Mike McCarty
Mike is a Senior Software Engineering Manager at NVIDIA, leading teams working on RAPIDS Cloud and HPC deployments, build infrastructure, and PyData projects. Mike is a former member of the advisory counsel at NumFOCUS and Prefect. He holds two BS degrees in Computer Science and Physics, and has over 20 years of experience in astronomy, computational sciences, data science, machine learning, and enterprise products.
Sessions
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.