GEOGG122 Scientific Computing

OPTION GEOGG122 - SCIENTIFIC COMPUTING
(15 credits)

Term 1 (2011)

Staff:
Prof. P. Lewis
Prof. J. French

Aims:

This module aims:

• to impart an understanding of scientific computing
• to give students a grounding in the basic principles of algorithm development and program construction
• to introduce principles of computer-based image analysis and model development
• to demonstrate the potential and practical implementation of parallel processing for computationally intensive modelling tasks

Content:

The module will cover:

• Introduction to programming (algorithms, data structures, control structures, I/O, languages and pseudocode)
• Introduction to linux environment (login, shell, file systems) and hardware
• Compilation and debugging
• Computing for image analysis (with reference to software such as ENVI/IDL)
• Computing for modelling (with reference to software such as Matlab)
• Data visualisation for scientific applications
• High performance computing, including code optimisation, SMP and cluster-based parallel computing

Assessment:

1 piece of coursework, 100% of the assessment

Format:
The course is based upon lectures, many with a strong practical component, and practical classes.

The module will be delivered through:

• Lectures (providing concepts, methods, examples and literature context)
• Computer laboratory work (extended practical sessions progressing technical aspects of understanding and providing hands-on experience of relevant software and computational problems).
• Moodle resources (hosting reading lists, lecture handouts, datasets, guides and practical support materials)

Learning outcomes:

At the end of the module, students should:

• have a working knowledge of linux / unix operating systems and have the knowledge and confidence to obtain, compile and install commonly available scientific software packages
• have an understanding of algorithm development and be able to use widely used scientific computing software to manipulate datasets and accomplish analytical tasks
• have an understanding of the technical issues specific to image-based analysis, model implementation and scientific visualisation