Shuvra S. Bhattacharyya, Sundararajan Sriram, and Edward A. Lee

ERL Technical Report UCB/ERL M95/2, University of California, Berkeley, CA 94720
January 5, 1995



This paper is concerned with multiprocessor implementations of embedded applications specified as iterative dataflow programs, in which synchronization overhead tends to be significant. We develop techniques to alleviate this overhead by determining a minimal set of processor synchronizations that are essential for correct execution. Our study is based in the context of self-timed execution of iterative dataflow programs. An iterative dataflow program consists of a dataflow representation of the body of a loop that is to be iterated an indefinite number of times; dataflow programming in this form has been studied and applied extensively, particularly in the context of signal processing software. Self-timed execution refers to a combined compile-time/ run-time scheduling strategy in which processors synchronize with one another only based on inter-processor communication requirements, and thus, synchronization of processors at the end of each loop iteration does not generally occur.

We introduce a new graph-theoretic framework for analyzing and optimizing synchronization overhead in self-timed, iterative dataflow programs. This framework is based on a data structure, which we call the inter-processor communication (IPC) graph, that was first proposed in [1] for analyzing the throughput of self-timed systems. We show that the comprehensive techniques that have been developed for removing redundant synchronizations in non-iterative programs can be extended in this framework to optimally remove redundant synchronizations in our context. We also introduce two new optimizations for reducing synchronization overhead in self-timed, iterative dataflow programs Q resynchronization and the conversion of the synchronization graph into a strongly connected graph.

1S. Sriram and E. A. Lee, "Statically Scheduling Communication Resources in Multiprocessor DSP architectures," Proceedings of the Asilomar Conference on Signals, Systems, and Computers, November, 1994.