ABSTRACT Title of thesis: REPRESENTATION AND SCHEDULING OF SCALABLE DATAFLOW GRAPH TOPOLOGIES

In dataflow-based application models, the underlying graph representations often consist of smaller sub-structures that repeat multiple times. In order to en-able concise and scalable specification of digital signal processing (DSP) systems, a graphical modeling construct called “topological pattern ” has been introduced in recent work [23]. In this thesis, we present new design capabilities for specifying and work-ing with topological patterns in the dataflow interchange format (DIF) framework, which is a software tool for model-based design and implementation of signal process-ing systems. We also present a plug-in to the DIF framework for deriving parameter-ized schedules, and a code generation module for generating code that implements these schedules. A novel schedule model called the scalable schedule tree (SST) is formulated. The SST model represents an important class of parameterized schedule structures in a form that is intuitive for representation, efficient for code generation, and flexible to support powerful forms of adaptation. We demonstrate our meth-Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information