06-06, 09:00–12:30 (Europe/London), Grand Hall
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.
Previous knowledge expected
Jeremy talks to people who talk to computers about talking to computers.
Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA supporting CUDA and Python. 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 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.
Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source Kubernetes Python framework kr8s in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.
Lawrence Mitchell works and thinks as part of the RAPIDS team at NVIDIA. His focus is on high-productivity, high-performance libraries for data analytics. He leads the technical design and implementation of the RAPIDS-accelerated Polars GPU engine. Prior to joining NVIDIA he was a lecturer in Computer Science and Applied Mathematics at the University of Durham with research interests in high performance simulation of continuum mechanics, structure-preserving numerical methods, and preconditioning techniques for coupled multiphysics problems. He was a founding co-lead and technical architect of the open source Firedrake project for finite element simulation.