Analysis of microbenchmarks for performance tuning of clusters

M. Sottile, R. Minnich
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引用次数: 46

Abstract

Microbenchmarks, i.e. very small computational kernels, have become commonly used for quantitative measures of node performance in clusters. For example, a commonly used benchmark measures the amount of time required to perform a fixed quantum of work. Unfortunately, this benchmark is one of many that violate well known rules from sampling theory, leading to erroneous, contradictory or misleading results. At a minimum, these types of benchmarks can not be used to identify time-based activities that may interfere with and hence limit application performance. Our original and primary goal remains to identify noise in the system due to periodic activities that are not part of user application code. We discuss why the 'fixed quantum of work' benchmark provides data that is of limited use for analysis; and we show code for, discuss, and analyze results from a microbenchmark which follows good rules of sampling hygiene, and hence provides useful data for analysis.
分析用于集群性能调优的微基准
微基准测试,即非常小的计算内核,已经被广泛用于集群中节点性能的定量测量。例如,常用的基准测量执行固定工作量所需的时间量。不幸的是,这个基准是许多违反众所周知的抽样理论规则的基准之一,导致错误、矛盾或误导性的结果。至少,这些类型的基准测试不能用于识别可能干扰并因此限制应用程序性能的基于时间的活动。我们最初和主要的目标仍然是识别系统中由于不属于用户应用程序代码一部分的周期性活动而产生的噪声。我们讨论了为什么“固定工作量”基准提供的数据对分析的用途有限;我们展示了一个遵循良好采样卫生规则的微基准测试的代码、讨论和分析结果,从而为分析提供了有用的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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