GPU-assisted evolutive image predictor generation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{110008152437,
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author = "Matthew McCawley and Seishi Takamura and
Hirohisa Jozawa",
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title = "GPU-assisted evolutive image predictor generation",
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journal = "IEICE Technical Report. Image Engineering (IE)",
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year = "2010",
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volume = "110",
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number = "275",
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pages = "25--28",
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month = nov,
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keywords = "genetic algorithms, genetic programming, GPU, CUDA,
lossless image coding",
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ISSN = "09135685",
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publisher = "IEICE",
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URL = "http://www.ieice.org/ken/paper/20101111b0co/eng/",
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URL = "http://ci.nii.ac.jp/naid/110008152437/",
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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.",
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notes = "NTT Cyber Space Laboratories, NTT Corporation",
- }
Genetic Programming entries for
Matthew McCawley
Seishi Takamura
Hirohisa Jozawa
Citations