HPC - Algorithms and Applications - Winter 13
- Winter 13/14
- Prof. Dr. Michael Bader
- Time and Place
- Lecture: Monday, 14.00-15.30, MI 02.07.023 (starts Oct 21);
Tutorial: Wednesday, 10-12, MI 02.07.023 (starts Oct 23, roughly bi-weekly)
- Elective topic in Informatics Bachelor/Master: students in mathematics or in any science or engineering discipline are welcome!
- Oliver Meister
- written or oral exam at end of semester
- Semesterwochenstunden / ECTS Credits
- 3 SWS (2V + 1Ü) / 4 ECTS
- https://campus.tum.de/tumonline/lv.detail?clvnr=950111465 (lecture)
https://campus.tum.de/tumonline/wbStpModHB.detailPage?&pKnotenNr=705979 (module description)
- From Nov 18, the lecture on Monday will start at 14.00 (instead of 14.15)
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).
The accompanying tutorials will include practical assignments, and will concentrate on the programming of GPU and accelerator platforms.
Slides and exercise sheets/solutions will be made available during the lecture.
Lecture slides will be published here after the lessons: See also the lecture from winter term 2012/13.
- Oct 21: Intro
- Oct 21, Oct 28, Nov 4: Fundamentals - Parallel Architectures, Models, and Languages
- additional material: Roofline: An Insightful Visual Performance Model for Floating-Point Programs and Multicore Architectures (technical report by Williams et al.)
- MPI examples: Cannon's Algorithm mpi_cannon.c (unsafe send/receive), mpi_cannon_sr.c (using MPI_Sendrecv), mpi_cannon_nbl.c (non-blocking communication)
- Oct 28, Nov 4, Nov 11: Dwarf No. 1 - Dense Linear Algebra;
- Nov 18: Dwarf no. 2 - Sparse Linear Algebra: Application example (page rank) and data structures
- Nov 25: Parallel Sparse Matrix-Vector Multiplication
Roughly every second week a two hour tutorial will take place (details at page top; days and time will be announced in TUMonline and in the lectures). The assignments and their solutions will be gradually posted here.
|Oct 23rd||Organizational remarks||-||-||-|
|Nov 6th||Introduction to CUDA||Worksheet 1||Exercise 1||Solution 1|
|Nov 13th||Further details on Dense LA in CUDA||Worksheet 2||Exercise 2||Solution 2|
|Nov 27th||Sparse LA in CUDA||Worksheet 3||Exercise 3|
Literature and Online Material
- R.H. Bisseling: Parallel Scientific Computing - A structured approach using BSP and MPI, Oxford University Press, 2004.
- Course notes on Rob Bisseling's lecture on Parallel Algorithms (based on the text book)
- T.G. Mattson, B.A. Sanders, B.L. Massingill: Patterns for Parallel Programming, Addison-Wesley, 2005
- D.B. Kirk, W.W. Hwu: Programming Massively Parallel Processors - A Hands-on Approach, Morgan-Kaufman, 2010
- J. Sanders, E. Kandrot: CUDA by Example, Addison-Wesley, 2011
Helpful, but not strictly required is knowledge in:
- basics of numerical methods (e.g.: lecture IN0019 Numerical Programming or similar)
- basics of parallel programming (lecture Parallel Programming, HPC - Programming Paradigms and Scalability, or similar)
Most important is a certain interest in problems from scientific computing and numerical simulation!