Abstract
Data race is the most widespread in concurrent program defects. Aiming at the problems of false positives, false negatives and high overheads in the process of data race detection. A multithreaded programs data race detection method based on the XGBoost model was proposed. The hybrid detection algorithm based on the sim-hb relationship of the thread segment and the lockset algorithm is used to detect data race, identify implicit synchronization pairs and perform instruction-level filtering, and extract instruction features to construct the XGBoost dynamic detection model. Designed and implemented the multithreaded programs data race detection tool XGRace, and performed data race detection on the classic multithreaded programs SPLASH-2 benchmark suite and Parsec benchmark program 3.0. The results show that: compared with Djit+, Eraser, Thread Sanitizer and AIRaceTest, XGrace has an average of 19.7% lower false negatives rate and 6.2% lower false positives rate, while the time and memory overhead are reduced by 34.1% and 29.8% respectively, which provides an effective and accurate data race detection method for multithreaded programs.
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Acknowledgment
This work was supported by the Key R&D Projects of Shaanxi Province (Grant No. 2020GY-010), the Postgraduate Innovation Fund Project of Xi’an University of Posts and Telecommunications (Grant No. CXJJLY2019072).
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Sun, J., Shan, L., Shu, X. (2022). XGBoost Dynamic Detection for Data Race in Multithreaded Programs. In: Xie, Q., Zhao, L., Li, K., Yadav, A., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-89698-0_128
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DOI: https://doi.org/10.1007/978-3-030-89698-0_128
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