{"id":8168,"date":"2012-09-07T14:52:41","date_gmt":"2012-09-07T11:52:41","guid":{"rendered":"http:\/\/hgpu.org\/?p=8168"},"modified":"2012-09-07T14:52:41","modified_gmt":"2012-09-07T11:52:41","slug":"gpu-computing-and-cuda-technology-used-to-accelerate-a-mesh-generator-application","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8168","title":{"rendered":"GPU Computing and CUDA technology used to accelerate a mesh generator application"},"content":{"rendered":"<p>The potential of GPU computing used in general purpose parallel programming has been amply shown. These massively parallel many-core multiprocessors are available to any users in every PCs, notebook, game console or workstation. In this work, we present the parallel version of a mesh-generating algorithm and its execution time reduction by using off-the-shelf GPU technology. We use commodities GPUs as a useful CPU co-processor to improve this kind of applications, characterized by a high level of data parallelism. Compared to the sequential algorithm, our techniques achieve 6X overall performance for GPU-CPU implementation; furthermore we achieve 50X speedup when implementing core operations of the algorithm. Results show that GPU provides a helpful platform for high performance computing to improve the execution time of these applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The potential of GPU computing used in general purpose parallel programming has been amply shown. These massively parallel many-core multiprocessors are available to any users in every PCs, notebook, game console or workstation. In this work, we present the parallel version of a mesh-generating algorithm and its execution time reduction by using off-the-shelf GPU technology. [&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":[11,89,3],"tags":[1782,14,20,1006],"class_list":["post-8168","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-tesla-c2070"],"views":2021,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8168","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=8168"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8168\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}