Detail publikace

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Originální název

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Anglický název

Effective Mapping of Grammatical Evolution to CUDA Hardware Model

Jazyk

en

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 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.

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 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.

BibTex


@inproceedings{BUT76335,
  author="Petr {Pospíchal} and Josef {Schwarz}",
  title="Effective Mapping of Grammatical Evolution to CUDA Hardware Model",
  annote="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.",
  address="Brno University of Technology",
  booktitle="Proceedings of the 17th Conference Student EEICT 2011 Volume 3",
  chapter="76335",
  edition="NEUVEDEN",
  howpublished="print",
  institution="Brno University of Technology",
  year="2011",
  month="april",
  pages="574--578",
  publisher="Brno University of Technology",
  type="conference paper"
}