A Parallelization Scheme Based on Work Stealing for a Class of SAT Solvers

Jurkowiak, Bernard; Li, Chu Min; Utard, Gil

doi:10.1007/s10817-005-1970-7

Cited by 35 publications

(17 citation statements)

References 21 publications

(27 reference statements)

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“…In [15] a parallelization scheme for a class of SAT solvers based on the DPLL procedure is presented. The scheme uses a dynamic load-balancing mechanism based on work-stealing techniques to deal with the irregularity of SAT problems.…”

Section: Previous Workmentioning

confidence: 99%

Diversification and Intensification in Parallel SAT Solving

Guo

Hamadi

Jabbour

et al. 2010

Principles and Practice of Constraint Programming – CP 2010

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Abstract. In this paper, we explore the two well-known principles of diversification and intensification in portfolio-based parallel SAT solving. These dual concepts play an important role in several search algorithms including local search, and appear to be a key point in modern parallel SAT solvers. To study their tradeoff, we define two roles for the computational units. Some of them classified as Masters perform an original search strategy, ensuring diversification. The remaining units, classified as Slaves are there to intensify their master's strategy. Several important questions have to be answered. The first one is what information should be given to a slave in order to intensify a given search effort? The second one is, how often, a subordinated unit has to receive such information? Finally, the question of finding the number of subordinated units along their connections with the search efforts has to be answered. Our results lead to an original intensification strategy which outperforms the best parallel SAT solver, and solves some open SAT instances.

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Section: Previous Workmentioning

confidence: 99%

Diversification and Intensification in Parallel SAT Solving

Guo

Hamadi

Jabbour

et al. 2010

Principles and Practice of Constraint Programming – CP 2010

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“…Minisat is added into some techniques like confliction clause learning and watched literal, achieving a good performance. Nowadays, there are many parallel algorithms of SAT, such as GrADSAT [4], NAGSAT [5], Satz [6] and PSATO [7]. There also exist parallel algorithms of Minisat like PMSat [8].…”

Section: Introductionmentioning

confidence: 99%

A Research on Minisat Using Coarse-grained Parallel Technique Based on Multi-core and Multi-platform

Zhu¹,

Jiang²,

Luo³

et al. 2017

Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)

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Abstract. In this paper, we make a research on a widely-used SAT solver, Minisat, aiming to improve its performance using coarse-grained parallel method on multi-core and multi-platform. Firstly, we parallel the Minisat by mean of OpenMP and test its performance with different threads by running a test set consisting of 2000 SAT problems on an X86 computer. Besides, a scheduling strategy with time sequence is added to the process and achieves a better speed-up ratio. Then, we move the algorithm to an ARM computer and repeat the same process, finding that the performance of Minisat on X86 is better than that on ARM, but ARM platform has a better scale effect than X86 platform when running at full load and is able to perform better than X86 when they have the same hardware configuration.

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“…We call plain partitioning an approach where a partition function is used once and each resulting instance is solved in parallel. A typical approach to splitting the search space is to use guiding path or semantic decomposition based techniques [7][8][9][10][11]. However, these techniques impose constraints on the underlying solver technology and are not ideal for grids and clouds since they require relatively frequent communication between jobs.…”

Section: Introductionmentioning

confidence: 99%

Partitioning SAT Instances for Distributed Solving

Hyvärinen

Junttila

Niemelä

2010

Logic for Programming, Artificial Intelligence, and Reasoning

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Abstract. In this paper we study the problem of solving hard propositional satisfiability problem (SAT) instances in a computing grid or cloud, where run times and communication between parallel running computations are limited. We study analytically an approach where the instance is partitioned iteratively into a tree of subproblems and each node in the tree is solved in parallel. We present new methods which combine clause learning and look-ahead to construct partitions, evaluate their efficiency experimentally, and finally demonstrate the power of the approach in a real grid environment by solving several instances that were not solved in a SAT solver competition.

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A Parallelization Scheme Based on Work Stealing for a Class of SAT Solvers

Cited by 35 publications

References 21 publications

Diversification and Intensification in Parallel SAT Solving

Diversification and Intensification in Parallel SAT Solving

A Research on Minisat Using Coarse-grained Parallel Technique Based on Multi-core and Multi-platform

Partitioning SAT Instances for Distributed Solving

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