Integrating automatic differentiation with object-oriented toolkits for high-performance scientific computing.

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

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Abate, J.; Benson, S.; Grignon, L.; Hovland, P.; McInnes, L. & Norris, B. November 1, 2000.

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

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  • 3rd International Conference/Workshop on Automatic Differentiation: From Simulation to Optimization, Nice (FR), 06/19/2000--06/23/2000; Other Information: PBD: 1 Nov 2000; PBD: 1 Nov 2000

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  • Report No.: ANL/MCS/CP-103184
  • Grant Number: W-31109-ENG-38
  • Office of Scientific & Technical Information Report Number: 768594
  • Archival Resource Key: ark:/67531/metadc720779

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  • November 1, 2000

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  • Sept. 29, 2015, 5:31 a.m.

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  • March 25, 2016, 11:57 a.m.

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Abate, J.; Benson, S.; Grignon, L.; Hovland, P.; McInnes, L. & Norris, B. Integrating automatic differentiation with object-oriented toolkits for high-performance scientific computing., article, November 1, 2000; Illinois. (digital.library.unt.edu/ark:/67531/metadc720779/: accessed November 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.