2026-07-13 –, Accelerated Computing
GPU-powered math libraries are the core of accelerated scientific computing. The nvmath-python package aims to provide intuitive pythonic APIs giving users full access to all features offered by NVIDIA's libraries in a variety of execution spaces. It is your one-stop shop for Pythonic math libraries on the GPU.
Installation Instructions: We will provide Nvidia Brev cloud instances. Attendees will only need their laptops and an Internet connection.
In this hands-on tutorial, we will explore the nvmath-python library, bringing the power of the CUDA-X math libraries to Python. You will learn:
- The landscape of CUDA Python libraries
- nvmath-python host, device, and distributed APIs
- How nvmath-python interoperates with existing array/tensor libraries
Basic knowledge of the Python scientific computing libraries
Installation Instructions:We will provide Nvidia Brev cloud instances. Attendees will only need their laptops and an Internet connection.
Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA leading the CUDA Education program. For over two decades, Katrina has worked extensively in the fields of scientific computing, machine learning, data science, and visualization. Most notably, she has helped lead data initiatives at the University of Texas Austin Applied Research Laboratory, Anaconda, Apple, Expedia Group, Cloudflare, and Snowflake. She is an active volunteer in the Python open-source scientific software community and currently serves on the Advisory Council for NumFOCUS.