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Parallel High Dimensional Self Organizing Maps Using CUDA

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4 Author(s)
Moraes, F.C. ; Centro de Cienc. Computacionais(C3), Univ. Fed. de Rio Grande, Rio Grande, Brazil ; Botelho, S.C. ; Filho, N.D. ; Gaya, J.F.O.

A common neural network used for complex data clustering is the Self Organizing Maps(SOM). This algorithm have a expensive training step, that occur mainly on high dimensional applications like image clustering. This makes impossible for some of these applications to be run in real time or even in a feasible time. On this paper we explore the use of GPUs with the NVIDIA CUDA language to decrease computational cost of SOM. We propose a three steps implementation able to reduce the computational complexity of the algorithm under SIMD paradigm and also making a good use of GPU's resources. At the end we were able to get a peak speed-up of 44 times against a C CPU implementation, fact that concludes about SOM's data parallelism.

Published in:

Robotics Symposium and Latin American Robotics Symposium (SBR-LARS), 2012 Brazilian

Date of Conference:

16-19 Oct. 2012