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Classification of Dataflow Actors with Satisfiability and Abstract Interpretation

Classification of Dataflow Actors with Satisfiability and Abstract Interpretation

Matthieu Wipliez, Mickaël Raulet
Copyright: © 2012 |Volume: 3 |Issue: 1 |Pages: 21
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781466611993|DOI: 10.4018/jertcs.2012010103
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MLA

Wipliez, Matthieu, and Mickaël Raulet. "Classification of Dataflow Actors with Satisfiability and Abstract Interpretation." IJERTCS vol.3, no.1 2012: pp.49-69. http://doi.org/10.4018/jertcs.2012010103

APA

Wipliez, M. & Raulet, M. (2012). Classification of Dataflow Actors with Satisfiability and Abstract Interpretation. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 3(1), 49-69. http://doi.org/10.4018/jertcs.2012010103

Chicago

Wipliez, Matthieu, and Mickaël Raulet. "Classification of Dataflow Actors with Satisfiability and Abstract Interpretation," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 3, no.1: 49-69. http://doi.org/10.4018/jertcs.2012010103

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Abstract

Dataflow programming has been used to describe signal processing applications for many years, traditionally with cyclo-static dataflow (CSDF) or synchronous dataflow (SDF) models that restrict expressive power in favor of compile-time analysis and predictability. More recently, dynamic dataflow is being used for the description of multimedia video standards as promoted by the RVC standard (ISO/IEC 23001:4). Dynamic dataflow is not restricted with respect to expressive power, but it does require runtime scheduling in the general case, which may be costly to perform on software. The authors presented in a previous paper a method to automatically classify actors of a dynamic dataflow program within more restrictive dataflow models when possible, along with a method to transform the actors classified as static to improve execution speed by reducing the number of FIFO accesses (Wipliez & Raulet, 2010). This paper presents an extension of the classification method using satisfiability solving, and details the precise semantics used for the abstract interpretation of actors. The extended classification is able to classify more actors than what could previously be achieved.

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