{"id":8301,"date":"2012-10-01T23:12:15","date_gmt":"2012-10-01T20:12:15","guid":{"rendered":"http:\/\/hgpu.org\/?p=8301"},"modified":"2012-10-01T23:12:15","modified_gmt":"2012-10-01T20:12:15","slug":"accelerated-pressure-projection-using-opencl-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8301","title":{"rendered":"Accelerated Pressure Projection using OpenCL on GPUs"},"content":{"rendered":"<p>A GPU version of the pressure projection solver using OpenCL is implemented. Then it has been compared with CPU version which is accelerated with OpenMP. The GPU version shows a sensible reduction in time despite using a simple algorithm in the kernel. The nal code is plugged into a commercial uid simulator software. Dierent kinds of algorithms and data transfer methods have been investigated. Overlapping the computation and communication showed a more than 3 times speed-up versus the serial communication-computation pattern. Finally we exploit methods for partitioning data and writing kernels to use many of the bene ts of computation on a heterogeneous system. We ran all the simulations on a machine with an Intel core i7-2600 cpu and 16 GB main memory coupled with a GeForce GTX 560 Ti graphic processing unit on a windows OS.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A GPU version of the pressure projection solver using OpenCL is implemented. Then it has been compared with CPU version which is accelerated with OpenMP. The GPU version shows a sensible reduction in time despite using a simple algorithm in the kernel. The nal code is plugged into a commercial uid simulator software. Dierent kinds [&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":[36,11,104,90,3],"tags":[1787,1782,1795,452,20,1089,1793,390],"class_list":["post-8301","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-fluid-dynamics","category-opencl","category-paper","tag-algorithms","tag-computer-science","tag-fluid-dynamics","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-560-ti","tag-opencl","tag-thesis"],"views":2310,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8301","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=8301"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8301\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8301"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}