{"id":10389,"date":"2013-08-23T23:56:02","date_gmt":"2013-08-23T20:56:02","guid":{"rendered":"http:\/\/hgpu.org\/?p=10389"},"modified":"2013-08-23T23:56:02","modified_gmt":"2013-08-23T20:56:02","slug":"implementation-of-kirchhoff-prestack-depth-migration-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10389","title":{"rendered":"Implementation of Kirchhoff prestack depth migration on GPU"},"content":{"rendered":"<p>The massively parallel nature of Graphics Processing Units has made them an attractive platform for some computationally intensive algorithms. This article presents a method to run 3D Kirchhoff prestack depth migration on GPU-based clusters. Compared to a CPU only version of the same algorithm, the new approach delivers a significantly greater efficiency. An actual production run with field data reveals the extent of the improvements.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The massively parallel nature of Graphics Processing Units has made them an attractive platform for some computationally intensive algorithms. This article presents a method to run 3D Kirchhoff prestack depth migration on GPU-based clusters. Compared to a CPU only version of the same algorithm, the new approach delivers a significantly greater efficiency. An actual production [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,303,192,3],"tags":[1787,14,1801,1798,20],"class_list":["post-10389","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-earth-and-space-sciences","category-geoscience","category-paper","tag-algorithms","tag-cuda","tag-earth-and-space-sciences","tag-geoscience","tag-nvidia"],"views":3223,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10389","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=10389"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10389\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}