Module manager: Mantas Mikaitis
Email: M.Mikaitis@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2026/27
COMP3221
This module is not approved as a discovery module
High-performance computing (HPC) is a cornerstone of modern science and technology. From climate modelling and drug discovery to machine learning and financial analytics, HPC enables breakthroughs that would be impossible on conventional systems. This module comprehensively explores the architecture, storage systems, and programming models that underpin high-performance computing (HPC). It provides students with an opportunity to program on a supercomputer and make use of parallel computing resources to solve scalable problems across diverse application domains. Students will gain hands-on experience with industry-standard tools and frameworks, learning how to write and optimise parallel programs using models such as MPI and OpenMP. The module emphasises practical skills in performance profiling, memory optimisation, and efficient resource management - essential for tackling computational challenges at scale.
This module aims to equip students with a deep understanding of high-performance computing architectures, storage systems, and programming models, enabling them to design, implement, and optimise parallel applications that efficiently leverage supercomputing resources for large-scale computational problems across diverse scientific and industrial domains.
On successful completion of the module students will be able to:
define and analyse a complex real-world problem to design and implement an HPC solution applying appropriate engineering design principles and engineering management processes to ensure quality and manage risk. (C1, M1, C2, M2, M3, M3, C5, M5, C6, M6, C9, M9, C14, M14, C15, M15)
identify and discuss legal, ethical, social, professional and sustainability issues relating to the application of high-performance computing. (C8, M8)
select and interpret sources of information to solve complex real-world problems. (C4, M4)
apply industrial best-practice, in the development of a solution considering security, sustainability and engineering design lifecycle. (C7, M7, C10, M10, C15, M15)
select and use tools to design, implement, test, analyse and evaluate project artefacts and identify limitations (C12, M12, C13, M13)
communicate effectively complex topics concerning computer systems to technical and non-technical audiences. (C17, M17)
reflect on their level of mastery of subject knowledge and skills and plan for personal development. (C18, M18)
Introduction to High Performance Computing:
Definition and evolution of HPC.
HPC architecture and infrastructure
Overview of HPC applications in research and industry.
Programming Models for HPC:
Introduction to parallel programming and patterns.
Detailed exploration of standard HPC programming models (e.g. OpenMP, MPI etc.).
Practical considerations and performance optimisation in HPC programming.
Theoretical exploration of advanced programming models (e.g., CUDA) and heterogeneous paradigms.
Case studies demonstrating the application of different programming models in real-world scenarios.
Future Trends in HPC:
Exploration of emerging trends in HPC hardware and software.
Discussion on the convergence of HPC, Big Data, and AI.
Sustainability, energy consumption and Green HPC.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 13 | 2 | 26 |
| Practical | 11 | 2 | 22 |
| Private study hours | 152 | ||
| Total Contact hours | 48 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Introduction to High Performance Computing:
Definition and evolution of HPC.
HPC architecture and infrastructure.
Overview of HPC applications in research and industry.
Programming Models for HPC:
Introduction to parallel programming and patterns.
Detailed exploration of standard HPC programming models (e.g. OpenMP, MPI etc.).
Practical considerations and performance optimisation in HPC programming.
Theoretical exploration of advanced programming models (e.g., CUDA) and heterogeneous paradigms.
Case studies demonstrating the application of different programming models in real-world scenarios.
Future Trends in HPC:
Exploration of emerging trends in HPC hardware and software.
Discussion on the convergence of HPC, Big Data, and AI.
Sustainability, energy consumption and Green HPC.
Check the module area in Minerva for your reading list
Last updated: 08/05/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team