Local Nontermination Detection for Parallel C++ Programs

Štill, Vladimír; Barnat, Jǐŕı

doi:10.1007/978-3-030-30446-1_20

Cited by 1 publication

(1 citation statement)

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“…In LTL model checking, the detection of bottom SCCs is used during the decomposition of the property automaton to speed up the model checking procedure [52]. Another example of an application where detection of BSCCs is crucial is detecting non-terminating sections of parallel programs written in C or C++ [55]. In models of dynamical systems, which are of our primary interest, BSCCs correspond to so-called attractors that determine the long-term behaviour of the system [43].…”

Section: Introductionmentioning

confidence: 99%

Computing Bottom SCCs Symbolically Using Transition Guided Reduction

Beneš

Brim

Pastva

et al. 2021

Computer Aided Verification

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Detection of bottom strongly connected components (BSCC) in state-transition graphs is an important problem with many applications, such as detecting recurrent states in Markov chains or attractors in dynamical systems. However, these graphs’ size is often entirely out of reach for algorithms using explicit state-space exploration, necessitating alternative approaches such as the symbolic one.Symbolic methods for BSCC detection often show impressive performance, but can sometimes take a long time to converge in large graphs. In this paper, we provide a symbolic state-space reduction method for labelled transition systems, called interleaved transition guided reduction (ITGR), which aims to alleviate current problems of BSCC detection by efficiently identifying large portions of the non-BSCC states.We evaluate the suggested heuristic on an extensive collection of 125 real-world biologically motivated systems. We show that ITGR can easily handle all these models while being either the only method to finish, or providing at least an order-of-magnitude speedup over existing state-of-the-art methods. We then use a set of synthetic benchmarks to demonstrate that the technique also consistently scales to graphs with more than $$2^{1000}$$ 2 1000 vertices, which was not possible using previous methods.

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Section: Introductionmentioning

confidence: 99%