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International Conference on High Performance Computing & simulation (HPCS)

作者： Ferreira, Florian Gratadour, Damien Sevin, Arnaud Doucet, Nicolas Univ Paris Diderot LESIA Observ Paris Meudon France

ISBN: (纸本)9781538678794

For ground-based astronomical telescopes, image resolution is directly dependent on aperture diameter. Practically however, atmospheric turbulence disturbs incoming wavefronts and sets dramatic limitations on imaging quality, which issue led to using Adaptive Optics (AO) systems to compensate wavefront disturbance and achieve optimal resolution. With the arrival of Extremely Large Telescopes, with aperture diameter above 20 meters, High Performance Computing (HPC) techniques are required to simulate and operate AO systems. With the advent of General Purpose Graphics Processing Units (GPGPU), many-core architectures of GPUs can be leveraged to accelerate AO simulation software. COMPASS (COMputing Platform for Adaptive opticS System) is an end-to-end AO simulation software based upon Nvidia's Compute Unified Device Architecture (CUDA) toolkit. simulations performed on state-of-the-art Nvidia DGX-1 servers equipped with 8 Tesla V100-SXM2 and Dual 20-core Intel Xeon E5-2698 v4 show a speed up factor of up to 362 as compared to YAO, a feature-equivalent software with multi-threaded CPU architecture. The COMPASS implementation ensures compatibility with any Nvidia architecture. A speed up by 5 was measured between a DGX-1 and a comparable server with 6-year old GPUs, with no line of code changed.

关键词： GPGPU parallelization of simulation Applied Modeling and simulation using HPC Large Scale Scientific Computing

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An analysis of the feasibility and benefits of GPU/multicore acceleration of the Weather Research and Forecasting model

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2016年第7期28卷 2052-2072页

作者： Vanderbauwhede, Wim Takemi, Tetsuya Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland Kyoto Univ Disaster Prevent Res Inst Uji Kyoto Japan

There is a growing need for ever more accurate climate and weather simulations to be delivered in shorter timescales, in particular, to guard against severe weather events such as hurricanes and heavy rainfall. Due to climate change, the severity and frequency of such events - and thus the economic impact - are set to rise dramatically. Hardware acceleration using graphics processing units (GPUs) or Field-Programmable Gate Arrays (FPGAs) could potentially result in much reduced run times or higher accuracy simulations. In this paper, we present the results of a study of the Weather Research and Forecasting (WRF) model undertaken in order to assess if GPU and multicore acceleration of this type of numerical weather prediction (NWP) code is both feasible and worthwhile. The focus of this paper is on acceleration of code running on a single compute node through offloading of parts of the code to an accelerator such as a GPU. The governing equations set of the WRF model is based on the compressible, non-hydrostatic atmospheric motion with multi-physics processes. We put this work into context by discussing its more general applicability to multi-physics fluid dynamics codes: in many fluid dynamics codes, the numerical schemes of the advection terms are based on finite differences between neighboring cells, similar to the WRF code. For fluid systems including multi-physics processes, there are many calls to these advection routines. This class of numerical codes will benefit from hardware acceleration. We studied the performance of the original code of the WRF model and proposed a simple model for comparing multicore CPU and GPU performance. Based on the results of extensive profiling of representative WRF runs, we focused on the acceleration of the scalar advection module. We discuss the implementation of this module as a data-parallel kernel in both OpenCL and OpenMP. We show that our data-parallel kernel version of the scalar advection module runs up to seven times fa

关键词： general-purpose computation on graphics processing units (GPGPU) parallelization of simulation large-scale scientific computing

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An analysis of the feasibility and benefits of GPU/multicore acceleration of the Weather Research and Forecasting model

An analysis of the feasibility and benefits of GPU/multicore...

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10th International Workshop on Science Gateways Computing Environments (GCE) held in conjunction with the 3rd Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE)

作者： Vanderbauwhede, Wim Takemi, Tetsuya Univ Glasgow Sch Comp Sci Glasgow G12 8QQ Lanark Scotland Kyoto Univ Disaster Prevent Res Inst Uji Kyoto Japan

关键词： general-purpose computation on graphics processing units (GPGPU) parallelization of simulation large-scale scientific computing

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TaskLocalRandom: a statistically sound substitute to pseudorandom number generation in parallel java tasks frameworks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2015年第13期27卷 3383-3398页

作者： Passerat-Palmbach, Jonathan Mazel, Claude Hill, David R. C. ISIMA F-63173 Aubiere France Univ Blaise Pascal CNRS LIMOS UMR 6158 Clermont Ferrand France Univ Clermont Ferrand 2 Clermont Ferrand France

Several software efforts have been produced over the past few years in various programming languages to help developers handle pseudorandom streams partitioning. Parallel and distributed stochastic simulations can obviously benefit from this kind of highlevel tools. The latest release of the Java Development Kit (JDK 7) tries to tackle this problem by providing facilities to partition a pseudorandom stream across various threads thanks to the new class ThreadLocalRandom. Meanwhile, Java 7 offers a framework to split a problem in a divide and conquer way through the new class called ForkJoinPool. As any other Java thread pool, ForkJoin exploits threads as workers and manipulates the tasks that will be run on the workers. In ThreadLocalRandom, pseudorandom number generation is handled at a thread level. As a consequence, a scientific application taking advantage of a Java thread pool to parallelize its computation may suffer from a bad pseudorandom stream partitioning due to the behavior of ThreadLocalRandom. The present work introduces TaskLocalRandom, a tasklevel alternative to ThreadLocalRandom that solves this partitioning problem and assigns an independent pseudorandom stream to each task run in the thread pool. TaskLocalRandom is compatible with existing Java thread pools such as Executors or ForkJoin. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： java threads tasks pseudorandom number generation parallelization of simulation software development tools and support object oriented programming & design

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Enabling a Quantum Monte Carlo Application for the DEEP architecture 13

Enabling a Quantum Monte Carlo Application for the DEEP arch...

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International Conference on High Performance Computing and simulation (HPCS)

作者： Emerson, Andrew Affinito, Fabio Cineca Supercomp Applicat & Innovat SCAI I-40033 Casalecchio Di Reno Italy

ISBN: (纸本)9781467378130

In the DEEP project a prototype Exascale system consisting of a standard Intel Xeon cluster linked to a "Booster" part containing Intel Xeon Phi nodes connected in a high-speed network, is being designed and constructed. In order to evaluate this novel architecture, expected to be available in the second half of 2015, a number of grand challenge applications in computational science and engineering are being modified and optimised. In this study we report on the efforts made by the Cineca project partner and DEEP support staff to enable one of these applications, the TurboRVB Quantum Monte Carlo simulation program, which can be used to study complex phenomena in materials such as superconductivity. The modified code, based on an implementation of the OmpSs offload task model, has been successfully tested on the MareNostrum supercomputer at the Barcelona Supercomputing Center.

关键词： Exascale Systems Modeling and simulation using HPC Systems parallelization of simulation

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Twinned Buffering: A Simple and Highly Effective Scheme for parallelization of Successive Over-Relaxation on GPUs and Other Accelerators 13

Twinned Buffering: A Simple and Highly Effective Scheme for ...

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International Conference on High Performance Computing and simulation (HPCS)

作者： Vanderbauwhede, Wim Takemi, Tetsuya Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland Kyoto Univ Disaster Prevent Res Inst Kyoto Japan

ISBN: (纸本)9781467378130

In this paper we present a new scheme for parallelization of the Successive Over-Relaxation method for solving the Poisson equation over a 3-D volume. Our new scheme is both simple and effective, outperforming the conventional Red-Black scheme by a factor of 16 on an NVIDIA GeForce GTX 590 GPU, a factor of 11 on an NVIDIA GeForce TITAN Black GPU and a factor of 5 on an Intel Xeon Phi. The speed-up compared to the fully optimised reference implementation running on an Intel Xeon CPU is 16 times on the GTX 590, 22 times on the TITAN and 5 times on the Xeon Phi. We explain the rationale and the implementation in OpenCL and present the performance evaluation results.

关键词： General-Purpose computation on Graphics Processing Units (GPGPU) parallelization of simulation Large Scale Scientific Computing

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Resilient workflows for high-performance simulation platforms

Resilient workflows for high-performance simulation platform...

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2010 International Conference on High Performance Computing and simulation, HPCS 2010

作者： Nguyên, Toàn Trifan, Laurentiu Désidéri, Jean-Antoine INRIA 655 Av. de l'Europe Montbonnot FR-38334 Saint-Ismier France

ISBN: (纸本)9781424468287

Workflows systems are considered here to support large-scale multiphysics simulations. Because the use of large distributed and parallel multi-core infrastructures is prone to software and hardware failures, the paper addresses the need for error recovery procedures. A new mechanism based on asymmetric checkpointing is presented. A rule-based implementation for a distributed workflow platform is detailed. ©2010 IEEE.

关键词： Fault-tolerant computing Large-scale scientific computing parallelization of simulation Workflow systems

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