K. De, A. Klimentov, S. Panitkin, M. Titov, A. Vaniachine, T. Wenaus, D. Yu, G. Záruba
{"title":"Poster: PanDA: Next Generation Workload Management and Analysis System for Big Data","authors":"K. De, A. Klimentov, S. Panitkin, M. Titov, A. Vaniachine, T. Wenaus, D. Yu, G. Záruba","doi":"10.1109/SC.Companion.2012.302","DOIUrl":null,"url":null,"abstract":"In real world any big science project implies to use a sophisticated Workload Management System (WMS) that deals with a huge amount of highly distributed data, which is often accessed by large collaborations. The Production and Distributed Analysis System (PanDA) is a high-performance WMS that is aimed to meet production and analysis requirements for a data-driven workload management system capable of operating at the Large Hadron Collider data processing scale. PanDA provides execution environments for a wide range of experimental applications, automates centralized data production and processing, enables analysis activity of physics groups, supports custom workflow of individual physicists, provides a unified view of distributed worldwide resources, presents status and history of workflow through an integrated monitoring system, archives and curates all workflow. PanDA is now being generalized and packaged, as a WMS already proven at extreme scales, for the wider use of the Big Data community.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"145 1","pages":"1523-1523"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SC.Companion.2012.302","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
In real world any big science project implies to use a sophisticated Workload Management System (WMS) that deals with a huge amount of highly distributed data, which is often accessed by large collaborations. The Production and Distributed Analysis System (PanDA) is a high-performance WMS that is aimed to meet production and analysis requirements for a data-driven workload management system capable of operating at the Large Hadron Collider data processing scale. PanDA provides execution environments for a wide range of experimental applications, automates centralized data production and processing, enables analysis activity of physics groups, supports custom workflow of individual physicists, provides a unified view of distributed worldwide resources, presents status and history of workflow through an integrated monitoring system, archives and curates all workflow. PanDA is now being generalized and packaged, as a WMS already proven at extreme scales, for the wider use of the Big Data community.