This blog post from
@nvidia is intended to develop a clearer way of reasoning about performance optimization for PyTorch models and other representations.
It explores questions such as: What makes one compiled engine better than another? Why can individually promising decisions fail end to end? How should accuracy constrain performance optimization?
It introduces the Global Performance Tuner, a feature for automatic performance tuning released in TensorRT 11.1, and explores these questions through an intuitive example and a mathematical framework.
Read the full blog post here: