Genetic programming on graphics processing units

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Robilliard:2009:GPEM,
  author =       "Denis Robilliard and Virginie Marion-Poty and 
                 Cyril Fonlupt",
  title =        "Genetic programming on graphics processing units",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2009",
  volume =       "10",
  number =       "4",
  pages =        "447--471",
  month =        dec,
  note =         "Special issue on parallel and distributed evolutionary
                 algorithms, part I",
  keywords =     "genetic algorithms, genetic programming, Graphics
                 processing units, GPU, Parallel processing",
  ISSN =         "1389-2576",
  URL =          "http://www-lil.univ-littoral.fr/~robillia/Publis/GPonGPU09.ps.gz",
  DOI =          "doi:10.1007/s10710-009-9092-3",
  size =         "25 pages",
  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 programmed in the CUDA language. In a
                 first work we have showed that this setup allows to
                 develop fine grain parallelization schemes to evaluate
                 several GP programs in parallel, while obtaining
                 speedups for usual training sets and program sizes.
                 Here we present another parallelization scheme and
                 optimizations about program representation and use of
                 GPU fast memory. This increases the computation speed
                 about three times faster, up to 4 billion GP operations
                 per second. The code has been developed within the well
                 known ECJ library and is open source.",
  notes =        "ECJ JNI Java Native Interface to CUDA. RPN. Thread
                 divergence. nVidia 8800 GTX. Sextic symbolic
                 regression, 6-mux and 11-multiplexor, intertwined
                 spirals, Mackey-Glass \cite{langdon:2008:eurogp}. Data
                 cache not faster???

                 Code via
                 http://www-lil.univ-littoral.fr/~robillia/GPUregression.html",
}

Genetic Programming entries for Denis Robilliard Virginie Marion-Poty Cyril Fonlupt

Citations