GPU Accelerated Python
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
I lead CUDA Python Product Management, working to make CUDA a Python native.
I received my Ph.D. from the University of Chicago in 2010, where Ibuilt domain-specific languages to generate high-performance code for physics simulations with the PETSc and FEniCS projects. After spending a brief time as a research professor at the University of Texas and Texas Advanced Computing Center, I have been a serial startup executive, including a founding team member of Anaconda.
I am a leader in the Python open data science community (PyData). A contributor to Python's scientific computing stack since 2006, I am most notably a co-creator of the popular Dask distributed computing framework, the Conda package manager, and the SymPy symbolic computing library. I was a founder of the NumFOCUS foundation. At NumFOCUS, I served as the president and director, leading the development of programs supporting open-source codes such as Pandas, NumPy, and Jupyter.