Extending the Monte Carlo Processor Modeling Technique: Statistical Performance Models of the Niagara 2 Processor

With the complexity of contemporary single- and multi-core, multi-threaded processors comes a greater need for faster methods of performance analysis and design. It is no longer practical to use only cycle-accurate processor simulators for design space analysis of modern processors and systems. Therefore, we propose a statistical processor modeling method that is based on Monte Carlo techniques. In this paper, we present new details of the methodology and the recent extensions that we have made to it, including the capability to model multi-core processors. We detail the steps to develop a new model and then present statistical performance models of the Sun Niagara 2 processor micro-architecture that, together with a previously published Itanium 2 Monte Carlo model, demonstrates the validity of the technique and its new capabilities. We show that we can accurately predict single and multi-core performance within 7% of actual on average, and we can use the models to quickly pinpoint performance problems at various components.