{"id":12028,"date":"2014-05-09T06:21:39","date_gmt":"2014-05-09T03:21:39","guid":{"rendered":"http:\/\/hgpu.org\/?p=12028"},"modified":"2014-05-09T06:21:39","modified_gmt":"2014-05-09T03:21:39","slug":"acceleration-of-lsb-algorithm-in-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12028","title":{"rendered":"Acceleration of LSB Algorithm in GPU"},"content":{"rendered":"<p>This paper presents a method for acceleration of LSB (Least Significant Bit) Algorithm in GPU (Graphics Processing Unit) using a programming model called CUDA. CUDA is a state-of-the-art parallel computing architecture developed by nVIDIA. CUDA allows the programmers to access the GPU directly by invoking the Kernel. In Image Steganography, parallelization of computations to a single pixel and the hybrid mix of message passing and shared memory access routines allows us to accelerate the LSB algorithm and thereby reducing the runtime of the program.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a method for acceleration of LSB (Least Significant Bit) Algorithm in GPU (Graphics Processing Unit) using a programming model called CUDA. CUDA is a state-of-the-art parallel computing architecture developed by nVIDIA. CUDA allows the programmers to access the GPU directly by invoking the Kernel. In Image Steganography, parallelization of computations to 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":[36,11,89,3],"tags":[1787,1782,14,20],"class_list":["post-12028","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia"],"views":2400,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12028","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=12028"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12028\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12028"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12028"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12028"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}