GTuner
Qi Sun, Xinyun Zhang, Hao Geng, Yuxuan Zhao, Yang Bai, Haisheng Zheng, Bei Yu
Abstract
It is an open problem to compile DNN models on GPU and improve the performance. A novel framework, GTuner, is proposed to jointly learn from the structures of computational graphs and the statistical features of codes to find the optimal code implementations. A Graph ATtention network (GAT) is designed as the performance estimator in GTuner. In GAT, graph neural layers are used to propagate the information in the graph and a multi-head self-attention module is designed to learn the complicated relationships between the features. Under the guidance of GAT, the GPU codes are generated through auto-tuning. Experimental results demonstrate that our method outperforms the previous arts remarkably.