# AIRES: Accelerating Out-of-Core GCNs via Algorithm-System Co-Design

arXiv: https://arxiv.org/abs/2507.02006 · HTML: https://mlsanity.com/paper/2507.02006/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 2 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Molecular graph generation with Graph Neural Networks](https://mlsanity.com/paper/2012.07397/) (2020)
- [MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks](https://mlsanity.com/paper/2202.02365/) (2022)
- [A Systematic Survey of General Sparse Matrix-Matrix Multiplication](https://mlsanity.com/paper/2002.11273/) (2020)
- [Sextans: A Streaming Accelerator for General-Purpose Sparse-Matrix Dense-Matrix Multiplication](https://mlsanity.com/paper/2109.11081/) (2021)
- [Exploiting Multiple Levels of Parallelism in Sparse Matrix-Matrix Multiplication](https://mlsanity.com/paper/1510.00844/) (2015)
- [HongTu: Scalable Full-Graph GNN Training on Multiple GPUs (via communication-optimized CPU data offloading)](https://mlsanity.com/paper/2311.14898/) (2023)
- [Helios: An Efficient Out-of-core GNN Training System on Terabyte-scale Graphs with In-memory Performance](https://mlsanity.com/paper/2310.00837/) (2023)
- [Accelerating CPU-Based Sparse General Matrix Multiplication With Binary Row Merging](https://mlsanity.com/paper/2206.06611/) (2022)
- [A Unified CPU-GPU Protocol for GNN Training](https://mlsanity.com/paper/2403.17092/) (2024)

## Abstract (arXiv)

Graph convolutional networks (GCNs) are fundamental in various scientific applications, ranging from biomedical protein-protein interactions (PPI) to large-scale recommendation systems. An essential component for modeling graph structures in GCNs is sparse general matrix-matrix multiplication (SpGEMM). As the size of graph data continues to scale up, SpGEMMs are often conducted in an out-of-core fashion due to limited GPU memory space in resource-constrained systems. Albeit recent efforts that aim to alleviate the memory constraints of out-of-core SpGEMM through either GPU feature caching, hybrid CPU-GPU memory layout, or performing the computation in sparse format, current systems suffer from both high I/O latency and GPU under-utilization issues. In this paper, we first identify the problems of existing systems, where sparse format data alignment and memory allocation are the main performance bottlenecks, and propose AIRES, a novel algorithm-system co-design solution to accelerate out-of-core SpGEMM computation for GCNs. Specifically, from the algorithm angle, AIRES proposes to alleviate the data alignment issues on the block level for matrices in sparse formats and develops a tiling algorithm to facilitate row block-wise alignment. On the system level, AIRES employs a three-phase dynamic scheduling that features a dual-way data transfer strategy utilizing a tiered memory system: integrating GPU memory, GPU Direct Storage (GDS), and host memory to reduce I/O latency and improve throughput. Evaluations show that AIRES significantly outperforms the state-of-the-art methods, achieving up to 1.8x lower latency in real-world graph processing benchmarks.