科研之友
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摘要
Data races widely exist in concurrent programs and the harmful races have caused severe failures. To detect the harmful races, previous tools verify all the races, identifying the harmful ones. However, efficiency is affected when there are a large number of races needed to be verified. The multicore technology trend worsens this problem. Unlike previous work, to detect the harmful races, we try to improve the efficiency through parallel verification. We use imprecise race detection to find the races, including benign races and harmful races. The races are divided into many parts, and each part is sent to one machine for verification. On each machine, the races are verified dynamically, identifying the harmful races that would lead to program failures. To our knowledge, this is the first work that parallelizes race verification to improve the efficiency. We have experimented on a number of real-world concurrent programs and all the known harmful races in known benchmarks are detected. Additionally, our tool could scale well as the number of machines increases, and the speedup can also be increased linearly with the number of machines. Comparing with many previous tools, our work imposes lower runtime overhead.
- 出版日期2015-8
- 单位中国人民解放军国防科学技术大学
