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  • 标题:On-the-fly Race Detection for Structured Fork-join Parallel Programs
  • 本地全文:下载
  • 作者:Ok-kyoon Ha
  • 期刊名称:International Journal of Software Engineering and Its Applications
  • 印刷版ISSN:1738-9984
  • 出版年度:2014
  • 卷号:8
  • 期号:4
  • 页码:11-22
  • DOI:10.14257/ijseia.2014.8.4.02
  • 出版社:SERSC
  • 摘要:It is still difficult and cumbersome to locate where a program runs into data races, because they may lead to unpredictable results of the program. To detect data races occurred during an execution of parallel programs, previous work provides large runtime and space overhead or focuses on reducing false positives. Thus, the prior detectors are still imprecise and inefficient, when applied to large scale parallel programs. This paper presents an efficient data race detection technique, called EDP that analyzes conflicting accesses to every shared memory location. Our technique precisely reports data races because it guarantees to locate at least one data race for each shared memory location, if there exists any, and provides a significant improvement of efficiency as O(1) space and time overheads for each access history.
  • 关键词:Data race detection; structured fork-join parallelism; dynamic analysis
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