Detail publikace
GPU-Based Acceleration of the Genetic Algorithm
Originální název
GPU-Based Acceleration of the Genetic Algorithm
Anglický název
GPU-Based Acceleration of the Genetic Algorithm
Jazyk
en
Originální abstrakt
Genetic algorithm, a robust, stochastic optimization technique, is effective in solving manypractical problems in science, engineering, and business domains. Unfortunatelly, executionusually takes long time. In this paper, we study a possibility of utilization consumer-levelgraphics cards for acceleration of GAs. We have designed a mapping of the parallel islandgenetic algorithm to the CUDA software model and tested our implementation on GeForce8800GTX and GTX285 GPUs using a Rosenbrock's, Griewank's and Michalewicz's benchmarkfunctions. Results indicates that our optimization leads to speedups up to seven thousand timescompared to single CPU thread while maintaing reasonable results quality.
Anglický abstrakt
Genetic algorithm, a robust, stochastic optimization technique, is effective in solving manypractical problems in science, engineering, and business domains. Unfortunatelly, executionusually takes long time. In this paper, we study a possibility of utilization consumer-levelgraphics cards for acceleration of GAs. We have designed a mapping of the parallel islandgenetic algorithm to the CUDA software model and tested our implementation on GeForce8800GTX and GTX285 GPUs using a Rosenbrock's, Griewank's and Michalewicz's benchmarkfunctions. Results indicates that our optimization leads to speedups up to seven thousand timescompared to single CPU thread while maintaing reasonable results quality.
BibTex
@inproceedings{BUT35531,
author="Petr {Pospíchal}",
title="GPU-Based Acceleration of the Genetic Algorithm",
annote="Genetic algorithm, a robust, stochastic optimization technique, is effective in
solving manypractical problems in science, engineering, and business domains.
Unfortunatelly, executionusually takes long time. In this paper, we study
a possibility of utilization consumer-levelgraphics cards for acceleration of
GAs. We have designed a mapping of the parallel islandgenetic algorithm to the
CUDA software model and tested our implementation on GeForce8800GTX and GTX285
GPUs using a Rosenbrock's, Griewank's and Michalewicz's benchmarkfunctions.
Results indicates that our optimization leads to speedups up to seven thousand
timescompared to single CPU thread while maintaing reasonable results quality.",
address="Faculty of Information Technology BUT",
booktitle="Proceedings of the 16th Conference Student EEICT 2010 Volume 5",
chapter="35531",
edition="NEUVEDEN",
howpublished="print",
institution="Faculty of Information Technology BUT",
year="2010",
month="april",
pages="234--238",
publisher="Faculty of Information Technology BUT",
type="conference paper"
}