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Transaction Parameterized Dataflow: A Model for Context-Dependent Streaming Applications

Xuan Do 1 Stephane Louise 1 Albert Cohen 2
2 Parkas - Parallélisme de Kahn Synchrone
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : Static dataflow programming models are well suited to the development of embedded many-core systems. However, complex signal and media processing applications often display dynamic behavior that do not fit the classical static restrictions. We propose Transaction Parameterized Dataflow (TPDF), a new model of computation combining integer parameters—to express dynamic rates—and a new type of control actor—to allow topology changes and time constraints enforcement. We present static analyses for liveness and bounded memory usage. We also introduce a static scheduling heuristic to map TPDF to massively parallel embedded platforms. We validate the model and associated methods using a cognitive radio application, demonstrating significant buffer size and performance improvements compared to state of the art models including Cyclo-Static Dataflow (CSDF).
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Contributor : Albert Cohen <>
Submitted on : Wednesday, January 4, 2017 - 1:48:31 AM
Last modification on : Tuesday, September 22, 2020 - 3:45:40 AM
Long-term archiving on: : Wednesday, April 5, 2017 - 1:24:24 PM


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  • HAL Id : hal-01425902, version 1



Xuan Do, Stephane Louise, Albert Cohen. Transaction Parameterized Dataflow: A Model for Context-Dependent Streaming Applications. Design, Automation & Test in Europe Conference & Exhibition (DATE), Mar 2016, Dresden, Germany. ⟨hal-01425902⟩



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