首页|Minimal Context-Switching Data Race Detection with Dataflow Tracking
Minimal Context-Switching Data Race Detection with Dataflow Tracking
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Data race is one of the most important concurrent anomalies in multi-threaded programs.Emerging con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race detector.However,this constraint-based approach has serious limitations on helping programmers analyze and understand data races.First,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the program.Second,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving process.This ad hoc recommendation imposes too many context switches,which complicates the data race analysis.To address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context switches.Specifically,we reduce the false positives by ana-lyzing and tracking the dataflow in the program.By this means,DFTracker thus reduces the unnecessary analysis of false race schedules.We further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data race.Our experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race detec-tor.Therefore,DFTracker can be used as an effective tool to understand the data race for programmers.
data racesatisfiability modulo theorymulti-threaded programdynamic detection
郑龙、李洋、辛杰、刘海峰、郑然、廖小飞、金海
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National Engineering Research Center for Big Data Technology and System,School of Computer Science and Technology Huazhong University of Science and Technology,Wuhan 430074,China
Services Computing Technology and System Laboratory,School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China
Cluster and Grid Computing Laboratory,School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China
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NSTL
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