MPIDepQBF: Towards Parallel QBF Solving without Knowledge Sharing

Jordan, Charles; Kaiser, Łukasz; Lonsing, Florian; Seidl, Martina

doi:10.1007/978-3-319-09284-3_32

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…As future work it would be interesting to consider further variants of Qresolution systems [2] (apart from traditional [14] and long-distance resolution [1,26]) as a means of diversification in HordeQBF, which would amount to a combination of QBF proof systems with different power. Further, it may be promising to equip HordeQBF with search space partitioning as in MPIDepQBF [13].…”

Section: Discussionmentioning

confidence: 99%

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HordeQBF: A Modular and Massively Parallel QBF Solver

Balyo

Lonsing

2016

Theory and Applications of Satisfiability Testing – SAT 2016

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Abstract. The recently developed massively parallel satisfiability (SAT) solver HordeSAT was designed in a modular way to allow the integration of any sequential CDCL-based SAT solver in its core. We integrated the QCDCL-based quantified Boolean formula (QBF) solver DepQBF in HordeSAT to obtain a massively parallel QBF solver-HordeQBF. In this paper we describe the details of this integration and report on results of the experimental evaluation of HordeQBF's performance. HordeQBF achieves superlinear average and median speedup on the hard application instances of the 2014 QBF Gallery.

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Section: Discussionmentioning

confidence: 99%

“…Search space is partitioned like in the SAT solver PSATO based on guiding paths [25]. The MPI-based solver MPIDepQBF [13] implements a master/worker architecture without knowledge sharing. A worker consists of an instance of the QCDCL solver DepQBF [19].…”

Section: Related Workmentioning

confidence: 99%

HordeQBF: A Modular and Massively Parallel QBF Solver

Balyo

Lonsing

2016

Theory and Applications of Satisfiability Testing – SAT 2016

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“…Unfortunately, the code of most of the early approaches is not available anymore. Following the success of Cube-and-Conquer-based searchspace splitting, the QBF solver MPIDepQBF has been presented [14]. While MPIDepQBF does not implement any sophisticated look-ahead mechanisms, it could demonstrate that even without knowledge-sharing considerable speedup could be achieved.…”

Section: Related Workmentioning

confidence: 99%

“…In general, only variables from the outermost quantifier block Q 1 X may be considered, because otherwise, the value of the formula might change. Jordan et al [14] observed that for QBF following the sequential order of the variables in the first quantifier block already leads to improvements compared to the sequential implementation of DepQBF. The already existing QBF solver module of ParaCooba (see section 4) relied on this observation: it traverses the prefix of a PCNF and splits each visited leaf into two sub-trees, respecting both universal and existential quantifiers, until a pre-defined maximum depth is reached.…”

Section: Paracoobamentioning

confidence: 99%

ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving

Heisinger¹,

Seidl²,

Biere³

2023

Tools and Algorithms for the Construction and Analysis of Systems

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Over the last years, innovative parallel and distributed SAT solving techniques were presented that could impressively exploit the power of modern hardware and cloud systems. Two approaches were particularly successful: (1) search-space splitting in a Divide-and-Conquer (D &C) manner and (2) portfolio-based solving. The latter executes different solvers or configurations of solvers in parallel. For quantified Boolean formulas (QBFs), the extension of propositional logic with quantifiers, there is surprisingly little recent work in this direction compared to SAT.In this paper, we present ParaQooba, a novel framework for parallel and distributed QBF solving which combines D &C parallelization and distribution with portfolio-based solving. Our framework is designed in such a way that it can be easily extended and arbitrary sequential QBF solvers can be integrated out of the box, without any programming effort. We show how ParaQooba orchestrates the collaboration of different solvers for joint problem solving by performing an extensive evaluation on benchmarks from QBFEval’22, the most recent QBF competition.

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“…Furthermore, they used a set of quite easy benchmarks, which does not sufficiently show the usability of their approach on harder problems. Jordan et al use a search space splitting approach without any knowledge exchange, and report significant improvements on up to 64 threads [130].…”

Section: Parallel Constraint Programmingmentioning

confidence: 99%

SAT and CP - Parallelisation and Applications

Ehlers¹

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In this thesis, we consider the parallelisation and application of SAT and CP solvers. In the first chapter, we consider SAT, the decision problem of propositional logic. We discuss details of the implementations of SAT solvers, and show how to improve upon existing sequential solvers. Furthermore, we discuss the parallelisation of these solvers with a focus on the communication of intermediate results within a parallel solver. The second chapter is concerned with Contraint Programing (CP) with learning. Contrary to classical Constraint Programming techniques, this incorporates learning mechanisms as they are used in the field of SAT solving. We present results from parallelising CHUFFED, a learning CP solver. In the final chapter, we discuss Sorting Networks, which are data oblivious sorting algorithms. Their independence of the input data lends them to parallel implementation. We consider the question how many parallel sorting steps are needed to sort some inputs, and present both lower and upper bounds for several cases.

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MPIDepQBF: Towards Parallel QBF Solving without Knowledge Sharing

Cited by 6 publications

References 15 publications

HordeQBF: A Modular and Massively Parallel QBF Solver

HordeQBF: A Modular and Massively Parallel QBF Solver

ParaQooba: A Fast and Flexible Framework for Parallel and Distributed QBF Solving

SAT and CP - Parallelisation and Applications

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