Abstract
A high-level Petri net model of the software architecture of an experimental MIMD multiprocessor system for Artificial Intelligence applications is derived by direct translation of the code corresponding to the assumed workload. Hardware architectural constraints are then easily added, and formal reduction rules are used to simplify the model, which is then further approximated to obtain a performance model of the system based on generalized stochastic Petri nets. From the latter model it is possible to estimate the optimal multiprogramming level of each processor so as to achieve the maximum performance in terms of overall throughput (number of tasks completed per unit time).
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