Performance prediction and tuning on a multiprocessor

This paper presents a methodology for modeling the behavior of a given class of applications executing in real workloads on a particular machine. The methodology is illustrated by modeling the execution of computationally bound, parallel applications running in real workloads on an Alliant FX/80. me model is constructed from real measured data obtained during normal machine operation and is capable of capturing intricate multiple job interactions, such as contention for shared resources. The model is a finitestate, discrete-time Markov model with rewards and costs associated with each state. The model is capable of predicting the distribution of completion times in real workloads for a given application. The predictions are useful in gauging how quickly an application will execute, or in predicting the performance impact of a system change. The model constructed in this study is validated with three separate sets of empirical data. In one validation, the model successfully predicts the effects of operating the machine with one less processor.