Multi-Node Acceleration for Large-Scale GCNs

doi:10.1109/TC.2022.3207127

	Multi-Node Acceleration for Large-Scale GCNs
	Sun, Gongjian 1,2; Yan, Mingyu 1,2; Wang, Duo 1,2; Li, Han 1,2; Li, Wenming 1,2; Ye, Xiaochun 1,2; Fan, Dongrui 1,2; Xie, Yuan 3
	2022-12-01
发表期刊	IEEE TRANSACTIONS ON COMPUTERS
ISSN	0018-9340
卷号	71 期号:12 页码:3140-3152
摘要	Limited by the memory capacity and computation power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-scale GCNs call for a multi-node acceleration system (MultiAccSys) like tensor processing unit (TPU) Pod for large-scale neural network. In this work, we aim to scale up single-node GCN accelerator to accelerate GCNs on large-scale graphs. We first identify the communication pattern and challenges of multi-node acceleration for GCNs on large-scale graphs. We observe that (1) irregular coarse-grained communication patterns exist in the execution of GCNs in MultiAccSys, which introduces massive amount of redundant network transmissions and off-chip memory accesses; (2) the acceleration of GCNs in MultiAccSys is mainly bounded by network bandwidth but tolerates network latency. Guided by the above observations, we then propose MultiGCN, an efficient MultiAccSys for large-scale GCNs that trades network latency for network bandwidth. Specifically, by leveraging the network latency tolerance, we first propose a topology-aware multicast mechanism with a one put per multicast message-passing model to reduce transmissions and alleviate network bandwidth requirements. Second, we introduce a scatter-based round execution mechanism which cooperates with the multicast mechanism and reduces redundant off-chip memory accesses. Compared to the baseline MultiAccSys, MultiGCN achieves 4 & SIM; 12x speedup using only 28%$\sim$& SIM;68% energy, while reducing 32% transmissions and 73% off-chip memory accesses on average. Besides, MultiGCN not only achieves 2.5 & SIM; 8x speedup over the state-of-the-art multi-GPU solution, but also scales to large-scale graph as opposed to single-node GCN accelerators.
关键词	Deep learning graph neural network hardware accelerator multi-node system communication optimization
DOI	10.1109/TC.2022.3207127
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61732018] ; National Natural Science Foundation of China[61872335] ; National Natural Science Foundation of China[62202451] ; Austrian-Chinese Cooperative RD Project[171111KYSB20200002] ; CAS Project for Young Scientists in Basic Research[YSBR-029] ; Open Research Projects of Zhejiang Lab[2022PB0AB01] ; CAS Project for Youth Innovation Promotion Association
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:000886309300007
出版者	IEEE COMPUTER SOC
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/20321
专题	中国科学院计算技术研究所期刊论文
通讯作者	Sun, Gongjian
作者单位	1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100045, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA
推荐引用方式 GB/T 7714	Sun, Gongjian,Yan, Mingyu,Wang, Duo,et al. Multi-Node Acceleration for Large-Scale GCNs[J]. IEEE TRANSACTIONS ON COMPUTERS,2022,71(12):3140-3152.
APA	Sun, Gongjian.,Yan, Mingyu.,Wang, Duo.,Li, Han.,Li, Wenming.,...&Xie, Yuan.(2022).Multi-Node Acceleration for Large-Scale GCNs.IEEE TRANSACTIONS ON COMPUTERS,71(12),3140-3152.
MLA	Sun, Gongjian,et al."Multi-Node Acceleration for Large-Scale GCNs".IEEE TRANSACTIONS ON COMPUTERS 71.12(2022):3140-3152.