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Current connectionist simulations require huge computational resources. Wedescribe a neural network simulator for the IBM GF11, an experimental SIMDmachine with 566 processors and a peak arithmetic performance of 11 Gigaflops.We present our parallel implementation of the backpropagation learningalgorithm, techniques for increasing efficiency, performance measurements onthe NetTalk text-to-speech benchmark, and a performance model for thesimulator. Our simulator currently runs the back-propagation learning algorithmat 900 million connections per second, where each "connection per second"includes both a forward and backward pass. This figure was obtained on themachine when only 356 processors were working; with all 566 processorsoperational, our simulation will run at over one billion connections persecond. 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 mostimportant for high performance.

An Implementation of Back-Propagation Learning on GF11, a Large SIMD Parallel Computer