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New HPC permanent page
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# On-site HPC trainings | ||
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We propose periodical on-site events for our users. They are free of charge and can be attended by anyone from the University of Luxembourg faculties and interdisciplinary centers. | ||
Additionally, we also accept users from LIST and LIH. If you are part of another public research center, please [contact us](mailto:hpc-school-for-beginners@uni.lu). | ||
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## HPC School for beginners | ||
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This event aims to equip you with essential skills and knowledge to embark on your High-Performance Computing journey. The event is organized monthly and is composed of two half days (usually 9am-12pm). | ||
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Next planned sessions: | ||
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- Nov. 13th and 14th, MNO - Belval campus | ||
- Dec. 11th and 12th, MNO - Belval campus | ||
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Feel free to only attend the second day session if: | ||
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- You can connect to the ULHPC | ||
- You are confortable with the command line interface | ||
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Limited spots available (usually 30 max). Please register by [emailing us](mailto:hpc-school-for-beginners@uni.lu) | ||
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### Morning 1 - Accessing the Cluster and Command Line Introduction | ||
Learn how to access the HPC cluster, set up your machine, and navigate the command line interface effectively. Gain confidence in interacting with the cluster environment. | ||
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### Morning 2 - Understanding HPC Workflow: Job Submission and Monitoring | ||
Explore the inner workings of HPC systems. Discover the process of submitting and managing computational tasks. Learn how to monitor and optimize job performance. | ||
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## Python HPC School | ||
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In this workshop, we will explore the process of improving Python code for efficient execution. Chances are, you 're already familiar with Python and Numpy. However, we will start by mastering profiling and efficient NumPy usage as these are crucial steps before venturing into parallelization. Once your code is fine-tuned with Numpy we will explore the utilization of Python's parallel libraries to unlock the potential of using multiple CPU cores. By the end, you will be well equipped to harness Python's potential for high-performance tasks on the HPC infrastructure. | ||
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### Target Audience Description | ||
The workshop is designed for individuals who are interested in advancing their skills and knowledge in Python-based scientific and data computing. The ideal participants would typically possess basic to intermediate Python and Numpy skills, along with some familiarity with parallel programming. This workshop will give a good starting point to leverage the usage of the HPC computing power to speed up your Python programs. | ||
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### Next planned sessions | ||
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- Nov. 17th and 20th, MNO - Belval campus | ||
- Additional dates will soon be announced | ||
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Limited spots available (usually 30 max). Please register by [emailing us](mailto:hpc-school-for-beginners@uni.lu) | ||
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### First day – Jupyter notebook on ULHPC / profiling efficient usage of Numpy | ||
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#### Program | ||
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- Setting up a Jupyter notebook on an HPC node - 10am to 11am | ||
- Taking time and profiling python code - 11am to 12pm | ||
- Lunch break - 12pm to 2pm | ||
- Numpy basics for replacing python loops for efficient computations - 2pm to 4pm | ||
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#### Requirements | ||
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- Having an HPC account to access the cluster. | ||
- Basic knowledge on SLURM (beginners HPC school). | ||
- A basic understanding of Python programming. | ||
- Familiarity with Jupyter Notebook (installed and configured). | ||
- A basic understanding of Numpy and linear algebra. | ||
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### Second day – Improving performance with python parallel packages | ||
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#### Program | ||
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- Use case understanding and Python implementation - 9am to 10am | ||
- Numpy implementation - 10am to 11am | ||
- Python’s Multiprocessing - 11am to 12pm | ||
- Lunch break - 12pm to 2pm | ||
- PyMP - 2pm to 3pm | ||
- Cython - 3pm to 4pm | ||
- Numba - 4pm to 5pm | ||
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#### Requirements | ||
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- Having an HPC account to access the cluster. | ||
- Basic knowledge on SLURM (beginners HPC school). | ||
- A basic understanding of Python programming. | ||
- Familiarity with Jupyter Notebook (installed and configured). | ||
- A basic understanding of Numpy and linear algebra. | ||
- Familiarity with parallel programming. |
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