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.
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Martínez-Zarzuela, M. et al. (2009). Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_19
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DOI: https://doi.org/10.1007/978-3-642-02478-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
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