Execution of OPS5 Production Systems on a Massively Parallel Machine

Abstract In recent years, the development of expert systems implemented by rule-based production systems has emerged as one of the dominant paradigms in the field of artificial intelligence. While production systems offer important advantages in large-scale AI applications, their use in such applications is typically very costly in execution time. In this paper, we describe an algorithm for executing production systems expressed in the OPS5 language on a massively parallel multiple-SIMD machine called NON-VON, portions of which are currently under construction at Columbia University. The algorithm, a parallel adaptation of Forgy's Rete Match, has been implemented and tested on an instruction-level simulator. We present a detailed performance analysis, based on the implemented code, for the averaged characteristics of six production systems having an average of 910 inference rules each. The analysis predicts an execution rate of more than 850 production firings per second using hardware comparable in cost to a VAX 11/780. By way of comparison, a LISP-based OPS5 interpreter running on a VAX 11/780 typically fires 1 to 5 rules per second, while a Bliss-based interpreter executes 5 to 12 rules per second.

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