Neighborhood Structures for GPU-based Local ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Neighborhood Structures for GPU-based Local Search Algorithms
Auteur(s) :
Luong, Thé Van [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Titre de la revue :
Parallel Processing Letters
Pagination :
307-324
Éditeur :
World Scientific Publishing
Date de publication :
2010
ISSN :
0129-6264
Mot(s)-clé(s) en anglais :
GPU-based metaheuristics
Parallel local search algorithms on GPU
Parallel local search algorithms on GPU
Discipline(s) HAL :
Informatique [cs]/Autre [cs.OH]
Résumé en anglais : [en]
Local search algorithms are powerful heuristics for solving computationally hard problems in science and industry. In these methods, designing neighborhood operators to explore large promising regions of the search space ...
Lire la suite >Local search algorithms are powerful heuristics for solving computationally hard problems in science and industry. In these methods, designing neighborhood operators to explore large promising regions of the search space may improve the quality of the obtained solutions at the expense of a high computation process. As a consequence, the use of GPU computing provides an efficient way to speed up the search. However, designing applications on GPU is still complex and many issues have to be faced. We provide a methodology to design and implement different neighborhood structures for LS algorithms on GPU. The work has been evaluated for binary problems and the obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.Lire moins >
Lire la suite >Local search algorithms are powerful heuristics for solving computationally hard problems in science and industry. In these methods, designing neighborhood operators to explore large promising regions of the search space may improve the quality of the obtained solutions at the expense of a high computation process. As a consequence, the use of GPU computing provides an efficient way to speed up the search. However, designing applications on GPU is still complex and many issues have to be faced. We provide a methodology to design and implement different neighborhood structures for LS algorithms on GPU. The work has been evaluated for binary problems and the obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.inria.fr/inria-00520461/document
- Accès libre
- Accéder au document
- https://hal.inria.fr/inria-00520461/document
- Accès libre
- Accéder au document
- http://hal.inria.fr/docs/00/52/04/61/PDF/PPL.pdf
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- PPL.pdf
- Accès libre
- Accéder au document
- PPL.pdf
- Accès libre
- Accéder au document