BibTeX
@ARTICLE{
         Huckelheim2018Pas,
       crossref = "Christianson2018Sio",
       author = "Jan C. H{\"u}ckelheim and Paul D. Hovland and Michelle M. Strout and
         Jens-Dominik M{\"u}ller",
       title = "Parallelizable adjoint stencil computations using transposed forward-mode algorithmic
         differentiation",
       journal = "Optimization Methods \& Software",
       volume = "33",
       number = "4--6",
       pages = "672--693",
       year = "2018",
       publisher = "Taylor \& Francis",
       doi = "10.1080/10556788.2018.1435654",
       url = "https://www.tandfonline.com/doi/abs/10.1080/10556788.2018.1435654",
       eprint = "https://www.tandfonline.com/doi/pdf/10.1080/10556788.2018.1435654",
       abstract = "Algorithmic differentiation (AD) is a tool for generating discrete adjoint solvers,
         which efficiently compute gradients of functions with many inputs, for example for use in
         gradient-based optimization. AD is often applied to large computations such as stencil operators,
         which are an important part of most structured-mesh PDE solvers. Stencil computations are often
         parallelized, for example by using OpenMP, and optimized by using techniques such as cache-blocking
         and tiling to fully utilize multicore CPUs and many-core accelerators and GPUs. Differentiating
         these codes with conventional reverse-mode AD results in adjoint codes that cannot be expressed as
         stencil operations and may not be easily parallelizable. They thus leave most of the compute power
         of modern architectures unused. We present a method that combines forward-mode AD and loop
         transformation to generate adjoint solvers that use the same memory access pattern as the original
         computation that they are derived from and can benefit from the same optimization techniques. The
         effectiveness of this method is demonstrated by generating a scalable adjoint CFD solver for
         multicore CPUs and Xeon Phi accelerators.",
       booktitle = "Special issue of Optimization Methods \& Software: Advances in
         Algorithmic Differentiation",
       editor = "Bruce Christianson and Shaun A. Forth and Andreas Griewank",
       ad_area = "Computational Fluid Dynamics",
       ad_tools = "TAPENADE",
       ad_theotech = "Adjoint, Code Optimization, control-flow reversal, Data Flow Analysis, data-flow
         reversal, Implementation Strategies, Parallelism, Performance, Reverse Mode, Source
         transformation"
}