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Efficient noise injection for exposing hidden data races

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A Correction to this article was published on 02 January 2020

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Abstract

The happens-before (HB) relation is widely used to precisely detect data races, which are among the major causes of concurrency bugs. However, the HB relation is sensitive to thread interleaving; thus, hidden races that can be revealed in other thread interleavings may remain despite the repeated running of a program with an HB detector. In this paper, we propose a noise injection-based race detection technique (RaceInducer) that can expose and detect hidden races by using the runtime HB analysis result to determine the noise injection instants. RaceInducer delays a thread execution after the occurrence of the first no-lock non-racing accesses of three types (read-exclusive, write-exclusive, and read-shared) to a shared variable during a precise HB detection (FastTrack). Using this technique, RaceInducer can efficiently disturb the normal locking sequences between two threads that generate HB edges hiding data races. We implemented RaceInducer on the RoadRunner instrumentation framework for Java and evaluated its detection capability and runtime overheads on 12 real-world benchmark programs. The evaluation results showed that RaceInducer generated a lower runtime overhead compared to random noise injection that randomly injected noise for no-lock non-racing accesses and can detect more races with the same noise strength. RaceInducer generates particularly lower runtime overhead than the random noise injection for large programs that generates a high number of shared-memory accesses. In addition, we experimentally demonstrated that RaceInducer incurred lower runtime overheads than a current hidden-race detector (DrFinder) for programs with a relatively large number of locking operations.

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Change history

  • 02 January 2020

    The Acknowledgements section contains an error. The correct wording is given below.

Notes

  1. Two accesses of different threads are not ordered by synchronization.

  2. At least one of two accesses is a write [47].

  3. https://github.com/stephenfreund/RoadRunner.

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Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.R0126-18-1101 (SW Star Lab) Software R&D for Model-based Analysis and Verification of Higher-order Large Complex System, and No.1711073912, Neuromorphic Computing Software Platform for Artificial Intelligence Systems).

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  1. 218 Gajeong-ro, Yuseong-gu, Daejeon, 34129, Korea

    Misun Yu & Yu-Seung Ma

  2. KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea

    Doo-Hwan Bae

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  1. Misun Yu
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Yu, M., Ma, YS. & Bae, DH. Efficient noise injection for exposing hidden data races. J Supercomput 76, 292–323 (2020). https://doi.org/10.1007/s11227-019-03031-0

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