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Partial expansion graphs: Exposing parallelism and dynamic scheduling opportunities for DSP applications

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 23rd International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2012
Pages86-93
Number of pages8
DOIs
Publication statusPublished - 2012
Publication typeA4 Article in a conference publication
Event2012 IEEE 23rd International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2012 - Delft, Netherlands
Duration: 9 Jul 201211 Jul 2012

Conference

Conference2012 IEEE 23rd International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2012
CountryNetherlands
CityDelft
Period9/07/1211/07/12

Abstract

Emerging Digital Signal Processing (DSP) algorithms and wireless communications protocols require dynamicadaptation and online reconfiguration for the implementedsystems at runtime. In this paper, we introduce the conceptof Partial Expansion Graphs (PEGs) as an implementationmodel and associated class of scheduling strategies. PEGsare designed to help realize DSP systems in terms of formsand granularities of parallelism that are well matched to thegiven applications and targeted platforms. PEGs also facilitatederivation of both static and dynamic scheduling techniques,depending on the amount of variability in task execution timesand other operating conditions. We show how to implementefficient PEG-based scheduling methods using real time operating systems, and to re-use pre-optimized libraries of DSPcomponents within such implementations. Empirical resultsshow that the PEG strategy can 1) achieve significant speedupson a state of the art multicore signal processor platform forstatic dataflow applications with predictable execution times,and 2) exceed classical scheduling speedups for applicationshaving execution times that can vary dynamically. This abilityto handle variable execution times is especially useful as DSPapplications and platforms increase in complexity and adaptive behavior, thereby reducing execution time predictability.

Keywords

  • Data?ow Graphs, Digital Signal Processing, Multiprocessor Dynamic Scheduling