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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F10%3APU89612%21RIV12-GA0-26230___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
Genetic algorithm, a robust, stochastic optimization technique, is effective insolving many practical problems in science, engineering, and business domains. Unfortunatelly, execution usually takes a long time. In this paper, I study possibility of utilization consumer-level graphics cards for acceleration of GA’s. A mapping of parallel island genetic algorithm to CUDA software model is designed and tested on GeForce 8800GTX, GTX260-SP216 and GTX285 GPU’s using Rosenbrock’s, Griewank’s and Michalewicz’s benchmark functions. Results indicates that this optimization leads to speedups up to seven thousand times compared to single CPU thread while maintaing reasonable results quality. Genetic algorithm, a robust, stochastic optimization technique, is effective insolving many practical problems in science, engineering, and business domains. Unfortunatelly, execution usually takes a long time. In this paper, I study possibility of utilization consumer-level graphics cards for acceleration of GA’s. A mapping of parallel island genetic algorithm to CUDA software model is designed and tested on GeForce 8800GTX, GTX260-SP216 and GTX285 GPU’s using Rosenbrock’s, Griewank’s and Michalewicz’s benchmark functions. Results indicates that this optimization leads to speedups up to seven thousand times compared to single CPU thread while maintaing reasonable results quality.
dcterms:title
GPU-Based Acceleration of the Genetic Algorithm GPU-Based Acceleration of the Genetic Algorithm
skos:prefLabel
GPU-Based Acceleration of the Genetic Algorithm GPU-Based Acceleration of the Genetic Algorithm
skos:notation
RIV/00216305:26230/10:PU89612!RIV12-GA0-26230___
n3:aktivita
n11:S n11:P n11:Z
n3:aktivity
P(GAP103/10/1517), S, Z(MSM0021630528)
n3:dodaniDat
n9:2012
n3:domaciTvurceVysledku
n12:5491134
n3:druhVysledku
n14:D
n3:duvernostUdaju
n6:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
260917
n3:idVysledku
RIV/00216305:26230/10:PU89612
n3:jazykVysledku
n16:eng
n3:klicovaSlova
Parallel Genetic Algorithm, PGA, CUDA, Island Model, Galib, Speedup, GPU
n3:klicoveSlovo
n5:Galib n5:Parallel%20Genetic%20Algorithm n5:GPU n5:Island%20Model n5:CUDA n5:Speedup n5:PGA
n3:kontrolniKodProRIV
[F93D58E578F9]
n3:mistoKonaniAkce
Češkovice
n3:mistoVydani
Brno
n3:nazevZdroje
Počítačové architektury a diagnostika 2010
n3:obor
n10:JC
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n7:GAP103%2F10%2F1517
n3:rokUplatneniVysledku
n9:2010
n3:tvurceVysledku
Pospíchal, Petr
n3:typAkce
n22:CST
n3:zahajeniAkce
2010-09-13+02:00
n3:zamer
n17:MSM0021630528
s:numberOfPages
6
n13:hasPublisher
Vysoké učení technické v Brně. Fakulta informačních technologií
n4:isbn
978-80-214-4140-8
n20:organizacniJednotka
26230