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.
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.
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.
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Study High Performance Computing (M.Sc. / P.Grad.Dip.) at Trinity
Provided by the School of Mathematics at Trinity College Dublin, this programme is a one year, full-time taught Master's in High Performance Computing.
AwardsNFQ Level 9
Professor Kirk Soodhalter
31st July 2024
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.
Click here for a full list of postgraduate fees
Get in Touch
+353 (0)1 8961485
Prof. Kirk Soodhalter (course coordinator): firstname.lastname@example.org
Register Your Interest
Register your interest in studying at Ireland’s leading university, Trinity College Dublin, the University of Dublin.
Trinity was one of the few schools with a focus on both Mathematics and high-performance computing, a perfect ground for developing my skills. I was also the recipient of the prestigious Government of Ireland Scholarship, which made Dublin and Ireland more compelling. The programme helped me establish a foundation for common techniques and algorithms used in Computational Science. It provided me with an platform for solving problems in high-performance computing on a large scale. Secondly, during my programme, I got familiar with educational, professional and cultural systems in Europe, which has helped me establish myself in the professional community.
Developer at NVIDIA Switzerland
The M.Sc. programme provided the perfect platform for me to pursue a career that combines my knowledge of Mathematics and my passion for Computer Science. The Master's opened up an entirely new avenue of computing for me. It has helped me build on the underpinning knowledge that I gained from my undergraduate degree. It has provided me with plentiful inspiration for ongoing and further research in the area of numerical analysis. I highly recommend the M.Sc. to anyone passionate about learning parallel computing as it provides the must-have knowledge on parallel systems. It will definitely open up more opportunities in both academia and industry. I am now pursuing a Ph.D. in Computational and Mathematical Finance at Dublin Institute of Technology.