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Improve and Stabilize Classification Results of DataRaceBench


Workshop: 7th International Workshop on Software Correctness for HPC Applications (Correctness '23)

Authors: Joachim Jenke and Simon Schwitanski (RWTH Aachen University)


Abstract: DataRaceBench is a benchmark using small kernel applications to classify the detection capabilities of data race detection tools. During our experiments of applying Archer to the benchmark suite we observed different short-comings. With recently added kernels, the turn-around time of a basic benchmark run increased from several minutes to more than an hour. Furthermore, we observed non-deterministic and unexpected results. In this presentation, we propose several changes to existing kernels to address these short-comings. In addition, we propose to use variants of the kernels with non-deterministic runtime schedules that explicitly enforce these different schedules. Finally, we provide an evaluation of the updated benchmark with Archer running in thread-centric and task-centric mode.





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