Katrina Riehl

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.

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.

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.

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.

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.

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.

If you have interest in NumPy, SciPy, Signal Processing, Simulation, DataFrames, or Graph Analysis, we'd love to hear what performance you're seeing and how you're measuring. We've been working to accelerate your favorite packages on GPUs

This talk will describe the typical and not-so-typical questions around data science tools, open source, privacy, security, communication, testing, deployment, and anything else that might come up while trying to blaze the machine learning trail.