A parallel general implementation of Kohonen's self-organizing map algorithm: performance and scalability

It is reported that a parallel implementation of the self-organizing map algorithm on a Beowulf commodity-class computing cluster scales very favorably with the number of available nodes and greatly speeds the computation of medium-to-large-scale cortical maps.

Abstract

Kohonen's self-organizing map algorithm provides computational neurobiology with a useful model of the primate cerebral cortex. However, simulations of only modestly sized maps quickly exceed the capacity of even very fast workstations. Here, we report that a parallel implementation of the algorithm on a Beowulf commodity-class computing cluster scales very favorably with the number of available nodes and greatly speeds the computation of medium-to-large-scale cortical maps.