Evolutionary Computing on Consumer Graphics Hardware   [EC] [CGH]

by

Fok, K.-L., Wong, T.-T. and Wong, M.-L.

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Info: IEEE Intelligent Systems (Journal), 2007, p. 69-78
Keywords:genetic algorithms, GPU, EP, computer graphic equipment, computer graphics, evolutionary computation, parallel algorithms, consumer graphics card, consumer-grade graphics hardware, evolutionary computing, high-performance computer, parallel evolutionary algorithm, evolutionary algorithms, parallel algorithm, pervasive computing, scientific computing on graphics-processing units, ubiquitous computing, SIMD
Abstract:
We propose implementing a parallel EA on consumer graphics cards, [GC] which we can find in many PCs. This lets more people use our parallel algorithm [PA] to solve large-scale, real-world problems [RP] such as data mining. Parallel evolutionary algorithms run [DM] [PEA] [EA] on consumer-grade graphics hardware [CGH] achieve better execution times than ordinary evolutionary algorithms [EA] and offer greater accessibility than those run on high-performance computers
Notes:
Chinese Univ. of Hong Kong, Shatin INSPEC Accession Number:9445531 nVidia GeForce 6800 Ultra. GPU wins for populations bigger than 800. Speedup ratio between 0.62 (slower) to 5.02
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BibTex:
@Article{Fok:2007:ieeeIS,
  author =       "Ka-Ling Fok and Tien-Tsin Wong and Man-Leung Wong",
  title =        "Evolutionary Computing on Consumer Graphics Hardware",
  journal =      "IEEE Intelligent Systems",
  year =         "2007",
  volume =       "22",
  number =       "2",
  pages =        "69--78",
  month =        mar # "-" # apr,
  keywords =     "genetic algorithms, GPU, EP, computer graphic
                 equipment, computer graphics, evolutionary computation,
                 parallel algorithms, consumer graphics card,
                 consumer-grade graphics hardware, evolutionary
                 computing, high-performance computer, parallel
                 evolutionary algorithm, evolutionary algorithms,
                 parallel algorithm, pervasive computing, scientific
                 computing on graphics-processing units, ubiquitous
                 computing, SIMD",
  ISSN =         "1541-1672",
  URL =          "http://ieeexplore.ieee.org/iel5/9670/4136845/04136862.pdf?tp=&isnumber=4136845&arnumber=4136862&punumber=9670",
  doi =          "doi:10.1109/MIS.2007.28",
  size =         "10 pages",
  abstract =     "We propose implementing a parallel EA on consumer
                 graphics cards, which we can find in many PCs. This
                 lets more people use our parallel algorithm to solve
                 large-scale, real-world problems such as data mining.
                 Parallel evolutionary algorithms run on consumer-grade
                 graphics hardware achieve better execution times than
                 ordinary evolutionary algorithms and offer greater
                 accessibility than those run on high-performance
                 computers",
  notes =        "Chinese Univ. of Hong Kong, Shatin INSPEC Accession
                 Number:9445531

                 nVidia GeForce 6800 Ultra. GPU wins for populations
                 bigger than 800. Speedup ratio between 0.62 (slower) to
                 5.02",
}