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Durham University

Faculty Handbook 2022-2023

Module Description

Please ensure you check the module availability box for each module outline, as not all modules will run each academic year.

Department: Computer Science

COMP4187: PARALLEL SCIENTIFIC COMPUTING II

Type Open Level 4 Credits 10 Availability Available in 2022/23 Module Cap None. Location Durham

Prerequisites

  • COMP3577 Parallel Scientific Computing I OR COMP3371 Computing Methodologies III

Corequisites

  • None

Excluded Combination of Modules

  • MATH3081 Numerical Differential Equations III AND MATH4221 Numerical Differential Equations IV

Aims

  • Introduce advanced scientific computing techniques
  • Familiarise student with distributed memory programming and MPI

Content

  • Basic spatial discretisation techniques for partial differential equations
  • Implicit time discretisation techniques for ordinary differential equations.
  • Advanced algorithms of scientific computing
  • Distributed memory programming paradigms.
  • Advanced parallel data structures.

Learning Outcomes

Subject-specific Knowledge:
  • On completion of the module, students will be able to demonstrate:
  • an in-depth knowledge of the state-of-the-art in scientific computing and accelerator programming
  • a critical awareness of the main open problems of current interest related to these areas
  • a comprehensive understanding of the research issues that relate to these problems, including recent developments and research trends, breaking technologies and opportunities for industrial innovation.
Subject-specific Skills:
  • On completion of the module, students will be able to demonstrate:
  • an ability to conduct significant self-study and critically evaluate research issues in the covered areas of scientific computing and accelerator programming
  • an ability to propose adaptations to numerical techniques and parallelisation methodologies to problems of current interest in the covered areas and evaluate their potential industrial implications.
Key Skills:
  • On completion of the module, students will be able to demonstrate:
  • an ability to read and understand technical papers
  • an ability to propose original solutions to problems of current interest
  • an ability to deliver working, performing, scaling simulation codes.

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

  • Lectures provide the students with a focus on the content described above.
  • Self-study/reading classes where application of the theory and familiarisation with current research issues are enabled.
  • A substantial summative assignment encourages and guides further independent study to be conducted.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
lectures 20 1 per week 1 hour 20
preparation and reading 80
total 100

Summative Assessment

Component: Coursework Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Summative Assignment 100% No

Formative Assessment:

Through coursework.


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



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