高能物理数据普及:ATLAS数据集普及案例研究

M. Grigorieva, E. Tretyakov, A. Klimentov, D. Golubkov, T. Korchuganova, A. Alekseev, A. Artamonov, T. Galkin
{"title":"高能物理数据普及:ATLAS数据集普及案例研究","authors":"M. Grigorieva, E. Tretyakov, A. Klimentov, D. Golubkov, T. Korchuganova, A. Alekseev, A. Artamonov, T. Galkin","doi":"10.1109/IVMEM51402.2020.00010","DOIUrl":null,"url":null,"abstract":"The amount of scientific data generated by the LHC experiments has hit the exabyte scale. These data are transferred, processed and analyzed in hundreds of computing centers. The popularity of data among individual physicists and University groups has become one of the key factors of efficient data management and processing. It was actively used during LHC Run 1 and Run 2 by the experiments for the central data processing, and allowed the optimization of data placement policies and to spread the workload more evenly over the existing computing resources. Besides the central data processing, the LHC experiments provide storage and computing resources for physics analysis to thousands of users. Taking into account the significant increase of data volume and processing time after the collider upgrade for the High Luminosity Runs (2027– 2036) an intelligent data placement based on data access pattern becomes even more crucial than at the beginning of LHC. In this study we provide a detailed exploration of data popularity using ATLAS data samples. In addition, we analyze the geolocations of computing sites where the data were processed, and the locality of the home institutes of users carrying out physics analysis. Cartography visualization, based on this data, allows the correlation of existing data placement with physics needs, providing a better understanding of data utilization by different categories of user’s tasks.","PeriodicalId":325794,"journal":{"name":"2020 Ivannikov Memorial Workshop (IVMEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"High Energy Physics Data Popularity : ATLAS Datasets Popularity Case Study\",\"authors\":\"M. Grigorieva, E. Tretyakov, A. Klimentov, D. Golubkov, T. Korchuganova, A. Alekseev, A. Artamonov, T. Galkin\",\"doi\":\"10.1109/IVMEM51402.2020.00010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The amount of scientific data generated by the LHC experiments has hit the exabyte scale. These data are transferred, processed and analyzed in hundreds of computing centers. The popularity of data among individual physicists and University groups has become one of the key factors of efficient data management and processing. It was actively used during LHC Run 1 and Run 2 by the experiments for the central data processing, and allowed the optimization of data placement policies and to spread the workload more evenly over the existing computing resources. Besides the central data processing, the LHC experiments provide storage and computing resources for physics analysis to thousands of users. Taking into account the significant increase of data volume and processing time after the collider upgrade for the High Luminosity Runs (2027– 2036) an intelligent data placement based on data access pattern becomes even more crucial than at the beginning of LHC. In this study we provide a detailed exploration of data popularity using ATLAS data samples. In addition, we analyze the geolocations of computing sites where the data were processed, and the locality of the home institutes of users carrying out physics analysis. Cartography visualization, based on this data, allows the correlation of existing data placement with physics needs, providing a better understanding of data utilization by different categories of user’s tasks.\",\"PeriodicalId\":325794,\"journal\":{\"name\":\"2020 Ivannikov Memorial Workshop (IVMEM)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Ivannikov Memorial Workshop (IVMEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVMEM51402.2020.00010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Ivannikov Memorial Workshop (IVMEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVMEM51402.2020.00010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

大型强子对撞机实验产生的科学数据量已达到eb级。这些数据在数百个计算中心进行传输、处理和分析。数据在物理学家个人和大学群体中的流行已经成为有效的数据管理和处理的关键因素之一。实验在LHC Run 1和Run 2期间积极使用它进行中央数据处理,并允许优化数据放置策略,并在现有计算资源上更均匀地分配工作负载。除了中央数据处理,大型强子对撞机实验还为成千上万的用户提供物理分析的存储和计算资源。考虑到高亮度运行(2027 - 2036)对撞机升级后数据量和处理时间的显著增加,基于数据访问模式的智能数据放置比LHC开始时更加重要。在本研究中,我们使用ATLAS数据样本对数据流行度进行了详细的探索。此外,我们还分析了处理数据的计算站点的地理位置,以及进行物理分析的用户所在机构的地理位置。基于这些数据的制图可视化允许将现有数据放置与物理需求相关联,从而更好地了解不同类别用户任务的数据利用情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High Energy Physics Data Popularity : ATLAS Datasets Popularity Case Study
The amount of scientific data generated by the LHC experiments has hit the exabyte scale. These data are transferred, processed and analyzed in hundreds of computing centers. The popularity of data among individual physicists and University groups has become one of the key factors of efficient data management and processing. It was actively used during LHC Run 1 and Run 2 by the experiments for the central data processing, and allowed the optimization of data placement policies and to spread the workload more evenly over the existing computing resources. Besides the central data processing, the LHC experiments provide storage and computing resources for physics analysis to thousands of users. Taking into account the significant increase of data volume and processing time after the collider upgrade for the High Luminosity Runs (2027– 2036) an intelligent data placement based on data access pattern becomes even more crucial than at the beginning of LHC. In this study we provide a detailed exploration of data popularity using ATLAS data samples. In addition, we analyze the geolocations of computing sites where the data were processed, and the locality of the home institutes of users carrying out physics analysis. Cartography visualization, based on this data, allows the correlation of existing data placement with physics needs, providing a better understanding of data utilization by different categories of user’s tasks.
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:481959085
Book学术官方微信