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

Adjoint MPI
M. Schanen, U. Naumann
Collaborators: Dr. Jean Utke, Argonne National Laboratory, USA, Dr. Laurent Hascoet,
INRIA, France
Funded by: Fond National de la Recherche of Luxembourg
We investigate the robust and efficient applicability of reverse mode algorithmic
differentiation to numerical simulation codes that use MPI. Since MPI is the de facto
parallelization standard in large scale simulation codes, there have been numerous attempts to
compute adjoints of MPI parallelized code. Until today, they all heavily rely on manual
manipulation of the original code. Our generic approach tries to reverse the entire flow of
computation and thus reverse all the MPI communication patterns automatically.
The focus has been on the programming languages C and Fortran. In Fortran, adjoint MPI has
been inserted in the development branch of the COMPAD project based on the NAG Fortran
Compiler. Additionally, covering the language C/C++, the technique is being used with the in
house developed dcc compiler and the dco overloading library.
The outcome should be a generic and versatile adjoint MPI library that may be coupled with
any algorithmic differentiation software. It is not restricted to any specific language or tool.
Uncertainty Quantification
M. Beckers, U. Naumann
Collaborators : Prof. B. Christianson, Phd., University of Hertfordshire, UK
Funded by: German Research School for Simulation Sciences
Uncertainty Quantification aims to determine the imprecision in the outputs of numerical
programs caused by (measurement) errors in the inputs. For a known error distribution of the
inputs, probabilistic methods are used to get information about the distribution of the outputs.
Such investigations are for example desired in the context of engineering or weather
simulations. Weather simulations are partly based on measured wind speeds and temperatures
used to forecast future weather conditions. Quantifications of the impreciseness in such
predictions is needed. Especially if important decisions have to be made based on such
simulations, uncertainty information has to be taken into account.
Our approach is based on a Taylor Series Expansion of the function implemented by the
simulation, yielding approximations of the mean and variance of the distribution of the
output. Because of the complexity of accurate derivative computations mostly first-order
methods are used in practice. AD allows the efficient computation of higher order derivatives
and therewith more precise approximations. At the moment we apply such higher order
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