Random forest instruction level detection model for data race in multithreaded programs
SUN Jiaze1,2, YANG Jiawei1, YANG Zijiang3
1. School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, China; 2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an 710121, China; 3. Department of Computer Science, Western Michigan University, Kalamazoo 49008-5466, USA
Abstract：Data race is a typical concurrency bug in multithreaded programs. Data race is difficult to detect due to the uncertain interleaving in multithreaded programs. A random forest instruction level data race detection model is developed for multithread programs using five attributes to identify the data race features. Firstly, data race detection at the instruction level is based on the happens-before relationship and the lockset algorithm. At the same time, the assembly source code is used to eliminate implicit synchronization pairs. Then, the analysis results from the happens-before relationship and the lockset algorithm are used to train a random forest detection model for multithreaded program data race detection. This data race detection tool for multithreaded programs, AIRaceTest, is implemented on Pin. The model is trained with the results of the multithreaded program instrumentation in GitHub as a sample set. The model accuracy reaches 92.1%. Test results on the classic multithreaded programs, Google data-race-test and Parsec benchmark 3.1, show that the false positives are reduced by about 10.6% and the false negatives are reduced by about 12.3% compared with Eraser, Djit+and Thread Sanitizer. For a large number of threads, the time overhead is reduced by 41.8% while the memory overhead is reduced by 22.4%.
孙家泽, 阳伽伟, 杨子江. 多线程程序数据竞争随机森林指令级检测模型[J]. 清华大学学报（自然科学版）, 2020, 60(10): 804-813.
SUN Jiaze, YANG Jiawei, YANG Zijiang. Random forest instruction level detection model for data race in multithreaded programs. Journal of Tsinghua University(Science and Technology), 2020, 60(10): 804-813.
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2020, Vol. 60 
