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Lectures

The following courses are regularly taught, partly in collaboration with the staff of the computing center. Visit our current teaching activities to check what is actually offered right now.

Introduction to High-Performance Computing (V3 + Ü1, Credits: 6)

This introductory course sets the stage for all other courses. The focus is on reducing complexity by means of exploiting the structure of a given problem as well as on the application of parallel computing technologies. It is regularly held by Prof. Bischof and is at the very core of his Vertiefungsfach "High-Performance Computing". (Before the move to the Ba/Ma system, this course was offered as V4 + Ü2.)

Parallel Numerical Algorithms (V4 + Ü2, ECTS credits: 8)

This lecture deals with techniques to improve performance on high-performance computers such as blocking and parallelizing. Algorithms for linear systems, eigenvalue problems, and numerical integration are discussed. The course is held by PD Dr. Lang.

Computational Geometry (V4 + Ü2, ECTS credits: 8)

This lecture covers algorithms and data structures for efficiently solving geometric problems, such as locating objects, determining next neighbors among a set of objects, determining (convex or other) hulls, finding intersection points, etc. Such problems arise in a variety of applications ranging from computer graphics to data retrieval and data evaluation. The course is held by PD Dr. Lang. (This course is no longer offered.)

Parallel Algorithms and Software for Iterative Methods (V2 + Ü2, ECTS credits: 4)

Large-scale problems are often tackled by parallel computers simply to get rid of the storage limitations imposed by conventional serial computers. Unfortunately, classical algorithms designed in the serial von Neuman-style tend to loose efficiency when implemented on parallel computers. This is the reason why algorithms specificly-designed with parallelism in mind are necessary. The course covers such algorithms and corresponding software for sparse linear systems. It is taught by PD Dr. Bücker.

Parallel Preconditioning Techniques for Linear Systems (V2 + Ü2, ECTS credits: 4)

The term preconditioning comprises a set ot techniques which modifies a given system of linear equations in such a way that its iterative solution is "easier" to compute than the solution of the original system. Traditional preconditioning techniques are often inherently serial. When designing a parallel preconditioner one should beware that increasing the degree of parallelism does not decrease the convergence behavior. The course is taught by PD Dr. Bücker.

Semantical Transformations (V2, ECTS credits: 3)

Numerical computing relies on the efficient mapping of mathematical concepts to computing resources. Typically the tool-assisted step of such a transformation is limited to the compilation of user-written code to a specific architecture, without any knowledge of what the user had in mind when he wrote that code. We discuss instances where a rule-based higher-level approach of semantical transformation tries to capture some aspect of the underlying mathematics in the compilation process. Examples include automatic differentiation and code generation for elementary numerical operations such as Fast Fourier Transforms specificly-tuned towards (parallel) architectures. The course is held by Prof. Bischof.

Computational Differentiation (V2, ECTS credits: 3)

Computational Differentiation is concerned with the accurate and efficient evaluation of derivatives for functions defined by computer programs. The resulting derivative values are useful in scientific computations including optimization, parameter identification, nonlinear equation solving, numerical integration of differential equations, to name a few. The lecture gives a comprehensive introduction to this chainrule-based technique for evaluating derivatives. The course is held by Prof. Bischof.

Combinatorial Problems in Scientific Computing (V2 + Ü2, ECTS credits: 4)

The term "Combinatorial Scientific Computing" (CSC) is commonly used to refer to the interdisciplinary field spanning scientific computing and algorithmic computer science. It involves identifying a problem in scientific computing, building an appropriate combinatorial model of the problem, and designing algorithms to solve the combinatorial problem. The course is taught by PD Dr. Bücker and Prof. Naumann.

Virtual Reality (V2 + Ü1, ECTS credits: 4)

Virtual Reality is characterized by a three-dimensional human-computer interface. In the industrial field, VR technology has only recently become a powerful tool for visualization of simulations and for prototyping. The lecture is about basic methods for the development and simulation of virtual environments. The course is held by Dr. Kuhlen.
RWTH Aachen
Computer Science
Scientific Computing
Computational Engineering
Center for Computing and Communication
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