Parallel Refinement for Multi-Threaded Program Verification

Liangze Yin, Wei Dong, Wanwei Liu, J. Wang
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引用次数: 3

Abstract

Program verification is one of the most important methods to ensuring the correctness of concurrent programs. However, due to the path explosion problem, concurrent program verification is usually time consuming, which hinders its scalability to industrial programs. Parallel processing is a mainstream technique to deal with those problems which require mass computing. Hence, designing parallel algorithms to improve the performance of concurrent program verification is highly desired. This paper focuses on parallelization of the abstraction refinement technique, one of the most efficient techniques for concurrent program verification. We present a parallel refinement framework which employs multiple engines to refine the abstraction in parallel. Different from existing work which parallelizes the search process, our method achieves the effect of parallelization by refinement constraint and learnt clause sharing, so that the number of required iterations can be significantly reduced. We have implemented this framework on the scheduling constraint based abstraction refinement method, one of the best methods for concurrent program verification. Experiments on SV-COMP 2018 show the encouraging results of our method. For those complex programs requiring a large number of iterations, our method can obtain a linear reduction of the iteration number and significantly improve the verification performance.
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多线程程序验证的并行优化
程序验证是保证并发程序正确性的重要方法之一。然而,由于路径爆炸问题,并发程序验证通常耗时,阻碍了其对工业程序的可扩展性。并行处理是处理需要大量计算的问题的主流技术。因此,设计并行算法来提高并发程序验证的性能是非常必要的。摘要本文主要研究并行化的抽象细化技术,它是并发程序验证中最有效的技术之一。我们提出了一个并行优化框架,该框架采用多个引擎并行地对抽象进行优化。与现有的并行化搜索过程的方法不同,我们的方法通过细化约束和学习子句共享来达到并行化的效果,从而大大减少了所需的迭代次数。该框架是在并行程序验证的最佳方法之一——基于调度约束的抽象改进方法上实现的。在SV-COMP 2018上的实验表明,我们的方法取得了令人鼓舞的结果。对于那些需要大量迭代的复杂程序,我们的方法可以获得迭代次数的线性减少,显著提高验证性能。
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