Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F11%3APU96038" target="_blank" >RIV/00216305:26230/11:PU96038 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Popis výsledku v původním jazyce

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 Ccode, respectively. As a result, the ordinary GPU, if used properly, can offer interesting performance boostfor solution the symbolic regression by the GE.

Název v anglickém jazyce

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Popis výsledku anglicky

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 Ccode, respectively. As a result, the ordinary GPU, if used properly, can offer interesting performance boostfor solution the symbolic regression by the GE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F10%2F1517" target="_blank" >GAP103/10/1517: Natural computing na nekonvenčních platformách</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 17th Conference Student EEICT 2011 Volume 3

ISBN

978-80-214-4273-3

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

574-578

Název nakladatele

Brno University of Technology

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

28. 4. 2011

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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