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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F11%3APU96181%21RIV13-MSM-26230___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken.  This paper deals with utilizing mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimization details are discussed and the NVCC compiler is analyzed.  We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCI-express bottleneck and main memory transactions.  This is the first occasion GE has been adapted for running on a GPU. We measure our implementation running on one core of CPU Core i7 and GPU GTX Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken.  This paper deals with utilizing mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimization details are discussed and the NVCC compiler is analyzed.  We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCI-express bottleneck and main memory transactions.  This is the first occasion GE has been adapted for running on a GPU. We measure our implementation running on one core of CPU Core i7 and GPU GTX
dcterms:title
Acceleration of Grammatical Evolution Using Graphics Processing Units Acceleration of Grammatical Evolution Using Graphics Processing Units
skos:prefLabel
Acceleration of Grammatical Evolution Using Graphics Processing Units Acceleration of Grammatical Evolution Using Graphics Processing Units
skos:notation
RIV/00216305:26230/11:PU96181!RIV13-MSM-26230___
n13:predkladatel
n21:orjk%3A26230
n3:aktivita
n11:S n11:P n11:Z
n3:aktivity
P(GAP103/10/1517), S, Z(MSM0021630528)
n3:dodaniDat
n15:2013
n3:domaciTvurceVysledku
n7:2457652 n7:5444284 n7:5491134
n3:druhVysledku
n23:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
184380
n3:idVysledku
RIV/00216305:26230/11:PU96181
n3:jazykVysledku
n22:eng
n3:klicovaSlova
CUDA, grammatical evolution, graphics chips, GPU, GPGPU, speedup, symbolic regression
n3:klicoveSlovo
n5:GPU n5:graphics%20chips n5:speedup n5:GPGPU n5:CUDA n5:symbolic%20regression n5:grammatical%20evolution
n3:kontrolniKodProRIV
[85C5194DD8B9]
n3:mistoKonaniAkce
Dublin
n3:mistoVydani
New York
n3:nazevZdroje
Proceedings of the 2011 GECCO conference companion on Genetic and evolutionary computation
n3:obor
n16:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n14:GAP103%2F10%2F1517
n3:rokUplatneniVysledku
n15:2011
n3:tvurceVysledku
Jaroš, Jiří Schwarz, Josef Pospíchal, Petr
n3:typAkce
n20:WRD
n3:zahajeniAkce
2011-07-12+02:00
n3:zamer
n10:MSM0021630528
s:numberOfPages
8
n19:hasPublisher
Association for Computing Machinery
n6:isbn
978-1-4503-0690-4
n12:organizacniJednotka
26230