BibTeX
@INPROCEEDINGS{
         Bucker2003APi,
       title = "Automatic Parallelism in Differentiation of {F}ourier Transforms",
       booktitle = "Proceedings of the 18th ACM Symposium on Applied Computing, Melbourne, Florida,
         USA, March~9--12, 2003",
       publisher = "ACM Press",
       pages = "148--152",
       doi = "http://doi.acm.org/10.1145/952532.952565",
       address = "New York",
       abstract = "For functions given in the form of a computer program, automatic differentiation is
         an efficient technique to accurately evaluate the derivatives of that function. Starting from a
         given computer program, automatic differentiation generates another program for the evaluation of
         the original function and its derivatives in a fully mechanical way. While the efficiency of this
         black box approach is already high as compared to numerical differentiation based on divided
         differences, automatic differentiation can be applied even more efficiently by taking into account
         high-level knowledge about the given computer program. We show that, in the case where the function
         involves a Fourier transform, the degree of parallelism in the program generated by automatic
         differentiation can be increased leading to a rich set of automatic parallelization strategies that
         are not available when employing a black box automatic parallelization approach. Experiments of the
         new automatic parallelization approach are reported on a SunFire~6800 server using up to 20
         processors.",
       ad_theotech = "Parallelism",
       year = "2003",
       author = "H. Martin B{\"u}cker and Bruno Lang and A. Rasch and Christian
         H.~Bischof"
}