{"title":"蜡笔:一个基于Azure云的GIS叠加操作并行系统","authors":"Dinesh Agarwal","doi":"10.1109/SC.Companion.2012.315","DOIUrl":null,"url":null,"abstract":"Processing of extremely large polygonal (vector-based) spatial datasets has been a long-standing research challenge for scientists in the Geographic Information Systems and Science (GIS) community. Surprisingly, it is not for the lack of individual parallel algorithm; we discovered that the irregular and data intensive nature of the underlying processing is the main reason for the meager amount of work by way of system design and implementation. Furthermore, of all the systems reported in the literature, very few deal with the complexities of vector-based datasets and none, including commercial systems, on the cloud platform. We have designed and implemented an open-architecture-based system named Crayons for Windows Azure cloud platform using state-of-the-art techniques. We have implemented three different architectures of Crayons with different load balancing schemes. Crayons scales well for sufficiently large data sets, achieving end-to-end absolute speedup of over 28-fold employing 100 Azure processors. For smaller and more irregular workload, it still yields over 10-fold speedup.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"312 1","pages":"1542-1543"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Crayons: An Azure Cloud Based Parallel System for GIS Overlay Operations\",\"authors\":\"Dinesh Agarwal\",\"doi\":\"10.1109/SC.Companion.2012.315\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Processing of extremely large polygonal (vector-based) spatial datasets has been a long-standing research challenge for scientists in the Geographic Information Systems and Science (GIS) community. Surprisingly, it is not for the lack of individual parallel algorithm; we discovered that the irregular and data intensive nature of the underlying processing is the main reason for the meager amount of work by way of system design and implementation. Furthermore, of all the systems reported in the literature, very few deal with the complexities of vector-based datasets and none, including commercial systems, on the cloud platform. We have designed and implemented an open-architecture-based system named Crayons for Windows Azure cloud platform using state-of-the-art techniques. We have implemented three different architectures of Crayons with different load balancing schemes. Crayons scales well for sufficiently large data sets, achieving end-to-end absolute speedup of over 28-fold employing 100 Azure processors. For smaller and more irregular workload, it still yields over 10-fold speedup.\",\"PeriodicalId\":6346,\"journal\":{\"name\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"volume\":\"312 1\",\"pages\":\"1542-1543\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"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.2012.315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","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.2012.315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Crayons: An Azure Cloud Based Parallel System for GIS Overlay Operations
Processing of extremely large polygonal (vector-based) spatial datasets has been a long-standing research challenge for scientists in the Geographic Information Systems and Science (GIS) community. Surprisingly, it is not for the lack of individual parallel algorithm; we discovered that the irregular and data intensive nature of the underlying processing is the main reason for the meager amount of work by way of system design and implementation. Furthermore, of all the systems reported in the literature, very few deal with the complexities of vector-based datasets and none, including commercial systems, on the cloud platform. We have designed and implemented an open-architecture-based system named Crayons for Windows Azure cloud platform using state-of-the-art techniques. We have implemented three different architectures of Crayons with different load balancing schemes. Crayons scales well for sufficiently large data sets, achieving end-to-end absolute speedup of over 28-fold employing 100 Azure processors. For smaller and more irregular workload, it still yields over 10-fold speedup.