Algorithms of Scientific Computing II - Winter 11
- Winter 11/12
- Prof. Dr. Michael Bader
- Time and Place
- Lecture: Wednesday, 10:30 - 12:00 Uhr, room MI 02.07.023, started Oct 26
- Tutorial: Monday, 16:00 -18:00, every second week, room MI 02.07.023, started Nov 7
- Elective topic in Informatik Bachelor/Master/Diplom subject area Algorithms and Scientific Computing
- Wirtschaftsinformatik Bachelor (Modul IN2002)
- Mathematik, Natur- und Ingenieurwissenschaften students are also welcome!
- Daniel Butnaru, M.Sc, Christoph Kowitz, M.Sc.
- details t.b.a.
- Semesterwochenstunden / ECTS Credits
- 3 SWS (2V + 1Ü) / 4 Credits
- The next tutorial will be on 21.11.2010 (15:30).
- In winter term 2011/12, this lecture will be held by Michael Bader with a focus on algorithms in high performance computing (and scientific computing).
- The lecture on Wednesday, Nov 2, was skipped due to the student's general assembly
The lecture will have a focus on parallel algorithms and implementation techniques in the field of numerical simulation and high performance computing, such as:
- linear algebra problems on dense and sparse matrices
- simulation on structured and unstructured meshes
- particle-based simulations (with long-range and short-range interactions)
- spectral methods (parallel FFT and related algorithms)
- Monte Carlo and statistical methods
(a.k.a. the seven dwarfs of HPC).
Lecture slides will be published here after the lessons:
- Oct 26: Intro
- Oct 26, Nov 9: Fundamentals - Parallel Architectures, Models, and Languages
- Nov 16: Dwarf no. 1 - Dense Linear Algebra;
additional material: article by Elmroth et al. in SIAM Review
Roughly every second week a two hour tutorial will take place (details at page top; days and time will be announced here and in the lectures). The assignments and their solutions will be gradually posted here.
|07.11.2011||Slides - Introduction to Cuda||cuda_mmult.cu, README-1.txt|
|21.11.2011||Slides - Further Details on Dense Linear Algebra|
Lecture IN0019 Numerical Programming or similar basic knowledge in numerical methods. Basic knowledge in parallel programming (lecture Parallel Programming, Parallele Algorithmen und Höchstleistungsrechnen, or similar) is helpful (as is a certain interest in problems from scientific computing and numerical simulation).