Publication: Expression templates for primal value taping in the reverse mode of algorithmic differentiation
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Expression templates for primal value taping in the reverse mode of algorithmic differentiation

- Article in a journal -
 

Area
General

Author(s)
Max Sagebaum , Tim Albring , Nicolas R. Gauger

Published in
Special issue of Optimization Methods & Software: Advances in Algorithmic Differentiation Optimization Methods & Software

Editor(s)
Bruce Christianson, Shaun A. Forth, Andreas Griewank

Year
2018

Publisher
Taylor & Francis

Abstract
The reverse mode of Algorithmic Differentiation (ad) can be implemented in several ways. The major choices are primal value taping vs. Jacobian taping, managed indices vs. unmanaged indices and operator level taping vs. statement level taping. Most of the current ad tools have implemented only one of the eight possible choices, and the data management of the implementation adds another complexity hierarchy. The focus in this paper is the implementation of primal value taping on a statement level. Statement level taping removes the need to create intermediate values on the ad tape which results in reduced memory compared to operator level taping. The implementation will be done for managed and unmanaged indices in the ad tool CoDiPack. Primal value taping with statement level taping has not yet been implemented in any other ad tool, thus we will analyse the properties of the taping approaches and highlight the important details for an efficient implementation. Furthermore, all existing taping approaches in CoDiPack will be compared with the new primal value taping approach. The comparison have been conducted on a simple toy problem and a fully featured computational fluid dynamics solver in the multi-physics suite SU2.

Cross-References
Christianson2018Sio

AD Tools
CoDiPack

AD Theory and Techniques
Adjoint, Black Box, Code Optimization, Implementation Strategies, Performance, Reverse Mode

BibTeX
@ARTICLE{
         Sagebaum2018Etf,
       title = "Expression templates for primal value taping in the reverse mode of algorithmic
         differentiation",
       author = "Max Sagebaum and Tim Albring and Nicolas R. Gauger",
       url = "https://doi.org/10.1080/10556788.2018.1471140",
       year = "2018",
       date = "2018-05-18",
       journal = "Optimization Methods \& Software",
       ad_area = "General",
       ad_tools = "CoDiPack",
       ad_theotech = "Adjoint, Black Box, Code Optimization, Implementation Strategies, Performance,
         Reverse Mode",
       crossref = "Christianson2018Sio",
       volume = "33",
       number = "4--6",
       pages = "1207--1231",
       publisher = "Taylor \& Francis",
       doi = "10.1080/10556788.2018.1471140",
       eprint = "https://doi.org/10.1080/10556788.2018.1471140",
       abstract = "The reverse mode of Algorithmic Differentiation (AD) can be implemented in several
         ways. The major choices are primal value taping vs. Jacobian taping, managed indices vs. unmanaged
         indices and operator level taping vs. statement level taping. Most of the current AD tools have
         implemented only one of the eight possible choices, and the data management of the implementation
         adds another complexity hierarchy. The focus in this paper is the implementation of primal value
         taping on a statement level. Statement level taping removes the need to create intermediate values
         on the AD tape which results in reduced memory compared to operator level taping. The implementation
         will be done for managed and unmanaged indices in the AD tool CoDiPack. Primal value taping with
         statement level taping has not yet been implemented in any other AD tool, thus we will analyse the
         properties of the taping approaches and highlight the important details for an efficient
         implementation. Furthermore, all existing taping approaches in CoDiPack will be compared with the
         new primal value taping approach. The comparison have been conducted on a simple toy problem and a
         fully featured computational fluid dynamics solver in the multi-physics suite SU2.",
       booktitle = "Special issue of Optimization Methods \& Software: Advances in
         Algorithmic Differentiation",
       editor = "Bruce Christianson and Shaun A. Forth and Andreas Griewank"
}


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