Titre :

A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem

Auteur(s) :

Melab, Nouredine [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Chakroun, Imen [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Mezmaz, Mohand [Auteur]
Institut de Mathématiques [Mons]
Tuyttens, Daniel [Auteur]
Institut de Mathématiques [Mons]

Titre de la manifestation scientifique :

14th IEEE International Conference on Cluster Computing, Cluster'12

Ville :

Beijin

Pays :

Chine

Date de début de la manifestation scientifique :

2012-09-24

Date de publication :

2012-09-24

Mot(s)-clé(s) en anglais :

Flow-Shop Scheduling
Massively Parallel Computing
GPU Computing
Branch-and-Bound Algorithms
Lower Bounding
Flow-Shop Scheduling.

Discipline(s) HAL :

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

Résumé en anglais : [en]

Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the use of GPU computing as a major complementary ...
Lire la suite >Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration methods for solving to optimality combinatorial optimization problems. In this paper, we investigate the use of GPU computing as a major complementary way to speed up those methods. The focus is put on the bounding mechanism of B&B algorithms, which is the most time consuming part of their exploration process. We propose a parallel B&B algorithm based on a GPU-accelerated bounding model. The proposed approach concentrate on optimizing data access management to further improve the performance of the bounding mechanism which uses large and intermediate data sets that do not completely fit in GPU memory. Extensive experiments of the contribution have been carried out on well known FSP benchmarks using an Nvidia Tesla C2050 GPU card. We compared the obtained performances to a single and a multithreaded CPU-based execution. Accelerations up to x100 are achieved for large problem instances.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