- 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",
