Computing - High Performance Computing
The M.Sc. in High-Performance Computing is a one year full-time programme run by the School of Mathematics at Trinity College, Dublin. The degree provides practical training in the emerging field of high-performance technical computing, which has applications in scientific simulation and mathematical modelling of systems in areas ranging from telecommunications to financial markets.
The aim of the course is to train students in practical applications of high-performance technical computing in industry, finance and research. During your year in Trinity, you will develop the expertise to make use of a large number of computer processing cores and use them to solve large numerical problems quickly, precisely and reliably. The course presents the practical mathematical skills needed to translate descriptions of complex systems into a form the computer can manipulate and solve efficiently.
The programme is taught in close collaboration with Research IT (previously The Trinity Centre for High Performance Computing) and the Schools of Physics and Chemistry, It has close links to the IITAC interdisciplinary research project in computational science.
Is This Course For Me?
The course is aimed at graduates with a degree in a technical discipline such as mathematics, physics, engineering, chemistry or mathematical finance. No prior programming experience is assumed but some familiarity with the concepts is useful. A background in basic mathematical concepts is also important.
The M.Sc. in High Performance Computing is a one-year full-time taught Masters degree.
To complete your M.Sc. studies, you should take a total of 60 ECTS units of coursework plus a 30 ECTS project, making a total of 90 ECTS. Many modules will have a written exam in the summer examination period. Most modules include some amount of continuous assessment.
Course topics range from computer architecture, software optimisation, and parallel programming through to classical simulation and stochastic modelling. Application areas include simulation of physical, chemical and biological systems, financial risk management, telecommunications performance modelling, optimisation and data mining. The course has a number of optional elements, allowing specialisation in application areas.
The course includes a strong practical element. Students have unlimited access to a dedicated teaching computing laboratory, and access to the facilities of Research IT (previously The Trinity Centre for High Performance Computing, TCHPC), which include large-scale parallel computers. Career opportunities include mathematical modeling, simulation and forecasting, data-base mining and resource management. The techniques covered during the year will allow students to work in advanced software development including parallel and concurrent software applications. High-performance technical computing methods are becoming increasingly widespread in research into mathematics, physics, chemistry and biotechnology, engineering and finance, providing a wide range of options for the student wishing to go on to further research.
Applicants should normally have a first or upper second-class (2.1) degree in a subject with a significant mathematical component and should have some knowledge of computing and numerical simulation methods.
Closing Date: 31st July 2024.
Please click on "Application Weblink" above.
1 year full-time.
Next Intake: September 2024.
Post Course Info
This programme equips students with the combination of programming skills and mathematical insight to enable them to go on to careers or academic research in large-scale modelling, simulation or numerical multi-core software development. To make use of powerful modern computing systems, you will learn the programming tools needed, the best algorithms adapted to solve different types of problem and how to maximise the impact of available resources.
Successful graduates of the course go on to careers in technical and scientific computing and modelling, either in industrial or academic positions. A substantial number of graduates begin research towards their Ph.D. directly after completing the course, studying topics as diverse as astrophysics, biomolecular modelling, fluid mechanics, and financial mathematics.