Python for High-Performance Computing

Software setup

Episodes

Reference

Walkthrough for module authors

Description

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.

Prerequisites

  • Basic experience with Python

  • Basic experience in working in a Linux-like terminal

  • Some prior experience in working with large or small datasets

Course Topics

  • 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

Target Audience

Level: 70% beginner, 30% intermediate

Prerequisites: Basic experience with programming in Python

Language: English

Technical Requirements

  • Python and its dependencies

  • Jupyter Notebook for interactive coding

  • Anaconda (optional) for managing dependencies

Instructors

Dominika Regeciova is a Lecturer at IT4Innovations, focusing on the intersection of AI and security. Previously, she worked as a Senior Researcher at Avast. She holds a master’s degree in Information Security from the Faculty of Information Technology at Brno University of Technology (FIT BUT). Her work is driven by a passion for bringing formal theory and advanced AI methods into practical, real-world applications with an emphasis on security and reliability.

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 data science, data visualization, and mathematical modeling leveraging statistical methods and deep neural networks.

Ghaith Chaabane is a researcher at the Advanced Data Analysis and Simulation Laboratory within the IT4Innovations National Supercomputing Center.

About the Infrastructure

Participants 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.

Learning outcomes

This material is for all researchers and engineers who work with large or small datasets and who want to learn powerful tools and best practices for writing more performant, parallelised, robust and reproducible data analysis pipelines.

By the end of this module, learners should:

  • Have a good overview of available tools and libraries for improving performance in Python (link to leaves in skill tree)

  • Knowing libraries for efficiently storing, reading and writing large data (link to leaves in skill tree)

  • Be comfortable working with NumPy arrays and Pandas dataframes for data analysis using Python (link to leaves in skill tree)

  • …

Credit

Don’t forget to check out additional course materials from IT4Innovations National Supercomputing Center. Please contact us if you want to reuse these course materials in your teaching. You can also join the XXX channel to share your experience and get more help from the community.

License

CC BY-SA for media and pedagogical material

Copyright © 2026 IT4Innovations National Supercomputing Center. This material is released by IT4Innovations National Supercomputing Center under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

Canonical URL: https://creativecommons.org/licenses/by-sa/4.0/

See the legal code

You are free to

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.

  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.

  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

  2. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

  3. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

This deed highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. You should carefully review all of the terms and conditions of the actual license before using the licensed material.

MIT for source code and code snippets

MIT License

Copyright (c) 2026, EVITA project, Dominika Regeciova, Tomas Martinovic, Ghaith Chaabane

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Note

To module authors: For code you may use any OSI-approved license as mentioned in https://spdx.org/licenses/, such as Apache License 2.0, GNU GPLv3, MIT. Please make sure to update the deed above and LICENSE.code file accordingly.