Today I started covering a tutorial on GPU computing. This repository contains a number of Jupyter notebooks with examples covering basic usage of numba and cupy on the gpu.
Since I don’t have a GPU, I followed the following steps to run the examples:
- Clone the repository into my local Google Drive directory.
- Using Google Colab, open any of the notebooks to interact with the code online.
- If the notebook necessitates the use of a GPU, go to ‘Runtime’, ‘Change Runtime’, and on ‘Hardware accelerator’ select ‘GPU’.
Google Colab is an online resource that allows interactive python execution through a hosted Jupyter notebook service. In particular, Google Colab provides a virtual machine with resources including CPU, GPU, and TPU for research and experimentation. This is particularly useful for students and researchers who do not have these resources locally.