The Living Thing / Notebooks :

GPU computation

mostly for python

Options for doing SIMD computation with fewer tears, for people, like me, who give not a damn about implementation details, but just want it to work fast enough.

Aside: you’re going to have to mess around with downloading proprietary GPU toolkits from the manufacturer. Tedious. Consider instead paying some cloud provider to rent their pre-configured machines.

Hip, but not so hop as FPGA computation.


  1. Just writing GSL shaders using your compiler and the relevant manufacturer toolboxes. Laborious and tangential, unless you are a GPU-algorithm researcher. But could be fun I s’pose. See the book of shaders.
  2. for data-oriented computational data flow graphs, use one of those toolkits from the deep_learning community. These are easy and performant, although not quite as general as just writing a damn shader.

Neither fits?

OK, try these (emphasis on integration with python):

The book of shaders

This book will focus on the use of GLSL pixel shaders. First we’ll define what shaders are; then we’ll learn how to make procedural shapes, patterns, textures and animations with them. You’ll learn the foundations of shading language and apply it to more useful scenarios such as: image processing (image operations, matrix convolutions, blurs, color filters, lookup tables and other effects) and simulations (Conway’s game of life, Gray-Scott’s reaction-diffusion, water ripples, watercolor effects, Voronoi cells, etc.). Towards the end of the book we’ll see a set of advanced techniques based on Ray Marching.