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

N. Melab, T-V. Luong, K. Boufaras, E-G. Talbi
Dolphin Project, INRIA Lille Nord Europe – LIFL/CNRS UMR 8022 – Universite de Lille1, 40 avenue Halley, 59650 Villeneuve d’Ascq Cedex France
Advances in Computational Intelligence, Lecture Notes in Computer Science, Volume 6691/2011, 401-408, 2011

@article{melab2011towards,

   title={Towards paradisEO-MO-GPU: a framework for GPU-based local search metaheuristics},

   author={Melab, N. and Luong, T. and Boufaras, K. and Talbi, E.},

   journal={Advances in Computational Intelligence},

   pages={401–408},

   year={2011},

   publisher={Springer}

}

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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 GPU-based 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.
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