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

Melab, Nouredine; Luong, Thé Van; Boufaras, Karima; Talbi, El-Ghazali

Type de document :

Communication dans un congrès avec actes

Titre :

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

Auteur(s) :

Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Luong, Thé Van [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Boufaras, Karima [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Titre de la manifestation scientifique :

ACM GECCO'2013

Ville :

Amsterdam

Pays :

Pays-Bas

Date de début de la manifestation scientifique :

2013-07-06

Date de publication :

2013-07-09

Mot(s)-clé(s) en anglais :

Algorithms
Design
Experimentation
Performance.
Performance

Discipline(s) HAL :

Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]

Résumé en anglais : [en]

In this paper, we propose a pioneering framework called ParadisEO-MO-GPU for the reusable design and implementation of parallel local search metaheuristics (S- Metaheuristics) on Graphics Processing Units (GPU). We revisit ...
Lire la suite >In this paper, we propose a pioneering framework called ParadisEO-MO-GPU for the reusable design and implementation of parallel local search metaheuristics (S- Metaheuristics) on Graphics Processing Units (GPU). We revisit the ParadisEO-MO software framework to allow its utilization on GPU accelerators focusing on the parallel iteration-level model, the major parallel model for S- Metaheuristics. It consists in the parallel exploration of the neighborhood of a problem solution. The challenge is on the one hand to rethink the design and implementation of this model optimizing the data transfer between the CPU and the GPU. On the other hand, the objective is to make the GPU as transparent as possible for the user minimizing his or her involvement in its management. In this paper, we propose solutions to this challenge as an extension of the ParadisEO framework. The first release of the new GPU-based ParadisEO framework has been experimented on the permuted perceptron problem. 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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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