Affiliation:
1. Ohio State University, USA
2. Carnegie Mellon University, USA
Abstract
Data races complicate programming language semantics, and a data race is often a bug. Existing techniques detect data races and define their semantics by detecting conflicts between synchronization-free regions (SFRs). However, such techniques either modify hardware or slow programs dramatically, preventing always-on use today. This paper describes Valor, a sound, precise, software-only region conflict detection analysis that achieves high performance by eliminating the costly analysis on each read operation that prior approaches require. Valor instead logs a region's reads and lazily detects conflicts for logged reads when the region ends. As a comparison, we have also developed FastRCD, a conflict detector that leverages the epoch optimization strategy of the FastTrack data race detector. We evaluate Valor, FastRCD, and FastTrack, showing that Valor dramatically outperforms FastRCD and FastTrack. Valor is the first region conflict detector to provide strong semantic guarantees for racy program executions with under 2X slowdown. Overall, Valor advances the state of the art in always-on support for strong behavioral guarantees for data races.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Graphics and Computer-Aided Design,Software
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Sound and efficient concurrency bug prediction;Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2021-08-18
2. BlockRace;Proceedings of the IEEE/ACM 1st International Conference on Automation of Software Test;2020-09-12
3. PLASMA;Proceedings of the Fifteenth European Conference on Computer Systems;2020-04-15
4. AdaptiveLock: Efficient Hybrid Data Race Detection Based on Real-World Locking Patterns;International Journal of Parallel Programming;2018-06-04