GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP

Helbecque, Guillaume; Krishnasamy, Ezhilmathi; Melab, Nouredine; Bouvry, Pascal

Document type :

Communication dans un congrès avec actes

Title :

GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP

Author(s) :

Helbecque, Guillaume [Auteur]
Université du Luxembourg [Uni.lu]
Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Krishnasamy, Ezhilmathi [Auteur]
Université du Luxembourg [Uni.lu]
Melab, Nouredine [Auteur]

Université de Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Inria Lille - Nord Europe
Optimisation de grande taille et calcul large échelle [BONUS]
Bouvry, Pascal [Auteur]
Université du Luxembourg [Uni.lu]

Conference title :

14th IEEE Workshop Parallel / Distributed Combinatorics and Optimization (PDCO 2024)

City :

San Francisco

Country :

Etats-Unis d'Amérique

Start date of the conference :

2024-05-31

English keyword(s) :

Chapel
Tree-Search
GPU computing
CUDA
HIP
N-Queens
Nvidia
AMD

HAL domain(s) :

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

English abstract : [en]

In the context of exascale programming, the PGAS-based Chapel is among the rare languages targeting the holistic handling of high-performance computing issues including the productivity-aware harnessing of Nvidia and AMD ...
Show more >In the context of exascale programming, the PGAS-based Chapel is among the rare languages targeting the holistic handling of high-performance computing issues including the productivity-aware harnessing of Nvidia and AMD GPUs. In this paper, we propose a pioneering proof-of-concept dealing with this latter issue in the context of tree-based exact optimization. Actually, we revisit the design and implementation of a generic multi-pool GPU-accelerated tree-search algorithm using Chapel. This algorithm is instantiated on the backtracking method and experimented on the N-Queens problem. For performance evaluation, the Chapel-based approach is compared to Nvidia CUDA and AMD HIP low-level counterparts. The reported results show that in a single-GPU setting, the high GPU abstraction of Chapel results in a loss of only 8% (resp. 16%) compared to CUDA (resp. HIP). In a multi-GPU setting, up to 80% (resp. 71%) of the baseline speed-up is achieved for coarse-grained problem instances on Nvidia (resp. AMD) GPUs.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Calcul ultra-scale pour la résolution de problèmes d'optimisation de grande taille

Collections :