The lock set method and the partial order method are two main approaches to guarantee that dynamic data race prediction remains efficient. There are many variations of these ideas. Common to all of them is the assumption that the events in a critical section belong to the same thread. We have evidence that critical sections in the wild do extend across thread boundaries even if the surrounding acquire and release events occur in the same thread. We introduce the novel concept of a cross-thread critical section to capture such situations, offer a theoretical comprehensive framework, and study their impact on state-of-the-art data race analyses. For the sound partial order relation WCP we can show that the soundness claim also applies to cross-thread critical sections. For DCtp the occurrence of cross-thread critical sections invalidates the soundness claim. For complete partial order relations such as WDP and PWR, cross-thread critical sections help to eliminate more false positives. The same (positive) impact applies to the lock set construction. Our experimental evaluation confirms that cross-thread critical sections arise in practice. For the complete relation PWR, we are able to reduce the number of false positives. The performance overhead incurred by tracking cross-thread critical sections slows down the analysis by 10%-20%, on average.

翻译：锁集方法和偏序方法是保证动态数据竞争检测高效性的两种主要手段，这些方法存在多种变体，其共同前提是临界区中的事件属于同一线程。我们证实，即使获取与释放事件发生在同一线程中，现实中的临界区确实会跨越线程边界。为此，我们提出跨线程临界区这一新概念以描述此类场景，构建了完整的理论框架，并研究其对现有顶尖数据竞争分析算法的影响。对于正确的偏序关系WCP，可证明其正确性同样适用于跨线程临界区。对于DCtp，跨线程临界区的出现会使其正确性失效。对于WDP和PWR等完备偏序关系，跨线程临界区有助于消除更多误报。这种（积极的）影响同样适用于锁集构造。实验评估证实了跨线程临界区在实践中的存在性。针对完备关系PWR，我们成功减少了误报数量。追踪跨线程临界区带来的性能开销平均使分析速度降低10%-20%。