Steve Kuo, Dan Moldovan
The performance of production programs can be improved by firing multiple rules in a production cycle. In this paper, we present the multiple-contexts-multiple-rules (MCMR) model which speeds up production program execution by firing multiple rule concurrently and guarantees the correctness of the solution. The MCMR model is implemented using the RUBIC parallel inference model on the Intel iPSC/2 hypercube. The Intel iPSC/2 hypercube is chosen because it is a cost-effective solution to large-scale application. To avoid unnecessary synchronization and improve performance, rules are executed asynchronously and messages are used to update the database. Preliminary implementation results for the RUBIC parallel inference environment on the Intel iPSC/2 hypercube are reported.