This paper presents a performance model tailored for warp specialization kernels, focusing on factors such as warp size, tilling size, input matrix size, memory bandwidth, and thread divergence. Our model offers accurate predictions of execution time by leveraging differential equations validated through simulations and experiments. The insights gained from this model not only enhance our understanding of warp specialization techniques but also have practical implications for optimizing GPU-accelerated applications through compiler optimizations, kernel parameter tuning, and algorithm design.
@article{arxiv.2506.11209,
title = {A Performance Model for Warp Specialization Kernels},
author = {Zhengyang Liu and Vinod Grover},
journal= {arXiv preprint arXiv:2506.11209},
year = {2025}
}