Compositionality in Synchronous Data Flow

1. Stavros Tripakis Dai Bui Bert Rodiers Edward A. Lee Marc Geilen Compositionality in Synchronous Data Flow Context Preliminary Implementation Profile Synthesis • Model-based design for embedded software: build software starting from high-level models • Synchronous Data Flow (SDF): popular model in embedded systems, signal processing • Ptolemy II: heterogeneous modeling environment that includes hierarchical SDF models Rate Analysis Unfolding SDF Schedule Graph Deadlock Analysis Hierarchical SDF Graphs Test Cases • Motivation: Incremental Compilation • Large Ptolemy models => long time to compile • Incremental compilation: generate code for parts of the model independently from others => modularity • Ongoing Work • Comparing clustering algorithms • Modular performance analysis • Refolding Actor Firing Clustering • Problem: SDF is not Compositional • Abstracting composite SDF into atomic SDF can result to deadlocks during feedback • References • S. Tripakis, D. Bui, B. Rodiers, and E.A. Lee. Compositionality in Synchronous Data Flow: Modular Code Generation from Hi- erarchical SDF Graphs. Technical Report UCB/EECS-2009-143, EECS Department, University of California, Berkeley, Oct 2009. • E.A. Lee and D.G. Messerschmitt. Static scheduling of synchronous data flow programs for digital signal processing. IEEE Trans. Comput., 36(1):24–35, 1987. • R. Lublinerman, C. Szegedy, and S. Tripakis. Modular code generation from synchronous block diagrams: modularity vs. code size. In Principles of Programming Languages – POPL’09, pages 78–89. ACM, January 2009. • J. Falk, J. Keinert, C. Haubelt, J. Teich, and S. Bhattacharyya. A generalized static data flow clustering algorithm for mpsoc scheduling of multimedia applications. In Embedded Software – EMSOFT’08, pages 189–198. ACM, 2008. Greedy Backward Disjoint Clustering • Our Proposal: Non-Monolithic Profiles • Each actor has multiple different firing functions Center for Hybrid and Embedded Software Systems