Publication result detail

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

POSPÍCHAL, P.; SCHWARZ, J.

Original Title

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

English Title

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

POSPÍCHAL, P.; SCHWARZ, J.

RIV year

2012

Released

28.04.2011

Publisher

Brno University of Technology

Location

Brno

ISBN

978-80-214-4273-3

Book

Proceedings of the 17th Conference Student EEICT 2011 Volume 3

Pages from

574

Pages to

578

Pages count

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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