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Adjoining Independent Computations

- incollection -
 

Area
Computational Fluid Dynamics

Author(s)
Laurent HascoŽt , Stefka Fidanova , Christophe Held

Published in
Automatic Differentiation of Algorithms: From Simulation to Optimization

Editor(s)
George Corliss, Christèle Faure, Andreas Griewank, Laurent Hascoët, Uwe Naumann

Year
2002

Publisher
Springer

Abstract
The reverse or adjoint mode of automatic differentiation is a software engineering technique that permits efficient computation of gradients. However, this technique requires a lot of temporary memory. In this paper, we present a refinement that reduces memory consumption in the case of parallel loops, and we give a proof of its correctness based on properties of the data-dependence graph of adjoint programs and parallel loops. This technique is particularly suitable for assembly loops that dominate in mesh-based computations. Application is done on the kernel of a realistic Navier-Stokes solver.

Cross-References
Corliss2002ADo

AD Tools
TAPENADE

AD Theory and Techniques
Reverse Mode

BibTeX
@INCOLLECTION{
         Hascoet2002AIC,
       author = "Laurent Hasco{\"e}t and Stefka Fidanova and Christophe Held",
       title = "Adjoining Independent Computations",
       pages = "299--304",
       abstract = "The {\em reverse} or {\em adjoint} mode of automatic differentiation is a
         software engineering technique that permits efficient computation of gradients. However, this
         technique requires a lot of temporary memory. In this paper, we present a refinement that reduces
         memory consumption in the case of parallel loops, and we give a proof of its correctness based on
         properties of the {\em data-dependence graph} of adjoint programs and parallel loops. This
         technique is particularly suitable for assembly loops that dominate in mesh-based computations.
         Application is done on the kernel of a realistic Navier-Stokes solver.",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "TAPENADE",
       ad_theotech = "Reverse Mode",
       chapter = "35",
       crossref = "Corliss2002ADo",
       booktitle = "Automatic Differentiation of Algorithms: From Simulation to Optimization",
       year = "2002",
       editor = "George Corliss and Christ{\`e}le Faure and Andreas Griewank and Laurent
         Hasco{\"e}t and Uwe Naumann",
       series = "Computer and Information Science",
       publisher = "Springer",
       address = "New York, NY",
       referred = "[Mancini2002APH], [Soulie2002EPR]."
}


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