{"id":7160,"date":"2012-02-17T06:13:52","date_gmt":"2012-02-17T04:13:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=7160"},"modified":"2012-02-17T06:13:52","modified_gmt":"2012-02-17T04:13:52","slug":"a-3d-radiative-transfer-framework-viii-opencl-implementation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7160","title":{"rendered":"A 3D radiative transfer framework. VIII. OpenCL implementation"},"content":{"rendered":"<p>AIMS: We discuss an implementation of our 3D radiative transfer (3DRT) framework with the OpenCL paradigm for general GPU computing. METHODS: We implemented the kernel for solving the 3DRT problem in Cartesian coordinates with periodic boundary conditions in the horizontal (x,y) plane, including the construction of the nearest neighbor ^* and the operator splitting step. RESULTS: We present the results of both a small and a large test case and compare the timing of the 3DRT calculations for serial CPUs and various GPUs. CONCLUSIONS: The latest available GPUs can lead to significant speedups for both small and large grids compared to serial (single core) computations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AIMS: We discuss an implementation of our 3D radiative transfer (3DRT) framework with the OpenCL paradigm for general GPU computing. METHODS: We implemented the kernel for solving the 3DRT problem in Cartesian coordinates with periodic boundary conditions in the horizontal (x,y) plane, including the construction of the nearest neighbor ^* and the operator splitting step. [&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":[96,90,3],"tags":[1794,7,255,604,97,20,988,226,530,251,601,1793,606,378],"class_list":["post-7160","post","type-post","status-publish","format-standard","hentry","category-astrophysics","category-opencl","category-paper","tag-astrophysics","tag-ati","tag-ati-radeon-hd-4870","tag-earth-and-planetary-astrophysics","tag-instrumentation-and-methods-for-astrophysics","tag-nvidia","tag-nvidia-geforce-8600-m","tag-nvidia-geforce-8800-gt","tag-nvidia-geforce-gt-120","tag-nvidia-geforce-gtx-285","tag-nvidia-quadro-fx-4800","tag-opencl","tag-solar-and-stellar-astrophysics","tag-tesla-c2050"],"views":3792,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7160","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=7160"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7160\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7160"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7160"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7160"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}