www.Autodiff.org - Publication: Integrating AD with Object-Oriented Toolkits for High-performance Scientific Computing

Integrating AD with Object-Oriented Toolkits for High-performance Scientific Computing

- incollection -

Author(s)
Jason Abate , Steve Benson , Lisa Grignon , Paul D. Hovland , Lois C. McInnes , Boyana Norris

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
Often the most robust and efficient algorithms for the solution of large-scale problems involving nonlinear PDEs and optimization require the computation of derivatives. We examine the use of automatic differentiation (ad) for computing first and second derivatives in conjunction with two parallel toolkits, the Portable, Extensible Toolkit for Scientific Computing (PETSc) and the Toolkit for Advanced Optimization (TAO). We discuss how the use of mathematical abstractions in PETSc and TAO facilitates the use of ad to automatically generate derivative codes and present performance data demonstrating the suitability of this approach.

Cross-References
Corliss2002ADo

AD Theory and Techniques
Toolkits

BibTeX
@INCOLLECTION{
         Abate2002IAw,
       author = "Jason Abate and Steve Benson and Lisa Grignon and Paul D. Hovland and Lois C. McInnes
         and Boyana Norris",
       title = "Integrating {AD} with Object-Oriented Toolkits for High-performance Scientific
         Computing",
       pages = "173--178",
       chapter = "20",
       crossref = "Corliss2002ADo",
       ad_theotech = "Toolkits",
       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",
       abstract = "Often the most robust and efficient algorithms for the solution of large-scale
         problems involving nonlinear PDEs and optimization require the computation of derivatives. We
         examine the use of automatic differentiation (AD) for computing first and second derivatives in
         conjunction with two parallel toolkits, the Portable, Extensible Toolkit for Scientific Computing
         (PETSc) and the Toolkit for Advanced Optimization (TAO). We discuss how the use of mathematical
         abstractions in PETSc and TAO facilitates the use of AD to automatically generate derivative codes
         and present performance data demonstrating the suitability of this approach.",
       publisher = "Springer",
       series = "Computer and Information Science",
       address = "New York, NY",
       comment = "Mathematics and Computer Science Division, Argonne National Laboratory preprint
         ANL/MCS-P820-0500",
       referred = "[Lee2002SAU], [More2002ADT]."
}

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