GPU-assisted evolutive image predictor generation

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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