Session: Programmable Parallelism

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

Wednesday, November 18, 2020

11:00 AM – 11:30 AM ET

Channel: Track 5

Author(s)

BS

Bradley Swain

Texas A&M University, Coderrect Inc, United States of America
YL

Yanze Li

Texas A&M University, United States of America
PL

Peiming Liu

Texas A&M University, United States of America
Ignacio Laguna

Lawrence Livermore National Laboratory, United States of America
GG

Giorgis Georgakoudis

Lawrence Livermore National Laboratory, United States of America
JH

Jeff Huang

Texas A&M University, United States of America

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.