Effective High-Performance Computing & Data Analytics
This course will focus on the effective exploitation of state-of-the-art hybrid High-Performance Computing (HPC) systems with a special focus on Data Analytics. The content of the course is tailored for intermediate graduate students interested in both learning parallel programming models, and having hands-on experience using HPC systems. Starting from an introductory explanation of the available systems at CSCS, the course will progress to more applied topics such as parallel programming on accelerators, code optimization, scientific libraries, and deep learning software frameworks. The course will be offered online to also allow double-degree Master students to enroll.
The following topics will be covered:
- GPU programming
- Performance optimization and scientific libraries
- Interactive supercomputing (JupyterLab)
- Python HPC libraries (Numpy/SciPy/Dask/Numba/CuPy)
- Introduction to Machine Learning and GPU optimized frameworks (Rapids)
- Deep Learning on HPC platforms (TensorFlow)
Extensive practical and exercise lab sessions.
For all additional information please refer to the course website from 2021: https://www.cscs.ch/events/upcoming-events/event-detail/cscs-usi-summer-school-2021/
Master of Science in Artificial Intelligence, Elective course, Lecture, 2nd year (6 ECTS)
Master of Science in Computational Science, Elective course, Lecture, 1st year (6 ECTS)
Master of Science in Computational Science, Elective course, Lecture, 2nd year (6 ECTS)
PhD programme of the Faculty of Informatics, Summer School, Lecture, 1st year
PhD programme of the Faculty of Informatics, Summer School, Lecture, 2nd year