Towards ParadisEO-MO-GPU: a Framework for GPU-based Local Search Metaheuristics

Nouredine Melab 1 Thé Van Luong 1 Boufaras Karima 1 El-Ghazali Talbi 1
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : This paper is a major step towards a pioneering software framework for the reusable design and implementation of parallel metaheuristics on Graphics Processing Units (GPU). The objective is to revisit the ParadisEO framework to allow its utilization on GPU accelerators. The focus is on local search metaheuristics and the parallel exploration of their neighborhood. The challenge is to make the GPU as transparent as possible for the user. The first release of the new GPU-based ParadisEO framework has been experimented on the Quadratic Assignment Problem (QAP). The preliminary results are convincing, both in terms of flexibility and easiness of reuse at implementation, and in terms of efficiency at execution on GPU.
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Communication dans un congrès
Joan Cabestany and Ignacio Rojas and Gonzalo Joya Caparros. 11th International Work-Conference on Artificial Neural Networks, 2011, Torremolinos-Málaga, Spain. Springer, 6691, 2011, Lecture Notes in Computer Science
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Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
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Nouredine Melab, Thé Van Luong, Boufaras Karima, El-Ghazali Talbi. Towards ParadisEO-MO-GPU: a Framework for GPU-based Local Search Metaheuristics. Joan Cabestany and Ignacio Rojas and Gonzalo Joya Caparros. 11th International Work-Conference on Artificial Neural Networks, 2011, Torremolinos-Málaga, Spain. Springer, 6691, 2011, Lecture Notes in Computer Science. 〈inria-00638809〉

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