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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Shuangchen Li Dimin Niu Yijin Guan Linyong Huang Zhe Zhang Hongzhong Zheng Yuan Xie |
| Abstract | 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 |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2022.3169423 |
| Journal | IEEE Access |
| Volume Number | 10 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2022-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Graph Neural Network Large-scale Graph Processing Memory Pool Near Data Processing |
| Content Type | Text |
| Resource Type | Article |
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