{"id":3136,"date":"2011-03-08T21:50:53","date_gmt":"2011-03-08T21:50:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=3136"},"modified":"2011-03-08T21:50:53","modified_gmt":"2011-03-08T21:50:53","slug":"preliminary-implementation-of-vq-image-coding-using-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3136","title":{"rendered":"Preliminary implementation of VQ image coding using GPGPU"},"content":{"rendered":"<p>GPGPU (general purpose computing on graphic processing unit) attracts a great deal of attention, that is used for general-purpose computations like numerical calculations as well as graphic processing. In this paper, as an example of hierarchical clustering algorithms, we evaluate PNN (pairwise nearest neighbor) on GPUs by using CUDA (compute unified device architecture). We also evaluate it from the viewpoint of the power consumption.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPGPU (general purpose computing on graphic processing unit) attracts a great deal of attention, that is used for general-purpose computations like numerical calculations as well as graphic processing. In this paper, as an example of hierarchical clustering algorithms, we evaluate PNN (pairwise nearest neighbor) on GPUs by using CUDA (compute unified device architecture). We also [&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":[36,89,33,3],"tags":[1787,14,1786,349,20],"class_list":["post-3136","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-nearest-neighbour","tag-nvidia"],"views":2082,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3136","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=3136"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3136\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3136"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3136"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3136"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}