Fast Sparse GPU Kernels for Accelerated Training of Graph Neural Networks.

Graph Neural Networks (GNNs) are gaining huge traction recently as they achieve state-of-the-art performance on various graph-related problems. GNN training typically follows the standard Message Passing Paradigm, in which SpMM and SDDMM are the two essential sparse kernels. However, existing sparse GPU kernels are inefficient and may suffer from load imbalance, dynamics in GNN computing, poor memory efficiency, and tail effect. We propose two new kernels, HybridParallel SpMM (HP-SpMM) and Hybrid-Parallel SDDMM (HPSDDMM), that efficiently perform SpMM and SDDMM on GPUs with a unified hybrid parallel strategy of mixing nodes and edges. In view of the emerging graph-sampling training, we design the Dynamic Task Partition (DTP) method to minimize the tail effect by exposing sufficient parallelism. We further devise the Hierarchical Vectorized Memory Access scheme to achieve aligned global memory accesses and enable vectorized instructions for improved memory efficiency. We also propose to enhance data locality by reordering the graphs with the Graph Clustering method. Experiments on extensive sparse matrices collected from real GNN applications demonstrate that our kernels achieve significant performance improvements over state-of-the-art implementations. We implement our sparse kernels in popular GNN frameworks and use them to train various GNN models, including the GCN model in full-graph mode and the GraphSAINT model in graph-sampling mode. Evaluation results show that our kernels can accelerate GNN training by up to 1.72x.