HPC Course

Support and exercise files for the HPC Course, designed to help students learn and practice high-performance computing concepts. Exercises are provided to be run in an HPC cluster, and some can also be executed in Google Colab.

Structure of the Repository

• Assignments: Practical assignments for each module of the course.
• Chapter Examples: Example notebooks to help you practice the concepts covered in the lessons.

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Loading Notebooks from GitHub in JupyterHub

You can load the following notebooks directly from GitHub into JupyterHub by using the "File -> Open from URL" option in Jupyter.

Assignments

M1.P1. Intro to Slurm

M1.P2. Simple Parallel Codes

M2.P1. OpenMP

M2.P2. MPI

M3.P1. Performance Tuning

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Chapter Examples (Lessons)

1.M1.S1. Evolution and Fundamentals of HPC (1.1)

2.M1.S2. Architectural Overview of HPC Systems (1.2)

3.M1.S3. Resource Management & Performance Metrics in Parallel

4.M1.S4. Cloud-based HPC and Virtualization Containers in HPC

5.M1.S5. HPC in Health & Neurosciences (1.5)

7.M2.S2. OpenMP (2.2)

8.M2.S3. Deep Dive MPI (2.3)

9.M2.S4. GPU Computing, OpenACC, CUDA Basics (2.4)

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How to Use

1. HPC Cluster: These notebooks are meant to be executed on an HPC cluster, but some can also run on platforms like Google Colab.
2. Direct URL Loading: Use the provided raw GitHub links to load the notebooks directly into Jupyter or JupyterHub using the "File -> Open from URL" option.

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Contributions

Feel free to contribute to the course materials by creating pull requests, adding additional exercises, or reporting issues.