If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python.


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Links from the show

Bryce Adelstein Lelbach on Twitter: @blelbach


Episode Deep Dive write up: talkpython.fm/blog


NVIDIA CUDA Python API: github.com

Numba (JIT Compiler for Python): numba.pydata.org

Applied Data Science Podcast: adspthepodcast.com

NVIDIA Accelerated Computing Hub: github.com

NVIDIA CUDA Python Math API Documentation: docs.nvidia.com

CUDA Cooperative Groups (CCCL): nvidia.github.io

Numba CUDA User Guide: nvidia.github.io

CUDA Python Core API: nvidia.github.io

Numba (JIT Compiler for Python): numba.pydata.org

NVIDIA’s First Desktop AI PC ($3,000): arstechnica.com

Google Colab: colab.research.google.com

Compiler Explorer (“Godbolt”): godbolt.org

CuPy: github.com

RAPIDS User Guide: docs.rapids.ai

Watch this episode on YouTube: youtube.com

Episode #509 deep-dive: talkpython.fm/509

Episode transcripts: talkpython.fm


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GPU Programming in Pure Python

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