高效、接近完整且通常可靠的混合动态数据竞争预测（扩展版）
Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)
摘要：
动态数据竞争预测的目标是基于由跟踪表示的单个程序运行来识别竞争。挑战是保持效率，同时尽可能健全和完整。高效意味着线性运行时间，否则该方法不可能用于现实世界的程序。我们介绍了一个有效的，接近完整的，通常是健全的动态数据竞争预测方法，结合锁集方法与几个改进的发生在该地区之前的方法。通过接近完成，我们的意思是该方法在理论上是完整的，但出于效率原因，实现应用了一些可能导致不完整性的优化。该方法可以被证明是两个线程的声音，但一般是不健全的。我们提供了大量的实验数据，表明我们的方法在实践中效果良好。
英文摘要：
Dynamic data race prediction aims to identify races based on a single program run represented by a trace. The challenge is to remain efficient while being as sound and as complete as possible. Efficient means a linear run-time as otherwise the method unlikely scales for real-world programs. We introduce an efficient, near complete and often sound dynamic data race prediction method that combines the lockset method with several improvements made in the area of happens-before methods. By near complete we mean that the method is complete in theory but for efficiency reasons the implementation applies some optimizations that may result in incompleteness. The method can be shown to be sound for two threads but is unsound in general. We provide extensive experimental data that shows that our method works well in practice.
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