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A Parallel Algorithm for GAC Filtering of the Alldifferent Constraint

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13292))

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Abstract

In constraint programming the Alldifferent constraint is one of the oldest and most used global constraints. The algorithm by Régin enforces generalized arc-consistency, which is the strongest level of consistency for a single constraint. It is also the most time consuming despite several optimizations that were developed by others.

This paper parallelizes the Alldifferent generalized arc-consistent filtering algorithm, which is one of the first attempts for any global constraint. It does so by using a parallel graph search algorithm for two major parts of Régin’s algorithm: finding a maximum matching and finding the strongly connected components. Most effective known optimizations are also ported. Experiments solving a large N-queens problem or a resource constrained scheduling problem show that generalized arc-consistent filtering can be significantly sped-up on a 64-core shared-memory system and on a 200-core distributed-memory system. We discuss also several scenarios where this algorithm should be applied or not.

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Authors and Affiliations

  1. Huawei Technologies France, Paris Research Center, CSI, Boulogne-Billancourt, France

    Wijnand Suijlen, Félix de Framond, Arnaud Lallouet & Antoine Petitet

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  1. Wijnand Suijlen
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  2. Félix de Framond
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  3. Arnaud Lallouet
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  4. Antoine Petitet
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Correspondence to Wijnand Suijlen .

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  1. UCLouvain, Louvain-la-Neuve, Belgium

    Pierre Schaus

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Suijlen, W., de Framond, F., Lallouet, A., Petitet, A. (2022). A Parallel Algorithm for GAC Filtering of the Alldifferent Constraint. In: Schaus, P. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2022. Lecture Notes in Computer Science, vol 13292. Springer, Cham. https://doi.org/10.1007/978-3-031-08011-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-08011-1_26

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