Postgraduate Programme and Module Handbook 2019-2020 (archived)

Module COMP52130: Core II B: Advanced Scientific and High Performance Computing

Department: Computer Science

COMP52130: Core II B: Advanced Scientific and High Performance Computing

Type	Tied	Level	5	Credits	30	Availability	Available in 2019/20

Prerequisites

Core I (PHYS 51430)

Corequisites

Excluded Combination of Modules

Aims

Provide advanced knowledge and critical understanding of paradigms, fundamental ideas, tools and methods of program performance analysis and engineering
Provide advanced knowledge and critical understanding of paradigms, fundamental ideas, algorithms and methods behind the modelling and simulation of continuous systems (partial differential equations)
Provide advanced knowledge and critical understanding of paradigms, fundamental ideas, algorithms and methods behind the modelling and simulation of discrete systems
Provide advanced knowledge and critical understanding of paradigms, fundamental ideas and methods behind advanced algorithms

Content

Performance Analysis and Engineering
Continuous Systems
Discrete Systems
Advanced Algorithms

Learning Outcomes

Subject-specific Knowledge:

understanding and critical reflection of advanced ideas and techniques behind the performance analysis and performance of scientific computing and data analysis codes
understanding and critical reflection of advanced engineering algorithms in high-performance computing and data analysis
understanding and critical reflection of advanced ideas, numerical techniques and algorithms used to study discrete models
understanding and critical reflection of advanced ideas, numerical techniques and algorithms used to study continuous models

Subject-specific Skills:

basic familiarity with state-of-the-art algorithms to solve large-scale and data intense challenges
competent and educated selection and statements on potential performance of specific codes plus skills to improve existing solutions
competence to translate continuous and discrete problem descriptions into algorithmic formulations; competent and educated selection of appropriate solution algorithms

Key Skills:

familiarity with advanced paradigms and modern algorithms underlying scientific computing both for discrete and continuous systems as well as High-Performance Computing

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total/Hours
Lectures in Performance Analysis and Performance Engineering	12	3 per week	60 minutes	12
Practical Classes in Performance Analysis and Performance Engineering	4	1 per week	60 minutes	4
Lectures in Advanced Algorithms	12	3 per week	60 minutes	12
Practical Classes in Advanced Algorithms	4	1 per week	60 minutes	4
Lectures in Continuous Systems	12	3 per week	60 minutes	12
Practical Classes in Continuous Systems	4	1 per week	60 minutes	4
Lectures in Discrete Systems	12	3 per week	60 minutes	12
Practical Classes in Discrete Systems	4	1 per week	60 minutes	4
Preparation, reading, and self-study				236

Summative Assessment

Component: Coursework		Component Weighting: 100%
Element	Length / duration	Element Weighting	Resit Opportunity
Performance Analysis and Engineering	5 weeks	25%
Advanced Algorithms	5 weeks	25%
Discrete Systems	5 weeks	25%
Continuous Systems	5 weeks	25%

Formative Assessment:

Formative feedback is given as part of the coursework feedback.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University