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
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
%0 Journal Article
%1 Fok:2007:ieeeIS
%A Fok, Ka-Ling
%A Wong, Tien-Tsin
%A Wong, Man-Leung
%D 2007
%J IEEE Intelligent Systems
%K EP, GPU, SIMD algorithm, algorithms, card, computation, computer computer, computing computing, consumer consumer-grade equipment, evolutionary genetic graphic graphics graphics, graphics-processing hardware, high-performance on parallel pervasive scientific ubiquitous units,
%N 2
%P 69--78
%R doi:10.1109/MIS.2007.28
%T Evolutionary Computing on Consumer Graphics Hardware
%U http://ieeexplore.ieee.org/iel5/9670/4136845/04136862.pdf?tp=&isnumber=4136845&arnumber=4136862&punumber=9670
%V 22
%X 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
@article{Fok:2007:ieeeIS,
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},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Fok, Ka-Ling and Wong, Tien-Tsin and Wong, Man-Leung},
biburl = {https://www.bibsonomy.org/bibtex/2e3c17065a986b771efcb4b1f6de9a21e/brazovayeye},
doi = {doi:10.1109/MIS.2007.28},
interhash = {c65363d024e77af3eb6d4a0658796df2},
intrahash = {e3c17065a986b771efcb4b1f6de9a21e},
issn = {1541-1672},
journal = {IEEE Intelligent Systems},
keywords = {EP, GPU, SIMD algorithm, algorithms, card, computation, computer computer, computing computing, consumer consumer-grade equipment, evolutionary genetic graphic graphics graphics, graphics-processing hardware, high-performance on parallel pervasive scientific ubiquitous units,},
month = {March-April},
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},
number = 2,
pages = {69--78},
size = {10 pages},
timestamp = {2008-06-19T17:39:40.000+0200},
title = {Evolutionary Computing on Consumer Graphics Hardware},
url = {http://ieeexplore.ieee.org/iel5/9670/4136845/04136862.pdf?tp=&isnumber=4136845&arnumber=4136862&punumber=9670},
volume = 22,
year = 2007
}