GPGPU Implementation of Fuzzy Rule-Based Classifiers
This paper presents a parallel implementation of fuzzy-rule-based classifiers using a GPGPU (General Purpose Graphics Processing Unit). There are two steps in the process of fuzzy rule-based classification: Fuzzy-rule generation from training data and classification of an unseen input pattern. The proposed implementation parallelizes these two steps. In the step of fuzzy-rule generation from training patterns, the membership calculation of a training pattern for available fuzzy sets is simultaneously processed. On the other hand, the membership calculation of an unseen pattern for the generated fuzzy if-then rules is simultaneously processed in the step of the classification of the pattern. The efficiency of the parallelization is evaluated through a series of computational experiments. The effect of the parallelization is evaluated for each step of fuzzy rule-based classifiers. The results of the computational experiments show that the proposed implementation successfully improve the speed of fuzzy rule-based classifiers.
KeywordsMemory Access Training Pattern Fuzzy Partition Antecedent Part General Purpose Graphic Processing Unit
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- 1.Nvidia cuda, http://www.nvidia.com/object/cuda_home.html
- 2.nVidia Corporation: Cuda cublas library (2010), http://developer.download.nvidia.com/compute/cuda/3_2_prod/toolkit/docs/CUBLAS_Library.pdf
- 3.Foundation, N.S., of Energy, D.: BLAS (2010), http://www.netlib.org/blas/
- 4.Fujimoto, N.: Faster matrix-vector multiplication on GeForce 8800GTX. In: IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2008, pp. 1–8 (2008), doi:10.1109/IPDPS.2008.4536350Google Scholar
- 5.Ishibuchi, H., Nakashima, T., Nii, M.: Classification And Modeling With Linguistic Information Granules: Advanced Approaches To Linguistic Data Mining, 1st edn. Springer-Verlag New York, Inc., Secaucus (2004)Google Scholar
- 6.Volkov, V., Demmel, J.: Lu, qr and cholesky factorizations using vector capabilities of gpus. Tech. Rep. UCB/EECS-2008-49, EECS Department, University of California, Berkeley (2008), http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-49.html