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Recomputations in Reverse Mode AD

  • Ralf Giering
  • Thomas Kaminski
Chapter

Abstract

The main challenge of the reverse (or adjoint) mode of automatic differentiation (AD) is providing the accurate values of required variables to the derivative code. We discuss different strategies to tackle this challenge. The ability to generate efficient adjoint code is crucial for handling large scale applications. For challenging applications, efficient adjoint code must provide at least a fraction of the values of required variables through recomputations, but it is essential to avoid unnecessary recomputations. This is achieved by the Efficient Recomputation Algorithm implemented in the Tangent linear and Adjoint Model Compiler and in Transformation of Algorithms in Fortran, which are source-to-source translation AD tools for Fortran programs. We describe the algorithm and discuss possible improvements.

Keywords

Large Scale Application Adjoint Variable Function Code Adjoint Statement Require Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Ralf Giering
  • Thomas Kaminski

There are no affiliations available

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