GPUMemSort: A High Performance Graphics Co-processors Sorting Algorithm for Large Scale In-Memory Data

Yin Ye, Zhihui Du, David A. Bader, Quan Yang, Weiwei Huo
{"title":"GPUMemSort: A High Performance Graphics Co-processors Sorting Algorithm for Large Scale In-Memory Data","authors":"Yin Ye, Zhihui Du, David A. Bader, Quan Yang, Weiwei Huo","doi":"10.5176/2010-2283_1.2.34","DOIUrl":null,"url":null,"abstract":"In this paper, we present a GPU-based sorting algorithm, GPUMemSort, which achieves high performance in sorting large-scale in-memory data by take advantage of GPU processors. It consists of two algorithms: an in-core algorithm, which is responsible for sorting data in GPU global memory efficiently, and an out-of-core algorithm, which is responsible for dividing large-scale data into multiple chunks that fit GPU global memory. GPUMemSort is implemented based on NVIDIA’s CUDA framework and some critical and detailed optimization methods are also presented. The tests of different algorithms have been run on multiple data sets. The experimental results show that our in-core sorting can outperform other comparison-based algorithms and GPUMemSort is highly effective in sorting large-scale inmemory data.","PeriodicalId":91079,"journal":{"name":"GSTF international journal on computing","volume":"78 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2014-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GSTF international journal on computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5176/2010-2283_1.2.34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

Abstract

In this paper, we present a GPU-based sorting algorithm, GPUMemSort, which achieves high performance in sorting large-scale in-memory data by take advantage of GPU processors. It consists of two algorithms: an in-core algorithm, which is responsible for sorting data in GPU global memory efficiently, and an out-of-core algorithm, which is responsible for dividing large-scale data into multiple chunks that fit GPU global memory. GPUMemSort is implemented based on NVIDIA’s CUDA framework and some critical and detailed optimization methods are also presented. The tests of different algorithms have been run on multiple data sets. The experimental results show that our in-core sorting can outperform other comparison-based algorithms and GPUMemSort is highly effective in sorting large-scale inmemory data.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
GPUMemSort:一种用于大规模内存数据的高性能图形协处理器排序算法
在本文中,我们提出了一种基于GPU的排序算法GPUMemSort,该算法利用GPU处理器实现了对内存中大规模数据的高效排序。它由两种算法组成:一种是核心算法,负责有效地对GPU全局内存中的数据进行排序,另一种是核心算法,负责将大规模数据划分为适合GPU全局内存的多个块。GPUMemSort是基于NVIDIA的CUDA框架实现的,并给出了一些关键和详细的优化方法。不同算法的测试已经在多个数据集上运行。实验结果表明,GPUMemSort算法的性能优于其他基于比较的算法,在大规模内存数据排序中具有很高的效率。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Cognitive Computing supported Medical Decision Support System for Patient’s Driving Assessment Propaganda Barometer : A Supportive Tool to Improve Media Literacy Towards Building a Critically Thinking Society A framework for the adoption of bring your own device (BYOD) in the hospital environment On developing adaptive vocabulary learning game for children with an early language delay Stroke Cognitive Medical Assistant (StrokeCMA)
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1