Accelerating GNNs on GPU Sparse Tensor Cores through N:M Sparsity-Oriented Graph Reordering

Conference · Fri Feb 28 04:00:00 EST 2025

DOI:https://doi.org/10.1145/3710848.3710881· OSTI ID:2545648

Chen, Jou-An ^[1]; Sung, Hsin-Hsuan ^[1]; Zhang, Ruifeng ^[1]; Li, Ang ^[2]; Shen, Xipeng ^[1]

---

1. North Carolina State University
2. BATTELLE (PACIFIC NW LAB)

Recent advancements in GPU hardware support have introduced the capability to leverage N:M sparse patterns for substantial performance gains. Graphs in Graph Neural Networks (GNNs) are typically sparse, but the sparsity is often irregular, not conforming to such sparse patterns. In this paper, we propose a novel graph reordering algorithm, the first of its kind, to reshape irregular graph data into the N:M structured sparse pattern at the tile level, allowing linear-algebra-based graph operations in GNNs to benefit from the N:M sparse hardware. The optimization is lossless, maintaining the accuracy of GNN. It can remove 98-100\% violations of the N:M sparse patterns at the vector level, and increase the proportion of conforming graphs in SuiteSparse collection from 5-9\% to 88.7-93.5\%. On A100 GPUs, the optimization accelerates Sparse Matrix Matrix (SpMM) by up to 43X (2.3X -- 7.5X on average) and speeds up the key graph operations in GNNs on real graphs by as much as 8.6X (3.5X on average).

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 2545648
• Report Number(s): PNNL-SA-193030
• Country of Publication: United States
• Language: English

Related Subjects

artificial intelligence
graph neural networks