Adaptive Dynamic Load Balancing in ...
Type de document :
Communication dans un congrès avec actes
Titre :
Adaptive Dynamic Load Balancing in Heterogenous Multiple GPUs-CPUs Distributed Setting: Case Study of B&B Tree Search
Auteur(s) :
Vu, Trong-Tuan [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Melab, Nouredine [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Derbel, Bilel [Auteur]

Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Melab, Nouredine [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Titre de la manifestation scientifique :
7th International Learning and Intelligent OptimizatioN Conference (LION)
Ville :
Catania
Pays :
Italie
Date de début de la manifestation scientifique :
2013-01-07
Éditeur :
Lecture Notes in Computer Science
Date de publication :
2013-01-07
Discipline(s) HAL :
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Résumé en anglais : [en]
The emergence of new hybrid and heterogenous multi-GPU multi-CPU large scale platforms offers new opportunities and pauses new challenges when solving difficult optimization problems. This paper targets irregular tree ...
Lire la suite >The emergence of new hybrid and heterogenous multi-GPU multi-CPU large scale platforms offers new opportunities and pauses new challenges when solving difficult optimization problems. This paper targets irregular tree search algorithms in which workload is unpredictable. We propose an adaptive distributed approach allowing to distribute the load dynamically at runtime while taking into account the computing abilities of either GPUs or CPUs. Using Branch-and-Bound and Flowshop as a case study, we deployed our approach using up to 20 GPUs jointly to up to 128 CPUs. Through extensive experiments in different system configurations, we report near optimal speedups, thus providing new insights into how to take full advantage of both GPUs and CPUs power in modern computing platforms.Lire moins >
Lire la suite >The emergence of new hybrid and heterogenous multi-GPU multi-CPU large scale platforms offers new opportunities and pauses new challenges when solving difficult optimization problems. This paper targets irregular tree search algorithms in which workload is unpredictable. We propose an adaptive distributed approach allowing to distribute the load dynamically at runtime while taking into account the computing abilities of either GPUs or CPUs. Using Branch-and-Bound and Flowshop as a case study, we deployed our approach using up to 20 GPUs jointly to up to 128 CPUs. Through extensive experiments in different system configurations, we report near optimal speedups, thus providing new insights into how to take full advantage of both GPUs and CPUs power in modern computing platforms.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.inria.fr/hal-00765199/document
- Accès libre
- Accéder au document
- https://hal.inria.fr/hal-00765199/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- paper_complete.pdf
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- paper_complete.pdf
- Accès libre
- Accéder au document