
Programme of the Tenth Euro AD Workshop
Thursday, June 3, 2010
 10^{00} –11^{00} Welcome coffee
 11^{00} –12^{00} first session
 Sri Hari Krishna Narayanan (Argonne National Laboratory)
The ROSE open source compiler infrastructure  A Pbound and ADIC2 perspective
We have used the ROSE Open Source Compiler Infrastructure developed at Lawrence Livermore National Laboratory to develop two sourcetransformation tools. PBound estimates upper performance bounds of C/C++ applications. It combines application signatures with architectural information to generate parametrized expressions for different types of memory accesses and integer and floatingpoint computations. ADIC2 is a tool for the automatic differentiation of C and C++ code through sourcetosource transformation. I will discuss the program representation provided by ROSE and how the tools interact with ROSE.
 Markus Beckers (RWTH Aachen)
Subgradient propagation of McCormick Relaxations in Reverse Mode
 12^{00} –13^{30} Lunch break
 13^{30} –15^{00} session on Taylor methods
 Mathias Wagner (Technische Universität Darmstadt)
Accessing strongly interacting matter at finite density with an implicit Taylor expansion
The microscopic building blocks of matter, i.e., quarks and gluons are subject of a fundamental force, the strong interaction. Similar to water that appears in different aggregate states under different conditions strongly interacting matter is expected to occur in different phases at different temperatures and densities. Experimentally these phases are probed in heavy ion collisions e.g., at the LHC. In theoretical physics the strong interaction is described by Quantum Chromodynamics. However, a theoretical description of the phase structure is not straightforward as standard approaches fail. Lattice simulations using MonteCarlo methods, carried out on supercomputers, are one possible abinito approach. These are however limited to vanishing densities. To circumvent this restriction Taylor expansion methods have been proposed to extrapolate to finite density. In this talk I present results of the calculation of higher order coefficients, which became accessible by means of the inverse Taylor expansion method available in ADOLC.
 Sebastian Walter (Humboldt Universität zu Berlin)
Univariate Taylor arithmetic for matrix factorizations.
We will present some recent work on univariate Taylor arithmetic
applied to matrix factorizations.
Particular focus will be the symmetric eigenvalue decomposition with
potentially multiple eigenvalues,
the cholesky decomposition and the QR decomposition.
The algorithms for the forward and reverse mode have been implemented
and tested in the Python AD tool ALGOPY.
We will present some numerical tests that indicate how accurate the
matrix factorizations are.
If time permits, some live examples will be shown to show how easy it
is to differentiate matrix valued functions.
 15^{00} –15^{30} Coffee break
 15^{30} –16^{00} Perspectives for AD 2010
 16^{00} –19^{00} Diskussion
 19^{00} Workshop dinner

Friday, June 4, 2010
 9^{00} –10^{30} Session on flow control
 Denise Holfeld (Universität Paderborn)
Delay of transition point using adjointbased control
The efficient provision of derivatives is indispensable for numerous applications in flow control. Thus,
we use adjoint information in order to reduce the numerical effort. We present our approach for
flow control using TollmienSchlichting waves as an example. Amongst others effects the boundary
layer increases the resistance of a body. In our example the boundary layer flows smoothly over
the streamlined shape of the body. Therefore, the resistance is minimal. Small disturbance waves
(TollmienSchlichting waves) extend in the boundary layer during time. Hence, TollmienSchlichting
waves cause the transition from a laminar to a turbulent boundary layer and therewith an increasing
resistance. We want to use electromagnetic forces to achieve a flow profile which is more stable. More
precisely, we want to achieve a larger Reynolds number for which the flow is still stable. Numerical
results for the flow control problem illustrate the resulting numerical effort for the computation of the
required adjoint information and the optimization.
 Emre Özkaya (Humboldt Universität zu Berlin)
Efficient adjoint code generation in CFD using AD
In many CFD applications it is desired to generate adjoint CFD solvers for
the optimization of given design problems. Usually CFD tools consist of
models for complex physical phenomena such that hand writing an adjoint
code in
order to compute sensitivities is a rather hard task. Apart from hand
coded discrete adjoints and continuous adjoint approaches, reverse mode
of AD offers a possibility in order to generate an adjoint solver in a
(semi)automatic fashion. However, the enormous memory demand of reverse AD
makes it quite infeasible for practical problems. Besides, usually AD
tools are uncapable of treating parallel codes which are almost a must in
CFD applications. To overcome this kind of problems, in this talk we focus
on the
reverse accumulation
approach to reduce the memory demand and treatment of MPI directives for
the reverse mode of AD with an application to a NavierStokes solver. We
present also some results based on a benchmark flow control problem.
 Philipp Stumm (Technische Universität Dresden)
StructureExploiting Adjoints for the nonlinear Optimization
In this talk are presented the concepts to employ Algorithmic Differentiation (AD) in optimal control
problems. Especially two problems are addressed. The first one is checkpointing. The computation of
the adjoint equation requires the complete forward solution to be stored. This may be impossible due
to lack of storage capacities. We present a multistage binomial checkpointing approach (storing parts
of the forward solution in the main memory and on disc for an a priori known number of time steps)
and an online checkpointing approach (storing parts of the forward solution in the main memory for an
a priori unknown number of time steps) in order to achieve an optimal runtime. The second problem to
be addressed is the determination of AD adjoints in optimal control problems, especially the possibilities
of structure exploitation. We provide an error analysis for the AD adjoints w.r.t. continuous adjoints
and show that the AD adjoints yield a version of the adjoint of the discretizethenoptimize approach.
Additionally, we show that exploiting spatial and time structure for AD adjoints yield a vast memory
reduction and gains in runtime. Numerical results underline the theoretical results.
 10^{30} –11^{00} coffee break
 11^{00} –12^{15} session on adjoints in applications
 Rene Schneider (Technische Universität Chemnitz)
Differentiation of a finite element solver for stationary NavierStokes
We present and compare different approaches to the differentiation of a
finite element code for stationary incompressible NavierStokes. The
solver utilises the problem structure to a high degree (preconditioning,
multigrid) to gain efficiency. AD using ADOLC is applied at different
abstraction levels of code and the resulting behaviour is compared to
hand differentiated code. Problems with size of up to 37 million degrees
of freedom are used in the comparison, thus structure exploitation is
mandatory for efficiency.
 Kshitij Kulshreshtha (Humboldt Universität zu Berlin)
Derivatives in a discretisation free model of flexible frames
Structural optimisation currently relies heavily on methods based on
discretisation. In simpler cases like the simulation of frames and
trusses, where discretisation is not necessary, only the elongation or
compression is considered and the joints are free, like ball and socket
joints, in order to avoid bending the trusses. In my dissertation a
discretisation free method for the modelling and optimisation of frames
is developed which considers bending of the beams along with compression
or elongation with joints between the beams being rigid. The main
difficulty in derivative computation for this model is the necessity for
Nested differentiation which normally requires special handling. I shall
present two aproaches for computing these derivatives.
 12^{15} –13^{30} lunch break
 13^{30} –14^{30} last session
 Viktor Mosenkis (RWTH Aachen)
Branch and Bound for Optimal Jacobian Accumulation
 Johannes Willkomm (RWTH Aachen)
Generating adjoint expressions for MATLAB
 14^{30} discussion and fare well coffee

