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COMP3577: PARALLEL SCIENTIFIC COMPUTING I

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 3
Credits 10
Availability Available in 2023/24
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • COMP2221 Programming Paradigms AND (COMP1021 Maths for Computer Science OR MATH1551 Maths for Engineers and Scientists OR (MATH1561 Single Mathematics A AND MATH1571 Single Mathematics B) OR (MATH1061 Calculus I AND MATH1017 Linear Algebra I))

Corequisites

  • None

Excluded Combinations of Modules

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

Aims

  • Introduce scientific computing techniques for the numerical solution of problems in science and engineering
  • Introduce and familiarise students with parallel programming approaches in scientific computing

Content

  • Fundamentals of numerical algorithms for ordinary differential equations.
  • Explicit time discretion techniques for ordinary differential equations.
  • Notions of error and stability analysis.
  • Approaches to programming for multiple processing units using shared memory.
  • Data parallel programming paradigms

Learning Outcomes

Subject-specific Knowledge:

  • On completion of the module, students will be able to demonstrate:
  • an understanding of typical approaches to the numerical solution of problems in science and engineering.
  • a knowledge and appreciation of some of the research challenges in scientific computing
  • understanding of basic parallelisation strategies and when to apply them

Subject-specific Skills:

  • On completion of the module, students will be able to demonstrate:
  • an ability to apply numerical techniques to solve ordinary differential equations
  • an ability to develop appropriate parallelisation schemes
  • an ability to critically evaluate how the subjectknowledge could be applied to various applications

Key Skills:

  • On completion of the module, students will be able to demonstrate:
  • an ability to propose appropriate solutions toproblems in scientific computing.
  • an ability to communicate technicalinformation.
  • an ability to learn independently

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

  • Lectures enable the students to learn new material related to the above content.
  • Formative exercises enable students toapply the material from lectures and enhance their understanding.
  • A summative assignment assesses the application ofmethods and techniques and the synthesis of the core concepts of the course.

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 and example exercises during the course.

More information

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