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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F11%3APU96038%21RIV12-MSM-26230___
rdf:type
n4:Vysledek skos:Concept
dcterms:description
Several papers have shown that symbolic regression is suitable for data analysis and prediction in finance markets. The Grammatical Evolution (GE) has been successfully applied in solvingvarious tasks including symbolic regression. However, performance of this method can limit the areaof possible applications. This paper deals with utilizing mainstream graphics processing unit (GPU)for acceleration of GE solving symbolic regression. With respect to various mentioned constrains,such as PCI-Express and main memory bandwidth bottleneck, we have designed effective mappingof the algorithm to the CUDA framework. Results indicate that for larger number of regression pointscan our algorithm run 636 or 39 times faster than GEVA library routine or a sequential C code, respectively. As a result, the ordinary GPU, if used properly, can offer interesting performance boostfor solution the symbolic regression by the GE. Several papers have shown that symbolic regression is suitable for data analysis and prediction in finance markets. The Grammatical Evolution (GE) has been successfully applied in solvingvarious tasks including symbolic regression. However, performance of this method can limit the areaof possible applications. This paper deals with utilizing mainstream graphics processing unit (GPU)for acceleration of GE solving symbolic regression. With respect to various mentioned constrains,such as PCI-Express and main memory bandwidth bottleneck, we have designed effective mappingof the algorithm to the CUDA framework. Results indicate that for larger number of regression pointscan our algorithm run 636 or 39 times faster than GEVA library routine or a sequential C code, respectively. As a result, the ordinary GPU, if used properly, can offer interesting performance boostfor solution the symbolic regression by the GE.
dcterms:title
Effective Mapping of Grammatical Evolution to CUDA Hardware Model Effective Mapping of Grammatical Evolution to CUDA Hardware Model
skos:prefLabel
Effective Mapping of Grammatical Evolution to CUDA Hardware Model Effective Mapping of Grammatical Evolution to CUDA Hardware Model
skos:notation
RIV/00216305:26230/11:PU96038!RIV12-MSM-26230___
n4:predkladatel
n5:orjk%3A26230
n6:aktivita
n11:Z n11:P
n6:aktivity
P(GAP103/10/1517), Z(MSM0021630528)
n6:dodaniDat
n9:2012
n6:domaciTvurceVysledku
n8:5491134 n8:5444284
n6:druhVysledku
n22:D
n6:duvernostUdaju
n14:S
n6:entitaPredkladatele
n23:predkladatel
n6:idSjednocenehoVysledku
196579
n6:idVysledku
RIV/00216305:26230/11:PU96038
n6:jazykVysledku
n21:eng
n6:klicovaSlova
GPU, Graphics Processing Units, Grammatical Evolution, CUDA, Symbolic Regression,Speedup, C
n6:klicoveSlovo
n13:GPU n13:CUDA n13:Symbolic%20Regression n13:C n13:Speedup n13:Grammatical%20Evolution n13:Graphics%20Processing%20Units
n6:kontrolniKodProRIV
[DEF9C1153452]
n6:mistoKonaniAkce
Brno
n6:mistoVydani
Brno
n6:nazevZdroje
Proceedings of the 17th Conference Student EEICT 2011 Volume 3
n6:obor
n16:IN
n6:pocetDomacichTvurcuVysledku
2
n6:pocetTvurcuVysledku
2
n6:projekt
n18:GAP103%2F10%2F1517
n6:rokUplatneniVysledku
n9:2011
n6:tvurceVysledku
Schwarz, Josef Pospíchal, Petr
n6:typAkce
n7:CST
n6:zahajeniAkce
2011-04-28+02:00
n6:zamer
n19:MSM0021630528
s:numberOfPages
5
n17:hasPublisher
Vysoké učení technické v Brně
n3:isbn
978-80-214-4273-3
n20:organizacniJednotka
26230