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Embarrassingly Parallel Search

Abstract : We propose the Embarrassingly Parallel Search, a simple and efficient method for solving constraint programming problems in parallel. We split the initial problem into a huge number of independent subproblems and solve them with available workers (i.e., cores of machines). The decomposition into subproblems is computed by selecting a subset of variables and by enumerating the combinations of values of these variables that are not detected inconsistent by the propagation mechanism of a CP Solver. The experiments on satisfaction problems and on optimization problems suggest that generating between thirty and one hundred subproblems per worker leads to a good scalability. We show that our method is quite competitive with the work stealing approach and able to solve some classical problems at the maximum capacity of the multi-core machines. Thanks to it, a user can parallelize the resolution of its problem without modifying the solver or writing any parallel source code and can easily replay the resolution of a problem.
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Contributor : Jean-Charles Regin <>
Submitted on : Monday, July 11, 2016 - 12:21:36 PM
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Jean-Charles Régin, Mohamed Rezgui, Arnaud Malapert. Embarrassingly Parallel Search. 19th International Conference on Principles and Practice of Constraint Programming (CP 2013), Sep 2013, Uppsala, Sweden. ⟨10.1007/978-3-642-40627-0_45⟩. ⟨hal-01344074⟩



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