{"id":8832,"date":"2013-01-24T00:01:11","date_gmt":"2013-01-23T22:01:11","guid":{"rendered":"http:\/\/hgpu.org\/?p=8832"},"modified":"2013-01-24T00:01:11","modified_gmt":"2013-01-23T22:01:11","slug":"gpu-based-3d-wavelet-transform","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8832","title":{"rendered":"GPU-based 3D Wavelet Transform"},"content":{"rendered":"<p>Wide amount of applications like volumetric medical data compression, video watermarking and video coding use the three-dimensional wavelet transform (3D-DWT) in their algorithms. In this work, we present GPU algorithms, based on both global and shared memory, to compute the 3D-DWT transform on both the GTX280 and the GMT540 platforms. The results obtained show that speed-ups of 19.7 and 10.65 on average can be obtained for the GTX280 and GMT540 platforms respectively when only the GPU&#8217;s global memory is used. Moreover, Speed-ups increase considerably to 87 and 25 when the shared memory in the device is used optimizing the memory access to avoid idle threads. Futhermore, we discuss speed-up evolution depending on the group of pictures size (GOP).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Wide amount of applications like volumetric medical data compression, video watermarking and video coding use the three-dimensional wavelet transform (3D-DWT) in their algorithms. In this work, we present GPU algorithms, based on both global and shared memory, to compute the 3D-DWT transform on both the GTX280 and the GMT540 platforms. The results obtained show that [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,832,1782,14,20,1268,234,859],"class_list":["post-8832","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-compression","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gt-540-m","tag-nvidia-geforce-gtx-280","tag-wavelet"],"views":3642,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8832","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=8832"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8832\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8832"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8832"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8832"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}