Runtime and Memory Evaluation of Data Race Detection Tools
Runtime and Memory Evaluation of Data Race Detection Tools
复制标题
数据竞争检测工具的运行时和内存评估
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
I. Karlin
中科院分区：
文献类型：
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作者：
Pei;C. Liao;M. Schordan;I. Karlin
分析工具的有用性取决于其运行时间和内存消耗是否保持在给定程序的合理范围内。在本文中，我们使用 DataRaceBench 1.1.1 版和 79 个微基准测试，对四种数据竞争检测工具：Archer、ThreadSanitizer、Helgrind 和 Intel Inspector 的内存消耗和运行时间进行了评估。我们的评估包括四种不同的分析：(1) 使用所有 DataRaceBench 微基准测试的四种数据竞争检测工具的运行时和内存消耗，(2) 评估工具中实现的分析技术的比较，(3) 对于选定的基准测试，使用 CPU 分析器和已识别的差异对运行时行为进行深入分析，(4) 数据分析以调查收集的数据中的相关性。我们还使用三个定量指标来展示这些工具的有效性：精确度、召回率和准确性。
An analysis tool’s usefulness depends on whether its runtime and memory consumption remain within reasonable bounds for a given program. In this paper we present an evaluation of the memory consumption and runtime of four data race detection tools: Archer, ThreadSanitizer, Helgrind, and Intel Inspector, using DataRaceBench version 1.1.1 using 79 microbenchmarks. Our evaluation consists of four different analyses: (1) runtime and memory consumption of the four data race detection tools using all DataRaceBench microbenchmarks, (2) comparison of the analysis techniques implemented in the evaluated tools, (3) for selected benchmarks an in-depth analysis of runtime behavior with CPU profiler and the identified differences, (4) data analysis to investigate correlations within collected data. We also show the effectiveness of the tools using three quantitative metrics: precision, recall, and accuracy.