{"id":1903,"date":"2010-12-08T13:07:08","date_gmt":"2010-12-08T13:07:08","guid":{"rendered":"http:\/\/hgpu.org\/?p=1903"},"modified":"2010-12-08T13:07:08","modified_gmt":"2010-12-08T13:07:08","slug":"exploiting-the-power-of-gpus-for-asymmetric-cryptography","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1903","title":{"rendered":"Exploiting the Power of GPUs for Asymmetric Cryptography"},"content":{"rendered":"<p>Modern Graphics Processing Units (GPU) have reached a dimension with respect to performance and gate count exceeding conventional Central Processing Units (CPU) by far. Many modern computer systems include &#8211; beside a CPU &#8211; such a powerful GPU which runs idle most of the time and might be used as cheap and instantly available co-processor for general purpose applications. In this contribution, we focus on the efficient realisation of the computationally expensive operations in asymmetric cryptosystems on such off-the-shelf GPUs. More precisely, we present improved and novel implementations employing GPUs as accelerator for RSA and DSA cryptosystems as well as for Elliptic Curve Cryptography (ECC). Using a recent Nvidia 8800GTS graphics card, we are able to compute 813 modular exponentiations per second for RSA or DSA-based systems with 1024 bit integers. Moreover, our design for ECC over the prime field P-224 even achieves the throughput of 1412 point multiplications per second.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern Graphics Processing Units (GPU) have reached a dimension with respect to performance and gate count exceeding conventional Central Processing Units (CPU) by far. Many modern computer systems include &#8211; beside a CPU &#8211; such a powerful GPU which runs idle most of the time and might be used as cheap and instantly available co-processor [&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":[36,11,89,3,287],"tags":[1787,1782,14,20,357,1800],"class_list":["post-1903","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","category-security","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-security"],"views":1985,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1903","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=1903"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1903\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1903"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1903"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1903"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}