Towards paradisEO-MO-GPU: a framework for GPU-based local search metaheuristics

Authors:
N. Melab;T.-V. Luong;K. Boufaras;E.-G. Talbi
Affiliations:
INRIA Lille Nord Europe - LIFL/CNRS UMR 8022 - Université de Lille1, Villeneuve d'Ascq Cedex France;INRIA Lille Nord Europe - LIFL/CNRS UMR 8022 - Université de Lille1, Villeneuve d'Ascq Cedex France;INRIA Lille Nord Europe - LIFL/CNRS UMR 8022 - Université de Lille1, Villeneuve d'Ascq Cedex France;INRIA Lille Nord Europe - LIFL/CNRS UMR 8022 - Université de Lille1, Villeneuve d'Ascq Cedex France
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Year:
2011

Citing 5
Cited 2

ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics

Journal of Heuristics
Grid computing for parallel bioinspired algorithms

Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Scalable Parallel Programming with CUDA

Queue - GPU Computing
Paper: Robust taboo search for the quadratic assignment problem

Parallel Computing
Local search algorithms on graphics processing units. a case study: the permutation perceptron problem

EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization

libCudaOptimize: an open source library of GPU-based metaheuristics

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper is a major step towards a pioneering software framework for the reusable design and implementation of parallel metaheuristics on Graphics Processing Units (GPU). The objective is to revisit the ParadisEO framework to allow its utilization on GPU accelerators. The focus is on local search metaheuristics and the parallel exploration of their neighborhood. The challenge is to make the GPU as transparent as possible for the user. The first release of the new GPUbased ParadisEO framework has been experimented on the Quadratic Assignment Problem (QAP). The preliminary results are convincing, both in terms of flexibility and easiness of reuse at implementation, and in terms of efficiency at execution on GPU.