Undergraduate Programme and Module Handbook 2022-2023 (archived)

# Module COMP3577: PARALLEL SCIENTIFIC COMPUTING I

## Department: Computer Science

### COMP3577: PARALLEL SCIENTIFIC COMPUTING I

Type | Open | Level | 3 | Credits | 10 | Availability | Available in 2022/23 | Module Cap | None. | Location | Durham |
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#### 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 Combination 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 subject knowledge 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 to problems in scientific computing.
- an ability to communicate technical information.
- 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 to apply the material from lectures and enhance their understanding.
- A summative assignment assesses the application of methods and techniques and the synthesis of the core concepts of the course.

#### 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 and example exercises during the course.

■ 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