OMPRacer: a scalable and precise static race detector for OpenMP programs
Bradley Swain, Yanze Li, Peiming Liu, Ignacio Laguna, Giorgis Georgakoudis, Jeff Huang
摘要
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 behaviour. 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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- Multi-stage On-Demand Program Slicing for Modular Analysis of Multi-threaded ProgramsJiawei Yang, Xiao Cheng, Jiawei Wang, Xiapu Luo 等ISSTA 2026
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