{"title":"使用固态磁盘进行节能分类","authors":"A. Beckmann, U. Meyer, P. Sanders, J. Singler","doi":"10.1109/GREENCOMP.2010.5598309","DOIUrl":null,"url":null,"abstract":"We take sorting of large data sets as case study for making data-intensive applications more energy-efficient. Using a low-power processor, solid state disks, and efficient algorithms, we beat the current records in the JouleSort benchmark for 10GB to 1 TB of data by factors of up to 5.1. Since we also use parallel processing, this usually comes without a performance penalty.","PeriodicalId":262148,"journal":{"name":"International Conference on Green Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"37","resultStr":"{\"title\":\"Energy-efficient sorting using solid state disks\",\"authors\":\"A. Beckmann, U. Meyer, P. Sanders, J. Singler\",\"doi\":\"10.1109/GREENCOMP.2010.5598309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We take sorting of large data sets as case study for making data-intensive applications more energy-efficient. Using a low-power processor, solid state disks, and efficient algorithms, we beat the current records in the JouleSort benchmark for 10GB to 1 TB of data by factors of up to 5.1. Since we also use parallel processing, this usually comes without a performance penalty.\",\"PeriodicalId\":262148,\"journal\":{\"name\":\"International Conference on Green Computing\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"37\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Green Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GREENCOMP.2010.5598309\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Green Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GREENCOMP.2010.5598309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We take sorting of large data sets as case study for making data-intensive applications more energy-efficient. Using a low-power processor, solid state disks, and efficient algorithms, we beat the current records in the JouleSort benchmark for 10GB to 1 TB of data by factors of up to 5.1. Since we also use parallel processing, this usually comes without a performance penalty.
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