3 papers
cs.DC2026
ParamSpMM: Adaptive and Efficient Sparse Matrix-Matrix Multiplication on GPUs for GNNs
Lixing Zhang, Guanhua Ye, Hongzheng Li +2
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.…
cs.DB2025
Piecewise Linear Approximation in Learned Index Structures: Theoretical and Empirical Analysis
Jiayong Qin, Xianyu Zhu, Qiyu Liu +7
A growing trend in the database and system communities is to augment conventional index structures, such as B+-trees, with machine learning (ML) models. Among these, error-bounded…
cs.LG2024
SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training
Xizhi Gu, Hongzheng Li, Shihong Gao +3
Graph Neural Networks (GNNs) have superior capability in learning graph data. Full-graph GNN training generally has high accuracy, however, it suffers from large peak memory usage…