An overview of parallel SAT solving

Martins, Ruben; Manquinho, Vasco; Lynce, Inês

doi:10.1007/s10601-012-9121-3

Cited by 53 publications

(34 citation statements)

References 84 publications

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“…More recently, algorithms such as the ASAT heuristics or Focused Metropolis Search, which incorporate even more stochastic aspects, seem to be among the most effective methods for solving random 3-SAT problems [4]. A few parallel implementations of Local Search solvers have been done, see for instance [9] and [52], but limited to multi-core machines (i.e., up to 8 cores). Recently, parallel extensions of several Local Search SAT solvers have been done on massively parallel machines up to several hundreds of cores [7,8].…”

Section: Local Search and Parallelismmentioning

confidence: 99%

“…The PaCCS solver has been reported [61] to perform well with a larger number of cores, in the range of the hundreds. For SAT solvers, which can be seen as a special case of finite domain constraint solvers (with {0, 1} domains), several multi-core parallel implementations have also been developed for complete solvers [24,43,67], see [52] for a survey focused on parallel SAT solvers for shared memory machines. SAT solvers have also been implemented on larger PC clusters; for instance [58] describes an implementation using a hierarchical shared memory model which tries to minimize communication between nodes.…”

Section: Introductionmentioning

confidence: 99%

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Large-scale parallelism for constraint-based local search: the costas array case study

et al. 2014

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We present the parallel implementation of a constraint-based Local Search algorithm and investigate its performance on several hardware platforms with several hundreds or thousands of cores. We chose as the basis for these experiments the Adaptive Search method, an efficient sequential Local Search method for Constraint Satisfaction Problems (CSP). After preliminary experiments on some CSPLib benchmarks, we detail the modeling and solving of a hard combinatorial problem related to radar and sonar applications: the Costas Array Problem. Performance evaluation on some classical CSP benchmarks shows that speedups are very good for a few tens of cores, and good up to a few hundreds of cores. However for a hard combinatorial search problem such as the Costas Array Problem, performance evaluation of the sequential version shows results outperforming previous Local Search implementations, while the parallel version shows nearly linear speedups up to 8,192 cores. The proposed parallel scheme is simple and based on independent multi-walks with no communication between processes during search. We also investigated a cooperative multi-walk scheme where processes share simple information, but this scheme does not seem to improve performance.

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Section: Local Search and Parallelismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large-scale parallelism for constraint-based local search: the costas array case study

et al. 2014

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“…This situation has been recognised as witness several recent efforts to automatically exploit the inherent parallelism found in constraint solving problems, be it with local search methods [4], [5], [6] or propagation-based complete solvers [8], [7].…”

Section: Introductionmentioning

confidence: 99%

On the Scalability of Constraint Programming on Hierarchical Multiprocessor Systems

Machado

Pedro

Abreu

2013

2013 42nd International Conference on Parallel Processing

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Abstract-Recent developments in computer architecture progress towards systems with large core count, which expose more parallelism to applications, creating a hierarchical setup at the node and cluster levels. To take advantage of all this parallelism, applications must carefully exploit the different levels of the system which, if ignored, may yield surprising results. This aggravates the already difficult task of parallel programming.Declarative approaches such as those based on constraints are attractive to parallel programming because they concentrate on the logic of the problem. They have been successfully applied to hard problems, which usually involve searching through large problem spaces, a computationally intensive task but with potential for parallelization. Tree search algorithms play an important role in research areas such as constraint satisfaction or optimisation, and artificial intelligence. Tree search lends itself naturally to parallelization by exploiting different branches of the tree but scalability may be harder to achieve due to the high dynamic load balancing requirements.In this paper we present a high-level declarative approach based on constraints and show how it benefits from an efficient dynamic load balancing based on work stealing targeted at largescale. We focus on the implementation of a hierarchical work stealing scheme using a different programming model, GPI. Experimentation brought encouraging results on up to 512 cores on large instances of satisfaction and optimisation problems.

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“…Since we focus on search space partitioning procedures, we will discuss the portfolio approach only briefly. A more detailed survey on parallel SAT solving is given in [29,30].…”

Section: Parallel Sat Solvingmentioning

confidence: 99%

Modern Cooperative Parallel SAT Solving

Manthey¹,

Lanti²,

Irfan³

EPiC Series in Computing

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Nowadays, powerful parallel SAT solvers are based on an algorithm portfolio. The alternative approach, (iterative) search space partitioning, cannot keep up, although, according to the literature, iterative partitioning systems should scale better than portfolio solvers. This rises often! In this paper we identify key problems in current parallel cooperative SAT solving approaches, most importantly communication, how to partition the search space, and how to utilize the sequential search engine. First, we improve on each problem separately. In a further step, we show that combining all the improvements leads to a state-of-the-art parallel SAT solver, which does not use the portfolio approach, but instead relies on iterative partitioning. The experimental evaluation of this system completely changes the picture about the performance of search space partitioning SAT solvers: on instances of a combined benchmark of recent SAT competitions, the presented approach can keep up with the winners of last years SAT competition. The combined improvements improve the existing cooperative solver splitter by 24%: instead of 561 out of 880 instances, the new solver Pcasso can solve 696 instances.

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An overview of parallel SAT solving

Cited by 53 publications

References 84 publications

Large-scale parallelism for constraint-based local search: the costas array case study

Large-scale parallelism for constraint-based local search: the costas array case study

On the Scalability of Constraint Programming on Hierarchical Multiprocessor Systems

Modern Cooperative Parallel SAT Solving

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