Performance of a decentralized knowledge base system

The binary predicate execution model (BPEM) is a computational model that combines logic programming, semantic nets, and message-driven computation into a paradigm for the construction of highly parallel knowledge-base systems. Simulation results are presented that demonstrate the ability of BPM to exploit effectively the resources of a loosely coupled computer network consisting of large numbers of independent processing elements. These simulations suggest performance on the order of 10⁵ logical inferences per second for 256 processing elements in an n-cube configuration. A very important feature of the BPEM is that it scales-up linearly under simple OR-parallelism and AND-parallelism. Hence, the BPEM can scale-up to exploit parallelism efficiently in very large semantic networks and knowledge bases