Postgraduate Course: Parallel Numerical Algorithms (PGPH11076)
Course Outline
| School | School of Physics and Astronomy |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Postgraduate (School of Physics and Astronomy) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | The demand for performance of scientific applications as the driver for massive parallelism in computational science is reviewed. Basic algorithmic complexity theory is described, and parallel scaling introduced. Computational patterns, sometimes known as the ¿seven dwarfs¿¿ and how they are implemented in serial and parallel are described, how they scale, and which applications use them. The use of
libraries such as ScaLAPACK and PETSc are reviewed.
Topics include:
- Computational science as the third methodology
- Fundamentals of algorithmic complexity O(N) etc
- Basic numerics, floating-point representation and exceptions
- Complexity theory and parallel scaling analysis
(weak and strong scaling)
- Implementing parallelism in the ¿seven dwarfs¿, scaling and example applications
(N-body/particle methods, Simple ODEs, Dense Linear Algebra ,algorithms and libraries (LAPACK)
- Sparse Linear Algebra
(PDEs, BVPs and their solution (pollution problem), IVPs and implicit methods)
- Spectral methods
(FFW and applications)
- Structured grids
- Unstructured grids
- Verification |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2013/14 Semester 1, Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
16/09/2013 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
63 )
|
| Additional Notes |
Please contact the School for further information
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
Summary of Intended Learning Outcomes
On completion of this course students should be able to:
- explain why computer simulation is an essential technique in many areas of science, and understand
its advantages and limitations
- Explain how real-valued quantities are represented on a computer as floating-point variables.
- Discuss the various sources of error relevant for computational simulation.
- Explain when different methods (particle, grid, stationary, time dependent) are applicable, and
compare the strengths and weaknesses of different parallelisation strategies.
- Convert simple partial differential equations into numerical form.
- Select and implement the most appropriate method for solving a given system of linear equations.
- Use standard numerical libraries in their own codes.
- Diagnose when a numerical algorithm may be failing due to limited machine precision or floating point
exceptions. |
Assessment Information
| 100% examination consisting of a two hour exam |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | PNA (S1) |
Contacts
| Course organiser | Dr Christopher Johnson
Tel:
Email: Chris.Johnson@ed.ac.uk |
Course secretary | Yuhua Lei
Tel: (0131 6) 517067
Email: yuhua.lei@ed.ac.uk |
|
© Copyright 2013 The University of Edinburgh - 13 January 2014 4:53 am
|
University Homepage