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Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing (1987)
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| Venue: | IEEE TRANSACTIONS ON COMPUTERS |
| Citations: | 587 - 37 self |
BibTeX
@ARTICLE{Lee87staticscheduling,
author = {Edward Ashford Lee and David G. Messerschmitt},
title = {Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing},
journal = {IEEE TRANSACTIONS ON COMPUTERS},
year = {1987},
volume = {36},
number = {1},
pages = {24--35}
}
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Abstract
Large grain data flow (LGDF) programming is natural and convenient for describing digital signal processing (DSP) systems, but its runtime overhead is costly in real time or cost-sensitive applications. In some situations, designers are not willing to squander computing resources for the sake of program-mer convenience. This is particularly true when the target machine is a programmable DSP chip. However, the runtime overhead inherent in most LGDF implementations is not required for most signal processing systems because such systems are mostly synchronous (in the DSP sense). Synchronous data flow (SDF) differs from traditional data flow in that the amount of data produced and consumed by a data flow node is specified a priori for each input and output. This is equivalent to specifying the relative sample rates in signal processing system. This means that the scheduling of SDF nodes need not be done at runtime, but can be done at compile time (statically), so the runtime overhead evaporates. The sample rates can all be different, which is not true of most current data-driven digital signal processing programming methodologies. Synchronous data flow is closely related to computation graphs, a special case of Petri nets. This self-contained paper develops the theory necessary to statically schedule SDF programs on single or multiple proces-sors. A class of static (compile time) scheduling algorithms is proven valid, and specific algorithms are given for scheduling SDF systems onto single or multiple processors.
Keyphrases
digital signal processing static scheduling synchronous data flow program synchronous data flow signal processing system compile time runtime overhead inherent sample rate self-contained paper dsp sense special case data flow node cost-sensitive application sdf system programmable dsp chip target machine lgdf implementation computation graph large grain data flow real time runtime overhead multiple proces-sors relative sample rate multiple processor runtime overhead evaporates petri net sdf node specific algorithm program-mer convenience current data-driven digital signal processing traditional data flow sdf program
