A simulated annealing method for mapping production systems onto multicomputers

A methodology for mapping rule-based expert systems onto a message-passing multicomputer is presented. The method is based on static load balancing using simulated annealing to achieve a nearly optimal allocation of multiple production rules to processor nodes. The goal is to balance the initial load distribution and to avoid serious communication overhead among processor nodes at run time. A formal model is developed and a cost function is defined in the annealing process. Heuristic swap functions and cooling policies which ensure the efficiency and quality of the annealing process are given. A software load-balancing package is implemented on a SUN 3/280 workstation to carry out the benchmark experiments. The overhead associated with this mapping method is O(m ln m), where m is the number of production rules in the system. The Monkey and Bananas expert system with 24 rules is mapped onto an 8-node hypercube. Experimental results verify the effectiveness of the mapping method. The method can be applied in practical parallel production systems and to achieve scalability in performance