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Advances in Automatic Differentiation pp 187–197Cite as

Development and First Applications of TAC++

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Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 64))

Summary

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.

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Author information

Authors and Affiliations

  1. FastOpt, Schanzenstr. 36, 20357, Hamburg, Germany

    Michael Voßbeck, Ralf Giering & Thomas Kaminski

Authors
  1. Michael Voßbeck
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  2. Ralf Giering
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  3. Thomas Kaminski
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Editor information

Editors and Affiliations

  1. Institute for Scientific Computing, RWTH Aachen University, 52056, Aachen, Germany

    Christian H. Bischof  & H. Martin Bücker  & 

  2. Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Ave, Argonne, IL, 60439, USA

    Paul Hovland  & Jean Utke  & 

  3. Software and Tools for Computational Engineering, RWTH Aachen University, 52056, Aachen, Germany

    Uwe Naumann

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© 2008 Springer-Verlag Berlin Heidelberg

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Voßbeck, M., Giering, R., Kaminski, T. (2008). Development and First Applications of TAC++. In: Bischof, C.H., Bücker, H.M., Hovland, P., Naumann, U., Utke, J. (eds) Advances in Automatic Differentiation. Lecture Notes in Computational Science and Engineering, vol 64. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68942-3_17

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