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.…