with
Dr. Katrina Riehl
(NumFOCUS; Snowflake; Georgetown University)
,
Lawrence Mitchell
,
Jeremy Tanner
,
Jacob Tomlinson
Accelerating Python using the GPU is much easier than you might think. We will explore the powerful CUDA-enabled Python ecosystem in this tutorial through hands-on examples using some of the most popular accelerated scientific computing libraries.
Topics include: - Introduction to General Purpose GPU Computing - GPU vs CPU - Which processor is best for which tasks - Introduction to CUDA - How to use CUDA with Python - Using Numba to write kernel functions - CuPy - cuDF
No prior experience with GPU's is necessary, but attendees should be familiar with Python.