Improvement of the Efficiency of Genetic Algorithms for Scalable Parallel Graph Partitioning in a Multi-Level Framework

Cédric Chevalier 1, 2 François Pellegrini 1, 2, *
* Corresponding author
2 SCALAPPLIX - Algorithms and high performance computing for grand challenge applications
INRIA Futurs, Université Bordeaux Segalen - Bordeaux 2, Université Sciences et Technologies - Bordeaux 1, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR5800
Abstract : Parallel graph partitioning is a difficult issue, because the best sequential graph partitioning methods known to date are based on iterative local optimization algorithms that do not parallelize nor scale well. On the other hand, evolutionary algorithms are highly parallel and scalable, but converge very slowly as problem size increases. This paper presents methods that can be used to reduce problem space in a dramatic way when using graph partitioning techniques in a multi-level framework, thus enabling the use of evolutionary algorithms as possible candidates, among others, for the realization of efficient scalable parallel graph partitioning tools. Results obtained on the recursive bipartitioning problem with a multi-threaded genetic algorithm are presented, which show that this approach outperforms existing state-of-the-art parallel partitioners.
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https://hal.archives-ouvertes.fr/hal-00402946
Contributor : François Pellegrini <>
Submitted on : Wednesday, July 8, 2009 - 5:22:21 PM
Last modification on : Tuesday, October 23, 2018 - 5:24:03 PM
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Cédric Chevalier, François Pellegrini. Improvement of the Efficiency of Genetic Algorithms for Scalable Parallel Graph Partitioning in a Multi-Level Framework. Euro-Par, Aug 2006, Dresden, Germany. pp.243-252, ⟨10.1007/11823285⟩. ⟨hal-00402946⟩

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