Parallel Variable Elimination on CNF Formulas

Gebhardt, Kilian; Manthey, Norbert

doi:10.1007/978-3-642-40942-4_6

Cited by 6 publications

(8 citation statements)

References 16 publications

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“…Finally, it should be noted that BVE, as introduced in [8,10,16,24], is not confluent, as noted by the authors of [16]. Due to dependency between variables, altering the elimination order of these variables may result in different simplified formulas.…”

Section: Related Workmentioning

confidence: 98%

“…MiniSat solving of original and simplified formulas (time in seconds). (8) 726 (8) 143 (8) 86 (8) 194 (8) 127 (8) 51 manol-pipe (10) 1978 (10) 3909 (10) 3106 (10) 1432 (10) 1239 101213(10) Tables 1, 2 and 3. In addition, it presents a comparison between our GPU simplification algorithms and directly ported to the CPU versions of the algorithms, to provide more insight into the contribution of the GPU parallelisation.…”

Section: Benchmarksmentioning

confidence: 99%

“…CSE trims the search space, thereby decreasing the solving time, and is stable, i.e., the outcome of CSE does not depend on the chosen clause ordering. Gebhardt and Manthey [10] presented the first attempt to parallelise SAT preprocessing on a multi-core CPU using a locking scheme to prevent threads corrupting the SAT formula. However, they reported only a very limited speedup of on average 1.88× when running on eight cores.…”

Section: Related Workmentioning

confidence: 99%

“…It tends to produce reasonable reductions in acceptable processing time. Many techniques based on, e.g., variable elimination, clause elimination, and equivalence reasoning are being used to simplify SAT problems, whether prior to the solving phase (preprocessing) [8,10,12,15,16,24] or during the search (inprocessing) [3,18]. However, applying variable and clause M. Osama-This work is part of the GEARS project with project number TOP2.…”

Section: Introductionmentioning

confidence: 99%

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Parallel SAT Simplification on GPU Architectures

Osama

Wijs

2019

Tools and Algorithms for the Construction and Analysis of Systems

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The growing scale of applications encoded to Boolean Satisfiability (SAT) problems imposes the need for accelerating SAT simplifications or preprocessing. Parallel SAT preprocessing has been an open challenge for many years. Therefore, we propose novel parallel algorithms for variable and subsumption elimination targeting Graphics Processing Units (GPUs). Benchmarks show that the algorithms achieve an acceleration of 66× over a state-of-the-art SAT simplifier (SatELite). Regarding SAT solving, we have conducted a thorough evaluation, combining both our GPU algorithms and SatELite with MiniSat to solve the simplified problems. In addition, we have studied the impact of the algorithms on the solvability of problems with Lingeling. We conclude that our algorithms have a considerable impact on the solvability of SAT problems.

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Section: Related Workmentioning

confidence: 98%

Section: Benchmarksmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parallel SAT Simplification on GPU Architectures

Osama

Wijs

2019

Tools and Algorithms for the Construction and Analysis of Systems

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“…In Glucose, the preprocessor works well on many formulas, but is very slow especially if there are many large clauses, or if some clauses are put on the subsumption queue repeatedly. Instead of using a (non-deterministic) parallel BVE algorithm [29], we improved upon the preprocessor from Glucose by implementing linear-time versions of the subsumption check and the resolution of two clauses, similar to the algorithm used in Cadical [16]. Furthermore, we equipped the clause headers with a boolean flag which indicates whether a clause already is on the subsumption queue or not.…”

Section: Solver Designmentioning

confidence: 99%

Tuning Parallel SAT Solvers

Ehlers¹,

Nowotka²

2018

EasyChair Preprints

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In this paper we present new implementation details and benchmarking results for our parallel portfolio solver TopoSAT2. In particular, we discuss ideas and implementation details for the exchange of learned clauses in a massively-parallel SAT solver which is designed to run more that 1, 000 solver threads in parallel. Furthermore, we go back to the roots of portfolio SAT solving, and discuss the impact of diversifying the solver by using different restart-, branching-and clause database management heuristics. We show that these techniques can be used to tune the solver towards different problems. However, in a case study on formulas derived from Bounded Model Checking problems we see the best performance when using a rather simple clause exchange strategy. We show details of these tests and discuss possible explanations for this phenomenon.As computing times on massively-parallel clusters are expensive, we consider it especially interesting to share these kind of experimental results.

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SIGmA: GPU Accelerated Simplification of SAT Formulas

Osama

Wijs

2019

Lecture Notes in Computer Science

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Parallel Variable Elimination on CNF Formulas

Cited by 6 publications

References 16 publications

Parallel SAT Simplification on GPU Architectures

Parallel SAT Simplification on GPU Architectures

Tuning Parallel SAT Solvers

SIGmA: GPU Accelerated Simplification of SAT Formulas

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