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Effective High-Performance Computing & Data Analytics

Description

COURSE OBJECTIVES

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 (3rd year Bachelor's students, Master’s students, and Ph.D 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.
                  

 

COURSE DESCRIPTION

The following topics will be covered:

  • GPU and ARM architectures
  • GPU programming (CUDA ), and ARM programming
  • Performance optimization and scientific libraries (Kokkos)
  • Interactive supercomputing (JupyterLab)
  • Python HPC libraries (Numpy/SciPy/Dask/Numba)
  • Introduction to Machine Learning and GPU optimized frameworks (Rapids)
  • Deep Learning on HPC platforms

 

LEARNING METHODS

Extensive practical and exercise lab sessions.

 

EXAMINATION INFORMATION
The examination method will be a quiz on the last day of the summer school program - Thursday, July 21, at 13:30 CEST (Central European Summer Time). The quiz will be done on iCorsi and covers all objectives and content of the course. It will include closed-ended questions (which will then be assessed automatically by the system) and open-ended questions (which will be assessed by the lecturers and professors). Students will be notified about the result of the examination by Friday, July 29, 2022.

 

REFERENCES
For all additional information please refer to a more detailed website at CSCS: https://www.cscs.ch/events/upcoming-events/event-detail/summer-university-on-hpc-and-data-analytics/

People

 

Schenk O.

Course director

Holt T. A. B.

Assistant

Lechekhab M.

Assistant

Pasadakis D.

Assistant

Additional information

Semester
Spring
Academic year
2021-2022
ECTS
4
Language
English
Education
Master of Science in Artificial Intelligence, Elective course, Lecture, 1st year (6 ECTS)
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