Parallelisation of Physics Calculations on GPUs with CUDA
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2017 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
|available module versions|
|SS 2017||SS 2011|
PH1351 is a semester module in German or English language at Master’s level which is offered in summer semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of student seminars for condensed matter physics
- Catalogue of student seminars for nuclear, particle, and astrophysics
- Catalogue of student seminars for Biophysics
- Catalogue of student seminars for Applied and Engineering Physics
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
|Total workload||Contact hours||Credits (ECTS)|
|120 h||30 h||4 CP|
Responsible coordinator of the module PH1351 is Stefan Recksiegel.
Content, Learning Outcome and Preconditions
Seminar talks and programming projects by students on parallelisation methods on GPUs in computational physics
After successful participation in the module the student can implement algorithms from computaional physics on highly parallel processors like GPUs. He/she understands how various parallelisation parameters influence the speed of execution.
Algorithms from Computational Physics (e.g. by simultaneous participation in the lecture Computational Physics II), C programming.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|PS||2||Parallelisation of physics calculations on GPUs with CUDA||Heidsieck, T. Recksiegel, S.||
singular or moved dates
Learning and Teaching Methods
The learning outcomes of this module will be developed through the literature search, the study of literature, the programming, the preparation of the presentation, the discussion with the teacher, the talk itself and answering questions about it.
Presentation materials, complementary literature, Computer with CUDA enabled GPU (available in the CIP pool).
CUDA programming guide by NVIDIA (more info), part 5 and part 6 of GPU Gems 3.