Katrina Riehl
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.
Sessions
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.
Scientific researchers need reproducible software environments for complex applications that can run across heterogeneous computing platforms. Modern open source tools, like Pixi, provide automatic reproducibility solutions for all dependencies while providing a high level interface well suited for researchers.
This tutorial will provide a practical introduction to using Pixi to easily create scientific and AI/ML environments that benefit from hardware acceleration, across multiple machines and platforms. The focus will be on CUDA applications, such as machine learning frameworks and use of CUDA Tile, as well as using pixi-build to construct bespoke CUDA enabled conda packages.
Installation Instructions: https://matthewfeickert-talks.github.io/reproducible-cuda-workflows-with-pixi-scipy-2026/setup/
This Birds of a Feather session will bring together developers, users, researchers, and educators interested in GPU-accelerated Python. The discussion will explore the current state of the ecosystem, new library developments, and strategies for making GPU acceleration more accessible to a broader scientific audience. Topics may include performance optimization, debugging and profiling, education and training, and opportunities for collaboration across projects and communities.