Analyzing the real-time properties of a dataflow execution paradigm using a synthetic aperture radar application

Abstract

Real-time signal processing applications are commonly designed using a data flow software architecture. The author attempts 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, he identifies inherent real-time properties of nodes in a PGM data flow graph, and demonstrates 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, he develops relationships between properties such as latency and buffer bounds and show how one may be traded-off for the other. The results assume only the existence of a simple EDF scheduler and thus can be easily applied in practice.