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).
keywordsload balancing mapping scheduling self-organizing feature maps
Unable to display preview. Download preview PDF.
- 1.T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics 43, pp. 59–69, 1982Google Scholar
- 2.T. Kohonen, Self-Organization and Associative Memory, Springer-Verlag, Berlin, 1984Google Scholar
- 3.H. Ritter, K. Schulten, Kohonen's Self-Organizing Feature Maps: Exploring their Computational Capabilities, in: Proceedings of the IEEE International Conference on Neural Networks 1988, San Diego, Vol. I, pp. 109–116, 1988Google Scholar
- 4.H. Ritter, K. Schulten, Convergence Properties of Kohonen's Topology Conserving Maps: Fluctuations, Stability, and Dimension Selection, Biological Cybernetics 60, pp. 59–71, 1989Google Scholar
- 5.H. Ritter, T. Martinez, K. Schulten, Neuronale Netze, Addison-Wesley, Bonn, 1990Google Scholar
- 6.H.-U. Bauer, K.R. Pawelzik, Quantifying the Neighborhood Preservation of Self-Organizing Feature Maps, IEEE Transactions on Neural Networks, Vol. 3, No. 4, pp. 570–579, July 1992Google Scholar
- 7.J.W. Meyer, A New Metric for Self-Organizing Feature Maps Allows Mapping of Arbitrary Parallel Programs, in Proc. 5th IEEE Int. Conf. on Tools with Artificial Intelligence, Boston, 1993Google Scholar
- 8.L. Ingber, Simulated Annealing: Practise Versus Theory, Mathl. Comput. Modelling, Vol. 18, No. 11, pp. 29–57, 1993Google Scholar