Benchmarking performance of massively parallel AI architectures

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.<>