Target your GPU easily with Numba’s CUDA JIT
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I’ve written about the Python library Numba before. Check my article out using the link below,
The TL;DR of the above was that I showed how to realise significant speed up in your Python code using Numba. Numba is a high-performance Python library designed to optimize your code for speed. At its core, Numba is a Just-In-Time (JIT) compiler that translates a subset of Python and NumPy code into fast machine code. This process is automatic and dynamic, allowing Python developers to gain real performance improvements with minimal changes to their original Python code.
The regular Numba JIT compiler is all about optimising code run-time for your CPU, but if you are lucky enough to have access to a GPU, in this article, I’ll show you how you can use Numba again, this time with its CUDA JIT, to accelerate your Python code even further by targeting the GPU to run code on.