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GPGPGPU: Evaluation of Parallelisation of Genetic Programming Using GPGPU

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Search Based Software Engineering (SSBSE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10452))

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Abstract

We evaluate different approaches towards parallelisation of Genetic Programming (GP) using General Purpose Computing on Graphics Processor Units (GPGPU). Unlike Genetic Algorithms, which uses a single or a fixed number of fitness functions, GP has to evaluate a diverse population of programs. Since GPGPU is based on the Single Instruction Multiple Data (SIMD) architecture, parallelisation of GP using GPGPU allows multiple approaches. We study three different parallelisation approaches: kernel per individual, kernel per generation, and kernel interpreter. The results of the empirical study using a widely studied symbolic regression benchmark show that no single approach is the best: the decision about parallelisation approach has to consider the trade-off between the compilation and the execution overhead of GPU kernels.

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Notes

  1. 1.

    Compute Unified Device Architecture from NVIDIA.

  2. 2.

    https://github.com/DEAP/deap.

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Authors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

    Jinhan Kim, Junhwi Kim & Shin Yoo

Authors
  1. Jinhan Kim
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  2. Junhwi Kim
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  3. Shin Yoo
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Corresponding author

Correspondence to Shin Yoo .

Editor information

Editors and Affiliations

  1. North Carolina State University, Morgantown, West Virginia, USA

    Tim Menzies

  2. Department of Computer Science, University College London, London, United Kingdom

    Justyna Petke

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Kim, J., Kim, J., Yoo, S. (2017). GPGPGPU: Evaluation of Parallelisation of Genetic Programming Using GPGPU. In: Menzies, T., Petke, J. (eds) Search Based Software Engineering. SSBSE 2017. Lecture Notes in Computer Science(), vol 10452. Springer, Cham. https://doi.org/10.1007/978-3-319-66299-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-66299-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66298-5

  • Online ISBN: 978-3-319-66299-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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