An Empirical Evaluation of a Programming Model for Context-Dependent Real-time Streaming Applications

We present a first evaluation of a Programming Model for real-time streaming applications on high performance embedded multi- and many-core systems. Realistic streaming applications are highly dependent on the execution context (usually of physical world), past learned strategies, and often real-time constraints. The proposed Programming Model encompasses both real- time requirements, determinism of execution and context dependency. It is an extension of the well-known Cyclo-Static Dataflow (CSDF), for its desirable properties (determinism and composability), with two new important data-flow filters: Select-duplicate, and Transaction which retain the main properties of CSDF graphs and also provide useful features to implement real-time computational embedded applications. We evaluate the relevance of our programming model thanks to several real-life case-studies and demonstrate that our approach overcomes a range of limitations that use to be associated with CSDF models