151-0116-10L High Performance Computing for Science and Engineering (HPCSE) for Engineers II
| Semester | Spring Semester 2020 |
| Lecturers | P. Koumoutsakos, S. M. Martin |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 151-0116-00 G | High Performance Computing for Science and Engineering (HPCSE) II Lecture: 13-15h Exercises: 10-12h The exercises begin in the second week of the semester. | 4 hrs |
| P. Koumoutsakos, S. M. Martin |
Catalogue data
| Abstract | This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Uncertainty Quantification and Propagation including the implementation of relevant algorithms on HPC architectures. |
| Objective | The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences |
| Content | High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation |
| Lecture notes | https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs20/ Class notes, handouts |
| Literature | - Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides |
| Prerequisites / Notice | Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L) |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 4 credits |
| Examiners | P. Koumoutsakos |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit. |
| Mode of examination | written 180 minutes |
| Additional information on mode of examination | Most probably a computer based examination involving theoretical questions and coding problems. Parts of the lecture documents and other materials will be made available online during the examination. |
| Written aids | You are allowed to bring a HANDWRITTEN summary of 3 A4 sheets, written on the front and back pages (6 pages total). Photocopies are not allowed. |
| Online examination | The examination may take place on the computer. |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| Main link | Course web page |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
