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Code Optimization Techniques in Source Transformations for Interpreted Languages-
incollection
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Author(s)
H. Martin Bücker
, Monika Petera
, Andre Vehreschild
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Published in Advances in Automatic Differentiation
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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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