{"id":3677,"date":"2011-04-22T20:31:10","date_gmt":"2011-04-22T20:31:10","guid":{"rendered":"http:\/\/hgpu.org\/?p=3677"},"modified":"2011-04-22T20:31:10","modified_gmt":"2011-04-22T20:31:10","slug":"preliminary-implementation-of-two-parallel-programs-for-fractal-image-coding-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3677","title":{"rendered":"Preliminary implementation of two parallel programs for fractal image coding on GPUs"},"content":{"rendered":"<p>GPGPU (General Purpose computing on Graphic Processing Unit) attracts a great deal of attention, which is used for general-purpose computations like numerical calculations as well as graphic processing. In this paper, we implement Fractal image coding algorithms on GPUs by using CUDA (Compute Unified Device Architecture) and evaluate the effectiveness of the shared memory using the coalesced communication.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPGPU (General Purpose computing on Graphic Processing Unit) attracts a great deal of attention, which is used for general-purpose computations like numerical calculations as well as graphic processing. In this paper, we implement Fractal image coding algorithms on GPUs by using CUDA (Compute Unified Device Architecture) and evaluate the effectiveness of the shared memory using [&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":[89,33,3],"tags":[14,1786,20],"class_list":["post-3677","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-image-processing","tag-nvidia"],"views":1709,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3677","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=3677"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3677\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3677"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3677"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3677"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}