Self-Organizing Processes

Self-organizing feature maps are presented as an effcient tool for mapping process graphs onto processor networks. Arbitrary process graphs can be mapped to most of the common parallel architectures (two-dimensional lattice, three-dimensinal torus, hypercube, etc.). Two extensions of the Kohonen algorithm for self-organizing feature maps were necessary. A special graph metric allows the support of arbitrary process graphs and a modification of the learning rule added a load balancing facility. The order of computational complexity is restricted to O(m3) (m denoting the number of processes) in the worst case. The method can be adapted to a wide variety of further graph mapping problems (e.g. circuit design, production planning, scheduling).