High Performance Computing

The High Performance Computing MSc program lasts 4 /four/ semesters. During the first semester the training is aimed at the study of fundamental courses followed by two semesters for more specialized courses and one semester internship focused on a practical MSc project and written MSc thesis. The studies have a strong practical focus and they have an opportunity to access a wide range of HPC platforms and technologies.

Included fundamental courses provide a broad-based coverage of the fundamentals of HPC, parallel computing, mathematics and data science. There is a set of optional courses focused on more specific in-depyh knowledge and more practical applications. All fundamental courses are offered as elective courses and students can choose from them depending on their educational needs.

The elective courses for additional specialized training provided in the third semester give an opportunity students to make their choice depending on the field they wish to specialize in.

During the education in the master's program, students have also the opportunity to study disciplines oriented towards the acquisition of so-called "soft skills".

As a summary, the content of the MSc Program includes three types of courses:

• Fundamental courses, providing base knowledge and practical skills related to four specific domains: Mathematics and statistics, Software engineering, Parallel programming and Computer architectures.
• Specialization courses, providing knowledge and skills to various application domains where HPC and Data Science have important contribution (physics, chemistry, biology, medicine, finance and others)
• Transversal courses providing additional soft skills 

The MSc Program has two main specializations:

• Numerical and data analysis for science domains (including knowledge areas like Parallel algorithms, Monte Carlo, Linear Programming, Sorting, Fast Fourier Transform, Computational Linear Algebra, Computational Numerical Methods, Quantum Algorithms, Computational Biology, Computational Physics, Computational Chemistry, Computational Fluid Dynamics, Data Management and Analytics, etc.)
• Software system development, support and performance analysis (including knowledge areas like AI, Data science, Computational Science, Data Management and Analytics, Heterogeneous Systems: CPU, GPU, FPGA, CGRA; Container Technologies, Virtualization Technologies, Performance Analysis, Operating Systems, Parallel File Systems, High-Speed Networking, Container Technologies, Virtualization Technologies, Integration of HPC and Cloud, Server administration, Security, Infrastructure Setup and Management)

Generalay, included courses are focused on providing the theoretical and technical background for HPC. The individual modules consist of lectures and hands-on sessions in and address the following objectives:

• Provide an overview of the various tools and technologies used in HPC and the different ways to apply them;
• Acquire competences in understanding the technical requirements of scientific and technical HPC applications to plan and set up HPC facilities;
• Provide an outlook on future developments of HPC.

The master program includes a part of the education focused on how HPC methodology (i.e. the combination of algorithms, hardware and software tools) can be exploited in specific problems. This part of the education is strongly focused on algorithms and methods, with the aim of letting the students acquire practical skills and experience. Thus students learn to match methodology to solve specific problems, and be introduced to key subjects of various scientific areas and industrial or engineering tasks requiring high-performance computations.

Students are required to directly implement and apply methodologies to solve technical problems of increasing difficulty.

In the last part of the master program, each student carries out an internship in specialized HPC center, followed by HPC-oriented project tailored to professional interests and expectations of the students.

In order to graduate with the degree of a Master in High Performance Computing, all students are advised to undertake a technically challenging project in collaboration with an on-going research team and/or an industrial partner. In the projects, students must apply the knowledge obtained in the previous sections of the program to a scientific or technical computational problem that could, for instance, require the addition or improvement of parallelization or optimization or a port to a new HPC technology, or a combination thereof to a scientific application. Project proposals must be submitted to and accepted by a committee and must be overseen by a qualified adviser.