Ultra fast SOM using CUDA

SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. For the efficient construction of large maps searching the best matching unit is usually the computationally heaviest operation in the SOM. The parallel nature of the algorithm and the huge computations involved makes it a good target for GPU-based parallel implementation. This paper presents an overall idea of the optimization strategies used for the parallel implementation of Basic-SOM on GPU using CUDA programming paradigm.

Keywords – Basic-SOM, Data Mining, CUDA.