A Hierarchical Multiprocessor Scheduling Framework for Synchronous Dataflow Graphs

This paper discusses a hierarchical scheduling framework to reduce the complexity of scheduling synchronous dataflow (SDF) graphs onto multiple processors. The core of this framework is a clustering algorithm that reduces the number of nodes before expanding the SDF graph into a precedence DAG (directed acyclic graph). The internals of the clusters are then scheduled with uniprocessor SDF schedulers which can optimize for memory usage. The clustering is done in such a manner as to leave ample parallelism exposed for the multiprocessor scheduler. The advantages of this framework are demonstrated with several practical, real-time examples.