使用HPC挑战基准评估高级语言的并行扩展

L. Humphrey, B. Guilfoos, H. Smith, A. Warnock, J. Unpingco, B. Elton, A. Chalker
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引用次数: 2

摘要

近年来,出现了许多新的高级语言并行扩展。然而,似乎还没有集中精力量化它们的性能或确定它们的可用性。为此,我们使用了几个并行扩展来实现四种高性能计算(HPC)挑战基准——fft、HPL、RandomAccess和stream——根据Class 2规范。这里使用的并行扩展包括pMatlab、Star-P和官方的MATLAB并行计算工具箱;matlab for Octave;Star-P代表Python。我们已经在俄亥俄超级计算中心的超级计算机Glenn以及几个国防部超级计算资源中心(DoD dsrc)上使用这些扩展记录了基准测试的性能结果。将这些结果与在Glenn上运行的原始C基准测试的结果进行比较。我们还重点介绍了这些并行扩展的一些特性,以及gridMathematica for Mathematica和IPython for Python的特性,这些特性还没有经过完全的基准测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Parallel Extensions to High Level Languages Using the HPC Challenge Benchmarks
Recent years have seen the development of many new parallel extensions to high level languages. However, there does not yet seem to have been a concentrated effort to quantify their performance or qualify their usability. Toward this end, we have used several parallel extensions to implement four of the high performance computing (HPC) Challenge benchmarks—FFT, HPL, RandomAccess, and STREAM—according to the Class 2 specifications. The parallel extensions used here include pMatlab, Star-P, and the official Parallel Computing Toolbox for MATLAB; pMatlab for Octave; and Star-P for Python. We have recorded performance results for the benchmarks using these extensions on the Ohio Supercomputing Center’s supercomputer Glenn as well as several of the Department of Defense Supercomputing Resource Centers (DoD DSRCs). These results are compared to those of the original C benchmarks as run on Glenn. We also highlight some of the features of these parallel extensions, as well as those of gridMathematica for Mathematica and IPython for Python, which have not yet been fully benchmarked.
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