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A review of literature on parallel constraint solving

Published online by Cambridge University Press:  02 August 2018

IAN P. GENT ,
IAN MIGUEL ,
PETER NIGHTINGALE Open the ORCID record for PETER NIGHTINGALE [Opens in a new window] ,
CIARAN MCCREESH ,
PATRICK PROSSER ,
NEIL C. A. MOORE  and
CHRIS UNSWORTH
IAN P. GENT
Affiliation:
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK (e-mail: [email protected], [email protected], [email protected])
IAN MIGUEL
Affiliation:
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK (e-mail: [email protected], [email protected], [email protected])
PETER NIGHTINGALE
Affiliation:
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK (e-mail: [email protected], [email protected], [email protected])
CIARAN MCCREESH
Affiliation:
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK (e-mail: [email protected], [email protected])
PATRICK PROSSER
Affiliation:
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK (e-mail: [email protected], [email protected])
NEIL C. A. MOORE
Affiliation:
Adobe Systems Incorporated, Edinburgh, UK (e-mail: [email protected])
CHRIS UNSWORTH
Affiliation:
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK (e-mail: [email protected])
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Abstract

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As multi-core computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multi-core computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation.

Keywords

Constraint programmingparallel computingpropositional satisfiabilityparallel search
Type
Survey Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 5-6: Special Issue on Parallel and Distributed Logic Programming , September 2018 , pp. 725 - 758
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

*We would like to thank EPSRC for funding this work through grants EP/E030394/1, EP/M003728/1, EP/P015638/1 and EP/P026842/1.

†This paper is a substantially revised and extended version of a paper that appeared in the PMCS 2011 Workshop on Parallel Methods for Constraint Solving (Gent et al. 2011).

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