Deadlocks are one of the most notorious concurrency bugs, and significant research has focused on detecting them efficiently. Dynamic predictive analyses work by observing concurrent executions, and reason about alternative interleavings that can witness concurrency bugs. Such techniques offer scalability and sound bug reports, and have emerged as an effective approach for concurrency bug detection, such as data races. Effective dynamic deadlock prediction, however, has proven a challenging task, as no deadlock predictor currently meets the requirements of soundness, high-precision, and efficiency. In this paper, we first formally establish that this tradeoff is unavoidable, by showing that (a) sound and complete deadlock prediction is intractable, in general, and (b) even the seemingly simpler task of determining the presence of potential deadlocks, which often serve as unsound witnesses for actual predictable deadlocks, is intractable. The main contribution of this work is a new class of predictable deadlocks, called sync(hronization)-preserving deadlocks. Informally, these are deadlocks that can be predicted by reordering the observed execution while preserving the relative order of conflicting critical sections. We present two algorithms for sound deadlock prediction based on this notion. Our first algorithm SyncPDOffline detects all sync-preserving deadlocks, with running time that is linear per abstract deadlock pattern, a novel notion also introduced in this work. Our second algorithm SyncPDOnline predicts all sync-preserving deadlocks that involve two threads in a strictly online fashion, runs in overall linear time, and is better suited for a runtime monitoring setting. We implemented both our algorithms and evaluated their ability to perform offline and online deadlock-prediction on a large dataset of standard benchmarks.

翻译：死锁是最臭名昭著的并发错误之一，大量研究聚焦于如何高效检测死锁。动态预测分析通过观察并发执行过程，并推理可能见证并发错误的替代交错序列。此类技术具有可扩展性和可靠的错误报告，已发展成为并发错误检测（如数据竞争）的有效方法。然而，有效的动态死锁预测始终是一项艰巨挑战，因为目前尚无死锁预测器能同时满足可靠性、高精度和高效性。本文首先通过理论论证正式表明这种权衡不可避免：（a）一般而言，可靠且完备的死锁预测在计算上难以处理；（b）甚至看似更简单的潜在死锁判定任务（这类死锁常作为实际可预测死锁的不可靠见证）同样难以处理。本研究的主要贡献是提出一类新的可预测死锁——同步保持死锁。直观而言，此类死锁可通过在保持冲突临界区相对顺序的前提下重新排列观测执行顺序来预测。我们基于该概念提出两种可靠的死锁预测算法：第一种算法SyncPDOffline可检测全部同步保持死锁，其运行时间与每个抽象死锁模式（本文提出的另一新概念）呈线性关系；第二种算法SyncPDOnline能以严格在线方式预测所有涉及两个线程的同步保持死锁，整体运行时间为线性，更适合运行时监控场景。我们实现了这两种算法，并在包含标准基准测试的大规模数据集上评估了其离线和在线死锁预测能力。