  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.
