{"id":11442,"date":"2014-02-23T02:07:26","date_gmt":"2014-02-23T00:07:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=11442"},"modified":"2014-02-23T02:07:26","modified_gmt":"2014-02-23T00:07:26","slug":"parallel-spectral-graph-partitioning-on-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11442","title":{"rendered":"Parallel Spectral Graph Partitioning on CUDA"},"content":{"rendered":"<p>Parallelization of scientific problems is a challenging task which has a wide application area both on distributed programming, cloud computing and recently on GPGPU. Spectral graph partitioning is a widely used technique in many fields such as image processing, scientific computing, machine learning etc. In this study we analyze spectral graph partitioning subroutines on a GPGPU framework. Each step is analyzed with differing techniques to lead a conclusion about usage of GPGPU on overall spectral graph partitioning algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Parallelization of scientific problems is a challenging task which has a wide application area both on distributed programming, cloud computing and recently on GPGPU. Spectral graph partitioning is a widely used technique in many fields such as image processing, scientific computing, machine learning etc. In this study we analyze spectral graph partitioning subroutines on a [&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":[36,89,33,3],"tags":[1787,14,1786,1025,20,1006],"class_list":["post-11442","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-image-processing","category-paper","tag-algorithms","tag-cuda","tag-image-processing","tag-machine-learning","tag-nvidia","tag-tesla-c2070"],"views":2529,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11442","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=11442"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11442\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11442"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11442"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11442"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}