Description:

In this workshop, you will learn about accelerating Python code with GPUs through practical examples. We will introduce two critical approaches to Python GPU-accelerated computing: using CuPy as a drop-in replacement for SciPy and NumPy and utilizing Numba for writing custom accelerated code without leaving the Python language. Throughout the session, we will demonstrate these tools with examples, equipping you with the knowledge to accelerate your Python codes using GPUs. This workshop will not be recorded. 

Prerequisites: Before attending this workshop, researchers new to Quest should:   

• Apply for a Quest allocation. We recommend “Research Allocation I” if you are new to Quest. If your lab has a joint Quest allocation, you can also apply to join that allocation. 
• Watch the Introduction To Quest video series to get an overview of the system, learn how to submit jobs, and become familiar with best practices. To learn more about Quest, see  About Quest. 
• Become familiar with Unix command line: see our Intro Command Line and Bash Scripting workshops and check Online Resource Guide for learning basic command line skills.