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Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA

Ogier Maitre, Laurent A. Baumes, Nicolas Lachiche, Avelino Corma, Pierre Collet
Universite de Strasbourg, Strasbourg, France
Proceedings of the 11th Annual conference on Genetic and evolutionary computation GECCO 09 (2009), Volume: C, Issue: 1403, Publisher: ACM Press, Pages: 1403

@conference{maitre2009coarse,

   title={Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA},

   author={Maitre, O. and Baumes, L.A. and Lachiche, N. and Corma, A. and Collet, P.},

   booktitle={Proceedings of the 11th Annual conference on Genetic and evolutionary computation},

   pages={1403–1410},

   year={2009},

   organization={ACM}

}

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This paper presents a straightforward implementation of a standard evolutionary algorithm that evaluates its population in parallel on a GPGPU card. Tests done on a benchmark and a real world problem using an old NVidia 8800GTX card and a newer but not top of the range GTX260 card show a roughly 30x (resp. 100x) speedup for the whole algorithm compared to the same algorithm running on a standard 3.6GHz PC. Knowing that much faster hardware is already available, this opens new horizons to evolutionary computation, as search spaces can now be explored 2 or 3 orders of magnitude faster, depending on the number of used GPGPU cards. Since these cards remains very difficult to program, the knowhow has been integrated into the old EASEA language, that can now output code for GPGPU (-cuda option).
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