Using GPU acceleration with Pytorch to make your algorithms 2000% faster

Most developers are aware that some algorithms can be run on a GPU, instead of a CPU, and see orders of magnitude speedups. However, many people assume that:

1. Only specialist areas like deep learning are suitable for GPU

2. Learning to program a GPU takes years of developing specialist knowledge

It turns out that neither assumption is true! Nearly any non-recursive algorithm that operates on datasets of 1000+ items can be accelerated by a GPU. And recent libraries like Pytorch make it nearly as simple to write a GPU accelerated algorithm as a regular CPU algorithm.

In this talk we’ll explain what the mean-shift clustering algorithm is, and why it’s important for many data science applications. We’ll first implement it in python (with numpy), and will then show how to port it to Pytorch, showing how to get a 20x performance improvement in the process.