Evaluation of training efficiency and training performance of graph neural network models based on distributed environment

doi:10.11772/j.issn.1001-9081.2024081140

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (8): 2409-2420.DOI: 10.11772/j.issn.1001-9081.2024081140

• National Open Distributed and Parallel Computing Conference 2024 (DPCS 2024） • Previous Articles

Evaluation of training efficiency and training performance of graph neural network models based on distributed environment

Yinchuan TU, Yong GUO, Heng MAO, Yi REN(), Jianfeng ZHANG, Bao LI

College of Computer Science and Technology，National University of Defense Technology，Changsha Hunan 410073，China

Received:2024-08-14 Revised:2024-09-14 Accepted:2024-09-23 Online:2024-09-25 Published:2025-08-10
Contact: Yi REN
About author:TU Yinchuan， born in 1996， M. S. candidate. His research interests include graph neural network， cloud computing， distributed machine learning.
GUO Yong， born in 1988， Ph. D.， assistant research fellow. His research interests include distributed computing， graph computing.
MAO Heng， born in 1996， M. S. candidate. His research interests include graph neural network， distributed machine learning， edge intelligence.
ZHANG Jianfeng， born in 1984， Ph. D.， associate research fellow. His research interests include operating system， cloud computing， network security， intelligent processing.
LI Bao， born in 1982， Ph. D.， associate research fellow. His research interests include cloud-edge collaborative computing， operating system， intelligent processing.
Supported by:
Youth Program of Natural Science Foundation of Hunan(2021JJ40678)

基于分布式环境的图神经网络模型训练效率与训练性能评估

涂银川, 郭勇, 毛恒, 任怡(), 张建锋, 李宝

国防科技大学计算机学院，长沙 410073

通讯作者: 任怡
作者简介:涂银川（1996—），男，湖北武汉人，硕士研究生，主要研究方向：图神经网络、云计算、分布式机器学习
郭勇（1988—），男，湖北荆州人，助理研究员，博士，主要研究方向：分布式计算、图计算
毛恒（1996—），男，安徽宿州人，硕士研究生，主要研究方向：图神经网络、分布式机器学习、边缘智能
张建锋（1984—），男，陕西宝鸡人，副研究员，博士，CCF会员，主要研究方向：操作系统、云计算、网络安全、智能处理
李宝（1982—），男，山东济南人，副研究员，博士，CCF会员，主要研究方向：云边协同计算、操作系统、智能处理。
基金资助:
湖南自然科学基金青年项目(2021JJ40678)

Abstract

Abstract:

With the rapid growth of graph data sizes， Graph Neural Network （GNN） faces computational and storage challenges in processing large-scale graph-structured data. Traditional stand-alone training methods are no longer sufficient to cope with increasingly large datasets and complex GNN models. Distributed training is an effective way to address these problems due to its parallel computing power and scalability. However， on one hand， the existing distributed GNN training evaluations mainly focus on the performance metrics represented by model accuracy and the efficiency metrics represented by training time， but pay less attention to the metrics of data processing efficiency and computational resource utilization； on the other hand， the main scenarios for algorithm efficiency evaluation are single machine with one card or single machine with multiple cards， and the existing evaluation methods are relatively simple in a distributed environment. To address these shortcomings， an evaluation method for model training in distributed scenarios was proposed， which includes three aspects： evaluation metrics， datasets， and models. Three representative GNN models were selected according to the evaluation method， and distributed training experiments were conducted on four large open graph datasets with different data characteristics to collect and analyze the obtained evaluation metrics. Experimental results show that all of model complexity， training time， computing node throughput and computing Node Average Throughput Ratio （NATR） are influenced by model architecture and data structure characteristics in distributed training； sample processing and data copying take up much time in training， and the time of one computing node waiting for other computing nodes cannot be ignored either； compared with stand-alone training， distributed training reduces the computing node throughput significantly， and further optimization of resource utilization for distributed systems is needed. It can be seen that the proposed evaluation method provides a reference for optimizing the performance of GNN model training in a distributed environment， and establishes an experimental foundation for further model optimization and algorithm improvement.

Key words: model evaluation, Graph Neural Network (GNN), distributed training, training efficiency, training performance

摘要：

随着图数据规模的快速增长，图神经网络（GNN）在处理大规模图结构数据时面临计算和存储方面的挑战。传统的单机训练方法已不足以应对日益庞大的数据集和复杂的GNN模型，分布式训练凭借并行计算能力和可扩展性，成为解决这些问题的有效途径。然而，一方面，已有的分布式GNN训练评估主要关注以模型精度为代表的性能指标和以训练时间为代表的效率指标，而较少关注数据处理效率和计算资源利用方面的指标；另一方面，算法效率评估的主要场景为单机单卡或单机多卡，而已有的评估方法在分布式环境中的应用相对简单。针对这些不足，提出针对分布式场景的模型训练的评估方法，涵盖评估指标、数据集和模型这3个方面。根据评估方法，选取3个代表性GNN模型，在4个具有不同数据特征的大型公开图数据集上进行分布式训练实验，并收集和分析得到的评估指标。实验结果表明，分布式训练中的模型架构和数据结构特征对模型复杂度、训练时间、计算节点吞吐量和计算节点平均吞吐量之比（NATR）均有影响；样本处理与数据拷贝占用了模型训练较多时间，计算节点互相等待的时间也不容忽视；相较于单机训练，分布式训练的计算节点吞吐量有显著降低，且需要进一步优化分布式系统中的资源利用。可见，所提评估方法为GNN模型在分布式环境中的训练性能优化提供了参考依据，并为模型的进一步优化和算法的改进奠定了实验基础。

关键词: 模型评估, 图神经网络, 分布式训练, 训练效率, 训练性能

CLC Number:

TP391

Yinchuan TU, Yong GUO, Heng MAO, Yi REN, Jianfeng ZHANG, Bao LI. Evaluation of training efficiency and training performance of graph neural network models based on distributed environment[J]. Journal of Computer Applications, 2025, 45(8): 2409-2420.

涂银川, 郭勇, 毛恒, 任怡, 张建锋, 李宝. 基于分布式环境的图神经网络模型训练效率与训练性能评估[J]. 《计算机应用》唯一官方网站, 2025, 45(8): 2409-2420.

Figures/Tables 11

References 45

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数据集	节点数	边数	平均度数	节点特征维度	类别数	应用领域
CoraFull^［36］	19 793	126 842	6.41	8 710	70	引文网络
ogbn-arxiv^［30］	169 343	1 166 243	6.89	128	40	引文网络
Reddit^［39］	232 965	114 615 892	491.99	602	41	社交网络
ogbn-products^［30］	2 449 029	61 859 140	25.26	100	47	推荐系统

数据集	节点数	边数	平均度数	节点特征维度	类别数	应用领域
CoraFull^［36］	19 793	126 842	6.41	8 710	70	引文网络
ogbn-arxiv^［30］	169 343	1 166 243	6.89	128	40	引文网络
Reddit^［39］	232 965	114 615 892	491.99	602	41	社交网络
ogbn-products^［30］	2 449 029	61 859 140	25.26	100	47	推荐系统

模型	基本概念	参数共享	计算效率	灵活性	抗过平滑
GCN	图卷积	是	高	低	低
GAT	注意力	否	中	高	高
GraphSAGE	采样聚合	是	高	高	中

模型	基本概念	参数共享	计算效率	灵活性	抗过平滑
GCN	图卷积	是	高	低	低
GAT	注意力	否	中	高	高
GraphSAGE	采样聚合	是	高	高	中

模型	聚合方式	学习率	激活函数	dropout
GCN	求和	0.003	ReLU	0.5
GAT	注意力机制	0.003	ReLU	0.5
GraphSAGE	均值	0.003	ReLU	0.5

Evaluation of training efficiency and training performance of graph neural network models based on distributed environment

基于分布式环境的图神经网络模型训练效率与训练性能评估

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Figures/Tables 11

References 45

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数据集名称	分区数量	分区特征	分区1	分区2	分区3	分区4	分区5	分区6	分区7	分区8
CoraFull	4	节点	6 245	7 054	6 580	6 056	—	—	—	—
		节点	5 095	5 095	5 095	4 584	—	—	—	—
		边	33 962	35 316	33 994	35 482	—	—	—	—
		边	31 797	31 656	30 933	32 456	—	—	—	—
	8	节点	3 548	3 979	3 896	3 561	3 776	3 576	4 031	3 681
		节点	2 545	2 546	2 396	2 161	2 530	2 539	2 543	2 533
		边	17 482	18 838	19 334	17 852	17 304	18 008	18 454	18 182
		边	15 783	16 305	16 216	14 874	15 312	16 184	16 040	16 128
ogbn-arxiv	4	节点	72 166	73 705	79 222	69 758	—	—	—	—
		节点	42 151	43 591	42 842	40 759	—	—	—	—
		边	349 845	386 028	408 835	410 273	—	—	—	—
		边	304 982	343 654	343 477	343 473	—	—	—	—
	8	节点	50 225	49 622	45 405	44 941	45 008	50 709	49 064	45 018
		节点	21 777	21 801	19 599	21 449	21 776	20 912	20 229	21 800
		边	215 378	212 008	223 712	213 756	186 270	208 424	201 564	192 607
		边	171 879	171 701	171 928	171 784	153 345	171 954	159 784	163 211
Reddit	4	节点	203 689	167 999	175 006	168 965	—	—	—	—
		节点	53 928	59 455	59 570	60 012	—	—	—	—
		边	34 882 726	33 785 415	33 495 268	33 354 912	—	—	—	—
		边	29 214 769	28 417 939	28 841 090	28 375 059	—	—	—	—
	8	节点	140 091	124 022	178 234	141 059	121 485	166 820	117 723	150 886
		节点	30 030	25 490	30 035	29 887	29 800	29 914	29 416	28 393
		边	17 578 366	19 521 642	20 797 883	19 818 501	16 708 704	16 124 892	16 446 438	19 275 925
		边	13 865 509	14 724 005	14 803 007	14 736 263	14 762 642	12 743 840	14 412 564	14 801 027
ogbn-products	4	节点	1 073 131	992 338	1 012 637	972 605	—	—	—	—
		节点	610 657	615 415	592 322	630 635	—	—	—	—
		边	35 653 651	34 895 467	31 336 778	34 031 337	—	—	—	—
		边	32 481 110	32 374 997	29 612 148	31 698 798	—	—	—	—
	8	节点	566 183	553 209	578 443	61 531	526 883	606 943	628 995	609 695
		节点	315 327	309 400	310 846	261 228	315 322	314 885	307 820	314 201
		边	16 941 573	16 402 542	17 432 828	17 087 742	17 007 390	17 469 477	17 848 086	17 167 751
		边	15 681 661	15 388 305	15 815 285	15 341 841	16 084 289	15 962 365	16 113 958	15 779 349

数据集	模型	总时间/s	样本采样与数据拷贝		前向传播		反向传播		参数更新
数据集	模型	总时间/s	时间/s	占比/%	时间/s	占比/%	时间/s	占比/%	时间/s	占比/%
CoraFull	GCN	26.84	5.98	22.28	4.69	17.47	1.50	5.59	1.03	3.84
	GAT	125.47	19.12	15.24	18.81	14.99	10.35	8.25	2.93	2.34
	GraphSAGE	178.64	23.29	13.04	19.11	10.70	10.23	5.73	2.06	1.15
ogbn-arxiv	GCN	44.98	12.18	27.08	8.68	19.30	2.62	5.82	1.41	3.13
	GAT	186.95	40.35	21.58	33.54	17.94	16.59	8.87	3.06	1.64
	GraphSAGE	256.18	63.97	24.97	38.65	15.09	15.83	6.18	2.48	0.97
Reddit	GCN	372.26	34.47	9.26	20.20	5.43	5.98	1.61	1.43	0.38
	GAT	569.16	35.93	6.31	31.45	5.53	13.02	2.29	2.51	0.44
	GraphSAGE	811.44	45.84	5.65	27.05	3.33	11.56	1.42	2.34	0.29
ogbn-products	GCN	295.20	58.39	19.78	22.82	7.73	7.34	2.49	1.69	0.57
	GAT	438.68	65.00	14.82	52.34	11.93	23.63	5.39	3.68	0.84
	GraphSAGE	599.61	76.82	12.81	54.42	9.08	24.84	4.14	3.27	0.55

模型	CoraFull	ogbn-arxiv	Reddit	ogbn-products
GCN	38.83	8.19	15.69	14.26
GAT	9.82	3.01	8.39	5.67
GraphSAGE	5.92	1.50	5.46	4.87

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