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Acceleration of grammatical evolution using graphics processing units

Author(s)
Pospichal, Petr  
Muphy, Eoin  
O'Neill, Michael  
Schwarz, Josef  
Jaros, Jiri  
Uri
http://hdl.handle.net/10197/3545
Date Issued
2011-07-12
Date Available
2012-03-29T15:55:40Z
Abstract
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 480 together with a GE library written in JAVA, GEVA. Results indicate that our algorithm offers the same con- vergence, and it is suitable for a larger number of regression points where GPU is able to reach speedups of up to 39 times faster when compared to GEVA on a serial CPU code written in C. In conclusion, properly utilized, GPU can offer an interesting performance boost for GE tackling symbolic regression.
Sponsorship
Science Foundation Ireland
Other funder
Other Sponsorship
Czech Science Foundation
Faculty of Information Technology, Brno University of Technology
Type of Material
Conference Publication
Publisher
ACM
Copyright (Published Version)
2011 ACM
Subjects

CUDA

Grammatical evolution...

GPU

GPGPU

Graphics chips

Speedup

Symbolic regression

Subject – LCSH
Evolutionary computation
Graphics processing units
Genetic programming (Computer science)
DOI
10.1145/2001858.2002030
Web versions
http://dx.doi.org/10.1145/2001858.2002030
Language
English
Status of Item
Peer reviewed
Journal
GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011
Conference Details
Presented at the CIGPU Workshop at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011
ISBN
978-1-4503-0690-4
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-sa/1.0/
File(s)
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wk305c-pospichal.pdf

Size

391.31 KB

Format

Adobe PDF

Checksum (MD5)

29d9f41d447d3acaabdc47b925390eb3

Owning collection
Computer Science Research Collection
Mapped collections
CASL Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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