{"title":"A System for GIS Polygonal Overlay Computation on Linux Cluster - An Experience and Performance Report","authors":"Dinesh Agarwal, S. Puri, Xi He, S. Prasad","doi":"10.1109/IPDPSW.2012.180","DOIUrl":null,"url":null,"abstract":"GIS polygon-based (also know as vector-based) spatial data overlay computation is much more complex than raster data computation. Processing of polygonal spatial data files has been a long standing research question in GIS community due to the irregular and data intensive nature of the underlying computation. The state-of-the-art software for overlay computation in GIS community is still desktop-based. We present a cluster-based distributed solution for end-to-end polygon overlay processing, modeled after our Windows Azure cloud-based Crayons system [1]. We present the details of porting Crayons system to MPI-based Linux cluster and show the improvements made by employing efficient data structures such as R-trees. We present performance report and show the scalability of our system, along with the remaining bottlenecks. Our experimental results show an absolute speedup of 15x for end-to-end overlay computation employing up to 80 cores.","PeriodicalId":378335,"journal":{"name":"2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"42","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPSW.2012.180","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 42
Abstract
GIS polygon-based (also know as vector-based) spatial data overlay computation is much more complex than raster data computation. Processing of polygonal spatial data files has been a long standing research question in GIS community due to the irregular and data intensive nature of the underlying computation. The state-of-the-art software for overlay computation in GIS community is still desktop-based. We present a cluster-based distributed solution for end-to-end polygon overlay processing, modeled after our Windows Azure cloud-based Crayons system [1]. We present the details of porting Crayons system to MPI-based Linux cluster and show the improvements made by employing efficient data structures such as R-trees. We present performance report and show the scalability of our system, along with the remaining bottlenecks. Our experimental results show an absolute speedup of 15x for end-to-end overlay computation employing up to 80 cores.