Ming-Hsuan Yang and Narendra Ahuja

We propose a method to partition training vectors into clusters for a parallel implementation of  self-organizing map (SOM) algorithm. The proposed algorithm assigns a cluster to a processor such that, in updating weights, the neighbourhoods of a winning node in a cluster do not overlap the neighboring nodes of some winning nodes in other clusters. It reduces the overheads caused by synchronization (i.e.,  maintaining coherency) of the weight matrices in the processors since the proposed algorithm allows multiple vectors to find their winning nodes and update weights in parallel. Our experimental results  show that an average speedup of 3.15 for a parallel implementation of a four processor simulation. 

Keywords: data partition; parallel self-organizing map; learning vectors; cluster; synchronization; weight matrices; image coding.

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