151-0116-00L  High Performance Computing for Science and Engineering (HPCSE) for CSE

SemesterSpring Semester 2020
LecturersP. Koumoutsakos, S. M. Martin
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
151-0116-00 GHigh 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
Mon10:00-12:00ER SA TZ »
10:15-12:00ML H 44 »
13:00-15:00ER SA TZ »
13:15-15:00ML H 44 »
P. Koumoutsakos, S. M. Martin
151-0116-00 PHigh Performance Computing for Science and Engineering (HPCSE) for CSE2 hrs
Fri08:15-10:00HG E 26.1 »
P. Koumoutsakos, S. M. Martin

Catalogue data

AbstractThis 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.
ObjectiveThe 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
ContentHigh 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 noteshttps://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 / NoticeAttendance 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 credits11 credits
ExaminersP. Koumoutsakos
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Additional information on mode of examinationThe 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 aidsYou 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.
Online examinationThe examination may take place on the computer.
Performance assessment as a semester course (other programmes)
ECTS credits7 credits
ExaminersP. Koumoutsakos
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 180 minutes
Additional information on mode of examinationComputer based examination involving theoretical questions and coding problems. Parts of the lecture documents and other materials will be made available online during the examination (for both HPCSE I and HPCSE II).
Written aidsYou 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.
Online examinationThe examination may take place on the computer.
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 linkCourse web page
Only public learning materials are listed.

Groups

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Restrictions

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Offered in

ProgrammeSectionType
Computational Science and Engineering BachelorModule AWInformation
Computational Science and Engineering BachelorCore CoursesOInformation