Mapping of SOM and LVQ algorithms on a tree shape parallel computer system

Abstract

Parallel mappings of Kohonen's self organizing map (SOM) and learning vector quantization (LVQ) algorithms are presented for a tree shape parallel computer system called TUTNC (Tampere University of Technology Neural Computer). The lattice of neurons in SOM is partitioned columnwise to parallel processors in a neuron parallel manner. In addition, an efficient method is presented for the neighborhood computation to make the computation time independent of SOM size and processor count. The tree shape architecture is shown to match well the requirements of mapped algorithms and their relations in such a prototype system TUTNC are studied. Performance has been measured for sample configurations and estimated for a larger system. Comparisons to other implementations on various platforms show, that good performance per processor has been achieved.