Parallel mappings of Kohonen's self organizing map (SOM) and learning
vector quantization (LVQ) algorithms are presented for a tree shape pa
rallel computer system called TUTNC (Tampere University of Technology
Neural Computer). The lattice of neurons in SOM is partitioned columnw
ise to parallel processors in a neuron parallel manner. In addition, a
n efficient method is presented for the neighborhood computation to ma
ke the computation time independent of SOM size and processor count. T
he 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 implementation
s on Various platforms show, that good performance per processor has b
een achieved.