A collection of convolution algorithms accelerated with NumPy, CuPy and Numba. Written for instructive purposes and to complement the somewhat lacking documentation on how to write CUDA kernels in CuPy and Numba, and it does so with a motivating/fun application -- and no, it's not Convolutional NNs 😒

Convolution is used here to evolve a Game of Life (the prototypical Cellular Automaton model). CAs, which compute the viability of a cell (ie a binary value in a 2D grid) based on the values of the 8 neighbouring cells (0 = dead, 1 = alive) , naturally benefit from the optimizations used in NumPy's various convolution algorithms.

There are multiple convolution methods in NumPy/SciPy and each of them can be further accelerated with CuPy or Numba. So how are we to choose one? This module aims to provide insights on this question in an easy and (hopefully) fun way. Apart from sheer speed, relevant issues that will emerge through experimentation are:

• Compatibility/universality: not all methods work on all systems. For starters, to use the GPU-accelerated versions, you need a working CUDA Developer's Toolkit installation. Furthermore, this code is untested on Linux-based systems (although it is expected to work).
• (Not so) subtle issues with array sizes/alignment -- for example there seems to be an issue with CuPy's RawKernel method for arrays whose dimensions are not powers of 2. I'm currently investigating this and will have something to say about it (a fix and/or explanation) in the update.
• Choice of certain parameters, such as the "threads per block" in CUDA kernels.

• If you like CAs (who doesn't), check out my other project, which has a full-fledged GUI that allows you to run, visualize and save CAs, among other things.
• To use CUDA functionality (CuPy, Numba's cuda.jit), you need to install the appropriate version of the CUDA Toolkit and CuPy. Head over to CuPy's installation guide for details. The existing functions have been tested on CUDA 11.0 and 11.1.
• Since there is no requirements.txt or setup.py, users should be aware that on Windows x64 system, NumPy must be of version other than 1.19.4 -- for the (gory) details of why this is, see here.