Designing fast LTL model checking algorithms for many-core GPUs

Barnat, Jǐŕı; Bauch, Petr; Brim, Luboš; EšKa, Milan

doi:10.1016/j.jpdc.2011.10.015

Cited by 43 publications

(26 citation statements)

References 20 publications

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“…1 From each type of model, we selected the variants with more than 9 million states. Our CNDFS algorithm is implemented in the multi-core backend of the LTSMIN model checking tool set [16], based on a dedicated scalable lock-free hash table and an off-the-shelf load balancer [15].…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…While the heuristic on-the-fly behavior seems to work well for some models, for others it does not. It must however be mentioned that the on-the-fly capabilities of this algorithm have recently been improved by changing its exploration order to be more DFS-like [1]. In [1], performance is reported on par with the LNDFS algorithm.…”

Section: Models With Accepting Cyclesmentioning

confidence: 99%

“…It must however be mentioned that the on-the-fly capabilities of this algorithm have recently been improved by changing its exploration order to be more DFS-like [1]. In [1], performance is reported on par with the LNDFS algorithm. Unfortunately, we do not have the means (a GPGPU) to reproduce any results here.…”

Section: Models With Accepting Cyclesmentioning

confidence: 99%

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Improved Multi-Core Nested Depth-First Search

Évangelista

Laarman

Petrucci

et al. 2012

Automated Technology for Verification and Analysis

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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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Section: Experimental Evaluationmentioning

confidence: 99%

Section: Models With Accepting Cyclesmentioning

confidence: 99%

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Improved Multi-Core Nested Depth-First Search

Évangelista

Laarman

Petrucci

et al. 2012

Automated Technology for Verification and Analysis

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“…Concerning model checking, [7] describes the only other GPU on-line exploration we found, but it uses both the CPU and GPU, restricting the GPU to neighbour gathering, and it uses bitstate hashing, hence it is not guaranteed to be exhaustive. In [8], explicit state spaces are analysed.…”

Section: Sparse Graph Search On Gpusmentioning

confidence: 99%

GPUexplore: Many-Core On-the-Fly State Space Exploration Using GPUs

Wijs

Bošnački

2014

Tools and Algorithms for the Construction and Analysis of Systems

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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.

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“…Furthermore, we restrict the scope of this paper to the evaluation of the solution step in the model checking process, as there exist several efficient and even parallel approaches for the construction of compact data representations in our setting [3,4,20], which can be used for the preprocessing of the input data.…”

Section: Introductionmentioning

confidence: 99%

On Parallel Software Verification Using Boolean Equation Systems

Deisting

Češka

Lüttgen

2012

Model Checking Software

Self Cite

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Abstract. Due to the recent technological developments multi-core and many-core hardware platforms have become widely accessible. These parallel architectures have been used to significantly accelerate many computationally demanding tasks. In this paper we describe a parallel approach to solve Boolean Equation Systems (BESs) in the context of model checking. We focus on the applicability of state-of-the-art, shared-memory parallel hardware -multi-core CPUs and many-core GPUs -to speed up the resolution procedure for BESs. For this setting, we experimentally show the scalability and competitiveness of our parallel approach, compared to an optimized sequential implementation, based on a large benchmark suite with examples from industry and academia.

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Designing fast LTL model checking algorithms for many-core GPUs

Cited by 43 publications

References 20 publications

Improved Multi-Core Nested Depth-First Search

Improved Multi-Core Nested Depth-First Search

GPUexplore: Many-Core On-the-Fly State Space Exploration Using GPUs

On Parallel Software Verification Using Boolean Equation Systems

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