The self-organizing map is a prominent unsupervised neural network model which lends itself to the analysis of high-dimensional input data and data mining applications. However, the high execution times required to train the map put a limit to its application in many high-performance data analysis application domains.
In this paper we discuss the /orSOM implementation, a software-based parallel implementation of the self-organizing map, and its optimization for the analysis of high-dimensional input data using distributed memory systems and clusters. The original /orSOM algorithm scales very well in a parallel execution environment with low communication latencies and exploits parallelism to cope with memory latencies. However it suffers from poor scalability on distributed memory computers. We present optimizations to further decouple the subprocesses, simplify the communication model and improve the portability' of the system.