Tech Report Announcement CMU-CS-89-208
Michael.Witbrock@CS.CMU.EDU
Michael.Witbrock at CS.CMU.EDU
Mon Feb 12 18:53:42 EST 1990
Requests for copies should go to the address at the bottom of this post,
not to the poster.
PLEASE DO NOT FORWARD TO OTHER BULLETIN BOARDS.
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An Implementation of
Back-Propagation Learning on GF11,
a Large SIMD Parallel Computer
Michael Witbrock and Marco Zagha
Carnegie Mellon University
CMU-CS-89-208
December 1989
Current connectionist simulations require huge computational
resources. We describe a neural network simulator for the IBM GF11,
an experimental SIMD machine with 566 processors and a peak
arithmetic
performance of 11 Gigaflops. We present our parallel
implementation of
the backpropagation learning algorithm, techniques for increasing
efficiency, performance measurements on the NetTalk
text-to-speech benchmark, and a performance model for the simulator.
Our simulator currently runs the back-propagation learning algorithm
at 900 million connections per second, where each ``connection per
second'' includes both a forward and backward pass. This figure was
obtained on the machine when only 356 processors were working; with
all 566 processors operational, our simulation will run at over one
billion connections per second. We conclude that the GF11 is
well-suited to neural network simulation, and we analyze our use of
the machine to determine which features are the most important for
high performance.
=========================================================================
====
PLEASE DO NOT FORWARD TO OTHER BULLETIN BOARDS.
Requests for copies should go to:
C Copetas
School of Computer Science
Carnegie Mellon University
Pittsburgh PA 15213-3890 USA
or to copetas at cs.cmu.edu
The technical report deals with implementation issues for fast parallel
connectionist
simulators. It may not be of any great interest to anyone not working in
this area.
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P.S. This TR is in postscript. Could the person who runs the ftp archive
tell
me how to go about submitting it? Thanks.
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