Optimization of the self-organizing feature map on parallel computers

In this paper, we propose two implementations of the self organisation feature map (SOFM) on parallel computers. One is for a MIMD computer, the other one is for a SIMD computer. We propose a new mapping of the neurons onto the processors which permits one to obtain an optimal load balancing. We propose a new learning method for the SOFM using a block strategy. This allows one to exploit the high performance level of the new generation of parallel computers. We show that the block strategy performs well on several examples outperforming classical implementations.