BibTeX
@INCOLLECTION{
         Hascoet2002AIC,
       author = "Laurent Hasco{\"e}t and Stefka Fidanova and Christophe Held",
       title = "Adjoining Independent Computations",
       pages = "299--304",
       abstract = "The {\em reverse} or {\em adjoint} mode of automatic differentiation is a
         software engineering technique that permits efficient computation of gradients. However, this
         technique requires a lot of temporary memory. In this paper, we present a refinement that reduces
         memory consumption in the case of parallel loops, and we give a proof of its correctness based on
         properties of the {\em data-dependence graph} of adjoint programs and parallel loops. This
         technique is particularly suitable for assembly loops that dominate in mesh-based computations.
         Application is done on the kernel of a realistic Navier-Stokes solver.",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "TAPENADE",
       ad_theotech = "Reverse Mode",
       chapter = "35",
       crossref = "Corliss2002ADo",
       booktitle = "Automatic Differentiation of Algorithms: From Simulation to Optimization",
       year = "2002",
       editor = "George Corliss and Christ{\`e}le Faure and Andreas Griewank and Laurent
         Hasco{\"e}t and Uwe Naumann",
       series = "Computer and Information Science",
       publisher = "Springer",
       address = "New York, NY",
       referred = "[Mancini2002APH], [Soulie2002EPR]."
}