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052112 VU Numerical High Performance Algorithms (2020W)

Continuous assessment of course work
Tu 19.01. 13:15-14:45 Digital

Registration/Deregistration

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

PLEASE NOTE: Due to the current COVID situation the course is online until further notice!

Thursday 01.10. 13:15 - 14:45 Digital
Tuesday 06.10. 13:15 - 14:45 Digital
Thursday 08.10. 13:15 - 14:45 Digital
Tuesday 13.10. 13:15 - 14:45 Digital
Thursday 15.10. 13:15 - 14:45 Digital
Tuesday 20.10. 13:15 - 14:45 Digital
Thursday 22.10. 13:15 - 14:45 Digital
Tuesday 27.10. 13:15 - 14:45 Digital
Thursday 29.10. 13:15 - 14:45 Digital
Tuesday 03.11. 13:15 - 14:45 Digital
Thursday 05.11. 13:15 - 14:45 Digital
Tuesday 10.11. 13:15 - 14:45 Digital
Thursday 12.11. 13:15 - 14:45 Digital
Tuesday 17.11. 13:15 - 14:45 Digital
Thursday 19.11. 13:15 - 14:45 Digital
Tuesday 24.11. 13:15 - 14:45 Digital
Thursday 26.11. 13:15 - 14:45 Digital
Tuesday 01.12. 13:15 - 14:45 Digital
Thursday 03.12. 13:15 - 14:45 Digital
Thursday 10.12. 13:15 - 14:45 Digital
Tuesday 15.12. 13:15 - 14:45 Digital
Thursday 17.12. 13:15 - 14:45 Digital
Thursday 07.01. 13:15 - 14:45 Digital
Tuesday 12.01. 13:15 - 14:45 Digital
Thursday 14.01. 13:15 - 14:45 Digital
Thursday 21.01. 13:15 - 14:45 Digital
Tuesday 26.01. 13:15 - 14:45 Digital
Thursday 28.01. 13:15 - 14:45 Digital

Information

Aims, contents and method of the course

PLEASE NOTE: Due to the current COVID situation the course is online until further notice!

Know and understand selected advanced numerical high performance algorithms (including divide-and-conquer eigensolver, GMRES, least squares solver, QR algorithm, communication-avoiding linear solver, etc.) for large and very large problems. Understand the interdependencies between problem data, algorithm, implementation of the algorithm, hardware, performance and accuracy. Understand basic techniques for analysis, implementation and optimization of numerical high performance algorithms. Implement and evaluate your own implementations.

Assessment and permitted materials

Two homework exercises (with theoretical and practical components - implementation, experimentation, analysis), presentation of assigned papers from the literature, and an individual semester project (involving literature research, implementation, experimentation, analysis), whose results have to be presented in class and documented in written form (project report, presentation slides) during the semester.

Minimum requirements and assessment criteria

The maximum possible score is 100 points (20 for the homework exercises, 30 for the paper presentations, 25 for the presentation of the semester project, 25 for the report of the semester project). At least 50 points are required for passing the course. For passing the course, in each component (homeworks, paper presentations, semester project) at least half of the available points have to be achieved.

Examination topics

There is no separate exam, grading takes into account discussions and questions for each component (homeworks, paper presentations, semester project).

Reading list

Slides presented in class, literature references given on the slides.

J. Demmel: Applied Numerical Linear Algebra
L. N. Trefethen and D, Bau, III: Numerical Linear Algebra
Golub & van Loan: Matrix Computations

Association in the course directory

Module: HPA APS

Last modified: Th 08.10.2020 11:28