The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
LiteRace: effective sampling for lightweight data-race detection
Abstract
Data races are one of the most common and subtle causes of pernicious concurrency bugs. Static techniques for preventing data races are overly conservative and do not scale well to large programs. Past research has produced several dynamic data race detectors that can be applied to large programs. They are precise in the sense that they only report actual data races. However, dynamic data race detectors incur a high performance overhead, slowing down a program's execution by an order of magnitude.
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Citations
Cites
The PARSEC benchmark suite: characterization and architectural implications
Bienia, C., Kumar, S., Singh, J. P., & Li, K. (2008) · Proceedings of the 17th international conference on Parallel architectures and compilation techniques · doi:10.1145/1454115.1454128 · Cited by 3,444
Cited by
ThreadSanitizer: data race detection in practice
Serebryany, K., & Iskhodzhanov, T. (2009) · Proceedings of the Workshop on Binary Instrumentation and Applications · doi:10.1145/1791194.1791203 · Cited by 451
Metadata
- Authors
- Marino, D., Musuvathi, M., & Narayanasamy, S.
- Venue
- Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation
- Publisher
- ACM
- Published
- 2009-06-15
- Pages
- 134-143
- ISSN
- 1531-7102 (ISSN-L)
- OpenAlex
- W2150602145
- Cited by
- 245
- References
- 1
- FWCI
- 14.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: LiteRace: effective sampling for lightweight data-race detection. https://app.cochat.ai/papers/10.1145/1542476.1542491
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- Marino, D., Musuvathi, M., & Narayanasamy, S. (2009). LiteRace: effective sampling for lightweight data-race detection. Proceedings of the 30th ACM SIGPLAN Conference on Programming Language Design and Implementation. https://doi.org/10.1145/1542476.1542491
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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