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PFinder: Efficiently detecting bugs in concurrent programs through parallelizing race verification

机译:PFinder:通过并行化种族验证有效地检测并发程序中的错误

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Races hidden in concurrent programs can lead to harmful bugs. These bugs are difficult to detect due to their non-deterministic characteristics. Previous work has tried to dynamically verify races in actual executions to check whether they would lead to failures. However, it is inefficient to verify all the races to find the harmful bugs if there are a large number of races. To improve the efficiency, PFinder is the first technique that uses a parallel method to verify multiple races on multiple machines simultaneously. We have implemented PFinder as a prototype tool and have experimented on a number of real-world concurrent programs. All the known bugs in known benchmarks are detected. Also, PFinder could scale well as the number of machines increases. Additionally, the speedup of PFinder can be increased linearly with the number of machines.
机译:隐藏在并发程序中的种族可能导致有害的错误。这些错误由于其不确定性特征而难以检测。先前的工作试图动态验证实际执行中的竞争,以检查它们是否会导致失败。但是,如果存在大量种族,则验证所有种族以查找有害错误的效率很低。为了提高效率,PFinder是第一种使用并行方法同时验证多台计算机上的多个种族的技术。我们已经将PFinder实施为原型工具,并在许多现实世界中的并发程序上进行了实验。检测到已知基准中的所有已知错误。而且,随着计算机数量的增加,PFinder可以很好地扩展。此外,PFinder的速度可以随着计算机数量的增加而线性增加。

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