Durham University
Programme and Module Handbook

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

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