AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns

AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns Int J Parallel Prog https://doi.org/10.1007/s10766-018-0579-5 AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns 1 2 2 Misun Yu · Joon-Sang Lee · Doo-Hwan Bae Received: 6 May 2017 / Accepted: 31 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Among the various types of concurrency bugs, the data race is one of the primary causes of other concurrency bugs. Thus, it is important to detect as many data races as possible during the development step of multithreaded programs. A hybrid data race detection technique that uses the Lockset algorithm and happens-before relation, can detect actually occurred and hidden data races in one execution trace. However, high runtime slowdown obstructs the frequent use of hybrid detectors. In this paper, we empirically demonstrate that most data race bugs are caused by the absence of a lock, and that multiple locks are rarely involved in a data race bug in the real world. Thus, we propose a fast hybrid detection algorithm that does not introduce additional false positives and false negatives to the current hybrid detectors. The suggested algorithm replaces the lock-set intersection by a simple comparison operation that focuses on exploring data-race-prone locking http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Parallel Programming Springer Journals

AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Theory of Computation; Processor Architectures; Software Engineering/Programming and Operating Systems
ISSN
0885-7458
eISSN
1573-7640
D.O.I.
10.1007/s10766-018-0579-5
Publisher site
See Article on Publisher Site

Abstract

Int J Parallel Prog https://doi.org/10.1007/s10766-018-0579-5 AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns 1 2 2 Misun Yu · Joon-Sang Lee · Doo-Hwan Bae Received: 6 May 2017 / Accepted: 31 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Among the various types of concurrency bugs, the data race is one of the primary causes of other concurrency bugs. Thus, it is important to detect as many data races as possible during the development step of multithreaded programs. A hybrid data race detection technique that uses the Lockset algorithm and happens-before relation, can detect actually occurred and hidden data races in one execution trace. However, high runtime slowdown obstructs the frequent use of hybrid detectors. In this paper, we empirically demonstrate that most data race bugs are caused by the absence of a lock, and that multiple locks are rarely involved in a data race bug in the real world. Thus, we propose a fast hybrid detection algorithm that does not introduce additional false positives and false negatives to the current hybrid detectors. The suggested algorithm replaces the lock-set intersection by a simple comparison operation that focuses on exploring data-race-prone locking

Journal

International Journal of Parallel ProgrammingSpringer Journals

Published: Jun 4, 2018

References

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