A Scalable Performance Prediction Method for Parallel Neural Network Simulations
نویسندگان
چکیده
A performance prediction method is presented for indicating the performance range of MIMD parallel processor systems for neural network simulations. The total execution time of a parallel application is modeled as the sum of its calculation and communication times. The method is scalable because based on the times measured on one processor and one communication link, the performance, speedup, and ef-ciency can be predicted for a larger processor system. It is validated quantitatively by applying it to two popular neural networks, backprop-agation and the Kohonen self-organizing feature map, decomposed on a GCel-512, a 512 transputer system. Agreement of the model with the measurements is within 9%.
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