{"title":"CudaGIS: report on the design and realization of a massive data parallel GIS on GPUs","authors":"Jianting Zhang, Simin You","doi":"10.1145/2442968.2442981","DOIUrl":null,"url":null,"abstract":"We report the preliminary design and realization of a high-performance, general purposed, parallel GIS (CudaGIS), based on the General Purpose computing on Graphics Processing Units (GPGPU) technologies. Still under active developments, CudaGIS currently supports major types of geospatial data (point, polyline, polygon and raster) and provides modules for spatial indexing, spatial join and other types of geospatial operations on such geospatial data types. Experiments have demonstrated 10-40X on main-memory systems due to GPU accelerations and 1000-10000X speedups over serial CPU implementations and disk-resident systems by integrating additional performance boosting techniques, such as efficient in-memory data structures and algorithmic engineering.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on GeoStreaming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2442968.2442981","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
We report the preliminary design and realization of a high-performance, general purposed, parallel GIS (CudaGIS), based on the General Purpose computing on Graphics Processing Units (GPGPU) technologies. Still under active developments, CudaGIS currently supports major types of geospatial data (point, polyline, polygon and raster) and provides modules for spatial indexing, spatial join and other types of geospatial operations on such geospatial data types. Experiments have demonstrated 10-40X on main-memory systems due to GPU accelerations and 1000-10000X speedups over serial CPU implementations and disk-resident systems by integrating additional performance boosting techniques, such as efficient in-memory data structures and algorithmic engineering.
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