Postgraduate Programme and Module Handbook 2018-2019 (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 | Not available in 2018/19 | Module Cap |
|---|
Prerequisites
Corequisites
Excluded Combination of Modules
Aims
- Provide advanced knowledge and critical understanding of paradigms, fundamental ideas, tools and methods of program performance analysis
- Provide advanced knowledge and critical understanding of paradigms, technologies, tools and trends in performance engineering incl skills how to use advanced HPC programming techniques
- 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
Content
- Performance models and analysis
- Performance engineering techniques in High-Performance Computing and accelerator programming
- Scientific Computing for Partial Differential Equations
- Scientific Computing for Discrete Systems (graph-based models, combinatorial optimisation, algorithms and complexity)
Learning Outcomes
Subject-specific Knowledge:
- understanding and critical reflection of advanced ideas and techniques behind the performance analysis and on performance statements of scientific computing and data analysis codes
- understanding and critical reflection of advanced paradigms and relevant techniques in high-performance computing and accelerator programming
- 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 software toolboxes to solve large-scale equation systems
- competent and educated selection and statements on potential performance of specific codes plus skills to identify shortcomings of existing solutions
- competent and educated selection and application of programming languages and analysis tools for specific problems
- 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 concepts 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 | 4 | 1 per week | 60 minutes | 4 | |
| Practical Classes in Performance Analysis | 12 | 3 per week | 60 minutes | 12 | |
| Lectures in Performance Engineering | 8 | 2 per week | 60 minutes | 8 | |
| Practical Classes in Performance Engineering | 8 | 2 per week | 60 minutes | 8 | |
| Lectures in Modelling and Simulation of Continuous Systems | 12 | 3 per week | 60 minutes | 12 | |
| Practical Classes in Modelling and Simulation of Continuous Systems | 4 | 1 per week | 60 minutes | 4 | |
| Lectures in Modelling and Simulation of Discrete Systems | 12 | 3 per week | 60 minutes | 12 | |
| Practical Classes in Modelling and Simulation of 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 |
| Discrete Systems with Performance Analysis and Engineering | 5 weeks | 50% | |
| Continuous Systems with Performance Analysis and Engineering | 5 weeks | 50% | |
Formative Assessment:
Formative feedback is given as part of the coursework feedback. The linear algebra workshop is purely formative. Yet, content is used in the lectures.
■ 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