Habitat - A Runtime-Based Computational Performance Predictor for Deep Neural Network Training

Explore a runtime-based computational performance predictor for deep neural network training in this USENIX ATC '21 conference talk. Dive into the challenges of selecting GPUs for DNN training, considering both performance and cost factors. Learn about Habitat, a new technique that leverages existing GPUs to make informed predictions about performance on other GPU options. Understand the concept of wave scaling and its application in predicting execution times across different GPU architectures. Discover how Habitat achieves accurate iteration execution time predictions for various DNN models across multiple GPU architectures. Gain insights into the implementation of Habitat as a Python library supporting PyTorch, and explore its potential for helping researchers and practitioners make cost-efficient GPU selections for their deep learning projects.