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Genetic programming on graphics processing units

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Abstract

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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Notes

  1. http://www.graphics.stanford.edu/projects/brookgpu/.

  2. http://www.libsh.org/.

  3. See http://www.gpgpu.org for a survey.

  4. http://www.gpgpgpu.com.

  5. see http://www.nvidia.com/object/cuda_home.html.

  6. iflte is a quaternary operator that stands for: if sibling1 less than sibling2 then sibling3 else sibling4.

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Acknowledgment

We thank W. B. Langdon for kindly giving access to his RPN breeding code.

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Authors and Affiliations

  1. Univ. Lille Nord de France, ULCO, LIL, Maison de la Recherche Blaise Pascal, 50 rue Ferdinand Buisson, BP 719, 62228, CALAIS Cedex, France

    Denis Robilliard, Virginie Marion-Poty & Cyril Fonlupt

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  1. Denis Robilliard
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  2. Virginie Marion-Poty
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  3. Cyril Fonlupt
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Correspondence to Denis Robilliard.

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Robilliard, D., Marion-Poty, V. & Fonlupt, C. Genetic programming on graphics processing units. Genet Program Evolvable Mach 10, 447–471 (2009). https://doi.org/10.1007/s10710-009-9092-3

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