Faster {GPU} Based Genetic Programming Using {A} Two Dimensional Stack

  • Chitty D
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Abstract

Genetic Programming (GP) is a computationally
intensive technique which also has a high degree of
natural parallelism. Parallel computing architectures
have become commonplace especially with regards
Graphics Processing Units (GPU). Hence, versions of GP
have been implemented that use these highly parallel
computing platforms enabling significant gains in the
computational speed of GP to be achieved. However,
recently a two dimensional stack approach to GP using a
multi-core CPU also demonstrated considerable
performance gains. Indeed, performances equivalent to
or exceeding that achieved by a GPU were demonstrated.
This paper will demonstrate that a similar two
dimensional stack approach can also be applied to a GPU
based approach to GP to better exploit the underlying
technology. Performance gains are achieved over a
standard single dimensional stack approach when using a
GPU. Overall, a peak computational speed of over 55
billion Genetic Programming Operations per Second are
observed, a two fold improvement over the best GPU
based single dimensional stack approach from the
literature

Author-supplied keywords

  • genetic algorithms
  • genetic programming

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Authors

  • Darren M Chitty

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