We'll explore best practices for writing CUDA kernels using Python, empowering developers to harness the full potential of GPU acceleration. Gain a clear understanding of the structure and functionality of CUDA kernels, learning how to effectively implement them within Python applications.

If you have interest in NumPy, SciPy, Signal Processing, Simulation, DataFrames, Linear Programming (LP), Vehicle Routing Problems (VRP), or Graph Analysis, we'd love to hear what performance you're seeing and how you're measuring.

As general purpose GPU programming has risen in popularity, many Python programmers have expressed a need to use this technology in their libraries and applications. They soon realize that the GPU landscape is vast and sometimes difficult to traverse for Python users.

In this talk, I will demystify the CUDA-enabled Accelerated Python landscape, focusing on the advantages and disadvantages of popular libraries, the common performance issues encountered, and the best practices to getting the most out of your GPU. Topics include CuPy, numba, nvmath-python, cuDF, and cuML.

This talk is beginner-friendly, but even the most seasoned programmer will gain insight into the Python GPU computing landscape.

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.

ABOUT THE TALK: This talk walks you through the structure of NumFOCUS, the programs, challenges, and vision for a sustainable, inclusive, and vibrant open source community. This talk will deep dive on sustainability endeavors, including diversity and inclusion, and how you can get involved in the NumFOCUS community.

ABOUT THE SPEAKER: Dr. Katrina Riehl is President of the Board of Directors at NumFOCUS, Head of the Streamlit Data Team at Snowflake, and Adjunct Lecturer at Georgetown University. For almost two decades, Katrina has worked extensively in the fields of scientific computing, machine learning, data mining, and visualization. Most notably, she has helped lead data science efforts at the University of Texas Austin Applied Research Laboratory, Apple, HomeAway (now, Vrbo), and Cloudflare.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

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