In recent years, the LTSmin model checker has been extended with support for several new modelling languages, including probabilistic (Mapa) and timed systems (Uppaal). Also, connecting additional language front-ends or ad-hoc state-space generators to LTSmin was simplified using custom C-code. From symbolic and distributed reachability analysis and minimisation, LTSmin's functionality has developed into a model checker with multi-core algorithms for on-the-fly LTL checking with partial-order reduction, and multi-core symbolic checking for the modal µ-calculus, based on the multi-core decision diagram package Sylvan. In LTSmin, the modelling languages and the model checking algorithms are connected through a Partitioned Next-State Interface (Pins), that allows to abstract away from language details in the implementation of the analysis algorithms and on-the-fly optimisations. In the current paper, we present an overview of the toolset and its recent changes, and we demonstrate its performance and versatility in two case studies.

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Abstract. This paper contributes to the multi-core model checking of timed automata (TA) with respect to liveness properties, by investigating checking of TA Büchi emptiness under the very coarse inclusion abstraction or zone subsumption, an open problem in this field. We show that in general Büchi emptiness is not preserved under this abstraction, but some other structural properties are preserved. Based on those, we propose a variation of the classical nested depth-first search (ndfs) algorithm that exploits subsumption. In addition, we extend the multi-core cndfs algorithm with subsumption, providing the first parallel LTL model checking algorithm for timed automata. The algorithms are implemented in LTSmin, and experimental evaluations show the effectiveness and scalability of both contributions: subsumption halves the number of states in the real-world FDDI case study, and the multi-core algorithm yields speedups of up to 40 using 48 cores.

Abstract. This paper presents CNDFS, a tight integration of two earlier multicore nested depth-first search (NDFS) algorithms for LTL model checking. CNDFS combines the different strengths and avoids some weaknesses of its predecessors. We compare CNDFS to an earlier ad-hoc combination of those two algorithms and show several benefits: It has shorter and simpler code and a simpler correctness proof. It exhibits more robust performance with similar scalability, while at the same time reducing memory requirements. The algorithm has been implemented in the multi-core backend of the LTSMIN model checker, which is now benchmarked for the first time on a 48 core machine (previously 16). The experiments demonstrate better scalability than other parallel LTL model checking algorithms, but we also investigate apparent bottlenecks. Finally, we noticed that the multi-core NDFS algorithms produce shorter counterexamples, surprisingly often shorter than their BFS-based counterparts.

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Abstract. State space exploration is a basic solution to many verification problems, but is limited by time and memory usage. Due to physical limits in modern CPUs, sequential exploration algorithms do not benefit automatically from the next generation of processors anymore, hence the need for multi-core solutions. 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.

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