{"id":9070,"date":"2013-03-24T07:30:12","date_gmt":"2013-03-24T05:30:12","guid":{"rendered":"http:\/\/hgpu.org\/?p=9070"},"modified":"2013-03-24T07:30:12","modified_gmt":"2013-03-24T05:30:12","slug":"efficiently-mapping-the-aes-encryption-algorithm-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9070","title":{"rendered":"Efficiently Mapping the AES Encryption Algorithm on GPUs"},"content":{"rendered":"<p>Warped AES is a high performance heterogeneous GPU\/CPU-SSE parallel method for encryption using GPUs. Considering the performance of encryption in GPU memory alone, our algorithm outperforms current published implementations on comparable hardware. In our ongoing research, we have also devised a speculative method for high throughput encryption on GPUs, while preserving low latency to client CPU applications. In this work we emphasize on techniques used to efficiently map the AES CTR encryption tasks for parallel execution on GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Warped AES is a high performance heterogeneous GPU\/CPU-SSE parallel method for encryption using GPUs. Considering the performance of encryption in GPU memory alone, our algorithm outperforms current published implementations on comparable hardware. In our ongoing research, we have also devised a speculative method for high throughput encryption on GPUs, while preserving low latency to client [&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":[36,11,89,3,287],"tags":[370,1787,1782,14,452,20,1800,1316,931],"class_list":["post-9070","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","category-security","tag-aes","tag-algorithms","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-security","tag-tesla-c2060","tag-tesla-m2050"],"views":2629,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9070","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=9070"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9070\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9070"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9070"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}