Publication: Hierarchical Algorithmic Differentiation A Case Study
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Hierarchical Algorithmic Differentiation A Case Study

- incollection -
 

Author(s)
Johannes Lotz , Uwe Naumann , Jörn Ungermann

Published in
Recent Advances in Algorithmic Differentiation

Editor(s)
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther

Year
2012

Publisher
Springer

Abstract
This case study in Algorithmic Differentiation (ad) discusses the semi-automatic generation of an adjoint simulation code in the context of an inverse atmospheric remote sensing problem. In-depth structural and performance analyses allow for the run time factor between the adjoint generated by overloading in C++ and the original forward simulation to be reduced to 3. 5. The dense Jacobian matrix of the underlying problem is computed at the same cost. This is achieved by a hierarchical ad using adjoint mode locally for preaccumulation and by exploiting interface contraction. For the given application this approach yields a speed-up over black-box tangent-linear and adjoint mode of more than 170. Furthermore, the memory consumption is reduced by a factor of 1,000 compared to applying black-box adjoint mode.

Cross-References
Forth2012RAi

AD Theory and Techniques
Hierarchical Approach

BibTeX
@INCOLLECTION{
         Lotz2012HAD,
       title = "Hierarchical Algorithmic Differentiation A Case Study",
       doi = "10.1007/978-3-642-30023-3_17",
       author = "Johannes Lotz and Uwe Naumann and J{\"o}rn Ungermann",
       abstract = "This case study in Algorithmic Differentiation (AD) discusses the semi-automatic
         generation of an adjoint simulation code in the context of an inverse atmospheric remote sensing
         problem. In-depth structural and performance analyses allow for the run time factor between the
         adjoint generated by overloading in C++ and the original forward simulation to be reduced to 3. 5.
         The dense Jacobian matrix of the underlying problem is computed at the same cost. This is achieved
         by a hierarchical AD using adjoint mode locally for preaccumulation and by exploiting interface
         contraction. For the given application this approach yields a speed-up over black-box tangent-linear
         and adjoint mode of more than 170. Furthermore, the memory consumption is reduced by a factor of
         1,000 compared to applying black-box adjoint mode.",
       pages = "187--196",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_theotech = "Hierarchical Approach"
}


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