An Analytical Approach to the Performance Evaluation of Master-Slave Computational Models

Many stochastic models and analysis techniques have been proposed in the literature during the last two decades for the performance evaluation of parallel and distributed systems. However few of them are directly applicable to practical systems which are generally too complex. In this paper we analyze performances of the master-slave computational model, one of the most commonly used models in parallel and distributed computations. We propose a hybrid analytical approach by using techniques from the theories of both stochastic task graphs and queueing networks. We apply this method to the analysis of a computational chemistry application running on a Transputer based system. The proposed method turns out to be not only very eecient in time for large systems but also very accurate compared to measuring.