Characterizing and Understanding GCNs on GPU | |
Yan, Mingyu1,2,3; Chen, Zhaodong2; Deng, Lei2; Ye, Xiaochun; Zhang, Zhimin1; Fan, Dongrui1,3; Xie, Yuan1,2 | |
刊名 | IEEE COMPUTER ARCHITECTURE LETTERS |
2020 | |
卷号 | 19期号:1页码:22-25 |
关键词 | Graph convolutional neural networks characterization execution pattern GPU |
ISSN号 | 1556-6056 |
DOI | 10.1109/LCA.2020.2970395 |
英文摘要 | Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of GCNs are accelerated with GPUs. Therefore, characterizing and understanding the execution pattern of GCNs on GPU is important for both software and hardware optimization. Unfortunately, to the best of our knowledge, there is no detailed characterization effort of GCN workloads on GPU. In this letter, we characterize GCN workloads at inference stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and exploration, we propose several useful guidelines for both software optimization and hardware optimization for the efficient execution of GCNs on GPU. |
资助项目 | National Natural Science Foundation of China[61732018] ; US National Science Foundation[1725447] |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | IEEE COMPUTER SOC |
WOS记录号 | WOS:000525233900003 |
内容类型 | 期刊论文 |
源URL | [http://119.78.100.204/handle/2XEOYT63/14266] |
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.Chinese Acad Sci, ICT, SKLCA, Beijing 100864, Peoples R China 2.Univ Calif Santa Barbara, Santa Barbara, CA 93106 USA 3.UCAS, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Yan, Mingyu,Chen, Zhaodong,Deng, Lei,et al. Characterizing and Understanding GCNs on GPU[J]. IEEE COMPUTER ARCHITECTURE LETTERS,2020,19(1):22-25. |
APA | Yan, Mingyu.,Chen, Zhaodong.,Deng, Lei.,Ye, Xiaochun.,Zhang, Zhimin.,...&Xie, Yuan.(2020).Characterizing and Understanding GCNs on GPU.IEEE COMPUTER ARCHITECTURE LETTERS,19(1),22-25. |
MLA | Yan, Mingyu,et al."Characterizing and Understanding GCNs on GPU".IEEE COMPUTER ARCHITECTURE LETTERS 19.1(2020):22-25. |
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