Preprocessing in Incremental SAT

Nadel, Alexander; Ryvchin, Vadim; Strichman, Ofer

doi:10.1007/978-3-642-31612-8_20

Cited by 9 publications

(2 citation statements)

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“…Finally, it would be interesting to combine the techniques presented in this paper with more recent results on MUS preprocessing [10] and preprocessing under assumptions [19,37] resp. inprocessing [38].…”

Section: Resultsmentioning

confidence: 99%

Factoring Out Assumptions to Speed Up MUS Extraction

Lagniez

Biere

2013

Lecture Notes in Computer Science

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Abstract. In earlier work on a limited form of extended resolution for CDCL based SAT solving, new literals were introduced to factor out parts of learned clauses. The main goal was to shorten clauses, reduce proof size and memory usage and thus speed up propagation and conflict analysis. Even though some reduction was achieved, the effectiveness of this technique was rather modest for generic SAT solving. In this paper we show that factoring out literals is particularly useful for incremental SAT solving, based on assumptions. This is the most common approach for incremental SAT solving and was pioneered by the authors of MINISAT. Our first contribution is to focus on factoring out only assumptions, and actually all eagerly. This enables the use of compact dedicated data structures, and naturally suggests a new form of clause minimization, our second contribution. As last main contribution, we propose to use these data structures to maintain a partial proof trace for learned clauses with assumptions, which gives us a cheap way to flush useless learned clauses. In order to evaluate the effectiveness of our techniques we implemented them within the version of MINISAT used in the publically available state-of-the-art MUS extractor MUSer. An extensive experimental evaluation shows that factoring out assumptions in combination with our novel clause minimization procedure and eager clause removal is particularly effective in reducing average clause size, improves running time and in general the state-of-the-art in MUS extraction.

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Section: Resultsmentioning

confidence: 99%

Factoring Out Assumptions to Speed Up MUS Extraction

Lagniez

Biere

2013

Lecture Notes in Computer Science

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“…Lingeling uses sophisticated pre-processing techniques, though less powerful in incremental settings. For example, Lingeling "freezes" latch variables in the last unrolled time-frame for bounded model checking [24,29], and it contains a function for "cloning" the SAT solver instance [42,43,44].…”

Section: Model Checkersmentioning

confidence: 99%

Hardware Model Checking Competition 2014: An Analysis and Comparison of Model Checkers and Benchmarks

Cabodi

Loiacono

Palena

et al. 2016

SAT

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Model checkers and sequential equivalence checkers have become essential tools for the semiconductor industry in recent years. The Hardware Model Checking Competition (HWMCC) was founded in 2006 with the purpose of intensifying research interest in these technologies, and establishing more of a science behind them. For example, the competition provided a standardized benchmark format, a challenging and diverse set of industriallyrelevant public benchmarks, and, as a consequence, a significant motivation for additional research to advance the state-of-the-art in model checkers for these verification problems. This paper provides a historical perspective, and an analysis of the tools and benchmarks submitted to the competition. It also presents a detailed analysis of the results collected in the 2014 edition of the contest, showing relations among tools, and among tools and benchmarks. It finally proposes a list of considerations, lessons learned, and hints for both future organizers and competitors.

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