HB&B@GRID: An heterogeneous grid-enabled ...
Document type :
Communication dans un congrès avec actes
Title :
HB&B@GRID: An heterogeneous grid-enabled Branch and Bound algorithm
Author(s) :
Chakroun, Imen [Auteur]
IMEC [IMEC]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
IMEC [IMEC]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Conference title :
2016 International Conference on High Performance Computing & Simulation (HPCS)
City :
Innsbruck
Country :
Autriche
Start date of the conference :
2016-07-18
Book title :
IEEE
Publication date :
2016-09-15
English keyword(s) :
GPU
Branch and Bound
heterogeneous resources
distributed computing
master-slave
Branch and Bound
heterogeneous resources
distributed computing
master-slave
HAL domain(s) :
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
English abstract : [en]
In this paper, we propose an extended version of the hybrid multi-core and GPU-accelerated Branch-and-Bound (B&B) algorithm previously introduced for massively-parallel distributed and heterogeneous environments. The ...
Show more >In this paper, we propose an extended version of the hybrid multi-core and GPU-accelerated Branch-and-Bound (B&B) algorithm previously introduced for massively-parallel distributed and heterogeneous environments. The proposed algorithm consists in hierarchically combining two levels of parallelism by (1) dividing the B&B tree exploration among multiple distributed resources using the B&B@GRID approach, and (2) exploring in parallel each sub-tree using an heterogeneous meta-algorithm. Using this portable, heterogeneous and self-adaptive approach allows to achieve high performance.Show less >
Show more >In this paper, we propose an extended version of the hybrid multi-core and GPU-accelerated Branch-and-Bound (B&B) algorithm previously introduced for massively-parallel distributed and heterogeneous environments. The proposed algorithm consists in hierarchically combining two levels of parallelism by (1) dividing the B&B tree exploration among multiple distributed resources using the B&B@GRID approach, and (2) exploring in parallel each sub-tree using an heterogeneous meta-algorithm. Using this portable, heterogeneous and self-adaptive approach allows to achieve high performance.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :