Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

compute the gradient of the cost function using forward and reverse mode AD. Further effort
has been taken to parallelize the AD solutions using MPI parallel concept.
Hybrid Approach to the generation of adjoint C++-Code
J. Lotz, U. Naumann
Collaborators: ICG-1, Forschungszentrum Jülich, Max Planck Institute for Meteorology,
Hamburg
Funded by: DFG
In scientific computing a major challenge are derivative computations of implemented
mathematical models. For example in (large-scale) Inverse Problems the derivative of the
forward model is to be evaluated. AD is a powerful concept for doing this efficiently when
the possibility of a reverse (adjoint) execution is provided. This execution reversal can be
accomplished by building a computational graph by operator overloading at runtime
representing the implemented function. Thereby the derivative is obtained by operations on
the created data-structure. Apart from that source code transformation can be used to create
C++-Code computing the adjoint. The first approach is robust and suitable for almost any
C++-Code but is in comparison relatively slow. Source code transformation on the other hand
is applicable only to syntactically simpler code but yields a high efficiency. The aim is to
merge both approaches to a hybrid treatment of derivative calculations to combine the
advantages, i.e., syntactically complex parts are handled by operator overloading while
compact computationally expensive parts are covered by source code transformation. Target
application is the JUelich RApid Spectral SImulation Code (mentioned above) and we aim to
consider a C-version of a new atmospheric and oceanic general circulation model (ICON)
developed by the Max-Planck-Institut für Meteorologie and the German Weather Service.
CompAD-III
U. Naumann, J. Riehme, D. Gendler
Collaborators: The Numerical Algorithms Group Ltd., University of Hertfordshire, UK
Funded by: EPSRC
The development of the differentiation-enabled NAG Fortran Compiler is a joint effort of the
University of Hertfordshire, UK, and the RWTH Aachen University, Germany. The target is
the integration of current (or new) Automatic Differentiation technology into the industrial
strength Fortran compiler of the Numerical Algorithm Group (NAG), Oxford, UK. The
project is currently in the third period of funding by EPSRC.
The AD-enabled NAG Fortran Compiler utilizes a hybrid approach combining automatic
datatype changes (source transformation) with overloading techniques. The compiler provides
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