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Toward Efficient Many-core Scheduling of Partial Expansion Graphs

Hai Nam Tran 1, 2 Shuvra Bhattacharyya 3 Jean-Pierre Talpin 2 Thierry Gautier 2
IBNM - Institut Brestois du Numérique et des Mathématiques, Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
2 TEA - Tim, Events and Architectures
Inria Rennes – Bretagne Atlantique , IRISA-D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : Transformation of synchronous data flow graphs (SDF) into equivalent homogeneous SDF representations has been extensively applied as a pre-processing stage when mapping signal processing algorithms onto parallel platforms. While this transformation helps fully expose task and data parallelism, it also presents several limitations such as an exponential increase in the number of actors and excessive communication overhead. Partial expansion graphs were introduced to address these limitations for multi-core platforms. However, existing solutions are not well-suited to achieve efficient scheduling on many-core architectures. In this article, we develop a new approach that employs cyclo-static data flow techniques to provide a simple but efficient method of coordinating the data production and consumption in the expanded graphs. We demonstrate the advantage of our approach through experiments on real application models.
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Contributor : Jean-Pierre Talpin <>
Submitted on : Tuesday, December 4, 2018 - 4:27:15 PM
Last modification on : Wednesday, August 5, 2020 - 3:46:31 AM
Long-term archiving on: : Tuesday, March 5, 2019 - 12:21:10 PM


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Hai Nam Tran, Shuvra Bhattacharyya, Jean-Pierre Talpin, Thierry Gautier. Toward Efficient Many-core Scheduling of Partial Expansion Graphs. SCOPES 2018 - 21st International Workshop on Software and Compilers for Embedded Systems, May 2018, Saint Goar, Germany. pp.1-4, ⟨10.1145/3207719.3207734⟩. ⟨hal-01926955⟩



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