Incorporating Learning in Grid-Based Randomized SAT Solving

Hyvärinen, Antti E. J.; Junttila, Tommi; Niemelä, Ilkka

doi:10.1007/978-3-540-85776-1_21

Cited by 11 publications

(10 citation statements)

References 17 publications

(10 reference statements)

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“…In contrast to existing Grid SAT solvers [49,50], lemma exchange in Satciety is topologyaware. For this purpose, the system classifies each neighbor node-all nodes within the view of a node-as either direct or indirect.…”

Section: Topology-aware Distributed Dynamic Learningmentioning

confidence: 91%

“…In [50] Hyvärinen et al present a distributed SAT solving method incorporating a limited form of dynamic learning which is tailored for Grids comprised of batch controlled resources, where individual jobs are not able to communicate directly. Their approach is based on competition parallelism where several randomized SAT solvers independently work on the same SAT instance until one finds a solution.…”

Section: Parallel Sat Solving On Gridsmentioning

confidence: 99%

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Parallel SAT Solving on Peer-to-Peer Desktop Grids

Sven

Blochinger

2010

J Grid Computing

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Satciety is a distributed parallel satisfiability (SAT) solver which focuses on tackling the domain-specific problems inherent to one of the most challenging environments for parallel computing-Peer-to-Peer Desktop Grids. Satciety efficiently addresses issues related to resource volatility and heterogeneity, limited node and network capabilities, as well as non-uniform communication costs. This is achieved through a sophisticated distributed task pool execution model, problem size reduction through multistage SAT formula preprocessing, context-aware memory management, and adaptive topologyaware distributed dynamic learning. Despite the demanding conditions prevailing in Desktop Grids, Satciety achieves considerable speedups compared to state-of-the-art sequential SAT solvers.

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Section: Topology-aware Distributed Dynamic Learningmentioning

confidence: 91%

Section: Parallel Sat Solving On Gridsmentioning

confidence: 99%

Parallel SAT Solving on Peer-to-Peer Desktop Grids

Sven

Blochinger

2010

J Grid Computing

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“…Although it uses fewer nodes and achieves good results, it does not scale well. In the author's subsequent implementation [24], the sharing of conflict-learning clauses has been ManySAT [19] runs four DPLL processes, each with a different restart strategy: increasing the restart interval in equal proportion, increasing the restart interval with an arithmetic sequence, increasing the restart interval with the luby sequence, and the last is the dynamic restart strategy. The clause sharing method is: the dynamic restarting kernel passes the learned clauses to two DPLL processes, the other DPLL processes do not share clauses.…”

Section: Competitive Parallelismmentioning

confidence: 99%

A survey of SAT solver

Gong¹,

Zhang²

2017

AIP Conference Proceedings

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“…Note that other pallel checking techniques exist. For example, one can launch in parallel many randomized check runs on the same problem instance with the aim to hit the instance-intrinsic minimum runtime [4]. Instead, in our methodology, we create a different but equi-checkable instance that has a potentially lower minimum runtime.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Circuit Duality to Speed up SAT

Amarù

Gaillardon

Mishchenko

et al. 2015

2015 IEEE Computer Society Annual Symposium on VLSI

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Abstract-In this paper, we establish a non-trivial duality between tautology and contradiction check to speed up circuit SAT. Tautology check determines if a logic circuit is true in every possible interpretation. Analogously, contradiction check determines if a logic circuit is false in every possible interpretation. A trivial transformation of a (tautology, contradiction) check problem into a (contradiction, tautology) check problem is the inversion of all outputs in a logic circuit. In this work, we show that exact logic inversion is not necessary. We give operator switching rules that selectively exchange tautologies with contradictions, and viceversa. Our approach collapses into logic inversion just for tautology and contradiction extreme points but generates non-complementary logic circuits in the other cases. This property enables computing benefits when an alternative, but equisolvable, instance of a problem is easier to solve than the original one. As a case study, we investigate the impact on SAT. There, our methodology generates a dual SAT instance solvable in parallel with the original one. This concept can be used on top of any other SAT approach and does not impose much overhead, except having to run two solvers instead of one, which is typically not a problem because multi-cores are widespread and computing resources are inexpensive. Experimental results show a 25% speed-up of SAT in a concurrent execution scenario. Also, statistical experiments confirmed that our runtime reduction is not of the random variation type.

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Incorporating Learning in Grid-Based Randomized SAT Solving

Cited by 11 publications

References 17 publications

Parallel SAT Solving on Peer-to-Peer Desktop Grids

Parallel SAT Solving on Peer-to-Peer Desktop Grids

A survey of SAT solver

Exploiting Circuit Duality to Speed up SAT

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