HPC - Algorithms and Applications - Winter 15
- Winter 15/16
- Prof. Dr. Michael Bader
- Time and Place
- Mon, 14.00-15.30, and Wed, 12.15-13.45, in room MI 02.07.023 (first lecture on Oct 19);
lectures and tutorials will alternate on Wednesdays; as Michael Bader is on research semester, some slots on Mon/Wed will be skipped
- Elective topic in Informatics Bachelor/Master: students in mathematics or in any science or engineering discipline are welcome!
- Alexander Pöppl, M.Sc.
- repeat exam on April 7th, 15:30-17:15 in MI 02.07.023
- Semesterwochenstunden / ECTS Credits
- 3 SWS (2V + 1Ü) / 4 ECTS
- added new article, PageRank Beyond the Web, which recently appeared in SIAM Review 57(3) (by David Gleich)
- Exercise on Dec 9th
- No exercise on Dec 16th
- No exercise on Dec 23th
- Exercise on Jan 13th
- No exercise on Jan 20th
- Q&A session on Jan 27th
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.
Lecture slides will be published here after the lessons: See also the lecture from winter term 2014/15.
- Oct 19: Intro
- Oct 26, Nov 2 & 4: Fundamentals - Parallel Architectures, Models, and Languages
- read the related paper: Roofline: An Insightful Visual Performance Model for Floating-Point Programs and Multicore Architectures (technical report by Williams et al.; published in Communications of the ACM 52 (4), 2009, p.65-76)
- further info: Cannon's Algorithm as MPI examples - mpi_cannon.c (unsafe send/receive), mpi_cannon_sr.c (using MPI_Sendrecv), mpi_cannon_nbl.c (non-blocking communication)
- Oct 26, Nov 2, 4, 9: Dwarf No. 1 - Dense Linear Algebra
- Nov 9, 11: Dwarf No. 2 - Sparse Linear Algebra: Application example (page rank) and data structures
- further info: The PageRank Citation Ranking: Bringing Order to the Web (technical report by L. Page, L. Brin, R. Motwani, T. Winograd, 1999)
- further info: The Anatomy of a Large-Scale Hypertextual Web Search Engine by S. Brin and L. Page (preprint; paper appeared in Computer Networks and ISDN Systems 30 (1-7), 1998)
- further info: PageRank Beyond the Web - article in SIAM Review 57(3) by David Gleich
- Nov 23, 25: Parallel Sparse Matrix-Vector Multiplication - Parallel SpMV, Cartesian Distribution
- Nov 30, Dec 7, 14: Dwarf No. 5 - Structured Grids
- articles by M. Frigo and V. Strumpen:
Cache oblivious stencil operations (preprint);
The memory behavior of cache oblivious stencil operations (preprint can be found via Google)
- article by K. Datta et al. in SIAM Review (preprint)
- articles by M. Frigo and V. Strumpen:
- Dec 14, 21: Dwarf No. 5 extended Structured Grids and Space-filling Curves (contents that were only presented in these slides will not be part of the exam)
- Jan 18, 25: Dwarf No. 6 - Unstructured Grids and Partitioning
- additional material: article by Karypis and Kumar: A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
- additional material: article by Hendrickson and Leland: A Multilevel Algorithm for Partitioning Graphs (this article was awarded the "Test of Time Award" at the 2014 Supercomputing Conference, SC14)
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 21th||Organizational remarks||-||-||-|
|Oct 28th||Introduction to CUDA||Worksheet 1||Exercise 1||included in Exercise 2|
|Nov 11th||Further details on Dense LA in CUDA||Worksheet 2||Exercise 2||Solution 2|
|Nov 18th||Sparse LA in CUDA||Worksheet 3||Template for Lecture Template for Homework||Memory accesses, Code included in Exercise 4|
|Dec 2nd||Solving the heat equation with CUDA||Worksheet 4||Homework template (revised)||Solution 4|
|Dec 9th||The Shallow Water Equations and CUDA||Worksheet 5||Exercise 5||included in Exercise 6|
|Jan 13th||Further topics on SWE and CUDA||Worksheet 6||Exercise 6||-|
- the same rules as for the written exam will apply (see below)
- if less than 10 participants register for the exam, it will be executed as a series of oral exams (announcement with details will follow by email)
- written exam on Thu, Apr 7, 2016, from 15.30 (room MI 02.07.023)
- In case of a written exam, please try to be in front of the room by 15:15 as the working time will start at 15.30. Announcements will be made prior to 15:30.
- Helping material: One sheet of A4 paper (two-sided) with hand-written notes on it.
- the exam will extend over all topics discussed in the lectures and tutorials:
- approx. 30% of the questions will deal with questions related to the tutorials; basic knowledge about GPU programming with CUDA is thus required
- in the last lecture and in the last tutorial, there will be the oportunity to ask questions on lecture topics and exercises, respectively
- the following worksheet contains some example questions (with solutions) from previous exams:
- exam questions with example solutions
- note that this collection of exercises does not reflect the extent of assignements in the exam
- note that the contents of the leture may have slightly changed compared to previous years, such that exercises can have a slightly different focus (this year, we did not cover the topics of exercises 2a and 2b, e.g.)
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)
- V. Eijkhout: Introduction to High-Performance Scientific Computing (textbook, available as PDF on the website)
- T.G. Mattson, B.A. Sanders, B.L. Massingill: Patterns for Parallel Programming, Addison-Wesley, 2005
- G. Hager, G. Wellein: Introduction to High Performance Computing for Scientists and Engineers, Chapman & Hall/CRC Computational Science, 2010
(all available as ebooks from TUM library)
Books on CUDA
- D.B. Kirk, W.W. Hwu: Programming Massively Parallel Processors - A Hands-on Approach, Morgan-Kaufman, 2nd edition, 2013
- J. Sanders, E. Kandrot: CUDA by Example, Addison-Wesley, 2011
(both available as ebooks from TUM library)
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!