Proc. of FGCS '94, ICOT, Tokyo, December 1994
The Evaluation of Parallel Inference Machines
Kouichi KUMON and Hiroyoshi HATAZAWA
Institute for New Generation Computer Technology
4-28, Mita 1-Chome, Minatoku, Tokyo 108, JAPAN
{kumon,f-hataza}@icot.or.jp
Abstract:
In this paper, we study the performance of various implementations
of KL1 processing systems, including the parallel inference machine
(PIM) system and the KLIC system. PIM systems are built on dedicated
parallel hardware developed in the FGCS project for efficient KL1
execution, while the KLIC system is KL1 ported to run on UNIX
workstations. This difference makes the design policies of both
systems quite different necessitating a quantitative analysis. KL1 is
a parallel logic language running on hardware ranging from a single
processor to several hundred processors. Thus performance measurements
must cover this range.
Firstly we use a set of small benchmark programs for single
processor performance. It includes a naive reverse program to compare
and characterize those systems. And we take a close look at the
execution of instructions in both the PIM KL1 processing system and
the KLIC system. The PIM/p system is a convenient platform because
both the conventional KL1 system and the sequential core of the KLIC
system are available on it, and thus we can compare both
implementations on the same base. We also compare both system using
the life program, which shows quite a different aspect to
the append execution.
Then, we evaluate the cluster performance for automatic load
balancing. Some PIM models have a cluster structure, in which
processors are connected by a common bus and share memory. In parallel
processing in a cluster, automatic load balancing is available to
reduce the burden of writing load distribution code. It is thus
meaningful to investigate load balancing mechanisms and measure the
speed-up in parallel performance. To do this, we make execution models
for a very small program, examine the parallel performance, and
estimate the distribution overhead in the cluster.
Finally, we investigate the distributed performance of the systems
by using simple communication dependent benchmark programs. The PIM
models use a dedicated network for communication between
clusters/nodes. However as the KLIC system uses PVM for message
passing primitives, we found that the performance of KLIC wholly
depends on the PVM performance.
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