CUDA stands for Compute Unified Device Architecture, and it’s the reason popular deep learning libraries like Tensorflow & PyTorch are considered “GPU-accelerated”. CUDA is Nvidia’s programming platform that enables developers to leverage the full parallel processng capabilities of GPUs for deep learning applications. Almost all of the major deep learning libraries use CUDA under the hood, but it’s not really something that most developers think about often. In this episode, I’ll demo some progressively more complex CUDA examples by Nvidia to show you how using CUDA results in algorithmic speedups. We’ll use Nvidia’s profiler to clock speeds, then we’ll analyze a pure-CUDA neural network by Sergey Bugrov to understand how a full neural pipeline on the GPU looks like. Enjoy!

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Notebook shown in the video can be found here. It’s kind of messy! It’s a compilation of various code samples by the Nvidia team + Sergey’s neural network. It’s also got the CUDA install steps for Colab:
bit.ly/2uzg9FZ

Nvidia’s CUDA Documentation:
docs.nvidia.com/

Some awesome tutorials by Nvidia on CUDA that helped me:
cuda-tutorial.readthedocs.io/en/latest/tutorials/t…
www.nvidia.com/docs/IO/116711/sc11-cuda-c-basics.p…
devblogs.nvidia.com/even-easier-introduction-cuda/

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Credits:
Nvidia team
Sergei Bugrov
Image assets are from across the Web, i take no credit for them

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