Classification of General Data Flow Actors into Known Models of Computation

Abstract

Applications in the signal processing domain are often modeled by data flow graphs which contain both dynamic and static data flow actors due to heterogeneous complexity requirements. Thus, the adopted notation to model the actors must be expressive enough to accommodate dynamic data flow actors. On the other hand, treating static data flow actors like dynamic ones hinders design tools in applying domain-specific optimization methods to static parts of the model, e.g., static scheduling. In this paper, we present a general notation and a methodology to classify an actor expressed by means of this notation into the synchronous and cyclo-static dataflow models of computation. This enables the use of a unified descriptive language to express the behavior of actors while still retaining the advantage to apply domain-specific optimization methods to parts of the system. In experiments we could improve both latency and throughput of a general data flow graph application using our proposed automatic classification in combination with a static single-processor scheduling approach by 57%.