Annotation
This training introduces participants to Python for high-performance computing, covering parallel programming, performance optimization, and HPC resource utilization. Designed for researchers and developers, the course includes hands-on sessions to enhance practical skills.
Target Audience and Purpose of the Course:
- Python's role in HPC and performance optimization
- Parallel programming techniques for efficient computing
- How to utilize HPC resources effectively
- Hands-on experience with lab exercises for practical skills
Participants will have access to the Karolina supercomputer for hands-on sessions, utilizing both CPU and GPU resources. Karolina, operational since 2021, is the most powerful supercomputer in the Czech Republic and ranks among Europe's top systems. It features a standard part with 720 nodes, delivering 11.6 PFlop/s for traditional HPC simulations, and an accelerated section comprising 72 servers, each equipped with 8 GPU accelerators, achieving up to 360 PFlop/s for AI computations.
This infrastructure supports complex scientific and industrial challenges, including numerical simulations, data analysis, and artificial intelligence applications.
Level
70% beginner, 30% intermediate
Language
English
Prerequisites
beginner experience with programming in Python
Technical requirements:
- Python and it’s dependencies
- Jupyter Notebook for interactive coding
- Anaconda (optional) for managing dependencies
Tutors
Tomas Martinovic is a senior researcher at the Advanced Data Analysis and Simulation Laboratory within the IT4Innovations National Supercomputing Center. His work primarily focuses on the data science, data visualisation, and mathematical modeling leveraging statistical methods and deep neural networks.
Ghaith Chaabane Researcher at the Advanced Data Analysis and Simulation Laboratory within the IT4Innovations National Supercomputing Center.
