Mapping precedence and communication relations of a large scale computation on a multiprocessor system

The authors present an efficient algorithm for mapping m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p homogeneous processors with processor and communication costs to achieve minimum computation time. Minimax optimization is performed on the objective function to obtain the best mapping. Experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithm. Computer simulation and experimental results are compared and discussed