A GPU implementation of parallel constraint-based local search
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Accepted Version
Date
2014-04-14
Authors
Arbelaez, Alejandro
Codognet, Philippe
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
In this paper we study the performance of constraint-based local search solvers on a GPU. The massively parallel architecture of the GPU makes it possible to explore parallelism at two different levels inside the local search algorithm. First, by executing multiple copies of the algorithm in a multi-walk manner and, second, by evaluating large neighborhoods in parallel in a single-walk manner. Experiments on three well-known problem benchmarks indicate that the current GPU implementation is up to 17 times faster than a well-tuned sequential algorithm implemented on a desktop computer.
Description
Keywords
Local Search , CSP , GPU
Citation
Arbelaez, A. and Codognet, P. (2014) 'A GPU implementation of parallel constraint-based local search', 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Turin, Italy, 12-14 February, pp. 648-655, doi: https://doi.org/10.1109/PDP.2014.28
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© 2014, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.