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HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation

文献类型：期刊论文

作者Xue, Runzhen3; Han, Dengke3; Yan, Mingyu1,3,4; Zou, Mo2; Yang, Xiaocheng; Wang, Duo3; Li, Wenming3; Tang, Zhimin3; Kim, John2; Ye, Xiaochun3
刊名IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
出版日期2024-07-01
卷号35期号:7页码:1122-1138
关键词Semantics Parallel processing Graph neural networks Vectors Graphics processing units Fuses Hardware GNN GNN accelerator graph neural network HGNN HGNN accelerator heterogeneous graph neural network
ISSN号1045-9219
DOI10.1109/TPDS.2024.3394841
英文摘要Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 40.0x and 8.3x speedup as well as 99.59% and 99.74% energy reduction with quintile the memory bandwidth of GPU A100.
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001224174400002
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38954]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yan, Mingyu
作者单位1.Shanghai Innovat Ctr Processor Technol, Shanghai 201203, Peoples R China
2.Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 305701, South Korea
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100045, Peoples R China
4.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
推荐引用方式
GB/T 7714		 Xue, Runzhen,Han, Dengke,Yan, Mingyu,et al. HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2024,35(7):1122-1138.
APA Xue, Runzhen.,Han, Dengke.,Yan, Mingyu.,Zou, Mo.,Yang, Xiaocheng.,...&Fan, Dongrui.(2024).HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,35(7),1122-1138.
MLA Xue, Runzhen,et al."HiHGNN: Accelerating HGNNs Through Parallelism and Data Reusability Exploitation".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 35.7(2024):1122-1138.

入库方式： OAI收割

来源：计算技术研究所

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