中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance

文献类型：期刊论文

作者Niu, Songjie; Chen, Shimin1
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2021-07-01
卷号36期号:4页码:778-791
关键词performance modeling distributed graph computation deep learning transfer learning
ISSN号1000-9000
DOI10.1007/s11390-021-1356-2
英文摘要It is challenging to model the performance of distributed graph computation. Explicit formulation cannot easily capture the diversified factors and complex interactions in the system. Statistical learning methods require a large number of training samples to generate an accurate prediction model. However, it is time-consuming to run the required graph computation tests to obtain the training samples. In this paper, we propose TransGPerf, a transfer learning based solution that can exploit prior knowledge from a source scenario and utilize a manageable amount of training data for modeling the performance of a target graph computation scenario. Experimental results show that our proposed method is capable of generating accurate models for a wide range of graph computation tasks on PowerGraph and GraphX. It outperforms transfer learning methods proposed for other applications in the literature.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000684229900005
出版者SCIENCE PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/17301]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chen, Shimin
作者单位1.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Niu, Songjie,Chen, Shimin. TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2021,36(4):778-791.
APA Niu, Songjie,&Chen, Shimin.(2021).TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,36(4),778-791.
MLA Niu, Songjie,et al."TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 36.4(2021):778-791.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。