Data Flow Computation Models

In the previous chapters, we justified our choice for data flow as the basis of a model of computation for embedded real-time streaming applications running on a multiprocessor. There are many flavors of data flow. The ones that are interesting for our stated goals are mostly the variants that exhibit behavior which is independent of data values, because of their analytical properties and the variants with deterministic, data value dependent behavior, because of their expressivity. In this chapter, we present notation for data flow models that we will use throughout the book, and the properties of several data flow computation models that are relevant to our stated goal. This is reference material. It can, for the most, be found elsewhere in the literature [10, 57, 80, 81].

[1]  Edward A. Lee,et al.  Scheduling dynamic dataflow graphs with bounded memory using the token flow model , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  Jean A. Peperstraete,et al.  Cycle-static dataflow , 1996, IEEE Trans. Signal Process..

[3]  Edward A. Lee,et al.  Code generation by using integer-controlled dataflow graph , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Raymond Reiter,et al.  Scheduling Parallel Computations , 1968, J. ACM.

[5]  Twan Basten,et al.  Task-level timing models for guaranteed performance in multiprocessor networks-on-chip , 2003, CASES '03.

[6]  Shuvra S. Bhattacharyya,et al.  Embedded Multiprocessors: Scheduling and Synchronization , 2000 .

[7]  E.A. Lee,et al.  Synchronous data flow , 1987, Proceedings of the IEEE.

[8]  Edward A. Lee,et al.  Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing , 1989, IEEE Transactions on Computers.

[9]  Ali Dasdan,et al.  Experimental analysis of the fastest optimum cycle ratio and mean algorithms , 2004, TODE.