Semi-Empirical Multiprocessor Performance Predictions

This paper presents a multiprocessor performance prediction methodology supported by experimental measurements, which predicts the execution time of large application programs on large parallel architectures based on a small set of sample data. We propose a graph model to describe application program behavior. In order to precisely abstract an architecture model for the prediction, important and implicit architecture parameters are obtained by experiments. We focus on performance predictions of application programs in shared-memory and data-parallel architectures. Real world applications are implemented using the shared-memory model on the KSR-1 and using the data-parallel model on the CM-5 for performance measurements and prediction validation. We show that experimental measurements provide strong support for performance predictions on multiprocessors with implicit communications and complex memory systems, such as shared-memory and data-parallel systems, while analytical techniques partially applied in the prediction signiicantly reduce computer simulation and measurement time.