In order to have an optimal performance during an execution of a parallel program, the tasks of the parallel computation must be mapped to processors such that the computational load is distributed as evenly as possible while highly communicating tasks are placed closely. We describe a new algorithm for static load balancing problem based on Kohonen Self-Organizing Maps (SOM) which preserves the neighborhood relationship of tasks. We define the input space of the SOM algorithm to be a unit square and divide it into ``number of processors'' regions. The tasks are represented by the neurons which are mapped to the regions randomly. We enforce load balancing by selecting training input from the region of the least loaded processor. We examine the impact of various input selection strategies and neighborhood functions on the accuracy of the mapping. The results show that our algorithm outperforms the other task mapping algorithms implemented with SOMs.

Key words: neural networks, self-organizing maps, kohonen, graph partitioning, load balancing