Analyzing the Real - Time Properties of a Data

Real-time signal processing applications are commonly designed using a dataaow software architecture. Here we attempt to understand fundamental real-time properties of such an architecture | the Navy's coarse-grain Processing Graph Method (PGM). By applying recent results in real-time scheduling theory to the subset of PGM employed by the ARPA RASSP Synthetic Aperture Radar benchmark application , we identify inherent real-time properties of nodes in a PGM dataaow graph, and demonstrate how these properties can be exploited to perform useful and important system-level analyses such as schedulability analysis , end-to-end latency analysis, and memory requirements analysis. More importantly, we develop relationships between properties such as latency and buuer bounds and show how one may be traded-oo for the other. Our results assume only the existence of a simple EDF scheduler and thus can be easily applied in practice.