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A review of literature on parallel constraint solving

Published online by Cambridge University Press:  02 August 2018

IAN P. GENT
Affiliation:
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK (e-mail: ian.gent@st-andrews.ac.uk, ijm@st-andrews.ac.uk, pwn1@st-andrews.ac.uk)
IAN MIGUEL
Affiliation:
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK (e-mail: ian.gent@st-andrews.ac.uk, ijm@st-andrews.ac.uk, pwn1@st-andrews.ac.uk)
PETER NIGHTINGALE
Affiliation:
School of Computer Science, University of St Andrews, St Andrews KY16 9SX, UK (e-mail: ian.gent@st-andrews.ac.uk, ijm@st-andrews.ac.uk, pwn1@st-andrews.ac.uk)
CIARAN MCCREESH
Affiliation:
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK (e-mail: ciaran.mccreesh@glasgow.ac.uk, Patrick.Prosser@glasgow.ac.uk)
PATRICK PROSSER
Affiliation:
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK (e-mail: ciaran.mccreesh@glasgow.ac.uk, Patrick.Prosser@glasgow.ac.uk)
NEIL C. A. MOORE
Affiliation:
Adobe Systems Incorporated, Edinburgh, UK (e-mail: nemoore@adobe.com)
CHRIS UNSWORTH
Affiliation:
School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK (e-mail: chris.unsworth79@gmail.com)
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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.

Type
Survey Article
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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