A Parallel Matching in AI Production Systems

인공지능 생성시스템에서의 병렬 매칭

One of the hardest problems that limit real application of production system is its slowness. One way to overcome this problem is to speed up the matching operation which occupies more than 90% of the total execution time. In this paper, we try to speed up the matching operation with parallel execution of a typical pattern matching algorithm, RETE, in a multiprocessor environment, This requires not only to make partitions of the rules but also to allocate the partitioned rules to processors, respectively. A partition strategy is proposed to make groups of similar rules by evaluating the similarity of rules according to the number of common conditions between rules. An allocation strategy is proposed to make the load of each processor even by assigning the different priority to the group of rules according to the expected amount of time required for matching operation. To compare with the existing methods, we perform simulation using OPS5 sample programs. The simulation results show that the proposed methods can improve the performance of production system.