{"id":1843,"date":"2010-12-05T09:20:33","date_gmt":"2010-12-05T09:20:33","guid":{"rendered":"http:\/\/hgpu.org\/?p=1843"},"modified":"2010-12-05T09:20:33","modified_gmt":"2010-12-05T09:20:33","slug":"improving-the-performance-of-spatial-raster-analysis-in-gis-using-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1843","title":{"rendered":"Improving the performance of spatial raster analysis in GIS using GPU"},"content":{"rendered":"<p>GIS spatial raster analysis has become a powerful tool for geographical phenomena. Unfortunately the computation-intensive raster operations are likely to create computer performance bottlenecks when running on the CPUs. Over the last few years, GPU performance has improved much more than CPU performance. For this reason, many researches have applied the GPUs for scientific, geometric and database computations beyond graphics. This paper demonstrates a general framework for the GPU-based implementation of GIS raster operations, and conducts experiments to compare the computation performance between GPU-based and CPU-based algorithms. The test results indicate that using GPU on spatial raster operations can significantly improve their computation performance. This means that realizing GIS spatial analysis on the GPU create new opportunities by drastically lowering the cost of raster operations on the same hardware performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GIS spatial raster analysis has become a powerful tool for geographical phenomena. Unfortunately the computation-intensive raster operations are likely to create computer performance bottlenecks when running on the CPUs. Over the last few years, GPU performance has improved much more than CPU performance. For this reason, many researches have applied the GPUs for scientific, geometric [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,303,192,3],"tags":[1787,1801,1798],"class_list":["post-1843","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-earth-and-space-sciences","category-geoscience","category-paper","tag-algorithms","tag-earth-and-space-sciences","tag-geoscience"],"views":2494,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1843","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1843"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1843\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1843"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1843"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1843"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}