DataRaceBench: A Benchmark Suite for Systematic
Evaluation of Data Race Detection Tools
Event Type
Paper
State of the Practice
TimeTuesday, November 14th11am -
11:30am
Location301-302-303
DescriptionData races in multi-threaded parallel applications are
notoriously damaging while extremely difficult to
detect. Many tools have been developed to help
programmers find data races. However, there is no
dedicated OpenMP benchmark suite to systematically
evaluate data race detection tools for their strengths
and limitations.
We present DataRaceBench, an open-source benchmark suite designed to systematically and quantitatively evaluate the effectiveness of data race detection tools. We focus on data race detection in programs written in OpenMP, the popular parallel programming model for multi-threaded applications. In particular, DataRaceBench includes a set of microbenchmark programs with or without data races.
We also define several metrics to represent effectiveness and efficiency of data race detection tools. We evaluate four different data race detection tools: Helgrind, ThreadSanitizer, Archer, and Intel Inspector. The results show that DataRaceBench is effective to provide comparable, quantitative results and discover strengths and weaknesses of the tools evaluated.
We present DataRaceBench, an open-source benchmark suite designed to systematically and quantitatively evaluate the effectiveness of data race detection tools. We focus on data race detection in programs written in OpenMP, the popular parallel programming model for multi-threaded applications. In particular, DataRaceBench includes a set of microbenchmark programs with or without data races.
We also define several metrics to represent effectiveness and efficiency of data race detection tools. We evaluate four different data race detection tools: Helgrind, ThreadSanitizer, Archer, and Intel Inspector. The results show that DataRaceBench is effective to provide comparable, quantitative results and discover strengths and weaknesses of the tools evaluated.
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