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CUDA-Python and RAPIDS for blazing fast scientific computing

Abe Stern from NVIDIA

In this video from the ECSS Symposium, Abe Stern from NVIDIA presents: CUDA-Python and RAPIDS for blazing fast scientific computing.

We will introduce Numba and RAPIDS for GPU programming in Python. Numba allows us to write just-in-time compiled CUDA code in Python, giving us easy access to the power of GPUs from a powerful high-level language. RAPIDS is a suite of tools with a Python interface for machine learning and dataframe operations. Together, Numba and RAPIDS represent a potent set of tools for rapid prototyping, development, and analysis for scientific computing. We will cover the basics of each library and go over simple examples to get users started. Finally, we will briefly highlight several other relevant libraries for GPU programming.

Dr. Abraham Stern is a solutions architect with NVIDIA focused on higher education and research. Abe’s interests lie at the intersection of scientific computing and machine learning, especially as applied to problems in the chemistry and materials science domain. Abe obtained his Ph.D. in computational chemistry from the University of South Florida. Previously, Abe was a postdoctoral scholar at the University of California, Irvine.

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