Deploying a trained neural network involves more than translating each operator into a GPU kernel. It is a compilation problem with many legitimate answers. For example, when NVIDIA TensorRT builds an engine, it makes interdependent decisions about graph-IR transformations, fusion, implementation selection, memory placement, and quantization. This process is called scheduling. Different scheduling choices can preserve model semantics yet produce very different inference performance, making scheduling central to deep learning compiler optimization.

No single scheduling strategy is best for every model and deployment environment. A transformation that accelerates one network may slow another. A kernel that is faster in isolation may interact poorly with fusion, tensor layouts, or memory traffic elsewhere in the graph. Aggressive scheduling may improve performance while violating an application’s accuracy requirement. These coupled, model-dependent effects often appear only after the complete engine is built and measured, making the right strategy increasingly difficult to predict.

This post is Part 1 of a two-part series that is intended to develop a clearer way of reasoning about performance optimization. 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 mathematical framework.