Parallel Recursive State Compression for Free

This paper focuses on reducing memory usage in enumerative model checking, while maintaining the multi-core scalability obtained in earlier work. We present a tree-based multi-core compression method, which works by leveraging sharing among sub-vectors of state vectors. An algorithmic analysis of both worst-case and optimal compression ratios shows the potential to compress even large states to a small constant on average (8 bytes). Our experiments demonstrate that this holds up in practice: the median compression ratio of 279 measured experiments is within 17% of the optimum for tree compression, and five times better than the median compression ratio of SPIN's COLLAPSE compression. Our algorithms are implemented in the LTSmin tool, and our experiments show that for model checking, multi-core tree compression pays its own way: it comes virtually without overhead compared to the fastest hash table-based methods.Comment: 19 page

Proceedings of SUMo and CompoNet 2011

International audienc

Memory Efficient State Space Storage in Explicit Software Model Checking

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Memory Efficient State Space Storage in Explicit Software Model Checking

Abstract. The limited amount of memory is the major bottleneck in model checking tools based on an explicit states enumeration. In this context, techniques allowing an efficient representation of the states are precious. We present in this paper a novel approach which enables to store the state space in a compact way. Though it belongs to the family of explicit storage methods, we qualify it as semi-explicit since all states are not explicitly represented in the state space. Our experiments report a memory reduction ratio up to 95 % with only a tripling of the computing time in the worst case.