Voyager: Input-Adaptive Algebraic Transformations for High-Performance Graph Neural Networks

作者：Yangjie Zhou, Wenting Shen, Jingwen Leng, Shuwen Lu, Zihan Liu, Weihao Cui, Zhendong Zhang, Wencong Xiao, Baole Ai, Yong Li, Wei Lin, Deze Zeng, Yun Liang, Quan Chen, Ning Liu, Minyi Guo · 年份：2025 · DOI：10.1145/3676642.3736121 · 被引用次数：1 · 研究领域：Graph Theory and Algorithms、Advanced Graph Neural Networks、Machine Learning in Materials Science

Graph neural networks (GNNs) are gaining popularity in diverse application domains and growing in complexity.As a result, it is crucial to achieve high-performance GNN execution.Among various techniques, algebraic transformations, including operator reordering and operator fusion, have been successfully applied to improve the computation and memory access efficiencies of DNN models.However,