SC20 Proceedings

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

OMPRacer: A Scalable and Precise Static Race Detector for OpenMP Programs

Authors: Bradley Swain (Texas A&M University, Coderrect Inc); Yanze Li and Peiming Liu (Texas A&M University); Ignacio Laguna and Giorgis Georgakoudis (Lawrence Livermore National Laboratory); and Jeff Huang (Texas A&M University)

Abstract: We present OMPRacer, a static tool that uses flow-sensitive, interprocedural analysis to detect data races in OpenMP programs. OMPRacer is fast, scalable, has high code coverage, and supports the most common OpenMP features by combining state-of-the-art pointer analysis, novel value-flow analysis, happens-before tracking, and generalized modelling of OpenMP APIs.

Unlike dynamic tools that currently dominate data race detection, OMPRacer achieves almost 100% code coverage using static analysis to detect a broader category of races without running the program or relying on specific input or runtime behavior. OMPRacer has competitive precision with dynamic tools like Archer and ROMP: passing 105/116 cases in DataRaceBench with a total accuracy of 91%.

OMPRacer has been used to analyze several Exascale Computing Project proxy applications containing over 2 million lines of code in under 10 minutes. OMPRacer has revealed previously unknown races in an ECP proxy app and a production simulation for COVID19.

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