Automatic differentiation of C++ codes for large-scale scientific computing

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3994 LNCS - IV, Issue , 2006, Pages 525-532

  Bartlett, Roscoe A ^a   Gay, David M ^a   Phipps, Eric T ^a  

^a SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATION; CODES (SYMBOLS); FINITE ELEMENT METHOD; NATURAL SCIENCES COMPUTING; PROBLEM SOLVING;

AUTOMATIC DIFFERENTIATION (AD); CONVECTION-DIFFUSION PROBLEM; DERIVATIVE COMPUTATIONS; FINITE-ELEMENT DISCRETIZATION;

C (PROGRAMMING LANGUAGE);

EID: 33746628117     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11758549_73     Document Type: Conference Paper

References (10)

1. Abate, J., Benson, S., Grignon, L., Hovland, P.D., McInnes, L.C., Norris, B.: Integrating AD with object-oriented toolkits for high-performance scientific computing. In Corliss, G., Faure, C., Griewank, A., Hascoët, L., Naumann, U., eds.: Automatic Differentiation of Algorithms: From Simulation to Optimization. Computer and Information Science. Springer, New York, NY (2002) 173-178
2. Tijskens, E., Roose, D., Ramon, H., De Baerdemaeker, J.: Automatic differentiation for nonlinear partial differential equations: An efficient operator overloading approach. Numerical Algorithms 30 (2002) 259-301
3. Griewank, A.: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. Number 19 in Frontiers in Appl. Math. SIAM, Philadelphia, PA (2000)
4. Aubert, P., Di Césaré, N., Pironneau, O.: Automatic differentiation in C++ using expression templates and application to a flow control problem. Computing and Visualisation in Sciences 3 (2001) 197-208
5. Gay, D.M.: Semiautomatic differentiation for efficient gradient computations. In Bücker, H.M., Corliss, G., Hovland, P., Naumann, U., Norris, B., eds.: Automatic Differentiation: Applications, Theory, and Tools. Lecture Notes in Computational Science and Engineering. Springer (2005)
6. Kee, R.J., Rupley, F.M., Miller, J.A., Coltrin, M.E., Grcar, J.F., Meeks, E., Moffat, H.K., Lutz, A.E., Dixon-Lewis, G., Smooke, M.D., Warnatz, J., Evans, G.H., Larson, R.S., Mitchell, R.E., Petzold, L.R., Reynolds, W.C., Caracotsios, M., Stewart, W.E., Glarborg, P., Wang, C., Adigun, O., Houf, W.G., Chou, C.P., Miller, S.F., Ho, P., Young, D.J.: CHEMKIN Release 4.0, San Diego, CA. (2004)
7. Bischof, C.H., Carle, A., Khademi, P., Mauer, A.: ADIFOR 2.0: Automatic differentiation of Fortran 77 programs. IEEE Computational Science & Engineering 3(3) (1996) 18-32
8. Utke, J.: OpenAD: Algorithm implementation user guide. Technical Memorandum ANL/MCS-TM-274, Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, 111. (2004)
9. Hascoët, L., Pascual, V.: TAPENADE 2.1 user's guide. Rapport technique 300, INRIA, Sophia-Antipolis (2004)
10. Hughes, T.J.R., Franca, L.P., Hulbert, G.M.: A new finite element formulation for computational fluid dynamics: VIII. the Galerkin/least-squares method for advective-diffusive equations. Computational Methods Applied Mechanics and Engineering 73 (1989) 173-189