SC21 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

OpenRace: An Open Source Framework for Statically Detecting Data Races


Workshop:Correctness 2021: 5th International Workshop on Software Correctness for HPC Applications

Authors: Bradley Swain, Jeff Huang, Bozhen Liu, Peiming Liu, Yanze Li, Addison Crump, and Rohan Khera (Coderrect Inc)


Abstract: Data races are a particularly nefarious type of bugs that can affect the correctness of parallel software. High performance computing applications are particularly vulnerable to data races as they generally involve massive levels of parallelism. Detecting data races in large scale, complex, and highly parallel applications can be nearly impossible without domain specific race detection tools.

We present the OpenRace framework, the only open source project aimed at providing the foundation needed to build a fast and precise static race detection tool for LLVM based languages. OpenRace is designed to be extensible and allow new parallel programming frameworks to be easily modeled without the need to write an entirely custom race detection engine.

The OpenRace tool has thus far passed 149 of the 172 C/C++ cases in DataRaceBench version 1.3.2, outperforming all dynamic tools and ranking second place overall among the tools with results published by the DataRaceBench authors.





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