A Basic Study on Implementation of Large Scale SOM in GPU Cluster

Abstract

When SOM learning algorithm is applied to a large amount of data, it can be an effective approach that a parallel and distributed processing is adopted. In this study, we have proposed a kind of implementation method which parallelize the SOM learning algorithm by dividing the learning dataset. Furthermore, GPGPU approach is also adopted for vector operations under SOM learning process and we have confirmed an effectiveness of these implementation methods with respect to a computation time. In this paper, we propose another kind of implementation of parallelized SOM which divide competitive layer of a large scale SOM.