{"id":1012,"date":"2010-10-28T21:07:07","date_gmt":"2010-10-28T21:07:07","guid":{"rendered":"http:\/\/hgpu.org\/?p=1012"},"modified":"2010-10-28T21:07:07","modified_gmt":"2010-10-28T21:07:07","slug":"gpu-packet-classification-using-opencl-a-consideration-of-viable-classification-methods","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1012","title":{"rendered":"GPU packet classification using OpenCL: a consideration of viable classification methods"},"content":{"rendered":"<p>Packet analysis is an important aspect of network security, which typically relies on a flexible packet filtering system to extrapolate important packet information from each processed packet. Packet analysis is a computationally intensive, highly parallelisable task, and as such, classification of large packet sets, such as those collected by a network telescope, can require significant processing time. We wish to improve upon this, through parallel classification on a GPU. In this paper, we first consider the OpenCL architecture and its applicability to packet analysis. We then introduce a number of packet demultiplexing and routing algorithms, and finally present a discussion on how some of these techniques may be leveraged within a GPGPU context to improve packet classification speeds.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Packet analysis is an important aspect of network security, which typically relies on a flexible packet filtering system to extrapolate important packet information from each processed packet. Packet analysis is a computationally intensive, highly parallelisable task, and as such, classification of large packet sets, such as those collected by a network telescope, can require significant [&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":[36,11,90,3],"tags":[1787,1782,20,1793],"class_list":["post-1012","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-opencl","category-paper","tag-algorithms","tag-computer-science","tag-nvidia","tag-opencl"],"views":2965,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1012","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=1012"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1012\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1012"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1012"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1012"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}