Publication: Development and First Applications of TAC++
Introduction
Applications
Tools
Research Groups
Workshops
Publications
   List Publications
   Advanced Search
   Info
   Add Publications
My Account
About

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
  

Contact:
autodiff.org
Username:
Password:
(lost password)