Benchmarking performance of massively parallel AI architectures

Author

DeMara, Ronald F. ; Kitano, Hiroaki

Author_Institution

Univ. of Southern California, Los Angeles, CA, USA

fYear

1992

fDate

19-21 Oct 1992

Firstpage

517

Lastpage

520

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

Keywords

artificial intelligence; knowledge based systems; parallel architectures; parallel processing; performance evaluation; Connection Machine-2; Parallel AI Computational Efficiency; SNAP-1; architectural evaluation; benchmarking performance; execution time; knowledge base; machine configurations; massively parallel AI architectures; parallel languages; portable benchmark set; scalability; speedup; Artificial intelligence; Computer architecture; Computer languages; Computer science; Kernel; Parallel machines; Parallel processing; Speech analysis; Time measurement; Topology;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers of Massively Parallel Computation, 1992., Fourth Symposium on the

Conference_Location

McLean, VA

Print_ISBN

0-8186-2772-7

Type

conf

DOI

10.1109/FMPC.1992.234865

Filename

234865