research-article

High-Performance Derivative Computations using CoDiPack

Authors:

Nicolas R. GaugerAuthors Info & Claims

ACM Transactions on Mathematical Software (TOMS), Volume 45, Issue 4

Article No.: 38, Pages 1 - 26

https://doi.org/10.1145/3356900

Published: 09 December 2019 Publication History

Abstract

There are several AD tools available that all implement different strategies for the reverse mode of AD. The most common strategies are primal value taping (implemented e.g. by ADOL-C) and Jacobian taping (implemented e.g. by Adept and dco/c++). Particulary for Jacobian taping, recent advances using expression templates make it very attractive for large scale software. However, the current implementations are either closed source or miss essential features and flexibility. Therefore, we present the new AD tool CoDiPack (Code Differentiation Package) in this paper. It is specifically designed for minimal memory consumption and optimal runtime, such that it can be used for the differentiation of large scale software. An essential part of the design of CoDiPack is the modular layout and the recursive data structures which not only allow the efficient implementation of the Jacobian taping approach but will also enable other approaches like the primal value taping or new research ideas. We will finally present the performance values of CoDiPack on a generic PDE example and on the SU2 code.

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Cited By

Wu CZhang Y(2024)Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence ModelsAerospace10.3390/aerospace1107052511:7(525)Online publication date: 27-Jun-2024
https://doi.org/10.3390/aerospace11070525
Zhi HXiao TQin NDeng SLu Z(2024)On-the-Fly Unsteady Adjoint Aerodynamic and Aeroacoustic Optimization MethodAIAA Journal10.2514/1.J064455(1-19)Online publication date: 13-Aug-2024
https://doi.org/10.2514/1.J064455
Naumann U(2024)A Matrix-Free Exact Newton MethodSIAM Journal on Scientific Computing10.1137/23M157017X46:3(A1423-A1440)Online publication date: 2-May-2024
https://doi.org/10.1137/23M157017X
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Index Terms

High-Performance Derivative Computations using CoDiPack
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Automatic differentiation
  2. Mathematical software
    1. Mathematical software performance
2. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software design engineering
      2. Software design tradeoffs

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cover image ACM Transactions on Mathematical Software

ACM Transactions on Mathematical Software Volume 45, Issue 4

December 2019

207 pages

ISSN:0098-3500

EISSN:1557-7295

DOI:10.1145/3375544

Editors:
Zhaojun Bai
University of California at Davis, USA
,
Wolfgang Bangerth
Colorado State University, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

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Association for Computing Machinery

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Publication History

Published: 09 December 2019

Accepted: 01 July 2019

Revised: 01 July 2019

Received: 01 October 2017

Published in TOMS Volume 45, Issue 4

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Enabling Performance Engineering in Hesse and Rhineland-Palatinate
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Cited By

Wu CZhang Y(2024)Flow Topology Optimization at High Reynolds Numbers Based on Modified Turbulence ModelsAerospace10.3390/aerospace1107052511:7(525)Online publication date: 27-Jun-2024
https://doi.org/10.3390/aerospace11070525
Zhi HXiao TQin NDeng SLu Z(2024)On-the-Fly Unsteady Adjoint Aerodynamic and Aeroacoustic Optimization MethodAIAA Journal10.2514/1.J064455(1-19)Online publication date: 13-Aug-2024
https://doi.org/10.2514/1.J064455
Naumann U(2024)A Matrix-Free Exact Newton MethodSIAM Journal on Scientific Computing10.1137/23M157017X46:3(A1423-A1440)Online publication date: 2-May-2024
https://doi.org/10.1137/23M157017X
Singh GRembser JMoneta LLange DVassilev V(2024)Making Likelihood Calculations Fast: Automatic Differentiation Applied to RooFitEPJ Web of Conferences10.1051/epjconf/202429506014295(06014)Online publication date: 6-May-2024
https://doi.org/10.1051/epjconf/202429506014
Becker RSagraloff M(2024)Counting solutions of a polynomial system locally and exactlyJournal of Symbolic Computation10.1016/j.jsc.2023.102222120:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.jsc.2023.102222
Fang LHe P(2024)A duality-preserving adjoint method for segregated Navier–Stokes solversJournal of Computational Physics10.1016/j.jcp.2024.112860503(112860)Online publication date: Apr-2024
https://doi.org/10.1016/j.jcp.2024.112860
Mayer DBeishuizen NPitsch HEconomon TCarrigan T(2024)Automatic adjoint-based design optimization for laminar combustion applicationsFuel10.1016/j.fuel.2024.131751370(131751)Online publication date: Aug-2024
https://doi.org/10.1016/j.fuel.2024.131751
Chen LRottmayer JKusch LGauger NYe Y(2024)Data-driven aerodynamic shape design with distributionally robust optimization approachesComputer Methods in Applied Mechanics and Engineering10.1016/j.cma.2024.117131429(117131)Online publication date: Sep-2024
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Thompson CCadambi Padmanaban UGanapathisubramani BSymon S(2024)The effect of variations in experimental and computational fidelity on data assimilation approachesTheoretical and Computational Fluid Dynamics10.1007/s00162-024-00708-y38:3(431-450)Online publication date: 2-Jul-2024
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Hückelheim JMenon HMoses WChristianson BHovland PHascoët L(2024)A taxonomy of automatic differentiation pitfallsWIREs Data Mining and Knowledge Discovery10.1002/widm.1555Online publication date: 2-Sep-2024
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