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

  • Paula Prata
  • Paulo Fazendeiro
  • Pedro Sequeira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)

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.

Keywords

GPGPU Parallel Genetic Algorithms OpenCL data parallelism 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paula Prata
    • 1
    • 2
  • Paulo Fazendeiro
    • 1
    • 2
  • Pedro Sequeira
    • 1
  1. 1.Department of InformaticsUniversity of Beira InteriorPortugal
  2. 2.Institute of Telecommunications (IT)University of Beira InteriorPortugal

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