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An Architecture for Parallelism of OPS5 Production Systems

Tsuyoshi KAWAGUCHI, Etsuro HONDA

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In this paper we propose an architecture and an algorithm for the parallel execution of OPS5 production systems. It is known that current OPS5 production system interpreters spend almost 90% of their execution time in the match step. Thus, in this paper we focus on the speedup of the match step. The match algorithm used in OPS5 is called Rete and the algorithm uses a special kind of a date-flow network compiled from the left hand sides of rules. To achieve the maximum degree of parallelism of a given OPS5 program by as few processors as possible, the proposed parallel machine uses loosely coupled multiprocessors. Parallel machines designed for fine-grain parallelism, such as DADO, also use loosely coupled multiprocessors. However, the proposed machine differs from such machines at the following points: use of powerful processors which have large amounts of memories and small cycle times; use of a shared Rete network (parallel machines designed for fine-grain parallelism use an unshared Rete network); high hardware utilization. Basic ideas of the proposed parallel machine are as follows. (1) Use of a modified Rete network in which node sharing is used only for constant-test nodes and each memory node is lumped with the child two-input node. (2) Static allocation of the nodes of the modified Rete network onto processors. (3) Partition of the set of processors into three subsets: constant-test node processors, two-input node processors and conflict-set processors. (4) Use of a ring network for the interconnection network among two-input node processors. In addition to an architecture for parallel execution of OPS5 production systems, we propose a scheme for mapping the modified Rete network into the proposed architecture. The results of simulation experiments showed that the proposed architecture is promising for parallel execution of OPS5 production systems.

Publication
IEICE TRANSACTIONS on Information Vol.E76-D No.8 pp.935-946
Publication Date
1993/08/25
Publicized
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DOI
Type of Manuscript
PAPER
Category
Artificial Intelligence and Cognitive Science

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