GPU-assisted evolutive image predictor generation

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{110008152437,
  author =       "Matthew McCawley and Seishi Takamura and 
                 Hirohisa Jozawa",
  title =        "GPU-assisted evolutive image predictor generation",
  journal =      "IEICE Technical Report. Image Engineering (IE)",
  year =         "2010",
  volume =       "110",
  number =       "275",
  pages =        "25--28",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming, GPU, CUDA,
                 lossless image coding",
  ISSN =         "09135685",
  publisher =    "IEICE",
  URL =          "http://www.ieice.org/ken/paper/20101111b0co/eng/",
  URL =          "http://ci.nii.ac.jp/naid/110008152437/",
  abstract =     "Evolutive Image Coding has shown promising results in
                 efficiency compared to other lossless coding methods,
                 but until now, the processing power required for the
                 fitness evaluation has limited its usefulness outside
                 of large computer clusters. Using the CUDA programming
                 language on comparatively inexpensive NVIDIA graphics
                 cards, we have obtained speed increases of up to 150
                 times for the fitness evaluation. Some of the
                 techniques we have used to improve performance include
                 using the GPU's fast shared memory whenever possible as
                 well as performing some calculations for which the GPU
                 is not as well suited, such as a histogram-based
                 calculation, on the CPU while the GPU simultaneously
                 calculates the fitness evaluation in order to minimize
                 idle time.",
  notes =        "NTT Cyber Space Laboratories, NTT Corporation",
}

Genetic Programming entries for Matthew McCawley Seishi Takamura Hirohisa Jozawa

Citations