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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014
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DRPS : Course Catalogue : School of Physics and Astronomy : Postgraduate (School of Physics and Astronomy)

Postgraduate Course: Parallel Numerical Algorithms (PGPH11076)

Course Outline
SchoolSchool of Physics and Astronomy CollegeCollege of Science and Engineering
Course typeStandard AvailabilityNot available to visiting students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaPostgraduate (School of Physics and Astronomy) Other subject areaNone
Course website None Taught in Gaelic?No
Course descriptionThe 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
KeywordsPNA (S1)
Contacts
Course organiserDr Christopher Johnson
Tel:
Email: Chris.Johnson@ed.ac.uk
Course secretary Yuhua Lei
Tel: (0131 6) 517067
Email: yuhua.lei@ed.ac.uk
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