SmartTrack:高效的预测赛跑检测

J. Roemer, K. Genç, Michael D. Bond
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引用次数: 23

摘要

广泛使用的数据竞赛检测器(包括最先进的FastTrack算法)会产生性能成本,这在常规的内部测试中是可以接受的,但在分析执行过程中却无法检测到竞赛。预测分析在被分析的执行中检测到的数据竞争比FastTrack检测到的要多,但性能成本要高得多。本文介绍了一种优化预测种族检测分析的算法SmartTrack,其中包括先前工作的两个分析和本文介绍的一个新分析。SmartTrack包含两个主要优化:(1)首次应用于预测分析的前期工作的epoch和所有权优化,以及(2)本文引入的新型冲突临界段优化。我们的评估表明,SmartTrack的性能与fasttrack相当,这是数据竞争检测技术的一个质的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SmartTrack: efficient predictive race detection
Widely used data race detectors, including the state-of-the-art FastTrack algorithm, incur performance costs that are acceptable for regular in-house testing, but miss races detectable from the analyzed execution. Predictive analyses detect more data races in an analyzed execution than FastTrack detects, but at significantly higher performance cost. This paper presents SmartTrack, an algorithm that optimizes predictive race detection analyses, including two analyses from prior work and a new analysis introduced in this paper. SmartTrack incorporates two main optimizations: (1) epoch and ownership optimizations from prior work, applied to predictive analysis for the first time, and (2) novel conflicting critical section optimizations introduced by this paper. Our evaluation shows that SmartTrack achieves performance competitive with FastTrack—a qualitative improvement in the state of the art for data race detection.
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