Paper Info
Title
Classification and transformation of dynamic dataflow programs
Abstract
Dataflow programming has been used to describe signal processing applications for many years, traditionally with cyclostatic dataflow (CSDF) or synchronous dataflow (SDF) models that restrict expressive power in favor of compile-time analysis and predictability. Dynamic dataflow is not restricted with respect to expressive power, but it does require runtime scheduling in the general case. Fortunately, most signal processing applications are far from being entirely dynamic, and parts with static behavior need not be dynamically scheduled. This paper presents a method to automatically analyze and classify blocks of a dynamic dataflow program within more restrictive dataflow models when possible, and to transform the blocks classified as static to improve execution speed by reducing the number of FIFO accesses. We used this method on actors of two dynamic dataflow descriptions of an MPEG-4 part 2 decoder, and study how classification and transformation increases decoding speed.
Year
DOI
Venue
2010
10.1109/DASIP.2010.5706280
Design and Architectures for Signal and Image Processing
Keywords
Field
DocType
data flow analysis,pattern classification,signal processing,compile time analysis,cyclo static dataflow,dataflow programming,dynamic dataflow program,signal processing,synchronous dataflow,Abstract Interpretation,Classification,Dataflow Programming,RVC-CAL
Programming language,Signal programming,Dataflow architecture,FIFO (computing and electronics),Computer science,Abstract interpretation,Parallel computing,Data-flow analysis,Real-time computing,Dataflow,Dataflow programming,Dynamic priority scheduling
Conference
ISBN
Citations 
PageRank 
978-1-4244-8733-2
23
1.43
References 
Authors
14
2
Name
Order
Citations
PageRank
Matthieu Wipliez124118.36
Mickaël Raulet237039.15