Probabilistic Application Modeling for System-Level Performance Analysis
SVM HeaderParse 0.2
The objective of this paper is to introduce the Stochastic Automata Networks (SANs) as an effective formalism for application modeling in system-level analysis. More precisely, we present a methodology for application modeling for system-level power/performance analysis that can help the designer to select the right platform and implement a set of target multimedia applications. We also show that, under various input traces, the steady-state behavior of the application itself is characterized by very different ‘clusterings’ of the probability distributions. Having this information available, not only helps to avoid lengthy profiling simulations for predicting power and performance figures, but also enables efficient mappings of the applications onto a chosen platform. We illustrate the benefits of our methodology using the MPEG-2 video decoder as the driver application.