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Optimized Sound and Complete Data Race Detection in Structured Parallel Programs

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Languages and Compilers for Parallel Computing (LCPC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11882))

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Abstract

Task parallel programs that are free of data race are guaranteed to be deterministic, serializable, and free of deadlock. Techniques for verification of data race freedom vary in both accuracy and asymptotic complexity. One work is particularly well suited to task parallel programs with isolation and lightweight threads. It uses the Java Pathfinder model checker to reason about different schedules and proves the presence or absence of data race in a program on a fixed input. However, it uses a direct and inefficient transitive closure on the happens-before relation to reason about data race. This paper presents Zipper, an alternative to this naïve algorithm, which identifies the presence or absence of data race in asymptotically superior time. Zipper is optimized for lightweight threads and, in the presence of many threads, has superior time complexity to leading vector clock algorithms. This paper includes an empirical study of Zipper and a comparison against the naïve computation graph algorithm, demonstrating the superior performance it achieves.

The research presented here is supported in part by the NSF under grant 1302524.

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Authors and Affiliations

  1. Brigham Young University, Provo, UT, 84601, USA

    Kyle Storey, Jacob Powell, Ben Ogles, Joshua Hooker, Peter Aldous & Eric Mercer

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  1. Kyle Storey
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  2. Jacob Powell
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  3. Ben Ogles
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  4. Joshua Hooker
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  5. Peter Aldous
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  6. Eric Mercer
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Correspondence to Eric Mercer .

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Editors and Affiliations

  1. University of Utah, Salt Lake City, UT, USA

    Mary Hall

  2. University of Utah, Salt Lake City, UT, USA

    Hari Sundar

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Storey, K., Powell, J., Ogles, B., Hooker, J., Aldous, P., Mercer, E. (2019). Optimized Sound and Complete Data Race Detection in Structured Parallel Programs. In: Hall, M., Sundar, H. (eds) Languages and Compilers for Parallel Computing. LCPC 2018. Lecture Notes in Computer Science(), vol 11882. Springer, Cham. https://doi.org/10.1007/978-3-030-34627-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-34627-0_8

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