A Distributed Version of Syrup

Audemard, Gilles; Lagniez, Jean-Marie; Szczepanski, Nicolas; Tabary, Sébastien

doi:10.1007/978-3-319-66263-3_14

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The authors of [5] report results for the same benchmark set and configurations on 128 and 256 cores, but with a time limit of only 900 seconds. The report 177 solved instances on 128 cores (73 SAT, 104 UNSAT), 184 solved instances (75 SAT, 109 UNSAT) on 256 cores.…”

Section: Comparison To Other Solversmentioning

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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Section: Comparison To Other Solversmentioning

confidence: 99%

“…Compared to the distributed version of Syrup [5] which uses locks in its shared memory communication, this lockless implementation avoids a potential bottleneck.…”

Section: Communicationmentioning

confidence: 99%

Tuning Parallel SAT Solvers

Ehlers¹,

Nowotka²

2018

EasyChair Preprints

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“…Portfolio approaches run multiple algorithms on the same formula, often in parallel, and typically have a mechanism for sharing learnt clauses. For instance: PLINGELING (Biere 2010), HORDESAT and its cloud-ready counterpart (Balyo, Sanders, and Sinz 2015;Schreiber and Sanders 2021), the multi-core SYRUP and its hybrid counterpart D-SYRUP (Audemard et al 2017).…”

Section: Introduction and Related Workmentioning

confidence: 99%

Dagster: Parallel Structured Search

Burgess

Gretton

Milthorpe

et al. 2023

AAAI

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We demonstrate Dagster, a system that implements a new approach to scheduling interdependent (Boolean) SAT search activities in high-performance computing (HPC) environments. Our system takes as input a set of disjunctive clauses (i.e., DIMACS CNF) and a labelled directed acyclic graph (DAG) structure describing how the clauses are decomposed into a set of interrelated problems. Component problems are solved using standard systematic backtracking search, which may optionally be coupled to (stochastic dynamic) local search and/or clause-strengthening processes. We demonstrate Dagster using a new Graph Maximal Determinant combinatorial case study. This demonstration paper presents a new case study, and is adjunct to the longer accepted manuscript at the Pacific Rim International Conference on Artificial Intelligence (2022).

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Reasoning with Propositional Logic: From SAT Solvers to Knowledge Compilation

Simon

2020

A Guided Tour of Artificial Intelligence Research

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A Distributed Version of Syrup

Cited by 5 publications

References 24 publications

Tuning Parallel SAT Solvers

Tuning Parallel SAT Solvers

Dagster: Parallel Structured Search

Reasoning with Propositional Logic: From SAT Solvers to Knowledge Compilation

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