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
PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing

文献类型：期刊论文

作者Ye, Xiaochun3; Zhang, Hao1,3; Wang, Da1,3; Li, Wenming3; Tang, Zhimin2,3; Sun, Ninghui3; Fan, Dongrui2,3
刊名SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
出版日期2019-03-01
卷号21页码:129-142
关键词PIM String operations Acceleration architecture Big data HMC
ISSN号2210-5379
DOI10.1016/j.suscom.2019.01.006
英文摘要The ever-growing data volume in big data era drives designers to find more efficient processor architectures both in performance and energy consumption. Among various computing patterns in big data applications, string operations are common but important parts of data processing. However, due to the consideration of generality, current general-purpose CPUs are not efficient in both performance and energy consumption when processing simple and fixed computation patterns of discrete string operations. On the other hand, moving massive data from memory to computing units through NoC, Cache hierarchies, and other memory access data paths is time-consuming and especially energy consuming. Fortunately, emerging technologies, such as Hybrid Memory Cube (HMC), enable the processing-in memory (PIM) functionality without transferring massive data to remote processing units. In this paper, we propose PIM-WEAVER, a high-efficiency novel acceleration architecture for string processing using PIM mechanism, which is the 3D integration technology that facilitates stacking logic and memory dies in a single package. The PIM-WEAVER is implemented using such technology by integrating string processing units into the real world HMC memory. In PIM-WEAVER, the general-purpose acceleration architecture for string operation is implemented within the memory cube, which can reduce the latency of data transfer and also save energy. We also propose a full-stack solution of programming interface and control mechanism of instruction level. Our comprehensive evaluations using typical string processing algorithms from big data applications show that the PIM-WEAVER gains an average speedup of 14.74x over high-performance Intel processor, and reduces the energy consumption by 82.1% on average, with tiny area overhead. (C) 2019 Published by Elsevier Inc.
资助项目National Natural Science Foundation of China[61732018] ; National Natural Science Foundation of China[61521092] ; National Key Research and Development Program[2017YFC0803401] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA18000000] ; Innovation Project Program of the State Key Laboratory of Computer Architecture[CARCH3303] ; Innovation Project Program of the State Key Laboratory of Computer Architecture[CARCH3407] ; Innovation Project Program of the State Key Laboratory of Computer Architecture[CARCH3502] ; Innovation Project Program of the State Key Laboratory of Computer Architecture[CARCH3505]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000461484700012
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/4137]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ye, Xiaochun
作者单位1.SmarCo Co Ltd, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China
3.Chinese Acad Sci, ICT, SKL Comp Architecture, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Ye, Xiaochun,Zhang, Hao,Wang, Da,et al. PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing[J]. SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS,2019,21:129-142.
APA Ye, Xiaochun.,Zhang, Hao.,Wang, Da.,Li, Wenming.,Tang, Zhimin.,...&Fan, Dongrui.(2019).PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing.SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS,21,129-142.
MLA Ye, Xiaochun,et al."PIM-WEAVER: A High Energy-efficient, General-purpose Acceleration Architecture for String Operations in Big Data Processing".SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 21(2019):129-142.

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。