论文标题

TSO和PSO内存模型的读取。

The Reads-From Equivalence for the TSO and PSO Memory Models

论文作者

Bui, Truc Lam, Chatterjee, Krishnendu, Gautam, Tushar, Pavlogiannis, Andreas, Toman, Viktor

论文摘要

由于过程间交流中的非确定性主义,对并发计划的验证仍然是一个公开挑战。在此挑战中,一个算法问题是并发执行的一致性验证。从读取中的读取中的一致性验证允许并发痕迹之间的读取(RF)等效性,并直接应用于诸如无状态模型检查(SMC)之类的区域。最近,RF等效性比标准的Mazurkiewicz等效性更粗糙,从而在SC(顺序一致性)下对SMC的可伸缩性提高了。但是,对于TSO和PSO(总/部分商店订单)的放松记忆模型,决定RF等效性及其对SMC的影响的算法问题是难以捉摸的。在这项工作中,我们分别解决了TSO和PSO存储器模型的一致性验证问题,分别为读取映射,分别表示为VTSO-RF和VPSO-RF。为了执行$ k $ threads和$ d $变量上的$ n $事件,我们建立了新颖的界限,该界限为$ n^{k+1} $,用于tso,为$ n^{k+1} \ cdot \ cdot \ min(n^{k^{k^2},2^{k \ cdot d})$ for pSO。基于我们解决这些问题的解决方案,我们在使用RF等价的TSO和PSO下开发了一种SMC算法。该算法是探索最佳的,从某种意义上说,它可以确保探索一次RF分区的每个类别,并在$ k $有限时每类花费多项式时间。我们在SMC工具Nidhugg中实现了所有算法,并对现有文献的基准进行了大量实验。我们的实验结果表明,对于VTSO-RF和VPSO-RF,我们的算法对标准替代方案提供了显着的可伸缩性提高。当用于SMC时,RF分区通常比用于TSO/PSO的标准shasha-snir分区要粗糙得多,TSO/PSO在模型检查任务中产生了显着的加速。

The verification of concurrent programs remains an open challenge due to the non-determinism in inter-process communication. One algorithmic problem in this challenge is the consistency verification of concurrent executions. Consistency verification under a reads-from map allows to compute the reads-from (RF) equivalence between concurrent traces, with direct applications to areas such as Stateless Model Checking (SMC). The RF equivalence was recently shown to be coarser than the standard Mazurkiewicz equivalence, leading to impressive scalability improvements for SMC under SC (sequential consistency). However, for the relaxed memory models of TSO and PSO (total/partial store order), the algorithmic problem of deciding the RF equivalence, as well as its impact on SMC, has been elusive. In this work we solve the problem of consistency verification for the TSO and PSO memory models given a reads-from map, denoted VTSO-rf and VPSO-rf, respectively. For an execution of $n$ events over $k$ threads and $d$ variables, we establish novel bounds that scale as $n^{k+1}$ for TSO and as $n^{k+1}\cdot \min(n^{k^2}, 2^{k\cdot d})$ for PSO. Based on our solution to these problems, we develop an SMC algorithm under TSO and PSO that uses the RF equivalence. The algorithm is exploration-optimal, in the sense that it is guaranteed to explore each class of the RF partitioning exactly once, and spends polynomial time per class when $k$ is bounded. We implement all our algorithms in the SMC tool Nidhugg, and perform a large number of experiments over benchmarks from existing literature. Our experimental results show that our algorithms for VTSO-rf and VPSO-rf provide significant scalability improvements over standard alternatives. When used for SMC, the RF partitioning is often much coarser than the standard Shasha-Snir partitioning for TSO/PSO, which yields a significant speedup in the model checking task.

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