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Faculty of Mathematics, Physics & Computer Science

Scientific Computing, Master of Science (M.Sc.)

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3. Practical Course on Parallel Numerical Methods

The 3. practical course on parallel numerical methods took place from 28.03.2022 - 08.04.2022 at the chair of Parallel and Distributed Systems.

Course instructor: Thomas Rau (Chair of Scientific Computing) 

Participants: Students of the master's program "Scientific Computing"

The programming lab course on Parallel Numerical Methods is one of the core modules of the master program "Scientific Computing". The goal of this interdisciplinary course is to link the contents of different lectures. For this purpose, the students implemented a C++ library for the parallel solution of large dimensional linear systems.


The focus of the course this year is programming with MPI. The schedule is as follows:

TimeDay 1Day 2Day 3Day 4Day 5
8:30 - 12:00

Lecture: Sparse Numerics and Software Tools

Lecture:  Programming and C++

Coding Practice

ILecture: Basics of Distributed and Parallel Programming

Coding Practice

Lecture: MPI

Coding Practice

Coding Practice
12:00 - 13:30LunchLunchLunchLunchLunch
13:30 - 17:30

Lecture: Programming and C++

Coding Practice

Coding PracticeCoding PracticeCoding PracticeCoding Practice


Time Day 6Day 7Day 8Day 9Day 10
10:00 - 12:00Lecture: Cluster and SlurmCoding PracticeCoding PracticeCoding Practice

Coding Practice

Presentation Preparation

12:00 - 13:30LunchLunchLunchLunchLunch
13:30 - 17:30Coding PracticeCoding PracticeCoding PracticeCoding Practice

Coding Practice

Presentation Preparation


The time for the final presentations is April 11th from 9:00 am to 11:00 am.


In this practical course, students implement manageable numerical problems (such as PCG method, finite element discretization of 2d Laplacian, etc.) on parallel computers using the programming language C/C++ and standard software libraries (LAPACK/BLAS, OpenMP, OpenMPI). The resulting parallel efficiency is observed depending on the chosen implementation (naive or advanced such as Schwarz methods).

Recommended Prerequisities

  • A1: Numerical Methods for Differential Equations
  • C1.3: Parallel and Distributed Systems I
  • D1.1: Efficient Treatment of Non-local Operators


Implementation and presentation of approaches; active participation and discussion

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