HordeSat: A Massively Parallel Portfolio SAT Solver

Balyo, Tomáš; Sanders, Peter; Sinz, Carsten

doi:10.1007/978-3-319-24318-4_12

Cited by 50 publications

(48 citation statements)

References 22 publications

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“…However, the manual constructions of parallel solvers is a very challenging task, as it often requires to design specific algorithms, rather than adapting existing sequential ones. One promising approach for exploiting the computational power provided by multicore machines is then to combine solvers into parallel portfolios, which have been recently introduced in areas such as SAT and ASP [1,17]. Notably, while work has been done in the area of sequential portfolios for argumentation [10], there is a lack of approaches aiming at combining solvers into parallel portfolios.…”

Section: Introductionmentioning

confidence: 99%

On the Combination of Argumentation Solvers into Parallel Portfolios

Vallati

Cerutti

Giacomin

2017

AI 2017: Advances in Artificial Intelligence

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Abstract. In the light of the increasing interest in efficient algorithms for solving abstract argumentation problems and the pervasive availability of multicore machines, a natural research issue is to combine existing argumentation solvers into parallel portfolios. In this work, we introduce six methodologies for the automatic configuration of parallel portfolios of argumentation solvers for enumerating the preferred extensions of a given framework. In particular, four methodologies aim at combining solvers in static portfolios, while two methodologies are designed for the dynamic configuration of parallel portfolios. Our empirical results demonstrate that the configuration of parallel portfolios is a fruitful way for exploiting multicore machines, and that the presented approaches outperform the state of the art of parallel argumentation solvers.

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Section: Introductionmentioning

confidence: 99%

On the Combination of Argumentation Solvers into Parallel Portfolios

Vallati

Cerutti

Giacomin

2017

AI 2017: Advances in Artificial Intelligence

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“…Processes send their learned clauses either if some time has passed since the last sending operation, or if their clause buffer is sufficiently full. Compared to the communication strategy of HordeSAT [10] where communication is performed synchronously and the amount of transmitted data is restricted to 1500 integers per process and communication cycle, our strategy allows us to send a larger number of clauses in situations where many good clauses are learned. By collecting several clauses before sending them we prevent the network from being congested by a large number of small messages.…”

Section: Communicationmentioning

confidence: 99%

“…When receiving unit clauses either from other processes via MPI or from solver threads via the shared memory communication, it checks these clauses against the clauses it has seen already, and only forwards unit clauses which have not yet been seen. In other solvers like TopoSAT [26] and HordeSAT [10], the communication threads hash clauses received either from solver threads within the same process or via MPI. They use Bloom filters for preventing clauses from being shared multiple times.…”

Section: Communicationmentioning

confidence: 99%

“…Bloom Filters as suggested e.g. in [10]. Instead of hashing clauses during import, we checked for duplicates whenever the clause DB was reduced.…”

Section: Case Study: Bounded Model Checkingmentioning

confidence: 99%

“…This is sound as every learned clause was derived by resolution, and thus is logically implied by the original formula. This clause exchange is crucial in improving the performance of parallel SAT solvers, especially on unsatisfiable formulas [26,10].…”

Section: Introductionmentioning

confidence: 99%

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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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Modular and Efficient Divide-and-Conquer SAT Solver on Top of the Painless Framework

Frioux

Baarir

Sopena

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

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Over the last decade, parallel SATisfiability solving has been widely studied from both theoretical and practical aspects. There are two main approaches. First, divide-and-conquer (D&C) splits the search space, each solver being in charge of a particular subspace. The second one, portfolio launches multiple solvers in parallel, and the first to find a solution ends the computation. However although D&C based approaches seem to be the natural way to work in parallel, portfolio ones experimentally provide better performances. An explanation resides on the difficulties to use the native formulation of the SAT problem (i.e., the CNF form) to compute an a priori good search space partitioning (i.e., all parallel solvers process their subspaces in comparable computational time). To avoid this, dynamic load balancing of the search subspaces is implemented. Unfortunately, this is difficult to compare load balancing strategies since state-of-the-art SAT solvers appropriately dealing with these aspects are hardly adaptable to various strategies than the ones they have been designed for. This paper aims at providing a way to overcome this problem by proposing an implementation and evaluation of different types of divideand-conquer inspired from the literature. These are relying on the Painless framework, which provides concurrent facilities to elaborate such parallel SAT solvers. Comparison of the various strategies are then discussed.

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HordeSat: A Massively Parallel Portfolio SAT Solver

Cited by 50 publications

References 22 publications

On the Combination of Argumentation Solvers into Parallel Portfolios

On the Combination of Argumentation Solvers into Parallel Portfolios

Tuning Parallel SAT Solvers

Modular and Efficient Divide-and-Conquer SAT Solver on Top of the Painless Framework

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