The LTL Model Checking problem is reducible to finding accepting cycles in a graph. The Nested Depth-First Search (Ndfs) algorithm detects accepting cycles efficiently: on-the-fly, with linear-time complexity and negligible memory overhead. The only downside of the algorithm is that it relies on an inherently-sequential, depth-first search. It has not been parallelized beyond running the independent nested search in a separate thread (dual core). In this paper, we introduce, for the first time, a multi-core Ndfs algorithm that can scale beyond two threads, while maintaining exactly the same worst-case time complexity. We prove this algorithm correct, and present experimental results obtained with an implementation in the LTSmin tool set on the entire Beem benchmark database. We measured considerable speedups compared to the current state of the art in parallel cycle detection algorithms.
Reasoning about the correctness of parallel and distributed systems requires automated tools. By now, the mCRL2 toolset and language have been developed over a course of more than fifteen years. In this paper, we report on the progress and advancements over the past six years. Firstly, the mCRL2 language has been extended to support the modelling of probabilistic behaviour. Furthermore, the usability has been improved with the addition of refinement checking, counterexample generation and a user-friendly GUI. Finally, several performance improvements have been made in the treatment of behavioural equivalences. Besides the changes to the toolset itself, we cover recent applications of mCRL2 in software product line engineering and the use of domain specific languages (DSLs).1 The source code is also archived on https://doi.org/10.5281/zenodo.2555054.
In recent years, General Purpose Graphics Processors (GPUs) have been successfully applied in multiple application domains to drastically speed up computations. Model checking is an automatic method to formally verify the correctness of a system specification. Such specifications can be viewed as implicit descriptions of a large directed graph or state space, and for most model checking operations, this graph must be analysed. Constructing it, or on-the-fly exploring it, however, is computationally intensive, so it makes sense to try to implement this for GPUs. In this paper, we explain the limitations involved, and how to overcome these. We discuss the possible approaches involving related work, and propose an alternative, using a new hash table approach for GPUs. Experimental results with our prototype implementations show significant speed-ups compared to the established sequential counterparts.
Abstract. This article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bug-hunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods to automatically generate them by exploiting system abstractions. We extend the algorithms to feature partial-order reduction and show how liveness problems can be adapted by lifting the search space. For deterministic, finite domains we instantiate the algorithms to directed symbolic, external and distributed search. For real-time domains we discuss the adaption of the algorithms to timed automata and for probabilistic domains we show the application to counterexample generation. Last but not least, we explain how directed model checking helps to accelerate finding solutions to scheduling problems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.