Static and Quasi-static Compositions of Stream Processing Applications from Dynamic Dataflow Programs

Dynamic dataflow models for their expressiveness properties have shown to represent more adequate and attractive solutions for describing state of the art signal processing applications. However, they are known to present potential run-time penalties when implementations are obtained by mapping and scheduling a dataflow network partition on a processing unit. In general terms, a completely static scheduling at compile-time of dynamic dataflow programs remains an unsolved problem. Several approaches for the composition of actors are promising approach that can significantly reduce the potential penalty of run-time scheduling thus increasing the overall performance of the system. This paper presents static and quasi-static composition techniques that results in a reduction of the portion of dynamic dataflow networks, by applying appropriate transformations to network partitions that after a specific analysis demonstrate to possess a predictable behaviour. Some experiments based on a video processing application ported on several system-on-chips show the achievable speedup corresponding to the reduction of the number of run-time scheduling decisions.