151-0116-00L High Performance Computing for Science and Engineering (HPCSE) for CSE
| Semester | Spring Semester 2019 |
| 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 | ||||||||||||
| 151-0116-00 P | High Performance Computing for Science and Engineering (HPCSE) for CSE | 2 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 Bayesian Uncertainty Quantification and Machine Learning including the implementation of these algorithms on HPC architectures. |
| Learning 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. - fundamentals of Deep Learning |
| 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 Machine Learning - Deep Neural Networks and Stochastic Gradient Descent - Deep Neural Networks for Data Compression (Autoencoders) - Recurrent Neural Networks |
| Lecture notes | http://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs19/ 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, Devinderjit Sivia - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides |
| Prerequisites / Notice | Attendance of HPCSE I |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a two-semester course together with 151-0107-20L High Performance Computing for Science and Engineering (HPCSE) I | |
| For programme regulations (Examination block) | Bachelor's Degree Programme in Computational Science and Engineering 2016; Version 27.03.2018 (Examination Block Core Courses) Bachelor's Programme in Computational Science and Engineering 2012; Version 13.12.2016 (Examination Block Core Courses) |
| ECTS credits | 11 credits |
| Examiners | P. Koumoutsakos, S. M. Martin |
| 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 | The class has one compulsory continuous performance assessment (mandatory project, comprising of 6 biweekly assignments). The final grade will be determined as a weighted average of the grades: 70% session examination and 30% project. The project will be divided into 6 homework assignments, each counting to 5% of the course grade, delivered and graded every 2 weeks. All assignments must be delivered on the due date. Late assignments will be awarded a grade of 1. The assignments rely on each other so it would be more difficult to do only few than all of them. The assignments are envisioned as critical elements of the class and as assistance to the successful completion of the exam. The exam will contain a written part and exercises on the computer and it will contain material that refers directly to the assignments in the project.“ |
| Written aids | According information given above |
| Digital exam | The exam takes place on devices provided by ETH Zurich. |
|
Performance assessment as a semester course
(other programmes)
| |
| ECTS credits | 7 credits |
| Examiners | P. Koumoutsakos, S. M. Martin |
| Type | end-of-semester examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered at the end after the course unit. Repetition only possible after re-enrolling. |
| If the course unit is part of an examination block, the credits are allocated for the successful completion of the whole block. 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. |
Offered in
| Programme | Section | Type | |
|---|---|---|---|
| Computational Science and Engineering Bachelor | Core Courses | O |  |