HC-SpMM: Accelerating Sparse Matrix-Matrix Multiplication for Graphs with Hybrid GPU Cores

Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to achieving efficient SpMM for graph data on GPUs. Recently, the introduction of new efficient computing cores within GPUs offers new opportunities for acceleration. In this paper, we present HC-SpMM, a pioneering algorithm that leverages Hybrid GPU Cores (Tensor cores and CUDA cores) to accelerate SpMM for graphs. To adapt to the computing characteristics ofdifferent GPU cores, we investigate the impact of sparse graph features on the performance of different cores, develop a data partitioning technique for the graph adjacency matrix, and devise a novel strategy for intelligently selecting the most efficient cores for processing each submatrix. Additionally, we optimize it by considering memory access and thread utilization. To support complex graph computing workloads, we integrate HC-SpMM into the GNN training pipeline. Furthermore, we propose a kernel fusion strategy to enhance data reuse, as well as a cost-effective graph layout reorganization method to mitigate the irregularity of real-world graphs, better fitting the computational models of hybrid GPU cores. Extensive experiments on 14 real-world datasets demonstrate that HC-SpMM achieves an average speedup of 1.33× and 1.23× over state-of-the-art SpMM kernels and GNN frameworks.