ManySAT: a Parallel SAT Solver

Hamadi, Youssef; Jabbour, Saïd; Saïs, Lakhdar

doi:10.3233/sat190070

Cited by 191 publications

(100 citation statements)

References 30 publications

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“…Each square in a configuration is placed in the lowest and leftmost possible slot. The aforementioned solution corresponds to the configuration (33,37,42,29,4,25,16,18,24,9,7,2,17,6,50,15,11,19,35,8,27).…”

Section: The Perfect-square Placement Problemmentioning

confidence: 99%

“…It is worth noticing that, in these domains, most of the attempts to take advantage the parallelism available in modern multicore architectures have targeted homogeneous systems, for instance, Intel or AMD-based machines, and make use of shared memory [2][3][4]. The different cores are working on shared data structures that somehow represent a global environment in which the subcomputations are taking place.…”

Section: Introductionmentioning

confidence: 99%

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Targeting the Cell Broadband Engine for constraint‐based local search

Díaz

Abreu

Codognet

2011

Concurrency and Computation

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SUMMARYWe investigated the use of the Cell Broadband Engine (Cell/BE) for constraint-based local search and combinatorial optimization applications. We presented a parallel version of a constraint-based local search algorithm that was chosen because it fits very well the Cell/BE architecture because it requires neither shared memory nor communication among processors. The performance study on several large optimization benchmarks shows mostly linear time speedups, sometimes even super linear. These experiments were carried out on a dual-Cell IBM (Armonk, NY, USA) blade with 16 processors. Besides getting speedups, the execution times exhibit a much smaller variance that benefits applications where a timely reply is critical. Copyright

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Section: The Perfect-square Placement Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Targeting the Cell Broadband Engine for constraint‐based local search

Díaz

Abreu

Codognet

2011

Concurrency and Computation

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“…The solvers also exchange information mainly in the form of learned clauses. This approach is referred to as portfolio-based parallel SAT solving and was first used in the SAT solver ManySat [14]. However, so far it was not clear whether this approach can scale to a large number of processors.…”

Section: Introductionmentioning

confidence: 99%

HordeSat: A Massively Parallel Portfolio SAT Solver

Balyo

Sanders

Sinz

2015

Lecture Notes in Computer Science

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Abstract.A simple yet successful approach to parallel satisfiability (SAT) solving is to run several different (a portfolio of) SAT solvers on the input problem at the same time until one solver finds a solution. The SAT solvers in the portfolio can be instances of a single solver with different configuration settings. Additionally the solvers can exchange information usually in the form of clauses. In this paper we investigate whether this approach is applicable in the case of massively parallel SAT solving. Our solver is intended to run on clusters with thousands of processors, hence the name HordeSat. HordeSat is a fully distributed portfolio-based SAT solver with a modular design that allows it to use any SAT solver that implements a given interface. HordeSat has a decentralized design and features hierarchical parallelism with interleaved communication and search. We experimentally evaluated it using all the benchmark problems from the application tracks of the 2011 and 2014 International SAT Competitions. The experiments demonstrate that HordeSat is scalable up to hundreds or even thousands of processors achieving significant speedups especially for hard instances.

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“…There has been a significant amount of work to automatically select or adapt the search strategy. Some successes have been obtained by running some algorithms in parallel in CP [8] and in SAT [10]. Offline and online machine learning based methods are popular.…”

Section: Significance Level Of the Resultsmentioning

confidence: 99%

Parallel Strategies Selection

Palmieri

Régin

Schaus³

2016

Lecture Notes in Computer Science

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We consider the problem of selecting the best variable-value strategy for solving a given problem in constraint programming. We show that the recent Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS proposes to solve a problem by decomposing it in a lot of subproblems and to give them on-demand to workers which run in parallel. Our method uses a part of these subproblems as a simple sample as defined in statistics for comparing some strategies in order to select the most promising one that will be used for solving the remaining subproblems. For each subproblem of the sample, the parallelism helps us to control the running time of the strategies because it gives us the possibility to introduce timeouts by stopping a strategy when it requires more than twice the time of the best one. Thus, we can deal with the great disparity in solving times for the strategies. The selections we made are based on the Wilcoxon signed rank tests because no assumption has to be made on the distribution of the solving times and because these tests can deal with the censored data that we obtain after introducing timeouts. The experiments we performed on a set of classical benchmarks for satisfaction and optimization problems show that our method obtain good performance by selecting almost all the time the best variable-value strategy and by almost never choosing a variable-value strategy which is dramatically slower than the best one. Our method also outperforms the portfolio approach consisting in running some strategies in parallel and is competitive with the multi armed bandit framework.

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ManySAT: a Parallel SAT Solver

Cited by 191 publications

References 30 publications

Targeting the Cell Broadband Engine for constraint‐based local search

Targeting the Cell Broadband Engine for constraint‐based local search

HordeSat: A Massively Parallel Portfolio SAT Solver

Parallel Strategies Selection

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