Welcome to HPC Workshop 8 - Performance Engineering for Python and Deep Learning — an essential part of your journey toward mastering High Performance Computing (HPC) with HSUper!

This course is part of the reimagined HSUper Workshops series, originally offered as a six-part on-site program. Now presented as flexible, self-paced digital trainings, these workshops are designed to empower HSUper users to independently develop the competencies needed to make the most of HPC resources.

Learning Goals

• Performance Engineering - Optimization: You will learn about Python libraries that you can use to replace traditional data structures and libraries for better performance, as well as about Just-In-Time compilation.

• Performance Engineering - Parallelization:* You will be introduced to running Python programs in parallel, both single-node and multi-node.

• GPU Computing: You will learn about machine learning on HSUper using GPUs, and about running scripts on the GPUs using SLURM. You will also learn about monitoring your code’s performance on GPUs.

Level of Difficulty

This workshop is tailored for HSUper users at the Advanced-Level who are already familiar with the basics HPC, can navigate around HSUper and are already working on projects using methods, software and hardware related to HPC.

Requirements

While this course is free to use for everyone, you will need access to HSUper and access to the campus network (VPN possible), to actually work through it.

• Access to HSUper

It might also be useful to have a look at the content from the first and second HPC Workshops.

• HPC Workshop 1 - A short introduction to HSUper and the Linux Terminal
• HPC Workshop 2 - Data Transfer and System Resources
• HPC Workshop 3 - Module Systems and Slurm Jobs
• HPC Workshop 4 - Advanced Slurm
• HPC Workshop 5 - Loading and Installing Software with Spack
• HPC Workshop 6 - Running Graphical Applications with X-Forwarding
• HPC Workshop 7 - Best Practices for HPC