Internal sorting algorithm for large-scale data based on GPU-assisted

Liu Shenghui, Mao Junfeng, Che Nan
{"title":"Internal sorting algorithm for large-scale data based on GPU-assisted","authors":"Liu Shenghui, Mao Junfeng, Che Nan","doi":"10.1109/MIC.2013.6758043","DOIUrl":null,"url":null,"abstract":"This paper presents an internal sorting algorithm by GPU assisted. It consists of two algorithms: a GPU-based internal sorting algorithm and a CPU-based multi-way merging algorithm. The algorithm divided the large-scale data into multiple chunks to fit GPU global memory. Then copy the chunks to the GPU's global memory one by one, and sort them by GPU quicksort algorithm. Then we merge these sub-sequences to one sorted sequence by CPU. We use the loser tree algorithm to reduce the number of comparisons when merging. Finally, this algorithm is tested using a variety of data distribution. The experimental results show that our algorithm improves the efficiency of large-scale data sorting effectively.","PeriodicalId":404630,"journal":{"name":"Proceedings of 2013 2nd International Conference on Measurement, Information and Control","volume":"90 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 2013 2nd International Conference on Measurement, Information and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIC.2013.6758043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

This paper presents an internal sorting algorithm by GPU assisted. It consists of two algorithms: a GPU-based internal sorting algorithm and a CPU-based multi-way merging algorithm. The algorithm divided the large-scale data into multiple chunks to fit GPU global memory. Then copy the chunks to the GPU's global memory one by one, and sort them by GPU quicksort algorithm. Then we merge these sub-sequences to one sorted sequence by CPU. We use the loser tree algorithm to reduce the number of comparisons when merging. Finally, this algorithm is tested using a variety of data distribution. The experimental results show that our algorithm improves the efficiency of large-scale data sorting effectively.
基于gpu辅助的大规模数据内部排序算法
本文提出了一种基于GPU辅助的内部排序算法。它包括两种算法:基于gpu的内部排序算法和基于cpu的多路合并算法。该算法将大规模数据分成多个块,以适应GPU的全局内存。然后将这些块逐个复制到GPU的全局内存中,并使用GPU快速排序算法进行排序。然后我们将这些子序列合并成一个由CPU排序的序列。我们使用输家树算法来减少合并时的比较次数。最后,利用多种数据分布对该算法进行了测试。实验结果表明,该算法有效地提高了大规模数据排序的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信