Lawrence Mitchell
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.
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.