ReLeTA: Reinforcement Learning for Thermal-Aware Task Allocation on Multicore

In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, \releta...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Yang, Shi-Gui, Wang, Yuan-Yuan, Liu, Di, Jiang, Xu, Fang, Hui, Yang, Yu, Zhao, Mingxiong
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Systems and Control
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	Yang, Shi-Gui Wang, Yuan-Yuan Liu, Di Jiang, Xu Fang, Hui Yang, Yu Zhao, Mingxiong
description	In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, \releta is able to effectively reduce the system peak temperature without compromising the application performance. We implement and evaluate \releta on a real platform in comparison with the state-of-the-art approaches. Experimental results show \releta can reduce the average peak temperature by 4 $^{\circ}$C and the maximum difference is up to 13 $^{\circ}$C.
doi_str_mv	10.48550/arxiv.1912.00189
format	Article
fullrecord	<record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1912_00189</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1912_00189</sourcerecordid><originalsourceid>FETCH-LOGICAL-a679-966ac06bb969428c619afb00c7d0d240f638a77583b7a277e154094d0568c41f3</originalsourceid><addsrcrecordid>eNotz8lqwzAUBVBtuihpP6Cr6gfsPtkauzOhEzgUgvbmWX5uRDwUxZ3-vmlSuHDhLi4cxm4E5NIqBXeYvuNnLpwocgBh3SXbbKkmX93zLcWpn1OgkaaF14RpitMbP07c7yiNOGTVFybiHg97Xg3DHHCJ88SP2XwMSwxzoit20eNwoOv_XjH_-ODXz1n9-vSyruoMtXGZ0xoD6LZ12snCBi0c9i1AMB10hYRelxaNUbZsDRbGkFASnOxAaRuk6MsVuz3fnjzNe4ojpp_mz9WcXOUvn05HBg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>ReLeTA: Reinforcement Learning for Thermal-Aware Task Allocation on Multicore</title><source>arXiv.org</source><creator>Yang, Shi-Gui ; Wang, Yuan-Yuan ; Liu, Di ; Jiang, Xu ; Fang, Hui ; Yang, Yu ; Zhao, Mingxiong</creator><creatorcontrib>Yang, Shi-Gui ; Wang, Yuan-Yuan ; Liu, Di ; Jiang, Xu ; Fang, Hui ; Yang, Yu ; Zhao, Mingxiong</creatorcontrib><description>In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, \releta is able to effectively reduce the system peak temperature without compromising the application performance. We implement and evaluate \releta on a real platform in comparison with the state-of-the-art approaches. Experimental results show \releta can reduce the average peak temperature by 4 $^{\circ}$C and the maximum difference is up to 13 $^{\circ}$C.</description><identifier>DOI: 10.48550/arxiv.1912.00189</identifier><language>eng</language><subject>Computer Science - Systems and Control</subject><creationdate>2019-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1912.00189$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1912.00189$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Yang, Shi-Gui</creatorcontrib><creatorcontrib>Wang, Yuan-Yuan</creatorcontrib><creatorcontrib>Liu, Di</creatorcontrib><creatorcontrib>Jiang, Xu</creatorcontrib><creatorcontrib>Fang, Hui</creatorcontrib><creatorcontrib>Yang, Yu</creatorcontrib><creatorcontrib>Zhao, Mingxiong</creatorcontrib><title>ReLeTA: Reinforcement Learning for Thermal-Aware Task Allocation on Multicore</title><description>In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, \releta is able to effectively reduce the system peak temperature without compromising the application performance. We implement and evaluate \releta on a real platform in comparison with the state-of-the-art approaches. Experimental results show \releta can reduce the average peak temperature by 4 $^{\circ}$C and the maximum difference is up to 13 $^{\circ}$C.</description><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8lqwzAUBVBtuihpP6Cr6gfsPtkauzOhEzgUgvbmWX5uRDwUxZ3-vmlSuHDhLi4cxm4E5NIqBXeYvuNnLpwocgBh3SXbbKkmX93zLcWpn1OgkaaF14RpitMbP07c7yiNOGTVFybiHg97Xg3DHHCJ88SP2XwMSwxzoit20eNwoOv_XjH_-ODXz1n9-vSyruoMtXGZ0xoD6LZ12snCBi0c9i1AMB10hYRelxaNUbZsDRbGkFASnOxAaRuk6MsVuz3fnjzNe4ojpp_mz9WcXOUvn05HBg</recordid><startdate>20191130</startdate><enddate>20191130</enddate><creator>Yang, Shi-Gui</creator><creator>Wang, Yuan-Yuan</creator><creator>Liu, Di</creator><creator>Jiang, Xu</creator><creator>Fang, Hui</creator><creator>Yang, Yu</creator><creator>Zhao, Mingxiong</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20191130</creationdate><title>ReLeTA: Reinforcement Learning for Thermal-Aware Task Allocation on Multicore</title><author>Yang, Shi-Gui ; Wang, Yuan-Yuan ; Liu, Di ; Jiang, Xu ; Fang, Hui ; Yang, Yu ; Zhao, Mingxiong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-966ac06bb969428c619afb00c7d0d240f638a77583b7a277e154094d0568c41f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Yang, Shi-Gui</creatorcontrib><creatorcontrib>Wang, Yuan-Yuan</creatorcontrib><creatorcontrib>Liu, Di</creatorcontrib><creatorcontrib>Jiang, Xu</creatorcontrib><creatorcontrib>Fang, Hui</creatorcontrib><creatorcontrib>Yang, Yu</creatorcontrib><creatorcontrib>Zhao, Mingxiong</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yang, Shi-Gui</au><au>Wang, Yuan-Yuan</au><au>Liu, Di</au><au>Jiang, Xu</au><au>Fang, Hui</au><au>Yang, Yu</au><au>Zhao, Mingxiong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ReLeTA: Reinforcement Learning for Thermal-Aware Task Allocation on Multicore</atitle><date>2019-11-30</date><risdate>2019</risdate><abstract>In this paper, we propose \textit{ReLeTA}: Reinforcement Learning based Task Allocation for temperature minimization. We design a new reward function and use a new state model to facilitate optimization of reinforcement learning algorithm. By means of the new reward function and state model, \releta is able to effectively reduce the system peak temperature without compromising the application performance. We implement and evaluate \releta on a real platform in comparison with the state-of-the-art approaches. Experimental results show \releta can reduce the average peak temperature by 4 $^{\circ}$C and the maximum difference is up to 13 $^{\circ}$C.</abstract><doi>10.48550/arxiv.1912.00189</doi><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier	DOI: 10.48550/arxiv.1912.00189
ispartof
issn
language	eng
recordid	cdi_arxiv_primary_1912_00189
source	arXiv.org
subjects	Computer Science - Systems and Control
title	ReLeTA: Reinforcement Learning for Thermal-Aware Task Allocation on Multicore
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T08%3A43%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=ReLeTA:%20Reinforcement%20Learning%20for%20Thermal-Aware%20Task%20Allocation%20on%20Multicore&rft.au=Yang,%20Shi-Gui&rft.date=2019-11-30&rft_id=info:doi/10.48550/arxiv.1912.00189&rft_dat=%3Carxiv_GOX%3E1912_00189%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true