Benchmarking performance of massively parallel AI architectures
DeMara, R.F.
Kitano, H.
Univ. of Southern California, Los Angeles, CA;
This paper appears in: Frontiers of Massively Parallel Computation, 1992., Fourth Symposium on the
Publication Date: 19-21 Oct 1992
On page(s): 517-520
Meeting Date: 10/19/1992 - 10/21/1992
Location: McLean, VA, USA
ISBN: 0-8186-2772-7
References Cited: 6
INSPEC Accession Number: 4459437
Digital Object Identifier: 10.1109/FMPC.1992.234865
Current Version Published: 2002-08-06
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
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