{"id":8262,"date":"2012-09-24T15:28:40","date_gmt":"2012-09-24T12:28:40","guid":{"rendered":"http:\/\/hgpu.org\/?p=8262"},"modified":"2012-09-24T15:28:40","modified_gmt":"2012-09-24T12:28:40","slug":"sound-speed-optimization-using-image-texture-on-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8262","title":{"rendered":"Sound Speed Optimization Using Image Texture on CUDA"},"content":{"rendered":"<p>The Compute Unified Device Architecture (CUDA) is a brand new parallel processing platform making use of the unified shader design of the most current Graphics Processing Units (GPUs) from NVIDIA. In this paper, we apply this revolutionary new technology to implement the sound speed optimization (SSO) with image texture analysis for medical ultrasound imaging. The sum and difference histogram of parallel texture mask production is also presented. This SSO method achieves 77ms for a group of alternative sound velocities that are from 1400 to 1700m\/s every 10m\/s a sample image with the size of 256 x 512, about 45 times faster than the CPU implementation. Testing results from GPU and CPU are compared in terms of decision results and program runtime with different image size.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Compute Unified Device Architecture (CUDA) is a brand new parallel processing platform making use of the unified shader design of the most current Graphics Processing Units (GPUs) from NVIDIA. In this paper, we apply this revolutionary new technology to implement the sound speed optimization (SSO) with image texture analysis for medical ultrasound imaging. The [&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":[14,1786,20,1090,298,208],"class_list":["post-8262","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cuda","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gtx-560","tag-optimization","tag-ultrasound"],"views":2069,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8262","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=8262"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8262\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}