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首页> 外文会议>2016 7th International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems >HPC Benchmarking: Problem Size Matters
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HPC Benchmarking: Problem Size Matters

机译：HPC基准测试：问题大小很重要

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摘要

In order to compare and rank the worlds fastest computers, benchmarks evaluating their performance are required. A single execution of HPL is used for the most widely recognized ranking: the TOP500. Lately, two benchmarks, arguably more representative of typical modern workloads, have been proposed: HPCG and HPGMG. Currently, all three benchmarks use the highest observed performance from a single problem size for ranking. In this paper we report benchmarking result for all three benchmarks with a wide range of problem sizes on six distinct hardware architectures, covering the full range of machines present on the TOP500 list. We find that the data holds significantly more information on the performance of the underlying hardware as compared to just the maximum performance observed. We therefore argue that an aggregate value derived from a whole range of problem sizes can significantly improve the sensitivity of a given benchmark to relevant hardware properties and thus be more representative. However, we refrain from proposing the specific way to best compose such an aggregate and invite the community to open the discussion on the topic.
机译：为了比较和排名世界上最快的计算机，需要评估其性能的基准。 HPL的一次执行用于最广泛认可的排名：TOP500。最近，提出了两个基准（可以说更能代表典型的现代工作负载）：HPCG和HPGMG。当前，所有三个基准使用单个问题大小中观察到的最高性能进行排名。在本文中，我们报告了这三个基准测试的基准测试结果，这些问题在六个不同的硬件体系结构上具有广泛的问题规模，涵盖了TOP500列表中列出的所有机器。我们发现，与仅观察到的最大性能相比，数据包含有关底层硬件性能的大量信息。因此，我们认为，从整个问题大小范围中得出的合计值可以显着提高给定基准对相关硬件属性的敏感性，从而更具代表性。但是，我们避免提出最佳方法来最好地构成这样一个集合，并邀请社区开放有关该主题的讨论。

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