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Detecting harmful data races through parallel verification

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Abstract

Data races widely exist in concurrent programs and the harmful races have caused severe failures. To detect the harmful races, previous tools verify all the races, identifying the harmful ones. However, efficiency is affected when there are a large number of races needed to be verified. The multicore technology trend worsens this problem. Unlike previous work, to detect the harmful races, we try to improve the efficiency through parallel verification. We use imprecise race detection to find the races, including benign races and harmful races. The races are divided into many parts, and each part is sent to one machine for verification. On each machine, the races are verified dynamically, identifying the harmful races that would lead to program failures. To our knowledge, this is the first work that parallelizes race verification to improve the efficiency. We have experimented on a number of real-world concurrent programs and all the known harmful races in known benchmarks are detected. Additionally, our tool could scale well as the number of machines increases, and the speedup can also be increased linearly with the number of machines. Comparing with many previous tools, our work imposes lower runtime overhead.

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Acknowledgments

We appreciate the kind comments and professional criticisms of the anonymous reviewers. This work is partially supported by National High-tech R&D Program of China (863 Program) under Grants 2012AA01A301 and 2012AA010901, by program for New Century Excellent Talents in University and by National Science Foundation (NSF) China 61402492, 61272142, 61103082, 61170261 and 61103193. Moreover, it is a part of Innovation Fund Sponsor Project of Excellent Postgraduate Student B130608. An earlier version of this paper [37] appeared in The 9th IEEE International Conference on Computer Engineering and Systems.

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Authors and Affiliations

  1. Science and Technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha, China

    Zhendong Wu, Kai Lu, Xiaoping Wang, Xu Zhou & Chen Chen

  2. College of Computer, National University of Defense Technology, Changsha, China

    Zhendong Wu, Kai Lu, Xiaoping Wang, Xu Zhou & Chen Chen

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  1. Zhendong Wu
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  2. Kai Lu
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  3. Xiaoping Wang
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  4. Xu Zhou
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  5. Chen Chen
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Correspondence to Zhendong Wu.

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Wu, Z., Lu, K., Wang, X. et al. Detecting harmful data races through parallel verification. J Supercomput 71, 2922–2943 (2015). https://doi.org/10.1007/s11227-015-1418-8

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  • DOI: https://doi.org/10.1007/s11227-015-1418-8

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