardware implementation was developed for FL targeting the current state-of-the-art GPUs from NVIDIA(Trademark). The GPU implementation on a NVIDIA(Trademark) GeForce(Trademark) GTX 580 achieves a throughput performance of 583.08 Mbits/sec (44.85 MSamples/sec) and an acceleration of at least 6 times a software implementation running on a 3.47 GHz single core Intel(Trademark) Xeon(Trademark) processor. This paper describes the design and implementation of the FL algorithm on the GPU. The massively parallel implementation will provide in the future a fast and practical real-time solution for airborne and space applications.

Real-time motion artifacts compensation of ToF sensors data on GPU

NASA Astrophysics Data System (ADS)

Lefloch, Damien; Hoegg, Thomas; Kolb, Andreas

2013-05-01

Over the last decade, ToF sensors attracted many computer vision and graphics researchers. Nevertheless, ToF devices suffer from severe motion artifacts for dynamic scenes as well as low-resolution depth data which strongly justifies the importance of a valid correction. To counterbalance this effect, a pre-processing approach is introduced to greatly improve range image data on dynamic scenes. We first demonstrate the robustness of our approach using simulated data to finally validate our method using sensor range data. Our GPU-based processing pipeline enhances range data reliability in real-time.

Problems Related to Parallelization of CFD Algorithms on GPU, Multi-GPU and Hybrid Architectures

NASA Astrophysics Data System (ADS)

Biazewicz, Marek; Kurowski, Krzysztof; Ludwiczak, Bogdan; Napieraia, Krystyna

2010-09-01

Computational Fluid Dynamics (CFD) is one of the branches of fluid mechanics, which uses numerical methods and algorithms to solve and analyze fluid flows. CFD is used in various domains, such as oil and gas reservoir uncertainty analysis, aerodynamic body shapes optimization (e.g. planes, cars, ships, sport helmets, skis), natural phenomena analysis, numerical simulation for weather forecasting or realistic visualizations. CFD problem is very complex and needs a lot of computational power to obtain the results in a reasonable time. We have implemented a parallel application for two-dimensional CFD simulation with a free surface approximation (MAC method) using new hardware architectures, in particular multi-GPU and hybrid computing environments. For this purpose we decided to use NVIDIA graphic cards with CUDA environment due to its simplicity of programming and good computations performance. We used finite difference discretization of Navier-Stokes equations, where fluid is propagated over an Eulerian Grid. In this model, the behavior of the fluid inside the cell depends only on the properties of local, surrounding cells, therefore it is well suited for the GPU-based architecture. In this paper we demonstrate how to use efficiently the computing power of GPUs for CFD. Additionally, we present some best practices to help users analyze and improve the performance of CFD applications executed on GPU. Finally, we discuss various challenges around the multi-GPU implementation on the example of matrix multiplication.

OpenACC acceleration of an unstructured CFD solver based on a reconstructed discontinuous Galerkin method for compressible flows

DOE PAGES

Xia, Yidong; Lou, Jialin; Luo, Hong; ...

2015-02-09

Here, an OpenACC directive-based graphics processing unit (GPU) parallel scheme is presented for solving the compressible Navier–Stokes equations on 3D hybrid unstructured grids with a third-order reconstructed discontinuous Galerkin method. The developed scheme requires the minimum code intrusion and algorithm alteration for upgrading a legacy solver with the GPU computing capability at very little extra effort in programming, which leads to a unified and portable code development strategy. A face coloring algorithm is adopted to eliminate the memory contention because of the threading of internal and boundary face integrals. A number of flow problems are presented to verify the implementationmore » of the developed scheme. Timing measurements were obtained by running the resulting GPU code on one Nvidia Tesla K20c GPU card (Nvidia Corporation, Santa Clara, CA, USA) and compared with those obtained by running the equivalent Message Passing Interface (MPI) parallel CPU code on a compute node (consisting of two AMD Opteron 6128 eight-core CPUs (Advanced Micro Devices, Inc., Sunnyvale, CA, USA)). Speedup factors of up to 24× and 1.6× for the GPU code were achieved with respect to one and 16 CPU cores, respectively. The numerical results indicate that this OpenACC-based parallel scheme is an effective and extensible approach to port unstructured high-order CFD solvers to GPU computing.« less

The novel implicit LU-SGS parallel iterative method based on the diffusion equation of a nuclear reactor on a GPU cluster

NASA Astrophysics Data System (ADS)

Zhang, Jilin; Sha, Chaoqun; Wu, Yusen; Wan, Jian; Zhou, Li; Ren, Yongjian; Si, Huayou; Yin, Yuyu; Jing, Ya

2017-02-01

GPU not only is used in the field of graphic technology but also has been widely used in areas needing a large number of numerical calculations. In the energy industry, because of low carbon, high energy density, high duration and other characteristics, the development of nuclear energy cannot easily be replaced by other energy sources. Management of core fuel is one of the major areas of concern in a nuclear power plant, and it is directly related to the economic benefits and cost of nuclear power. The large-scale reactor core expansion equation is large and complicated, so the calculation of the diffusion equation is crucial in the core fuel management process. In this paper, we use CUDA programming technology on a GPU cluster to run the LU-SGS parallel iterative calculation against the background of the diffusion equation of the reactor. We divide one-dimensional and two-dimensional mesh into a plurality of domains, with each domain evenly distributed on the GPU blocks. A parallel collision scheme is put forward that defines the virtual boundary of the grid exchange information and data transmission by non-stop collision. Compared with the serial program, the experiment shows that GPU greatly improves the efficiency of program execution and verifies that GPU is playing a much more important role in the field of numerical calculations.

Accelerating Climate Simulations Through Hybrid Computing

NASA Technical Reports Server (NTRS)

Zhou, Shujia; Sinno, Scott; Cruz, Carlos; Purcell, Mark

2009-01-01

Unconventional multi-core processors (e.g., IBM Cell B/E and NYIDIDA GPU) have emerged as accelerators in climate simulation. However, climate models typically run on parallel computers with conventional processors (e.g., Intel and AMD) using MPI. Connecting accelerators to this architecture efficiently and easily becomes a critical issue. When using MPI for connection, we identified two challenges: (1) identical MPI implementation is required in both systems, and; (2) existing MPI code must be modified to accommodate the accelerators. In response, we have extended and deployed IBM Dynamic Application Virtualization (DAV) in a hybrid computing prototype system (one blade with two Intel quad-core processors, two IBM QS22 Cell blades, connected with Infiniband), allowing for seamlessly offloading compute-intensive functions to remote, heterogeneous accelerators in a scalable, load-balanced manner. Currently, a climate solar radiation model running with multiple MPI processes has been offloaded to multiple Cell blades with approx.10% network overhead.

Ultraviolet Communication for Medical Applications

DTIC Science & Technology

2015-06-01

In the previous Phase I effort, Directed Energy Inc.’s (DEI) parent company Imaging Systems Technology (IST) demonstrated feasibility of several key...accurately model high path loss. Custom photon scatter code was rewritten for parallel execution on a graphics processing unit (GPU). The NVidia CUDA

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Multi-phase SPH modelling of violent hydrodynamics on GPUs

NASA Astrophysics Data System (ADS)

Mokos, Athanasios; Rogers, Benedict D.; Stansby, Peter K.; Domínguez, José M.

2015-11-01

This paper presents the acceleration of multi-phase smoothed particle hydrodynamics (SPH) using a graphics processing unit (GPU) enabling large numbers of particles (10-20 million) to be simulated on just a single GPU card. With novel hardware architectures such as a GPU, the optimum approach to implement a multi-phase scheme presents some new challenges. Many more particles must be included in the calculation and there are very different speeds of sound in each phase with the largest speed of sound determining the time step. This requires efficient computation. To take full advantage of the hardware acceleration provided by a single GPU for a multi-phase simulation, four different algorithms are investigated: conditional statements, binary operators, separate particle lists and an intermediate global function. Runtime results show that the optimum approach needs to employ separate cell and neighbour lists for each phase. The profiler shows that this approach leads to a reduction in both memory transactions and arithmetic operations giving significant runtime gains. The four different algorithms are compared to the efficiency of the optimised single-phase GPU code, DualSPHysics, for 2-D and 3-D simulations which indicate that the multi-phase functionality has a significant computational overhead. A comparison with an optimised CPU code shows a speed up of an order of magnitude over an OpenMP simulation with 8 threads and two orders of magnitude over a single thread simulation. A demonstration of the multi-phase SPH GPU code is provided by a 3-D dam break case impacting an obstacle. This shows better agreement with experimental results than an equivalent single-phase code. The multi-phase GPU code enables a convergence study to be undertaken on a single GPU with a large number of particles that otherwise would have required large high performance computing resources.

Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.

PubMed

Richmond, Paul; Buesing, Lars; Giugliano, Michele; Vasilaki, Eleni

2011-05-04

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated.

CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions.

PubMed

Liu, Yongchao; Wirawan, Adrianto; Schmidt, Bertil

2013-04-04

The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases. We present CUDASW++ 3.0, a fast Smith-Waterman protein database search algorithm, which couples CPU and GPU SIMD instructions and carries out concurrent CPU and GPU computations. For the CPU computation, this algorithm employs SSE-based vector execution units as accelerators. For the GPU computation, we have investigated for the first time a GPU SIMD parallelization, which employs CUDA PTX SIMD video instructions to gain more data parallelism beyond the SIMT execution model. Moreover, sequence alignment workloads are automatically distributed over CPUs and GPUs based on their respective compute capabilities. Evaluation on the Swiss-Prot database shows that CUDASW++ 3.0 gains a performance improvement over CUDASW++ 2.0 up to 2.9 and 3.2, with a maximum performance of 119.0 and 185.6 GCUPS, on a single-GPU GeForce GTX 680 and a dual-GPU GeForce GTX 690 graphics card, respectively. In addition, our algorithm has demonstrated significant speedups over other top-performing tools: SWIPE and BLAST+. CUDASW++ 3.0 is written in CUDA C++ and PTX assembly languages, targeting GPUs based on the Kepler architecture. This algorithm obtains significant speedups over its predecessor: CUDASW++ 2.0, by benefiting from the use of CPU and GPU SIMD instructions as well as the concurrent execution on CPUs and GPUs. The source code and the simulated data are available at http://cudasw.sourceforge.net.

Revisiting Molecular Dynamics on a CPU/GPU system: Water Kernel and SHAKE Parallelization.

PubMed

Ruymgaart, A Peter; Elber, Ron

2012-11-13

We report Graphics Processing Unit (GPU) and Open-MP parallel implementations of water-specific force calculations and of bond constraints for use in Molecular Dynamics simulations. We focus on a typical laboratory computing-environment in which a CPU with a few cores is attached to a GPU. We discuss in detail the design of the code and we illustrate performance comparable to highly optimized codes such as GROMACS. Beside speed our code shows excellent energy conservation. Utilization of water-specific lists allows the efficient calculations of non-bonded interactions that include water molecules and results in a speed-up factor of more than 40 on the GPU compared to code optimized on a single CPU core for systems larger than 20,000 atoms. This is up four-fold from a factor of 10 reported in our initial GPU implementation that did not include a water-specific code. Another optimization is the implementation of constrained dynamics entirely on the GPU. The routine, which enforces constraints of all bonds, runs in parallel on multiple Open-MP cores or entirely on the GPU. It is based on Conjugate Gradient solution of the Lagrange multipliers (CG SHAKE). The GPU implementation is partially in double precision and requires no communication with the CPU during the execution of the SHAKE algorithm. The (parallel) implementation of SHAKE allows an increase of the time step to 2.0fs while maintaining excellent energy conservation. Interestingly, CG SHAKE is faster than the usual bond relaxation algorithm even on a single core if high accuracy is expected. The significant speedup of the optimized components transfers the computational bottleneck of the MD calculation to the reciprocal part of Particle Mesh Ewald (PME).

Implementation of metal-friendly EAM/FS-type semi-empirical potentials in HOOMD-blue: A GPU-accelerated molecular dynamics software

DOE PAGES

Yang, Lin; Zhang, Feng; Wang, Cai-Zhuang; ...

2018-01-12

We present an implementation of EAM and FS interatomic potentials, which are widely used in simulating metallic systems, in HOOMD-blue, a software designed to perform classical molecular dynamics simulations using GPU accelerations. We first discuss the details of our implementation and then report extensive benchmark tests. We demonstrate that single-precision floating point operations efficiently implemented on GPUs can produce sufficient accuracy when compared against double-precision codes, as demonstrated in test simulations of calculations of the glass-transition temperature of Cu 64.5Zr 35.5, and pair correlation function of liquid Ni 3Al. Our code scales well with the size of the simulating systemmore » on NVIDIA Tesla M40 and P100 GPUs. Compared with another popular software LAMMPS running on 32 cores of AMD Opteron 6220 processors, the GPU/CPU performance ratio can reach as high as 4.6. In conclusion, the source code can be accessed through the HOOMD-blue web page for free by any interested user.« less

Implementation of metal-friendly EAM/FS-type semi-empirical potentials in HOOMD-blue: A GPU-accelerated molecular dynamics software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Lin; Zhang, Feng; Wang, Cai-Zhuang

We present an implementation of EAM and FS interatomic potentials, which are widely used in simulating metallic systems, in HOOMD-blue, a software designed to perform classical molecular dynamics simulations using GPU accelerations. We first discuss the details of our implementation and then report extensive benchmark tests. We demonstrate that single-precision floating point operations efficiently implemented on GPUs can produce sufficient accuracy when compared against double-precision codes, as demonstrated in test simulations of calculations of the glass-transition temperature of Cu 64.5Zr 35.5, and pair correlation function of liquid Ni 3Al. Our code scales well with the size of the simulating systemmore » on NVIDIA Tesla M40 and P100 GPUs. Compared with another popular software LAMMPS running on 32 cores of AMD Opteron 6220 processors, the GPU/CPU performance ratio can reach as high as 4.6. In conclusion, the source code can be accessed through the HOOMD-blue web page for free by any interested user.« less

Matrix Algebra for GPU and Multicore Architectures (MAGMA) for Large Petascale Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dongarra, Jack J.; Tomov, Stanimire

2014-03-24

The goal of the MAGMA project is to create a new generation of linear algebra libraries that achieve the fastest possible time to an accurate solution on hybrid Multicore+GPU-based systems, using all the processing power that future high-end systems can make available within given energy constraints. Our efforts at the University of Tennessee achieved the goals set in all of the five areas identified in the proposal: 1. Communication optimal algorithms; 2. Autotuning for GPU and hybrid processors; 3. Scheduling and memory management techniques for heterogeneity and scale; 4. Fault tolerance and robustness for large scale systems; 5. Building energymore » efficiency into software foundations. The University of Tennessee’s main contributions, as proposed, were the research and software development of new algorithms for hybrid multi/many-core CPUs and GPUs, as related to two-sided factorizations and complete eigenproblem solvers, hybrid BLAS, and energy efficiency for dense, as well as sparse, operations. Furthermore, as proposed, we investigated and experimented with various techniques targeting the five main areas outlined.« less

Embedded-Based Graphics Processing Unit Cluster Platform for Multiple Sequence Alignments

PubMed Central

Wei, Jyh-Da; Cheng, Hui-Jun; Lin, Chun-Yuan; Ye, Jin; Yeh, Kuan-Yu

2017-01-01

High-end graphics processing units (GPUs), such as NVIDIA Tesla/Fermi/Kepler series cards with thousands of cores per chip, are widely applied to high-performance computing fields in a decade. These desktop GPU cards should be installed in personal computers/servers with desktop CPUs, and the cost and power consumption of constructing a GPU cluster platform are very high. In recent years, NVIDIA releases an embedded board, called Jetson Tegra K1 (TK1), which contains 4 ARM Cortex-A15 CPUs and 192 Compute Unified Device Architecture cores (belong to Kepler GPUs). Jetson Tegra K1 has several advantages, such as the low cost, low power consumption, and high applicability, and it has been applied into several specific applications. In our previous work, a bioinformatics platform with a single TK1 (STK platform) was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform). Complex system installation and setup are necessary procedures at first. Then, 2 job assignment modes are designed for the MTK platform to provide services for users. Finally, ClustalW v2.0.11 and ClustalWtk will be ported to the MTK platform. The experimental results showed that the speedup ratios achieved 5.5 and 4.8 times for ClustalW v2.0.11 and ClustalWtk, respectively, by comparing 6 TK1s with a single TK1. The MTK platform is proven to be useful for multiple sequence alignments. PMID:28835734

Porting ONETEP to graphical processing unit-based coprocessors. 1. FFT box operations.

PubMed

Wilkinson, Karl; Skylaris, Chris-Kriton

2013-10-30

We present the first graphical processing unit (GPU) coprocessor-enabled version of the Order-N Electronic Total Energy Package (ONETEP) code for linear-scaling first principles quantum mechanical calculations on materials. This work focuses on porting to the GPU the parts of the code that involve atom-localized fast Fourier transform (FFT) operations. These are among the most computationally intensive parts of the code and are used in core algorithms such as the calculation of the charge density, the local potential integrals, the kinetic energy integrals, and the nonorthogonal generalized Wannier function gradient. We have found that direct porting of the isolated FFT operations did not provide any benefit. Instead, it was necessary to tailor the port to each of the aforementioned algorithms to optimize data transfer to and from the GPU. A detailed discussion of the methods used and tests of the resulting performance are presented, which show that individual steps in the relevant algorithms are accelerated by a significant amount. However, the transfer of data between the GPU and host machine is a significant bottleneck in the reported version of the code. In addition, an initial investigation into a dynamic precision scheme for the ONETEP energy calculation has been performed to take advantage of the enhanced single precision capabilities of GPUs. The methods used here result in no disruption to the existing code base. Furthermore, as the developments reported here concern the core algorithms, they will benefit the full range of ONETEP functionality. Our use of a directive-based programming model ensures portability to other forms of coprocessors and will allow this work to form the basis of future developments to the code designed to support emerging high-performance computing platforms. Copyright © 2013 Wiley Periodicals, Inc.

Embedded-Based Graphics Processing Unit Cluster Platform for Multiple Sequence Alignments.

PubMed

Wei, Jyh-Da; Cheng, Hui-Jun; Lin, Chun-Yuan; Ye, Jin; Yeh, Kuan-Yu

2017-01-01

High-end graphics processing units (GPUs), such as NVIDIA Tesla/Fermi/Kepler series cards with thousands of cores per chip, are widely applied to high-performance computing fields in a decade. These desktop GPU cards should be installed in personal computers/servers with desktop CPUs, and the cost and power consumption of constructing a GPU cluster platform are very high. In recent years, NVIDIA releases an embedded board, called Jetson Tegra K1 (TK1), which contains 4 ARM Cortex-A15 CPUs and 192 Compute Unified Device Architecture cores (belong to Kepler GPUs). Jetson Tegra K1 has several advantages, such as the low cost, low power consumption, and high applicability, and it has been applied into several specific applications. In our previous work, a bioinformatics platform with a single TK1 (STK platform) was constructed, and this previous work is also used to prove that the Web and mobile services can be implemented in the STK platform with a good cost-performance ratio by comparing a STK platform with the desktop CPU and GPU. In this work, an embedded-based GPU cluster platform will be constructed with multiple TK1s (MTK platform). Complex system installation and setup are necessary procedures at first. Then, 2 job assignment modes are designed for the MTK platform to provide services for users. Finally, ClustalW v2.0.11 and ClustalWtk will be ported to the MTK platform. The experimental results showed that the speedup ratios achieved 5.5 and 4.8 times for ClustalW v2.0.11 and ClustalWtk, respectively, by comparing 6 TK1s with a single TK1. The MTK platform is proven to be useful for multiple sequence alignments.

Massively parallel algorithm and implementation of RI-MP2 energy calculation for peta-scale many-core supercomputers.

PubMed

Katouda, Michio; Naruse, Akira; Hirano, Yukihiko; Nakajima, Takahito

2016-11-15

A new parallel algorithm and its implementation for the RI-MP2 energy calculation utilizing peta-flop-class many-core supercomputers are presented. Some improvements from the previous algorithm (J. Chem. Theory Comput. 2013, 9, 5373) have been performed: (1) a dual-level hierarchical parallelization scheme that enables the use of more than 10,000 Message Passing Interface (MPI) processes and (2) a new data communication scheme that reduces network communication overhead. A multi-node and multi-GPU implementation of the present algorithm is presented for calculations on a central processing unit (CPU)/graphics processing unit (GPU) hybrid supercomputer. Benchmark results of the new algorithm and its implementation using the K computer (CPU clustering system) and TSUBAME 2.5 (CPU/GPU hybrid system) demonstrate high efficiency. The peak performance of 3.1 PFLOPS is attained using 80,199 nodes of the K computer. The peak performance of the multi-node and multi-GPU implementation is 514 TFLOPS using 1349 nodes and 4047 GPUs of TSUBAME 2.5. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

3D brain tumor localization and parameter estimation using thermographic approach on GPU.

PubMed

Bousselham, Abdelmajid; Bouattane, Omar; Youssfi, Mohamed; Raihani, Abdelhadi

2018-01-01

The aim of this paper is to present a GPU parallel algorithm for brain tumor detection to estimate its size and location from surface temperature distribution obtained by thermography. The normal brain tissue is modeled as a rectangular cube including spherical tumor. The temperature distribution is calculated using forward three dimensional Pennes bioheat transfer equation, it's solved using massively parallel Finite Difference Method (FDM) and implemented on Graphics Processing Unit (GPU). Genetic Algorithm (GA) was used to solve the inverse problem and estimate the tumor size and location by minimizing an objective function involving measured temperature on the surface to those obtained by numerical simulation. The parallel implementation of Finite Difference Method reduces significantly the time of bioheat transfer and greatly accelerates the inverse identification of brain tumor thermophysical and geometrical properties. Experimental results show significant gains in the computational speed on GPU and achieve a speedup of around 41 compared to the CPU. The analysis performance of the estimation based on tumor size inside brain tissue also presented. Copyright © 2017 Elsevier Ltd. All rights reserved.

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

PubMed

Jayaraj, P B; Ajay, Mathias K; Nufail, M; Gopakumar, G; Jaleel, U C A

2016-01-01

In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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.

GPU Accelerated Vector Median Filter

NASA Technical Reports Server (NTRS)

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 .

GPU-based Green's function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models.

PubMed

Yang, Yiqun; Urban, Matthew W; McGough, Robert J

2018-05-15

Shear wave calculations induced by an acoustic radiation force are very time-consuming on desktop computers, and high-performance graphics processing units (GPUs) achieve dramatic reductions in the computation time for these simulations. The acoustic radiation force is calculated using the fast near field method and the angular spectrum approach, and then the shear waves are calculated in parallel with Green's functions on a GPU. This combination enables rapid evaluation of shear waves for push beams with different spatial samplings and for apertures with different f/#. Relative to shear wave simulations that evaluate the same algorithm on an Intel i7 desktop computer, a high performance nVidia GPU reduces the time required for these calculations by a factor of 45 and 700 when applied to elastic and viscoelastic shear wave simulation models, respectively. These GPU-accelerated simulations also compared to measurements in different viscoelastic phantoms, and the results are similar. For parametric evaluations and for comparisons with measured shear wave data, shear wave simulations with the Green's function approach are ideally suited for high-performance GPUs.

Graphics Processing Unit Assisted Thermographic Compositing

NASA Technical Reports Server (NTRS)

Ragasa, Scott; McDougal, Matthew; Russell, Sam

2012-01-01

Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often great, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques. Technical Methodology/Approach: Apply massively parallel algorithms and data structures to the specific analysis requirements presented when working with thermographic data sets.

Matrix decomposition graphics processing unit solver for Poisson image editing

NASA Astrophysics Data System (ADS)

Lei, Zhao; Wei, Li

2012-10-01

In recent years, gradient-domain methods have been widely discussed in the image processing field, including seamless cloning and image stitching. These algorithms are commonly carried out by solving a large sparse linear system: the Poisson equation. However, solving the Poisson equation is a computational and memory intensive task which makes it not suitable for real-time image editing. A new matrix decomposition graphics processing unit (GPU) solver (MDGS) is proposed to settle the problem. A matrix decomposition method is used to distribute the work among GPU threads, so that MDGS will take full advantage of the computing power of current GPUs. Additionally, MDGS is a hybrid solver (combines both the direct and iterative techniques) and has two-level architecture. These enable MDGS to generate identical solutions with those of the common Poisson methods and achieve high convergence rate in most cases. This approach is advantageous in terms of parallelizability, enabling real-time image processing, low memory-taken and extensive applications.

Graphics-processing-unit-accelerated finite-difference time-domain simulation of the interaction between ultrashort laser pulses and metal nanoparticles

NASA Astrophysics Data System (ADS)

Nikolskiy, V. P.; Stegailov, V. V.

2018-01-01

Metal nanoparticles (NPs) serve as important tools for many modern technologies. However, the proper microscopic models of the interaction between ultrashort laser pulses and metal NPs are currently not very well developed in many cases. One part of the problem is the description of the warm dense matter that is formed in NPs after intense irradiation. Another part of the problem is the description of the electromagnetic waves around NPs. Description of wave propagation requires the solution of Maxwell’s equations and the finite-difference time-domain (FDTD) method is the classic approach for solving them. There are many commercial and free implementations of FDTD, including the open source software that supports graphics processing unit (GPU) acceleration. In this report we present the results on the FDTD calculations for different cases of the interaction between ultrashort laser pulses and metal nanoparticles. Following our previous results, we analyze the efficiency of the GPU acceleration of the FDTD algorithm.

FPGA Implementation of the Coupled Filtering Method and the Affine Warping Method.

PubMed

Zhang, Chen; Liang, Tianzhu; Mok, Philip K T; Yu, Weichuan

2017-07-01

In ultrasound image analysis, the speckle tracking methods are widely applied to study the elasticity of body tissue. However, "feature-motion decorrelation" still remains as a challenge for the speckle tracking methods. Recently, a coupled filtering method and an affine warping method were proposed to accurately estimate strain values, when the tissue deformation is large. The major drawback of these methods is the high computational complexity. Even the graphics processing unit (GPU)-based program requires a long time to finish the analysis. In this paper, we propose field-programmable gate array (FPGA)-based implementations of both methods for further acceleration. The capability of FPGAs on handling different image processing components in these methods is discussed. A fast and memory-saving image warping approach is proposed. The algorithms are reformulated to build a highly efficient pipeline on FPGA. The final implementations on a Xilinx Virtex-7 FPGA are at least 13 times faster than the GPU implementation on the NVIDIA graphic card (GeForce GTX 580).

FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks

PubMed Central

Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

2015-01-01

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out. PMID:25602758

FastGCN: a GPU accelerated tool for fast gene co-expression networks.

PubMed

Liang, Meimei; Zhang, Futao; Jin, Gulei; Zhu, Jun

2015-01-01

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.

Preliminary Study of Image Reconstruction Algorithm on a Digital Signal Processor

DTIC Science & Technology

2014-03-01

5.2 Comparison of CPU-GPU, CPU-FPGA, and CPU-DSP Designs The work for implementing VHDL description of the back-projection algorithm on a physical...FPGA was not complete. Hence, the DSP implementation results are compared with the simulated results for the VHDL design. Simulating VHDL provides an...rather than at the software level. Depending on an application’s characteristics, FPGA implementations can provide a significant performance

JESPP: Joint Experimentation on Scalable Parallel Processors Supercomputers

DTIC Science & Technology

2010-03-01

were for the relatively small market of scientific and engineering applications. Contrast this with GPUs that are designed to improve the end- user...experience in mass- market arenas such as gaming. In order to get meaningful speed-up using the GPU, it was determined that the data transfer and...Included) Conference Year Effectively using a Large GPGPU-Enhanced Linux Cluster HPCMP UGC 2009 FLOPS per Watt: Heterogeneous-Computing’s Approach

2005 6th Annual Science and Engineering Technology Conference

DTIC Science & Technology

2005-04-21

BioFAC VBAIDS Hybrid: PCR/Immuno Fast PCR Fast Immunoassay Mass Spec (Pyrolysis) SIBS UV -LIF IR Fluorochrome Charge Detect. BioCADS Trigger Advanced...Weights Beam forming Signal Processing mapped to GPU architecture Vector Processor STAP (STAP-BOY) GaN High Frequency Transistor (WBG-RF) UV Laser...Service anti- counterfeiting • Embedded security strips Technology Limitations and Barriers • Training and cost (training intensive) Land Borders North Land

Interactive Light Stimulus Generation with High Performance Real-Time Image Processing and Simple Scripting.

PubMed

Szécsi, László; Kacsó, Ágota; Zeck, Günther; Hantz, Péter

2017-01-01

Light stimulation with precise and complex spatial and temporal modulation is demanded by a series of research fields like visual neuroscience, optogenetics, ophthalmology, and visual psychophysics. We developed a user-friendly and flexible stimulus generating framework (GEARS GPU-based Eye And Retina Stimulation Software), which offers access to GPU computing power, and allows interactive modification of stimulus parameters during experiments. Furthermore, it has built-in support for driving external equipment, as well as for synchronization tasks, via USB ports. The use of GEARS does not require elaborate programming skills. The necessary scripting is visually aided by an intuitive interface, while the details of the underlying software and hardware components remain hidden. Internally, the software is a C++/Python hybrid using OpenGL graphics. Computations are performed on the GPU, and are defined in the GLSL shading language. However, all GPU settings, including the GPU shader programs, are automatically generated by GEARS. This is configured through a method encountered in game programming, which allows high flexibility: stimuli are straightforwardly composed using a broad library of basic components. Stimulus rendering is implemented solely in C++, therefore intermediary libraries for interfacing could be omitted. This enables the program to perform computationally demanding tasks like en-masse random number generation or real-time image processing by local and global operations.

A GPU-accelerated 3D Coupled Sub-sample Estimation Algorithm for Volumetric Breast Strain Elastography

PubMed Central

Peng, Bo; Wang, Yuqi; Hall, Timothy J; Jiang, Jingfeng

2017-01-01

Our primary objective of this work was to extend a previously published 2D coupled sub-sample tracking algorithm for 3D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3D coupled sub-sample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking (TM) phantom and in vivo breast ultrasound data. The performance of this 3D sub-sample tracking algorithm was compared with the conventional 3D quadratic sub-sample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3D sub-sample estimation algorithm can provide high-quality strain data (i.e. high correlation between the pre- and the motion-compensated post-deformation RF echo data and high contrast-to-noise ratio strain images), as compared to the conventional 3D quadratic sub-sample algorithm. Using the GPU implementation of the 3D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 seconds per volume [2.5 cm × 2.5 cm × 2.5 cm]). PMID:28166493

cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on CPU+GPU.

PubMed

Zhang, Jing; Wang, Hao; Feng, Wu-Chun

2017-01-01

BLAST, short for Basic Local Alignment Search Tool, is a ubiquitous tool used in the life sciences for pairwise sequence search. However, with the advent of next-generation sequencing (NGS), whether at the outset or downstream from NGS, the exponential growth of sequence databases is outstripping our ability to analyze the data. While recent studies have utilized the graphics processing unit (GPU) to speedup the BLAST algorithm for searching protein sequences (i.e., BLASTP), these studies use coarse-grained parallelism, where one sequence alignment is mapped to only one thread. Such an approach does not efficiently utilize the capabilities of a GPU, particularly due to the irregularity of BLASTP in both execution paths and memory-access patterns. To address the above shortcomings, we present a fine-grained approach to parallelize BLASTP, where each individual phase of sequence search is mapped to many threads on a GPU. This approach, which we refer to as cuBLASTP, reorders data-access patterns and reduces divergent branches of the most time-consuming phases (i.e., hit detection and ungapped extension). In addition, cuBLASTP optimizes the remaining phases (i.e., gapped extension and alignment with trace back) on a multicore CPU and overlaps their execution with the phases running on the GPU.

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

PubMed

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.

Online Tracking Algorithms on GPUs for the P̅ANDA Experiment at FAIR

NASA Astrophysics Data System (ADS)

Bianchi, L.; Herten, A.; Ritman, J.; Stockmanns, T.; Adinetz, A.; Kraus, J.; Pleiter, D.

2015-12-01

P̅ANDA is a future hadron and nuclear physics experiment at the FAIR facility in construction in Darmstadt, Germany. In contrast to the majority of current experiments, PANDA's strategy for data acquisition is based on event reconstruction from free-streaming data, performed in real time entirely by software algorithms using global detector information. This paper reports the status of the development of algorithms for the reconstruction of charged particle tracks, optimized online data processing applications, using General-Purpose Graphic Processing Units (GPU). Two algorithms for trackfinding, the Triplet Finder and the Circle Hough, are described, and details of their GPU implementations are highlighted. Average track reconstruction times of less than 100 ns are obtained running the Triplet Finder on state-of- the-art GPU cards. In addition, a proof-of-concept system for the dispatch of data to tracking algorithms using Message Queues is presented.

POM.gpu-v1.0: a GPU-based Princeton Ocean Model

NASA Astrophysics Data System (ADS)

Xu, S.; Huang, X.; Oey, L.-Y.; Xu, F.; Fu, H.; Zhang, Y.; Yang, G.

2015-09-01

Graphics processing units (GPUs) are an attractive solution in many scientific applications due to their high performance. However, most existing GPU conversions of climate models use GPUs for only a few computationally intensive regions. In the present study, we redesign the mpiPOM (a parallel version of the Princeton Ocean Model) with GPUs. Specifically, we first convert the model from its original Fortran form to a new Compute Unified Device Architecture C (CUDA-C) code, then we optimize the code on each of the GPUs, the communications between the GPUs, and the I / O between the GPUs and the central processing units (CPUs). We show that the performance of the new model on a workstation containing four GPUs is comparable to that on a powerful cluster with 408 standard CPU cores, and it reduces the energy consumption by a factor of 6.8.

High-throughput GPU-based LDPC decoding

NASA Astrophysics Data System (ADS)

Chang, Yang-Lang; Chang, Cheng-Chun; Huang, Min-Yu; Huang, Bormin

2010-08-01

Low-density parity-check (LDPC) code is a linear block code known to approach the Shannon limit via the iterative sum-product algorithm. LDPC codes have been adopted in most current communication systems such as DVB-S2, WiMAX, WI-FI and 10GBASE-T. LDPC for the needs of reliable and flexible communication links for a wide variety of communication standards and configurations have inspired the demand for high-performance and flexibility computing. Accordingly, finding a fast and reconfigurable developing platform for designing the high-throughput LDPC decoder has become important especially for rapidly changing communication standards and configurations. In this paper, a new graphic-processing-unit (GPU) LDPC decoding platform with the asynchronous data transfer is proposed to realize this practical implementation. Experimental results showed that the proposed GPU-based decoder achieved 271x speedup compared to its CPU-based counterpart. It can serve as a high-throughput LDPC decoder.

Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction.

PubMed

Guo, Jian; Qian, Kun; Zhang, Gongxuan; Xu, Huijie; Schuller, Björn

2017-12-01

The advent of 'Big Data' and 'Deep Learning' offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for 'feeding' the subsequent classifiers. With increasing numbers of biomedical data, extracting features from these 'big' data is an intensive and time-consuming task. In this case study, we employ a Graphics Processing Unit (GPU) via Python to extract features from a large corpus of snore sound data. Those features can subsequently be imported into many well-known deep learning training frameworks without any format processing. The snore sound data were collected from several hospitals (20 subjects, with 770-990 MB per subject - in total 17.20 GB). Experimental results show that our GPU-based processing significantly speeds up the feature extraction phase, by up to seven times, as compared to the previous CPU system.

GPU Particle Tracking and MHD Simulations with Greatly Enhanced Computational Speed

NASA Astrophysics Data System (ADS)

Ziemba, T.; O'Donnell, D.; Carscadden, J.; Cash, M.; Winglee, R.; Harnett, E.

2008-12-01

GPUs are intrinsically highly parallelized systems that provide more than an order of magnitude computing speed over a CPU based systems, for less cost than a high end-workstation. Recent advancements in GPU technologies allow for full IEEE float specifications with performance up to several hundred GFLOPs per GPU, and new software architectures have recently become available to ease the transition from graphics based to scientific applications. This allows for a cheap alternative to standard supercomputing methods and should increase the time to discovery. 3-D particle tracking and MHD codes have been developed using NVIDIA's CUDA and have demonstrated speed up of nearly a factor of 20 over equivalent CPU versions of the codes. Such a speed up enables new applications to develop, including real time running of radiation belt simulations and real time running of global magnetospheric simulations, both of which could provide important space weather prediction tools.

Rapid Parallel Calculation of shell Element Based On GPU

NASA Astrophysics Data System (ADS)

Wanga, Jian Hua; Lia, Guang Yao; Lib, Sheng; Li, Guang Yao

2010-06-01

Long computing time bottlenecked the application of finite element. In this paper, an effective method to speed up the FEM calculation by using the existing modern graphic processing unit and programmable colored rendering tool was put forward, which devised the representation of unit information in accordance with the features of GPU, converted all the unit calculation into film rendering process, solved the simulation work of all the unit calculation of the internal force, and overcame the shortcomings of lowly parallel level appeared ever before when it run in a single computer. Studies shown that this method could improve efficiency and shorten calculating hours greatly. The results of emulation calculation about the elasticity problem of large number cells in the sheet metal proved that using the GPU parallel simulation calculation was faster than using the CPU's. It is useful and efficient to solve the project problems in this way.

CUDA Fortran acceleration for the finite-difference time-domain method

NASA Astrophysics Data System (ADS)

Hadi, Mohammed F.; Esmaeili, Seyed A.

2013-05-01

A detailed description of programming the three-dimensional finite-difference time-domain (FDTD) method to run on graphical processing units (GPUs) using CUDA Fortran is presented. Two FDTD-to-CUDA thread-block mapping designs are investigated and their performances compared. Comparative assessment of trade-offs between GPU's shared memory and L1 cache is also discussed. This presentation is for the benefit of FDTD programmers who work exclusively with Fortran and are reluctant to port their codes to C in order to utilize GPU computing. The derived CUDA Fortran code is compared with an optimized CPU version that runs on a workstation-class CPU to present a realistic GPU to CPU run time comparison and thus help in making better informed investment decisions on FDTD code redesigns and equipment upgrades. All analyses are mirrored with CUDA C simulations to put in perspective the present state of CUDA Fortran development.

Accelerating NBODY6 with graphics processing units

NASA Astrophysics Data System (ADS)

Nitadori, Keigo; Aarseth, Sverre J.

2012-07-01

We describe the use of graphics processing units (GPUs) for speeding up the code NBODY6 which is widely used for direct N-body simulations. Over the years, the N2 nature of the direct force calculation has proved a barrier for extending the particle number. Following an early introduction of force polynomials and individual time steps, the calculation cost was first reduced by the introduction of a neighbour scheme. After a decade of GRAPE computers which speeded up the force calculation further, we are now in the era of GPUs where relatively small hardware systems are highly cost effective. A significant gain in efficiency is achieved by employing the GPU to obtain the so-called regular force which typically involves some 99 per cent of the particles, while the remaining local forces are evaluated on the host. However, the latter operation is performed up to 20 times more frequently and may still account for a significant cost. This effort is reduced by parallel SSE/AVX procedures where each interaction term is calculated using mainly single precision. We also discuss further strategies connected with coordinate and velocity prediction required by the integration scheme. This leaves hard binaries and multiple close encounters which are treated by several regularization methods. The present NBODY6-GPU code is well balanced for simulations in the particle range 104-2 × 105 for a dual-GPU system attached to a standard PC.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, Chi-kwan; Psaltis, Dimitrios; Özel, Feryal

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 comparingmore » 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.« less

Efficient gaussian density formulation of volume and surface areas of macromolecules on graphical processing units.

PubMed

Zhang, Baofeng; Kilburg, Denise; Eastman, Peter; Pande, Vijay S; Gallicchio, Emilio

2017-04-15

We present an algorithm to efficiently compute accurate volumes and surface areas of macromolecules on graphical processing unit (GPU) devices using an analytic model which represents atomic volumes by continuous Gaussian densities. The volume of the molecule is expressed by means of the inclusion-exclusion formula, which is based on the summation of overlap integrals among multiple atomic densities. The surface area of the molecule is obtained by differentiation of the molecular volume with respect to atomic radii. The many-body nature of the model makes a port to GPU devices challenging. To our knowledge, this is the first reported full implementation of this model on GPU hardware. To accomplish this, we have used recursive strategies to construct the tree of overlaps and to accumulate volumes and their gradients on the tree data structures so as to minimize memory contention. The algorithm is used in the formulation of a surface area-based non-polar implicit solvent model implemented as an open source plug-in (named GaussVol) for the popular OpenMM library for molecular mechanics modeling. GaussVol is 50 to 100 times faster than our best optimized implementation for the CPUs, achieving speeds in excess of 100 ns/day with 1 fs time-step for protein-sized systems on commodity GPUs. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Graphics performance in rich Internet applications.

PubMed

Hoetzlein, Rama C

2012-01-01

Rendering performance for rich Internet applications (RIAs) has recently focused on the debate between using Flash and HTML5 for streaming video and gaming on mobile devices. A key area not widely explored, however, is the scalability of raw bitmap graphics performance for RIAs. Does Flash render animated sprites faster than HTML5? How much faster is WebGL than Flash? Answers to these questions are essential for developing large-scale data visualizations, online games, and truly dynamic websites. A new test methodology analyzes graphics performance across RIA frameworks and browsers, revealing specific performance outliers in existing frameworks. The results point toward a future in which all online experiences might be GPU accelerated.

Efficient implementation of the 3D-DDA ray traversal algorithm on GPU and its application in radiation dose calculation.

PubMed

Xiao, Kai; Chen, Danny Z; Hu, X Sharon; Zhou, Bo

2012-12-01

The three-dimensional digital differential analyzer (3D-DDA) algorithm is a widely used ray traversal method, which is also at the core of many convolution∕superposition (C∕S) dose calculation approaches. However, porting existing C∕S dose calculation methods onto graphics processing unit (GPU) has brought challenges to retaining the efficiency of this algorithm. In particular, straightforward implementation of the original 3D-DDA algorithm inflicts a lot of branch divergence which conflicts with the GPU programming model and leads to suboptimal performance. In this paper, an efficient GPU implementation of the 3D-DDA algorithm is proposed, which effectively reduces such branch divergence and improves performance of the C∕S dose calculation programs running on GPU. The main idea of the proposed method is to convert a number of conditional statements in the original 3D-DDA algorithm into a set of simple operations (e.g., arithmetic, comparison, and logic) which are better supported by the GPU architecture. To verify and demonstrate the performance improvement, this ray traversal method was integrated into a GPU-based collapsed cone convolution∕superposition (CCCS) dose calculation program. The proposed method has been tested using a water phantom and various clinical cases on an NVIDIA GTX570 GPU. The CCCS dose calculation program based on the efficient 3D-DDA ray traversal implementation runs 1.42 ∼ 2.67× faster than the one based on the original 3D-DDA implementation, without losing any accuracy. The results show that the proposed method can effectively reduce branch divergence in the original 3D-DDA ray traversal algorithm and improve the performance of the CCCS program running on GPU. Considering the wide utilization of the 3D-DDA algorithm, various applications can benefit from this implementation method.

GPU-accelerated Monte Carlo convolution/superposition implementation for dose calculation.

PubMed

Zhou, Bo; Yu, Cedric X; Chen, Danny Z; Hu, X Sharon

2010-11-01

Dose calculation is a key component in radiation treatment planning systems. Its performance and accuracy are crucial to the quality of treatment plans as emerging advanced radiation therapy technologies are exerting ever tighter constraints on dose calculation. A common practice is to choose either a deterministic method such as the convolution/superposition (CS) method for speed or a Monte Carlo (MC) method for accuracy. The goal of this work is to boost the performance of a hybrid Monte Carlo convolution/superposition (MCCS) method by devising a graphics processing unit (GPU) implementation so as to make the method practical for day-to-day usage. Although the MCCS algorithm combines the merits of MC fluence generation and CS fluence transport, it is still not fast enough to be used as a day-to-day planning tool. To alleviate the speed issue of MC algorithms, the authors adopted MCCS as their target method and implemented a GPU-based version. In order to fully utilize the GPU computing power, the MCCS algorithm is modified to match the GPU hardware architecture. The performance of the authors' GPU-based implementation on an Nvidia GTX260 card is compared to a multithreaded software implementation on a quad-core system. A speedup in the range of 6.7-11.4x is observed for the clinical cases used. The less than 2% statistical fluctuation also indicates that the accuracy of the authors' GPU-based implementation is in good agreement with the results from the quad-core CPU implementation. This work shows that GPU is a feasible and cost-efficient solution compared to other alternatives such as using cluster machines or field-programmable gate arrays for satisfying the increasing demands on computation speed and accuracy of dose calculation. But there are also inherent limitations of using GPU for accelerating MC-type applications, which are also analyzed in detail in this article.

Integrative multicellular biological modeling: a case study of 3D epidermal development using GPU algorithms

PubMed Central

2010-01-01

Background Simulation of sophisticated biological models requires considerable computational power. These models typically integrate together numerous biological phenomena such as spatially-explicit heterogeneous cells, cell-cell interactions, cell-environment interactions and intracellular gene networks. The recent advent of programming for graphical processing units (GPU) opens up the possibility of developing more integrative, detailed and predictive biological models while at the same time decreasing the computational cost to simulate those models. Results We construct a 3D model of epidermal development and provide a set of GPU algorithms that executes significantly faster than sequential central processing unit (CPU) code. We provide a parallel implementation of the subcellular element method for individual cells residing in a lattice-free spatial environment. Each cell in our epidermal model includes an internal gene network, which integrates cellular interaction of Notch signaling together with environmental interaction of basement membrane adhesion, to specify cellular state and behaviors such as growth and division. We take a pedagogical approach to describing how modeling methods are efficiently implemented on the GPU including memory layout of data structures and functional decomposition. We discuss various programmatic issues and provide a set of design guidelines for GPU programming that are instructive to avoid common pitfalls as well as to extract performance from the GPU architecture. Conclusions We demonstrate that GPU algorithms represent a significant technological advance for the simulation of complex biological models. We further demonstrate with our epidermal model that the integration of multiple complex modeling methods for heterogeneous multicellular biological processes is both feasible and computationally tractable using this new technology. We hope that the provided algorithms and source code will be a starting point for modelers to develop their own GPU implementations, and encourage others to implement their modeling methods on the GPU and to make that code available to the wider community. PMID:20696053

High-speed real-time animated displays on the ADAGE (trademark) RDS 3000 raster graphics system

NASA Technical Reports Server (NTRS)

Kahlbaum, William M., Jr.; Ownbey, Katrina L.

1989-01-01

Techniques which may be used to increase the animation update rate of real-time computer raster graphic displays are discussed. They were developed on the ADAGE RDS 3000 graphic system in support of the Advanced Concepts Simulator at the NASA Langley Research Center. These techniques involve the use of a special purpose parallel processor, for high-speed character generation. The description of the parallel processor includes the Barrel Shifter which is part of the hardware and is the key to the high-speed character rendition. The final result of this total effort was a fourfold increase in the update rate of an existing primary flight display from 4 to 16 frames per second.

galario: Gpu Accelerated Library for Analyzing Radio Interferometer Observations

NASA Astrophysics Data System (ADS)

Tazzari, Marco; Beaujean, Frederik; Testi, Leonardo

2017-10-01

The galario library exploits the computing power of modern graphic cards (GPUs) to accelerate the comparison of model predictions to radio interferometer observations. It speeds up the computation of the synthetic visibilities given a model image (or an axisymmetric brightness profile) and their comparison to the observations.