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- Daniel Marino
- University of California, Los Angeles, Los Angeles, CA, USA
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- Madanlal Musuvathi
- Microsoft Research, Redmond, Redmond, WA, USA
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- Satish Narayanasamy
- University of Michigan, Ann Arbor, Ann Arbor, MI, USA
書誌事項
- タイトル別名
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- effective sampling for lightweight data-race detection
- 公開日
- 2009-05-28
- 権利情報
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- https://www.acm.org/publications/policies/copyright_policy#Background
- DOI
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- 10.1145/1543135.1542491
- 公開者
- Association for Computing Machinery (ACM)
この論文をさがす
説明
<jats:p>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.</jats:p> <jats:p>In this paper we present LiteRace, a very lightweight data race detector that samples and analyzes only selected portions of a program's execution. We show that it is possible to sample a multithreaded program at a low frequency, and yet, find infrequently occurring data races. We implemented LiteRace using Microsoft's Phoenix compiler. Our experiments with several Microsoft programs, Apache, and Firefox show that LiteRace is able to find more than 70% of data races by sampling less than 2% of memory accesses in a given program execution.</jats:p>
収録刊行物
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- ACM SIGPLAN Notices
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ACM SIGPLAN Notices 44 (6), 134-143, 2009-05-28
Association for Computing Machinery (ACM)
