Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation

doi:10.1109/TC.2020.3042663

	Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation
	Wen, Shengyan 1; Wang, Xiaohang 1,2; Singh, Amit Kumar 3; Jiang, Yingtao 4; Yang, Mei 4
	2022
发表期刊	IEEE TRANSACTIONS ON COMPUTERS
ISSN	0018-9340
卷号	71 期号:1 页码:92-106
摘要	As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for "hot" tasks to be migrated to run on a "cool" core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this article, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50 percent reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess.
关键词	Task analysis Heuristic algorithms Resource management Runtime System performance Heating systems Thermal management Task migration many-core dynamic resource allocation dark cores
DOI	10.1109/TC.2020.3042663
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61971200] ; Natural Science Foundation of Guangdong Province[2018A030313166] ; Pearl River S&T Nova Program of Guangzhou[201806010038] ; State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences[CARCH201916] ; Fundamental Research Funds for the Central Universities[2019MS087] ; Zhejiang Lab[2021LE0AB01] ; Key Laboratory of Big Data and Intelligent Robot (South China University of Technology), Ministry of Education
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic
WOS记录号	WOS:000730414800008
出版者	IEEE COMPUTER SOC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17913
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Xiaohang
作者单位	1.South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 3.Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England 4.Univ Nevada, Dept Elect & Comp Engn, Las Vegas, NV 89154 USA
推荐引用方式 GB/T 7714	Wen, Shengyan,Wang, Xiaohang,Singh, Amit Kumar,et al. Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation[J]. IEEE TRANSACTIONS ON COMPUTERS,2022,71(1):92-106.
APA	Wen, Shengyan,Wang, Xiaohang,Singh, Amit Kumar,Jiang, Yingtao,&Yang, Mei.(2022).Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation.IEEE TRANSACTIONS ON COMPUTERS,71(1),92-106.
MLA	Wen, Shengyan,et al."Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation".IEEE TRANSACTIONS ON COMPUTERS 71.1(2022):92-106.