2026-07-22T16:55:34Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:7272 2024-03-06T10:39:44Z ddc:004 open_access

What'’s the Optimal Performance of Precise Dynamic Race Detection? –A Redundancy Perspective Huang, Jeff Rajagopalan, Arun K. Data Race Detection Dynamic Analysis Concurrency Redundancy In a precise data race detector, a race is detected only if the execution exhibits a real race. In such tools, every memory access from each thread is typically checked by a happens-before algorithm. What’s the optimal runtime performance of such tools? In this paper, we identify that a significant percentage of memory access checks in real-world program executions are often redundant: removing these checks affects neither the precision nor the capability of race detection. We show that if all such redundant checks were eliminated with no cost, the optimal performance of a state-of-the-art dynamic race detector, FastTrack, could be improved by 90%, reducing its runtime overhead from 68X to 7X on a collection of CPU intensive benchmarks. We further develop a purely dynamic technique, ReX, that efficiently filters out redundant checks and apply it to FastTrack. With ReX, the runtime performance of FastTrack is improved by 31% on average. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Jeff Huang and Arun K. Rajagopalan 2017 Is Part Of LIPIcs, Volume 74, 31st European Conference on Object-Oriented Programming (ECOOP 2017) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ECOOP.2017.15 urn:nbn:de:0030-drops-72722 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2017.15 eng https://creativecommons.org/licenses/by/3.0/legalcode