Abstract
In the development of efficient parallel applications, reliable performance predictions are essential. However, many performance modelling formalisms, such as queueing networks, are not directly suitable for modelling parallel applications, while for other formalisms the analysis is too expensive. We present a methodology for performance modelling of parallel processing systems (Glamis), based on extended queueing networks, aiming to overcome these problems. The methodology yields reliable performance predictions for a class of parallel machines and programs at relatively low (polynomial time) analysis cost. Additional reductions of analysis cost are obtained by exploiting inherent replications in parallel systems.KeywordsCompletion TimeTask GraphParallel SectionMemory BankDelay CentreThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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