Performance Optimization of Many-Core Systems by Exploiting Task Migration and Dark Core Allocation
文献类型：期刊论文
| 作者 | Wen, Shengyan4; Wang, Xiaohang3,4; Singh, Amit Kumar2; Jiang, Yingtao1; Yang, Mei1 |
| 刊名 | IEEE TRANSACTIONS ON COMPUTERS
 |
| 出版日期 | 2022 |
| 卷号 | 71期号:1页码:92-106 |
| 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 |
| ISSN号 | 0018-9340 |
| DOI | 10.1109/TC.2020.3042663 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000730414800008 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17912]  |
| 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.Univ Nevada, Dept Elect & Comp Engn, Las Vegas, NV 89154 USA 2.Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England 3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 4.South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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