Detail publikačního výsledku

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

POSPÍCHAL, P.; SCHWARZ, J.

Originální název

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Anglický název

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Druh

Stať ve sborníku mimo WoS a Scopus

Originální abstrakt

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 solving
various tasks including symbolic regression. However, performance of this method can limit the area
of 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 mapping
of the algorithm to the CUDA framework. Results indicate that for larger number of regression points
can 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 boost
for solution the symbolic regression by the GE.

Anglický abstrakt

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 solving
various tasks including symbolic regression. However, performance of this method can limit the area
of 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 mapping
of the algorithm to the CUDA framework. Results indicate that for larger number of regression points
can 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 boost
for solution the symbolic regression by the GE.

Klíčová slova

GPU, Graphics Processing Units, Grammatical Evolution, CUDA, Symbolic Regression,
Speedup, C

Klíčová slova v angličtině

GPU, Graphics Processing Units, Grammatical Evolution, CUDA, Symbolic Regression,
Speedup, C

Autoři

POSPÍCHAL, P.; SCHWARZ, J.

Rok RIV

2012

Vydáno

28.04.2011

Nakladatel

Brno University of Technology

Místo

Brno

ISBN

978-80-214-4273-3

Kniha

Proceedings of the 17th Conference Student EEICT 2011 Volume 3

Strany od

574

Strany do

578

Strany počet

5

URL

https://www.fit.vut.cz/research/publication/9595/

BibTex

@inproceedings{BUT76335,
  author="Petr {Pospíchal} and Josef {Schwarz}",
  title="Effective Mapping of Grammatical Evolution to CUDA Hardware Model",
  booktitle="Proceedings of the 17th Conference Student EEICT 2011 Volume 3",
  year="2011",
  pages="574--578",
  publisher="Brno University of Technology",
  address="Brno",
  isbn="978-80-214-4273-3",
  url="https://www.fit.vut.cz/research/publication/9595/"
}

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