Postgraduate Course: Parallel Numerical Algorithms (PGPH11076)

Course Outline
School	School of Physics and Astronomy	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Postgraduate)	Availability	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	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 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 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
Course description	Not entered

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2017/18, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
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 Information (Learning and Teaching)	Please contact the School for further information
Assessment (Further Info)	Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment)	100% examination consisting of a two hour exam
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)			2:00

Learning Outcomes
On completion of this course, the student will 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.