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COMP4187: PARALLEL SCIENTIFIC COMPUTING II

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type Open
Level 4
Credits 10
Availability Not available in 2023/24
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • COMP3577 Parallel Scientific Computing I

Corequisites

  • None

Excluded Combinations 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

ActivityNumberFrequencyDurationTotalMonitored
lectures201 per week1 hour20 
preparation and reading80 
total100 

Summative Assessment

Component: CourseworkComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Summative Assignment100No

Formative Assessment

Through coursework.

More information

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