Publication: Code Optimization Techniques in Source Transformations for Interpreted Languages
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Code Optimization Techniques in Source Transformations for Interpreted Languages

- incollection -
 

Author(s)
H. Martin Bücker , Monika Petera , Andre Vehreschild

Published in
Advances in Automatic Differentiation

Editor(s)
Christian H. Bischof, H. Martin Bücker, Paul D. Hovland, Uwe Naumann, J. Utke

Year
2008

Publisher
Springer

Abstract
A common approach to implement automatic differentiation (ad) is based on source-to-source transformation. In contrast to the standard case in mathematical software that is concerned with compiled languages, ad for interpreted languages is considered. Here, techniques to improve code performance are introduced in transformations on a high-level rather than by an optimizing compiler carrying out these transformations on a lower-level intermediate representation. The languages MATLAB and CapeML are taken as examples to demonstrate these issues and quantify performance differences of codes generated by the ad tools ADiMat and ADiCape using the five code optimization techniques constant folding, loop unrolling, constant propagation, forward substitution, and common subexpression elimination.

Cross-References
Bischof2008AiA

AD Tools
ADiCape, ADiMat

AD Theory and Techniques
Code Optimization, Performance

BibTeX
@INCOLLECTION{
         Bucker2008COT,
       author = "H. Martin B{\"u}cker and Monika Petera and Andre Vehreschild",
       title = "Code Optimization Techniques in Source Transformations for Interpreted Languages",
       doi = "10.1007/978-3-540-68942-3_20",
       abstract = "A common approach to implement automatic differentiation (AD) is based on
         source-to-source transformation. In contrast to the standard case in mathematical software that is
         concerned with compiled languages, AD for interpreted languages is considered. Here, techniques to
         improve code performance are introduced in transformations on a high-level rather than by an
         optimizing compiler carrying out these transformations on a lower-level intermediate representation.
         The languages MATLAB and CapeML are taken as examples to demonstrate these issues and quantify
         performance differences of codes generated by the AD tools ADiMat and ADiCape using the five code
         optimization techniques constant folding, loop unrolling, constant propagation, forward
         substitution, and common subexpression elimination.",
       crossref = "Bischof2008AiA",
       pages = "223--233",
       booktitle = "Advances in Automatic Differentiation",
       publisher = "Springer",
       editor = "Christian H. Bischof and H. Martin B{\"u}cker and Paul D. Hovland and Uwe
         Naumann and J. Utke",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2008",
       ad_tools = "ADiCape, ADiMat",
       ad_theotech = "Code Optimization, Performance"
}


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