A parallel input-output system for resolving spatial data challenges: an agent-based model case study

Eric Shook, Shaowen Wang
{"title":"A parallel input-output system for resolving spatial data challenges: an agent-based model case study","authors":"Eric Shook, Shaowen Wang","doi":"10.1145/2070770.2070773","DOIUrl":null,"url":null,"abstract":"With recent advances in data collection technologies such as remote sensing and global positioning systems, the amount of spatial data being produced has been increasing at a staggering rate. Simultaneously, a shift is being experienced in computing from single-core to multi-core processors. To effectively utilize the computational power afforded by these new generation of processors for serving data-intensive geospatial applications, parallel computing techniques need to be employed. Parallel computing, however, raises new challenges associated with handling the input and output of spatial data in parallel. This paper describes a Parallel Input/Output System (PIOS) to address challenges associated with handling large amounts of diverse spatial data. The PIOS is based on a hierarchical structure that uses a scalable file partitioning strategy and combines data and metadata to enable efficient handling of terabyte-scale data sets in parallel. A spatially-explicit agent-based model is developed as a case study. Computational experiments were conducted on a supercomputer supported by the National Science Foundation. PIOS achieved ten times speedup in parallel input/output time, and was demonstrated to efficiently scale to over one thousand processing cores and handle multiple terabytes of data.","PeriodicalId":246527,"journal":{"name":"Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM SIGSPATIAL Second International Workshop on High Performance and Distributed Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2070770.2070773","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

With recent advances in data collection technologies such as remote sensing and global positioning systems, the amount of spatial data being produced has been increasing at a staggering rate. Simultaneously, a shift is being experienced in computing from single-core to multi-core processors. To effectively utilize the computational power afforded by these new generation of processors for serving data-intensive geospatial applications, parallel computing techniques need to be employed. Parallel computing, however, raises new challenges associated with handling the input and output of spatial data in parallel. This paper describes a Parallel Input/Output System (PIOS) to address challenges associated with handling large amounts of diverse spatial data. The PIOS is based on a hierarchical structure that uses a scalable file partitioning strategy and combines data and metadata to enable efficient handling of terabyte-scale data sets in parallel. A spatially-explicit agent-based model is developed as a case study. Computational experiments were conducted on a supercomputer supported by the National Science Foundation. PIOS achieved ten times speedup in parallel input/output time, and was demonstrated to efficiently scale to over one thousand processing cores and handle multiple terabytes of data.
解决空间数据挑战的并行输入输出系统:基于代理的模型案例研究
随着遥感和全球定位系统等数据收集技术的最新进展,所产生的空间数据量正以惊人的速度增加。与此同时,计算领域正在经历从单核处理器到多核处理器的转变。为了有效地利用这些新一代处理器提供的计算能力来服务数据密集型地理空间应用程序,需要采用并行计算技术。然而,并行计算提出了与并行处理空间数据的输入和输出相关的新挑战。本文描述了一个并行输入/输出系统(PIOS)来解决与处理大量不同空间数据相关的挑战。PIOS基于分层结构,该结构使用可伸缩的文件分区策略,并结合数据和元数据,从而能够并行地有效处理tb级数据集。以空间显式主体模型为例进行了研究。计算实验在美国国家科学基金会资助的一台超级计算机上进行。PIOS在并行输入/输出时间上实现了10倍的加速,并且被证明可以有效地扩展到超过1000个处理内核并处理数tb的数据。
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