Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

052112 VU Numerical High Performance Algorithms (2020W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

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
  • Tuesday 19.01. 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: Fr 12.05.2023 00:13