Abstract: Data race is a major source of concurrency bugs.Numerous static and dynamic program analysis techniques have been proposed to detect data races.However,some of detectors may cause a large detection overhead and some of detectors may miss lots of true races.In this paper,a dynamic hybrid data race detection algorithm AsampleLock is proposed,which is based on the optimized FastTrack algorithm and lock mode.It uses the sampling technique,monitoring the function pairs from concurrent threads running simultaneously at the same time,and obtains memory access pairs that really involve data race through the preliminary data race detection,thereby reducing analysis overhead of race detection.In order to reduce the influence of the algorithm on thread scheduling,AsampleLock adopts nolock-hb relation to judge the concurrency relationship of access events,adopts map to record read and write informations of shared variables,and adopts the locking patterns to perform dynamic data race detection,thereby reducing false positives and false negatives.On the basis of the above methods,this paper implements the prototype system,named AsampleLock,and chooses the Parsec benchmark suite to evaluate the race detectors.Experiments compared to FastTrack algorithm,LiteRace algorithm and Multilock-HB algorithm.The results show that the time overhead of AsampleLock algorithm is reduced by 8% compared with FastTrack algorithm.Compared with LiteRace algorithm and FastTrack algorithm,the data race detection rate of AsampleLock algorithm is increased by 39% and 27%,respectively.

Key words: Data race detection, Locking patterns, Multithreaded program, Preliminary data race