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10th International Conference on Parallel Processing and Applied Mathematics (PPAM)

作者： Szalkowski, Dominik Stpiczynski, Przemyslaw Marie Curie Sklodowska Univ Inst Math PL-20031 Lublin Poland

ISBN: (纸本)9783642552243

The aim of this paper is to show that the multidimensional Monte Carlo integration can be efficiently implemented on clusters with hybrid GPU-accelerated nodes using recently developed parallel versions of LCG and LFG pseudorandom number generators. We explain how to utilize multiple GPUs and all available cores of CPUs within a single node and how to extend computations on all available nodes of a cluster using MPI. The results of experiments performed on a Tesla-based GPU cluster are also presented and discussed.

关键词： Multidimensional integration Monte Carlo methods parallelized pseudorandom number generators GPU clusters

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Using distributed memory parallel computers and GPU clusters for multidimensional Monte Carlo integration

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2015年第4期27卷 923-936页

作者： Szalkowski, Dominik Stpiczynski, Przemyslaw Marie Curie Sklodowska Univ Inst Math PL-20031 Lublin Poland

The aim of this paper is to show that the multidimensional Monte Carlo integration can be efficiently implemented on various distributed memory parallel computers and clusters of multicore nodes using recently developed parallel versions of linear congruential generator and lagged Fibonacci generator pseudorandom number generators. We show how to accelerate the overall performance by offloading some computations to Graphics Processing Units (GPUs), and we discuss how to transform Message Passing Interface (MPI)+OpenMP programs to MPI+OpenMP+CUDA model. We explain how to utilize multiple cores of CPUs together with multiple GPU accelerators within a single node and how to achieve reasonable load balancing of all computational resources of GPU-accelerated multicore nodes. We present and discuss the results of experiments performed on the following target architectures: IBM Blue Gene/Q parallel computer, a cluster of Intel Xeon E5-2660 servers, and a Tesla-based GPU cluster with Intel Xeon X5650 multicore processors. The results are presented from two points of view: strong scaling and weak scaling. We also compare the performance of all considered architectures. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： multidimensional integration Monte Carlo methods parallelized pseudorandom number generators linear congruential generator lagged Fibonacci generator distributed memory parallel computers GPU clusters efficiency scalability

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