Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA

Authors:
M. Martínez-Zarzuela;F. J. Pernas;A. Tejero Pablos;M. Antón Rodríguez;J. F. Higuera;D. Boto Giralda;D. González Ortega
Affiliations:
Higher School of Telecommunications Engineering, University of Valladolid, (Spain);Higher School of Telecommunications Engineering, University of Valladolid, (Spain);Higher School of Telecommunications Engineering, University of Valladolid, (Spain);Higher School of Telecommunications Engineering, University of Valladolid, (Spain);Higher School of Telecommunications Engineering, University of Valladolid, (Spain);Higher School of Telecommunications Engineering, University of Valladolid, (Spain);Higher School of Telecommunications Engineering, University of Valladolid, (Spain)
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

Programming of Graphics Processing Units (GPUs) has evolved in a way they can be used to address and speed-up computation of algorithms exemplified by data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training under specific conditions.