Speed Cubing for Machine Learning - Episode 3
N. Morizet, Towards Data Science, March 22nd, 2021.
Abstract: In episode 1, we described how to generate 3D data as fast as possible to eventually feed some Generative Adversarial Networks, using CPUs, multithreading and Cloud resources. We reached a rate of 2 billion data points per second.
In episode 2, we used a local GPU, a framework called RAPIDS, and libraries such as CuPy and VisPy. We went even faster reaching almost 5 billion data points per second!
In this final episode, we will still focus on speed but, this time, we are going to perform actual computation with the help of several GPUs.