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151-0116-00L 7 Credits BSC D-MATH
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High Performance Computing for Science and Engineering (HPCSE) for CSE

Lecturers: Dr. Petros Koumoutsakos, Dr. Sergio Miguel Martin
Examiners: Dr. Petros Koumoutsakos
VVZ CR n/a

Last Updated: 2026-02-05 15:41:16

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.

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

Resources

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

Learning Materials (Links)

General Information

Language
English
Levels
BSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
You are allowed to bring a HANDWRITTEN summary of 7 A4 sheets, written on the front and back pages (14 pages total). Photocopies are not allowed.
Digital
The exam takes place on devices provided by ETH Zurich.
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.

Course Components

Type Title Time & Place Hours
lecture with exercise 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.
  • Mon 10:00-12:00 (ER SA TZ)
  • Mon 10:15-12:00 (ML H 44)
  • Mon 13:00-15:00 (ER SA TZ)
  • Mon 13:15-15:00 (ML H 44)
4 h weekly
practical/laboratory course High Performance Computing for Science and Engineering (HPCSE) for CSE
  • Fri 08:15-10:00 (HG E 26.1)
2 h weekly

Offered In