Numerical Linear Algebra for Scientific High Performance Computing (Wintersemester 2020/21)
- Dozent*in: Dr. Hartwig Anzt
- Veranstaltungen: Vorlesung (0110650)
- Semesterwochenstunden: 2
In this course, we will cover different aspects of scientific high performance computing:
- Methods and algorithms for scientific HPC
- Programming models and languages for HPC (CUDA, HIP, OpenMP, DPC++, MPI)
- Understanding and optimizing performance on HPC systems
- Sustainable Software Development
The course will be virtual: https://kit-lecture.zoom.us/j/67845776476
Lecture slides will be collected in the git repo: https://git.scc.kit.edu/nla4hpc_winter20-21/lecture-slides.git
If you want to take this course, please send an email to hartwig.anzt@kit.edu
| Termine | ||
|---|---|---|
| Vorlesung: | Montag 10:00-11:30 | Online |
| Lehrende | ||
|---|---|---|
| Dozent | Dr. Hartwig Anzt | |
| Sprechstunde: | ||
| Zimmer 3.017 Kollegiengebäude Mathematik (20.30) | ||
| Email: hartwig.anzt@kit.edu | ||
Programming:
Shared Memory/Distributed Memory, Bulk-Synchronous Programming Model, Synchronization, Mutex, One-sided Communication, OpenMP, Fork-Join Model, Private/Public Variables, Map-Reduce, Scheduling, MPI, CUDA (GPU Programming)
Performance:
GFLOPs, Moore’s Law, Amdahl’s Law, Performance tools, Performance Modeling, Roofline Model
Dense NLA:
BLAS operations, LAPACK, ScaLAPACK, LU/QR/Cholesky decomposition, Singular Value Decomposition (SVD)
Iterative Sparse Linear Algebra:
Fix-Point Iteration, Kylov subspace methods, ILU preconditioning, Jacobi/block-Jacobi preconditioning, Finite differences (Laplace), Domain Decomposition Methods (Additive/Multiplicative Schwarz)
Prüfung
Homework:
- Focus on sustainable software development.
- Homework will be peer-reviewed by other students and both the homework and the peer review performance will be graded.
- Homework usually includes programming, performance experiments, and a report.
- Programming in C/C++ (+ we learn CUDA, OpenMP, HIP)
Project:
- Topic of general interest to the course.
- The idea is to read three or four papers from the literature (references will be provided).
- Implement the problem/application.
- New ideas and extensions are welcome, as well as optimized implementations of existing algorithms.
- I.e., the class project can be an HPC implementation of a different project you are working on (Master’s thesis, Seminar, etc.)
- I will also distribute a list with project ideas.
- Synthesize them in terms of a report (~10-15 pages).
- Present your report to class (~30 mins).
Literaturhinweise
The Sourcebook of Parallel Computing, Edited by Jack Dongarra, Ian Foster, Geoffrey Fox, William Gropp, Ken Kennedy, Linda Torczon, Andy White, 2002, 760 pages, ISBN 1-55860-871-0, Morgan Kaufmann Publishers.
Introduction to High-Performance Scientific Computing, by Victor Eijkhout with Edmond Chow, Robert Van De Geijn, February 2010.
Introduction to High Performance Computing for Scientists and Engineers, by Georg Hager, Gerhard Wellein, CRC Press, 2010.