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

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, directly applying these methods to GNNs results in suboptimal performance. This is primarily due to the diversity of input graph structures, making it challenging to predict performance benefits. Additionally, the complex dependencies introduced by these input graph structures limit the effectiveness of existing optimization techniques.