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Towards Cost-Effective Bio-inspired Optimization: A Prospective Study on the GPU Architecture

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7077))

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Abstract

This paper studies the impact of varying the population’s size and the problem’s dimensionality in a parallel implementation, for an NVIDIA GPU, of a canonical GA. The results show that there is an effective gain in the data parallel model provided by modern GPU’s and enhanced by high level languages such as OpenCL. In the reported experiments it was possible to obtain a speedup higher than 140 thousand times for a population’s size of 262 144 individuals.

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© 2011 Springer-Verlag Berlin Heidelberg

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Prata, P., Fazendeiro, P., Sequeira, P. (2011). Towards Cost-Effective Bio-inspired Optimization: A Prospective Study on the GPU Architecture. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27242-4_8

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  • DOI: https://doi.org/10.1007/978-3-642-27242-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27241-7

  • Online ISBN: 978-3-642-27242-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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