{"id":3553,"date":"2011-04-11T20:03:02","date_gmt":"2011-04-11T20:03:02","guid":{"rendered":"http:\/\/hgpu.org\/?p=3553"},"modified":"2011-04-11T20:03:02","modified_gmt":"2011-04-11T20:03:02","slug":"simulation-of-bevel-gear-cutting-with-gpgpus-performance-and-productivity","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3553","title":{"rendered":"Simulation of bevel gear cutting with GPGPUs-performance and productivity"},"content":{"rendered":"<p>The desire for general purpose computation on graphics processing units caused the advance of new programming paradigms, e.g. OpenCL C\/C++, CUDA C or the PGI Accelerator Model. In this paper, we apply these programming approaches to the software KegelSpan for simulating bevel gear cutting. This engineering application simulates an important manufacturing process in the automotive industry. The results obtained are compared to an OpenMP implementation on various hardware configurations. The discussion covers performance results, but also productivity of code development realized in this effort.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The desire for general purpose computation on graphics processing units caused the advance of new programming paradigms, e.g. OpenCL C\/C++, CUDA C or the PGI Accelerator Model. In this paper, we apply these programming approaches to the software KegelSpan for simulating bevel gear cutting. This engineering application simulates an important manufacturing process in the automotive [&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":[11,89,90,3],"tags":[514,1782,14,20,1793,252],"class_list":["post-3553","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-computational-engineering","tag-computer-science","tag-cuda","tag-nvidia","tag-opencl","tag-openmp"],"views":1784,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3553","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=3553"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3553\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3553"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3553"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}