基于集群的缓存无关Peano曲线的Hadoop方法

Gurpinder Kaur, Sachin Bagga, K. Mann
{"title":"基于集群的缓存无关Peano曲线的Hadoop方法","authors":"Gurpinder Kaur, Sachin Bagga, K. Mann","doi":"10.1109/IACC.2017.0037","DOIUrl":null,"url":null,"abstract":"Hadoop is one of the most popular technologies used in the big data landscape for evaluating the data through Hadoop Distributed File System and Map-Reduce. Problems which are larger in size are becoming tough to handle by a single system these days because the execution time for such problems will be very high in such platform. Instead of processing the tasks in a sequential approach, when the processing is done in parallel through the MapReduce method, then results with better efficiency can be expected. In the present method, firstly the Map task decomposes the input into the intermediate keys and then the intermediate keys are sent to the reduce function for processing of data. The algorithm used for performing matrix multiplication is cache oblivious in nature, for better utilization of the memory hierarchy. Processing with the cache oblivious approach increases the re-usability power of the elements and thus decreases the overall execution time. The proposed work for matrix multiplication shall be fault tolerant in nature as there is a replication of data at three places on three different data nodes.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":" 51","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hadoop Approach to Cluster Based Cache Oblivious Peano Curves\",\"authors\":\"Gurpinder Kaur, Sachin Bagga, K. Mann\",\"doi\":\"10.1109/IACC.2017.0037\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Hadoop is one of the most popular technologies used in the big data landscape for evaluating the data through Hadoop Distributed File System and Map-Reduce. Problems which are larger in size are becoming tough to handle by a single system these days because the execution time for such problems will be very high in such platform. Instead of processing the tasks in a sequential approach, when the processing is done in parallel through the MapReduce method, then results with better efficiency can be expected. In the present method, firstly the Map task decomposes the input into the intermediate keys and then the intermediate keys are sent to the reduce function for processing of data. The algorithm used for performing matrix multiplication is cache oblivious in nature, for better utilization of the memory hierarchy. Processing with the cache oblivious approach increases the re-usability power of the elements and thus decreases the overall execution time. The proposed work for matrix multiplication shall be fault tolerant in nature as there is a replication of data at three places on three different data nodes.\",\"PeriodicalId\":248433,\"journal\":{\"name\":\"2017 IEEE 7th International Advance Computing Conference (IACC)\",\"volume\":\" 51\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 7th International Advance Computing Conference (IACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IACC.2017.0037\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 7th International Advance Computing Conference (IACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IACC.2017.0037","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

Hadoop是大数据领域中最流行的技术之一,通过Hadoop分布式文件系统和Map-Reduce来评估数据。现在单个系统很难处理规模较大的问题,因为在这样的平台上,这类问题的执行时间会非常高。当通过MapReduce方法并行处理任务时,可以期望得到效率更高的结果,而不是以顺序方法处理任务。在该方法中,Map任务首先将输入分解为中间键,然后将中间键发送给reduce函数进行数据处理。用于执行矩阵乘法的算法本质上是缓存无关的,以便更好地利用内存层次结构。使用缓参无关方法进行处理可以提高元素的可重用性,从而减少总体执行时间。由于在三个不同的数据节点上的三个地方存在数据复制,因此建议的矩阵乘法工作本质上应该是容错的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hadoop Approach to Cluster Based Cache Oblivious Peano Curves
Hadoop is one of the most popular technologies used in the big data landscape for evaluating the data through Hadoop Distributed File System and Map-Reduce. Problems which are larger in size are becoming tough to handle by a single system these days because the execution time for such problems will be very high in such platform. Instead of processing the tasks in a sequential approach, when the processing is done in parallel through the MapReduce method, then results with better efficiency can be expected. In the present method, firstly the Map task decomposes the input into the intermediate keys and then the intermediate keys are sent to the reduce function for processing of data. The algorithm used for performing matrix multiplication is cache oblivious in nature, for better utilization of the memory hierarchy. Processing with the cache oblivious approach increases the re-usability power of the elements and thus decreases the overall execution time. The proposed work for matrix multiplication shall be fault tolerant in nature as there is a replication of data at three places on three different data nodes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信