Acceleration of grammatical evolution using graphics processing units

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Title: Acceleration of grammatical evolution using graphics processing units
Authors: Pospichal, Petr
Muphy, Eoin
O'Neill, Michael
Schwarz, Josef
Jaros, Jiri
Permanent link: http://hdl.handle.net/10197/3545
Date: 12-Jul-2011
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.
Funding Details: Science Foundation Ireland
Other funder
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2011 ACM
Keywords: 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
Language: en
Status of Item: Peer reviewed
Is part of: 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
Appears in Collections:Computer Science Research Collection
CASL Research Collection

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