BibTeX
@INPROCEEDINGS{
         Bucker2002ELS,
       author = "H. M. B{\"u}cker and B. Lang and A. Rasch and C. H. Bischof and D.~an~Mey",
       title = "Explicit Loop Scheduling in {OpenMP} for Parallel Automatic Differentiation",
       booktitle = "Proceedings of the 16th Annual International Symposium on High Performance
         Computing Systems and Applications, Moncton, NB, Canada, June~16--19, 2002",
       editor = "J. N. Almhana and V. C. Bhavsar",
       pages = "121--126",
       address = "Los Alamitos, CA",
       publisher = "IEEE Computer Society Press",
       doi = "10.1109/HPCSA.2002.1019144",
       url = "http://doi.ieeecomputersociety.org/10.1109/HPCSA.2002.1019144",
       abstract = "Derivatives of almost arbitrary functions can be evaluated efficiently by automatic
         differentiation whenever the functions are given in the form of computer programs in a high-level
         programming language such as Fortran, C, or C++. In contrast to numerical differentiation, where
         derivatives are only approximated, automatic differentiation generates derivatives that are accurate
         up to machine precision. Sophisticated software tools implementing the technology of automatic
         differentiation are capable of automatically generating code for the product of the Jacobian matrix
         and a so-called seed matrix. It is shown how these tools can benefit from concepts of shared memory
         programming to parallelize, in a completely mechanical fashion, the gradient operations associated
         with each statement of the given code. The feasibility of our approach is demonstrated by numerical
         experiments. They were performed with a code that was generated automatically by the Adifor system
         and augmented with OpenMP directives.",
       year = "2002",
       ad_theotech = "Parallelism"
}