Graphite: Hardware-Aware GNN Reshaping for Acceleration With GPU Tensor Cores

Graph neural networks (GNNs) have emerged as powerful tools for addressing non-Euclidean problems. GNNs operate through two key execution phases: i) aggregation and ii) combination. In the aggregation phase, the feature data of neighboring graph nodes are gathered, which is expressed as sparse-dense matrix multiplication (SpMM) between an adjacency matrix and a feature embedding table. The combination phase takes the aggregated feature embedding as input to a neural network model with learnable weights. Typically, the adjacency matrix is extremely sparse due to inherent graph structures, making the aggregation phase a significant bottleneck in GNN computations. This paper introduces Graphite, a GNN acceleration framework to overcome the challenge of SpMM operations and enable graphics processing units (GPUs) to exploit massive thread-level parallelism more efficiently via existing dense acceleration units (i.e., tensor cores). To that end, Graphite employs three techniques for GNN acceleration. Firstly, hardware-aware sparse graph reshaping (HAS) rearranges graph structures to replace sparse operations with dense computations, enabling hardware acceleration through GPU tensor cores. Additionally, balanced thread block scheduling (BTS) distributes sparse thread blocks evenly across streaming multiprocessors in GPUs, and zero-aware warp skipping (ZAWS) eliminates ineffective threads that operate on meaningless zeros. Experimental results show that Graphite achieves an average compression rate of 84.1% for adjacency matrices using HAS. Combined with BTS and ZAWS, Graphite delivers an average 1.55x speedup over the conventional SpMM-based GNN computation method.