HPC- mixpbench:用于混合精度分析的HPC基准套件

K. Parasyris, I. Laguna, Harshitha Menon, M. Schordan, D. Osei-Kuffuor, G. Georgakoudis, Michael O. Lam, T. Vanderbruggen
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引用次数: 6

摘要

随着将近似计算应用于高性能计算应用的兴趣日益增加,需要有代表性的基准来评估和比较各种近似计算算法和编程框架。为此,我们提出了HPC- mixpbench,这是一个由广泛应用于HPC领域的具有代表性的内核和基准组成的基准套件。HPC-MixPBench有一个测试工具框架,可以在其中插入不同的工具并对一组基准进行评估。我们通过评估FloatSmith(浮点混合精度近似分析工具)中实现的几种混合精度算法来证明我们的基准套件的有效性。我们报告了一些关于我们比较的混合精度算法的见解,我们希望可以帮助这些方法的用户根据他们的工作负载选择正确的方法。我们设想这个基准套件将发展成为一组标准的HPC基准,用于比较不同的近似计算技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HPC-MixPBench: An HPC Benchmark Suite for Mixed-Precision Analysis
With the increasing interest in applying approximate computing to HPC applications, representative benchmarks are needed to evaluate and compare various approximate computing algorithms and programming frameworks. To this end, we propose HPC-MixPBench, a benchmark suite consisting of a representative set of kernels and benchmarks that are widely used in HPC domain. HPC-MixPBench has a test harness framework where different tools can be plugged in and evaluated on the set of benchmarks. We demonstrate the effectiveness of our benchmark suite by evaluating several mixed-precision algorithms implemented in FloatSmith, a tool for floating-point mixed-precision approximation analysis. We report several insights about the mixed-precision algorithms that we compare, which we expect can help users of these methods choose the right method for their workload. We envision that this benchmark suite will evolve into a standard set of HPC benchmarks for comparing different approximate computing techniques.
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