放松记忆模型的动态偏序约简

Naling Zhang, Markus Kusano, Chao Wang
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引用次数: 75

摘要

在宽松内存模型(如TSO或PSO)下,在共享内存多处理器上运行的并发程序可能会观察到两种类型的不确定性:线程调度中的不确定性和存储缓冲中的不确定性。尽管在减轻运行时验证期间的调度不确定性方面有大量的工作,但缺乏有效减轻存储缓冲不确定性的方法。提出了一种新的动态偏序约简算法,用于TSO和PSO下并发程序的验证。我们的方法依赖于在一个统一的框架中对两种类型的不确定性进行建模,这允许我们将现有的POR技术扩展到TSO和PSO,而无需修改验证算法。除了声音POR之外,我们还提出了一种缓冲边界方法来更积极地减少状态空间。我们已经在一个无状态模型检查工具中实现了我们的新方法,并在一组多线程C基准测试中证明了它们的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic partial order reduction for relaxed memory models
Under a relaxed memory model such as TSO or PSO, a concurrent program running on a shared-memory multiprocessor may observe two types of nondeterminism: the nondeterminism in thread scheduling and the nondeterminism in store buffering. Although there is a large body of work on mitigating the scheduling nondeterminism during runtime verification, methods for soundly mitigating the store buffering nondeterminism are lacking. We propose a new dynamic partial order reduction (POR) algorithm for verifying concurrent programs under TSO and PSO. Our method relies on modeling both types of nondeterminism in a unified framework, which allows us to extend existing POR techniques to TSO and PSO without overhauling the verification algorithm. In addition to sound POR, we also propose a buffer-bounding method for more aggressively reducing the state space. We have implemented our new methods in a stateless model checking tool and demonstrated their effectiveness on a set of multithreaded C benchmarks.
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