GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing

• URL: http://arxiv.org/abs/2111.00680v1
• Date: Mon, 1 Nov 2021 03:47:07 GMT
• Title: GCNear: A Hybrid Architecture for Efficient GCN Training with Near-Memory Processing
• Authors: Zhe Zhou and Cong Li and Xuechao Wei and Guangyu Sun
• Abstract summary: Graph Convolutional Networks (GCNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. It is challenging to realize efficient GCN training, especially on large graphs. This paper presents GCNear, a hybrid architecture to tackle these challenges.
• Score: 8.130391367247793
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Graph Convolutional Networks (GCNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, it is challenging to realize efficient GCN training, especially on large graphs. The reasons are many-folded: 1) GCN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory to buffer the intermediate data for back-propagation. 2) GCN training involves both memory-intensive data reduction and computation-intensive features/gradients update operations. Such a heterogeneous nature challenges current CPU/GPU platforms. 3) The irregularity of graphs and the complex training dataflow jointly increase the difficulty of improving a GCN training system's efficiency. This paper presents GCNear, a hybrid architecture to tackle these challenges. Specifically, GCNear adopts a DIMM-based memory system to provide easy-to-scale memory capacity. To match the heterogeneous nature, we categorize GCN training operations as memory-intensive Reduce and computation-intensive Update operations. We then offload Reduce operations to on-DIMM NMEs, making full use of the high aggregated local bandwidth. We adopt a CAE with sufficient computation capacity to process Update operations. We further propose several optimization strategies to deal with the irregularity of GCN tasks and improve GCNear's performance. We also propose a Multi-GCNear system to evaluate the scalability of GCNear.

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