R. F. DeMara and H. Kitano, "Benchmarking Performance of Massively Parallel AI 
Architectures," in Proceedings of the Fourth Symposium on the Frontiers of 
Massively Parallel Computation, pp. 517 - 520, McLean, Virginia, U.S.A., October 
19 - 21, 1992.

Abstract: The authors address the architectural evaluation of massively parallel 
machines suitable for artificial intelligence (AI). The approach is to identify 
the impact of specific algorithm features by measuring execution time on a SNAP-
1 and a Connection Machine-2 using different knowledge base and machine 
configurations. Since a wide variety of parallel AI languages and processing 
architectures are in use, the authors developed a portable benchmark set for 
Parallel AI Computational Efficiency (PACE). PACE provides a representative set 
of processing workloads, knowledge base topologies, and performance indices. The 
authors also analyze speedup and scalability of fundamental AI operations in 
terms of the massively parallel paradigm.

Complete Paper Available at:
http://www.cal.ucf.edu/journal/C48_demara_kitano_MPC92.pdf