Fueled by the ability to mine real-world graph data, GNN applications have experienced phenomenal growth. Sparse Matrix-Matrix Multiplication (SpMM) is a critical operator in GNNs. However, existing SpMM designs for GNNs struggle to adapt to diverse input characteristics. In this paper, we first conduct a comprehensive analysis of existing SpMM optimizations, revealing their limitations through statistical and empirical evidence. Based on this analysis, we introduce ParamSpMM, a parametric approach for highly adaptive and efficient SpMM computation in GNNs. It incorporates a new data structure, the Parameterized Compressed Sparse Row (PCSR), to flexibly integrate existing optimization techniques. ParamSpMM enables the configuration of these optimization techniques according to various input characteristics. Furthermore, we complement ParamSpMM with an ML-based SpMM-decider that predicts optimal configurations based on carefully crafted input features. Our evaluations demonstrate that ParamSpMM outperforms Nvidia cuSPARSE with an average speedup of 1.92x, significantly enhancing GNN training efficiency.
@article{arxiv.2605.15695,
title = {ParamSpMM: Adaptive and Efficient Sparse Matrix-Matrix Multiplication on GPUs for GNNs},
author = {Lixing Zhang and Guanhua Ye and Hongzheng Li and Shigang Li and Yingxia Shao},
journal= {arXiv preprint arXiv:2605.15695},
year = {2026}
}
Comments
Accepted by the 9th International Workshop on Graph Data Management and Analysis (GDMA 2025), held in conjunction with the 30th International Conference on Database Systems for Advanced Applications (DASFAA 2025). 15 pages, 6 figures