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 | Module Cap |
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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