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COMP40730

Academic Year 2024/2025

High Performance Computing (COMP40730)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Assoc Professor Alexey Lastovetsky
Trimester:
Autumn
Mode of Delivery:
Online
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module is suitable for students who are experienced programmers, with knowledge of the C/C++ programming languages. The aim of this module is to introduce students to design and development of parallel programs for different parallel architectures. There is a particular emphasis on practical implementation of shared-memory parallel algorithms using Pthreads and OpenMP and message-passing parallel algorithms using the message-passing interface MPI with the C programming language.
Students are introduced to some simple but typical computationally intensive problems, and strategies for solving these problems on multi-processor machines are introduced. Methods to analyse the performance of parallel algorithms on the executing parallel architecture are also introduced. Students are required to implement and experiment with a number of parallel applications. Topics covered in the module include: Vector and superscalar processors: architecture and programming model, optimizing compilers (dependency analysis and code generation), array libraries (BLAS), parallel languages (Fortran 90); Shared-memory multi-processors and multicores: architecture and programming models, optimizing compilers, thread libraries (Pthreads), parallel languages (OpenMP); Distributed-memory multi-processors: architecture and programming model, performance models, message-passing libraries (MPI); Hybrid parallel computing on lusters of multicore CPUs with MPI+OpenMP.

NB. This is a professional module which is part of a professional MSc

About this Module

Learning Outcomes:

By the end of this module, students should: Understand the main principles of parallel computing and orient themselves in parallel computing technologies; Be able to apply simple performance models to performance analysis of parallel algorithms; Be able to write parallel programs using MPI, OpenMP and Pthreads, and MPI+OpenMP; Be able to to theoretically and experimentally analyse the performance of parallel applications.

Indicative Module Content:

Vector and superscalar processors: architecture and programming model, optimizing compilers (dependency analysis and code generation), array libraries (BLAS), parallel languages (Fortran 90).

Shared-memory multi-processors and multicore CPUs: architecture and programming models, optimizing compilers, thread libraries (Pthreads), parallel languages (OpenMP).

Distributed-memory multi-processors: architecture and programming model, performance models, message-passing libraries (MPI), parallel languages (HPF).

Hybrid parallel programming for clusters of mutlicore CPUs with MPI+OpenMP.

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

140
Lectures

24
Practical

36
Total

200

Approaches to Teaching and Learning:
Each topic will be covered at lectures. During lab sessions students will work on individual practical assignments under the guidance of the TA and demonstrators. Practical assignments will require development and implementation of parallel scientific programs, conducting experiments, analysing the results and writing reports.

Requirements, Exclusions and Recommendations
Learning Recommendations:

Fluency in C and familiarity with Unix programming would be an advantage.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (Open Book): Final examination Week 14, Week 15 Standard conversion grade scale 40% No
50
 No
Assignment(Including Essay): Lab. assignments Week 4, Week 7, Week 9, Week 10, Week 12 Standard conversion grade scale 40% No
50
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Assignments' grades are released with comments from TA. Student can ask TA for further individual feedback regarding assessments.

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