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In addition to focusing on its performance, the issues of its energy consumption and environmental problem have gradually attracted the concerns of researchers, computer architects, and developers. Current researches only consider single-task scheduling for saving energy, lacking the focus on energy saving from scheduling the overall tasks. In view of the shortcomings of current researches, we propose a METS (Minimizing Execution Time Slot) approach to reduce energy by rationally allocating the tasks across GPUs. It first collects the number of tasks and the corresponding estimated performance information. Next, it decides whether to turn the problem into a 0\u20131 knapsack problem or to use FIFO method based on the number of tasks. Then, we conduct our experiment on typical platform to verify our proposed approach. The experimental results show that METS can save on average 8.43% of energy when compared with the existing approaches. This shows that the proposed METS method is effective, reasonable and feasible. <\/jats:p>","DOI":"10.1142\/s0218126620501947","type":"journal-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T07:55:56Z","timestamp":1580370956000},"page":"2050194","source":"Crossref","is-referenced-by-count":2,"title":["Minimizing Energy of Heterogeneous Computing Systems by Task Scheduling Approach,"],"prefix":"10.1142","volume":"29","author":[{"given":"Junke","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"},{"name":"Key Laboratory of Machine Learning, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2053-9321","authenticated-orcid":false,"given":"Junwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}]},{"given":"Mingjiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"},{"name":"Key Laboratory of Machine Learning, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}]},{"given":"Guanyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"},{"name":"Key Laboratory of Machine Learning, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}]},{"given":"Jincheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}]},{"given":"Yu","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, P.\u00a0R.\u00a0China"}]},{"given":"Deguang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Technology, Luoyang Normal University, Luoyang, Henan 471934, P.\u00a0R.\u00a0China"}]},{"given":"Yanhui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer, Sichuan University, Chengdu, Sichuan 610065, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2020,2,19]]},"reference":[{"key":"S0218126620501947BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2914447"},{"key":"S0218126620501947BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2019.2935042"},{"key":"S0218126620501947BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2019.01.003"},{"key":"S0218126620501947BIB004","first-page":"2215","author":"Zhou J.","year":"2018","journal-title":"IEEE Trans. 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