Andy Terrel
I lead CUDA Python Product Management, working closely with RAPIDS, Omniverse, and Math Libraries to unify NVIDIA's foundational offering for Python developers and the Python community.
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.
Sessions
Python promises productivity, GPUs promise performance, but if you ever try to fire up a program on a GPU you will find that it is often slower than a CPU. Over the last decade, the Python ecosystem has embraced GPUs in numerous libraries and techniques. We survey what works with GPUs and some of the libraries that one can use to accelerate the Python workflow on a GPU.