Paper Info
Title
Classification of Dataflow Actors with Satisfiability and Abstract Interpretation
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.
Year
DOI
Venue
2012
10.4018/jertcs.2012010103
IJERTCS
Keywords
Field
DocType
dynamic dataflow,dynamic dataflow program,classification method,dataflow actors,extended classification,restrictive dataflow model,abstract interpretation,expressive power,previous paper,cyclo-static dataflow,dataflow programming,synchronous dataflow sdf model
Programming language,Signal programming,Dataflow architecture,FIFO (computing and electronics),Abstract interpretation,Computer science,Scheduling (computing),Theoretical computer science,Dataflow,Dataflow programming,Semantics
Journal
Volume
Issue
Citations 
3
1
7
PageRank 
References 
Authors
0.51
15
2
Name
Order
Citations
PageRank
Matthieu Wipliez124118.36
Mickaël Raulet237039.15