This means that Python developers already have everything they need to run on the GPU, without having to learn the low-level details of CUDA programming and parallel operations.

GPUs combined with CUDA have been a game-changer for the AI industry, which benefited from the massive improvements in GPU computation power and ease of programmability.

Nvidia wants to extend the success of the GPU beyond graphics and deep learning to the full data science experience. Open source Python library Dask is the key to this.

What is CUDA programming, exactly? According to Nvidia, CUDA is a parallel computing platform and programming model that enables developers to write code and build applications on Nvidia's GPUs.

CUDA-enabled GPUs offer dedicated features for computing, including the Parallel Data Cache, which allows 128, 1.35GHz processor cores in newest generation NVIDIA GPUs to cooperate with each other ...

CUDA Cores shine the brightest when handling tasks that benefit from parallel computation. Tensor Cores use AI to upscale graphics in video games.

The new Scale toolkit promises to be a drop-in replacement for Nvidia CUDA, and that could have major implications.

You can also use nvprof — included with the CUDA software — to get a lot of detailed information about things running on the GPU. Try putting nvprof in front of the two example gocuda lines above.