High performance genetic programming on GPU

  • Robilliard D
  • Marion V
  • Fonlupt C
  • 48

    Readers

    Mendeley users who have this article in their library.
  • 30

    Citations

    Citations of this article.

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 pro- grammed in the CUDA language. We compare two par- allelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of compu- tations over the elementary processors greatly impacts per- formances. We also present memory and representation op- timizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library.

Author-supplied keywords

  • genetic algorithms
  • genetic programming
  • graphics process-
  • ing units
  • parallel processing

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Denis Robilliard

  • Virginie Marion

  • Cyril Fonlupt

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free