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

Conference · Wed Nov 01 00:00:00 EST 2000

OSTI ID:768594

Abate, J; Benson, S; Grignon, L; Hovland, P; McInnes, L; Norris, B

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.

• Research Organization: Argonne National Lab., IL (US)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: W-31109-ENG-38
• OSTI ID: 768594
• Report Number(s): ANL/MCS/CP-103184; TRN: US200304%%204
• Resource Relation: Conference: 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; PBD: 1 Nov 2000
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ALGORITHMS
MATRICES
OPTIMIZATION
PERFORMANCE
SIMULATION
VECTORS