GNNPilot: A Holistic Framework for High-Performance Graph Neural Network Computations on GPUs

Graph Neural Networks (GNNs) have emerged as powerful tools for graph-based machine learning tasks, but their performance is often constrained by inefficient sparse operators and limited hardware utilization during multi-operator workflows. This article presents GNNPilot, a holistic optimization framework that addresses these challenges through three key innovations. First, we introduce two packing strategies for gather operators, including neighbor packing for load balancing in sparser graphs, and bin packing with a new sparse format for enhanced data locality in denser graphs. Second, we propose dynamic parallelization methods and a novel row panel-based kernel fusion technique to optimize complex multi-operator GNN models. Third, we develop a lightweight sampling-based auto-tuning mechanism that adapts the framework’s optimization strategies to varying input characteristics. Built upon tensor expression-based intermediate representations, GNNPilot maintains the flexibility to optimize both popular and customized GNN models. Extensive experiments across diverse GNN models and graph datasets demonstrate that GNNPilot achieves substantial speedups over state-of-the-art implementations in both the performance of single operators and the efficiency of end-to-end inference. These results establish GNNPilot as an efficient and adaptive solution for accelerating GNN computations on modern GPU architectures.