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Practical Near-Data-Processing Architecture for Large-Scale Distributed Graph Neural Network
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DOI：10.1109/ACCESS.2022.3169423.png)
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摘要
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Graph Neural Networks have drawn tremendous attention in the past few years due to their convincing performance and high interpretability in various graph-based tasks like link prediction and node classification. With the ever-growing graph size in the real world, especially for industrial graphs at a billion-level, the storage of graphs can easily consume Terabytes so that the process of GNNs has to be processed in a distributed manner. As a result, the execution could be inefficient due to the expensive cross-node communication and irregular memory access. Various GNN accelerators have been proposed for efficient GNN processing. They, however, mainly focused on small and medium-size graphs, which is not applicable to large-scale distributed graphs. In this paper, we present a practical Near-Data-Processing architecture based on a memory-pool system for large-scale distributed GNNs. We propose a customized memory fabric interface to construct the memory pool for low-latency and high throughput cross-node communication, which can provide flexible memory allocation and strong scalability. A practical Near-Data-Processing design is proposed for efficient work offloading and bandwidth utilization improvement. Moreover, we also introduce a partition and scheduling scheme to further improve performance and achieve workload balance. Comprehensive evaluations demonstrate that the proposed architecture can achieve up to 27 x and 8 x higher training speed compared to two state-of-the-art distributed GNN frameworks: Deep Graph Library and P-3, respectively.
Keyword:
Graph neural network
large-scale graph processing
memory pool
near data processing
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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