Acceleration of grammatical evolution using graphics processing units

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dc.contributor.author Pospichal, Petr
dc.contributor.author Muphy, Eoin
dc.contributor.author O'Neill, Michael
dc.contributor.author Schwarz, Josef
dc.contributor.author Jaros, Jiri
dc.date.accessioned 2012-03-29T15:55:40Z
dc.date.available 2012-03-29T15:55:40Z
dc.date.copyright 2011 ACM en
dc.date.issued 2011-07-12
dc.identifier.isbn 978-1-4503-0690-4
dc.identifier.uri http://hdl.handle.net/10197/3545
dc.description Presented at the CIGPU Workshop at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011 en
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland en
dc.description.sponsorship Other funder en
dc.format.extent 400705 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher ACM en
dc.relation.ispartof GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011 en
dc.relation.requires CASL Research Collection en
dc.rights This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in the GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, http://dx.doi.org/10.1145/2001858.2002030 en
dc.subject CUDA en
dc.subject Grammatical evolution en
dc.subject GPU en
dc.subject GPGPU en
dc.subject Graphics chips en
dc.subject Speedup en
dc.subject Symbolic regression en
dc.subject.lcsh Evolutionary computation en
dc.subject.lcsh Graphics processing units en
dc.subject.lcsh Genetic programming (Computer science) en
dc.title Acceleration of grammatical evolution using graphics processing units en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://dx.doi.org/10.1145/2001858.2002030 en
dc.status Peer reviewed en
dc.identifier.doi 10.1145/2001858.2002030
dc.neeo.contributor Pospichal|Petr|aut| en
dc.neeo.contributor Muphy|Eoin|aut| en
dc.neeo.contributor O'Neill|Michael|aut| en
dc.neeo.contributor Schwarz|Josef|aut| en
dc.neeo.contributor Jaros|Jiri|aut| en
dc.description.othersponsorship Czech Science Foundation en
dc.description.othersponsorship Faculty of Information Technology, Brno University of Technology en
dc.description.admin ti, sp, ke, ab, co, li- TS 23.02.12 en


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