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COMP52315: Performance Modelling, Vectorisation and GPU Programming

It is possible that changes to modules or programmes might need to be made during the academic year, in response to the impact of Covid-19 and/or any further changes in public health advice.

Type Tied
Level 5
Credits 15
Availability Available in 2024/2025
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • PHYS52015 Introduction to Scientific and High Performance Computing

Corequisites

  • None

Excluded Combinations of Modules

  • n/a

Aims

  • Provide advanced knowledge and critical understanding of paradigms, fundamental ideas, algorithms and methods of program performance analysis, modelling and engineering
  • Provide advanced knowledge and critical understanding of low-level code optimisation paradigms, and how they may be applied in real-world scenarios

Content

  • Performance modelling and engineering
  • Vectorisation and low-level code optimisation
  • GPU programming

Learning Outcomes

Subject-specific Knowledge:

  • Understanding and critical reflection of advanced ideas and techniques behind the performance analysis of scientific computing and data analysis codes
  • Understanding of modern computer architectures, and how to design and implement codes to perform optimally on this hardware

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

Key Skills:

  • Familiarity with advanced paradigms and modern algorithms underlying scientific computing for discrete systems, and their analysis
  • Familiarity with programming paradigms for modern wide-vector CPUs, and GPUs

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

  • Teaching will be by lectures and workshops
  • The lectures provide the means to give a concise, focused presentation of the subject matter of the module
  • When appropriate, the lectures will also be supported by the distribution of written material, or by information and relevant links on Ultra
  • Regular problem exercises and workshops will give students the chance to develop their theoretical understanding and problem solving skills
  • Students will be able to obtain further help in their studies by approaching their lecturers, either after lectures or at other mutually convenient times
  • Student performance will be summatively assessed through coursework
  • The formative exercises provide opportunities for feedback, for students to gauge their progress and for staff to monitor progress throughout the duration of the module

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
Lectures 162 per week60 minutes16 
Practical Classes 162 per week60 minutes16 
Self Study118 
Total150 

Summative Assessment

Component: Summative CourseworkComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Coursework3 weeks100Yes

Formative Assessment

n/a

More information

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Current Students: Please contact your department.