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Genetic programming on graphics processing units

Denis Robilliard, Virginie Marion-Poty, Cyril Fonlupt
Univ. Lille Nord de France, ULCO, LIL, CALAIS Cedex, France 62228
Genetic Programming and Evolvable Machines, Volume 10, Number 4, 447-471

@article{robilliard2009genetic,

   title={Genetic programming on graphics processing units},

   author={Robilliard, D. and Marion-Poty, V. and Fonlupt, C.},

   journal={Genetic Programming and Evolvable Machines},

   volume={10},

   number={4},

   pages={447–471},

   issn={1389-2576},

   year={2009},

   publisher={Springer}

}

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when programmed in the CUDA language. In a first work we have showed that this setup allows to develop fine grain parallelization schemes to evaluate several GP programs in parallel, while obtaining speedups for usual training sets and program sizes. Here we present another parallelization scheme and optimizations about program representation and use of GPU fast memory. This increases the computation speed about three times faster, up to 4 billion GP operations per second. The code has been developed within the well known ECJ library and is open source.
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