A Semantics-Driven Approach to Improving DataRaceBench's OpenMP Standard Coverage

摘要

DataRaceBench is a benchmark suite designed to systematically and quantitatively evaluate the effectiveness of data race detection tools. Its initial release in 2017 contained 72 C99 microbenchmarks with and without data races and was successfully used to evaluate several popular data race detection tools. In this paper, we describe a novel semantics-driven approach to improving DataRaceBench's OpenMP standard coverage. Based on a traditional definition of data races, we define several semantic categories for parallelism, data-sharing attributes, and synchronization. This allows us to assign semantic labels to constructs, clauses and data-sharing rules in the OpenMP 4.5 specification. Based on these labels we then analyze the coverage of the initial release of DataRaceBench and add 44 new C and C++ microbenchmarks to improve the OpenMP standard coverage. Finally, we re-evaluate two popular data race detection tools with the new microbenchmarks, and show that the new version of DataRaceBench gives new insights about the selected tools.