|
|
Development and First Applications of TAC++-
incollection
- | |
|
Author(s)
Michael Voßbeck
, Ralf Giering
, Thomas Kaminski
|
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 The paper describes the development of the software tool Transformation of Algorithms in C++ (TAC++) for automatic differentiation (ad) of C(++) codes by source-to-source translation. We have transferred to TAC++ a subset of the algorithms from its well-established Fortran equivalent, Transformation of Algorithms in Fortran (TAF). TAC++ features forward and reverse as well as scalar and vector modes of ad. Efficient higher order derivative code is generated by multiple application of TAC++. High performance of the generated derivate code is demonstrated for five examples from application fields covering remote sensing, computer vision, computational finance, and aeronautics. For instance, the run time of the adjoints for simultaneous evaluation of the function and its gradient is between 1.9 and 3.9 times slower than that of the respective function codes. Options for further enhancement are discussed. |
Cross-References Bischof2008AiA |
AD Tools TAC++ |
BibTeX
@INCOLLECTION{
Vossbeck2008DaF,
author = "Michael Vo{\ss}beck and Ralf Giering and Thomas Kaminski",
title = "Development and First Applications of {TAC++}",
doi = "10.1007/978-3-540-68942-3_17",
abstract = "The paper describes the development of the software tool Transformation of
Algorithms in C++ (TAC++) for automatic differentiation (AD) of C(++) codes by source-to-source
translation. We have transferred to TAC++ a subset of the algorithms from its well-established
Fortran equivalent, Transformation of Algorithms in Fortran (TAF). TAC++ features forward and
reverse as well as scalar and vector modes of AD. Efficient higher order derivative code is
generated by multiple application of TAC++. High performance of the generated derivate code is
demonstrated for five examples from application fields covering remote sensing, computer vision,
computational finance, and aeronautics. For instance, the run time of the adjoints for simultaneous
evaluation of the function and its gradient is between 1.9 and 3.9 times slower than that of the
respective function codes. Options for further enhancement are discussed.",
crossref = "Bischof2008AiA",
pages = "187--197",
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 = "TAC++"
}
| |
back
|
|