Deep Learning Compilers: Architecture, Optimization, and Empirical Characterization
AI-generated summary This paper presents a systematic analysis of deep learning compilers, covering their architectural evolution, compilation workflows, optimization techniques, and emerging research directions. Experiments using PyTorch and TorchInductor on an NVIDIA RTX A6000 demonstrate speedups of up to 8.21x for fusion-friendly computation graphs, while other workloads exhibit limited improvements.