Hierarchical static scheduling of dataflow graphs onto multiple processors

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 technique that reduces the number of actors before expanding the SDF graph into an directed acyclic graph (DAG). 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 authors illustrate this framework with a real-time example that has been constructed in Ptolemy