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Publication Detail
Genetic improvement of GPU software
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Langdon WB, Lam BYH, Modat M, Petke J, Harman M
  • Publication date:
    01/03/2017
  • Pagination:
    5, 44
  • Journal:
    Genetic Programming and Evolvable Machines
  • Volume:
    18
  • Issue:
    1
  • Status:
    Published
  • Print ISSN:
    1389-2576
Abstract
© 2016, Springer Science+Business Media New York.We survey genetic improvement (GI) of general purpose computing on graphics cards. We summarise several experiments which demonstrate four themes. Experiments with the gzip program show that genetic programming can automatically port sequential C code to parallel code. Experiments with the StereoCamera program show that GI can upgrade legacy parallel code for new hardware and software. Experiments with NiftyReg and BarraCUDA show that GI can make substantial improvements to current parallel CUDA applications. Finally, experiments with the pknotsRG program show that with semi-automated approaches, enormous speed ups can sometimes be had by growing and grafting new code with genetic programming in combination with human input.
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