{"id":13860,"date":"2015-04-14T23:22:17","date_gmt":"2015-04-14T20:22:17","guid":{"rendered":"http:\/\/hgpu.org\/?p=13860"},"modified":"2015-04-14T23:22:17","modified_gmt":"2015-04-14T20:22:17","slug":"a-parallel-tree-pattern-query-processing-algorithm-for-graph-databases-using-a-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13860","title":{"rendered":"A Parallel Tree Pattern Query Processing Algorithm for Graph Databases using a GPGPU"},"content":{"rendered":"<p>Large amounts of data are modeled and stored as graphs in order to express complex data relationships. Consequently, query processing on graph structures is becoming an important component in real-world applications. The most commonly used query format is that of tree pattern queries. We present a new parallel SIMD algorithm, GGQ (GPU Graph data base Query), for answering tree pattern queries on graph databases, using a GPU. We present the results of extensive experimentation of GGQ on large graph databases using known benchmarks that show that GGQ is an effective and competitive algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large amounts of data are modeled and stored as graphs in order to express complex data relationships. Consequently, query processing on graph structures is becoming an important component in real-world applications. The most commonly used query format is that of tree pattern queries. We present a new parallel SIMD algorithm, GGQ (GPU Graph data base [&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":[1787,451,1782,14,667,20,379],"class_list":["post-13860","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-benchmarking","tag-computer-science","tag-cuda","tag-databases","tag-nvidia","tag-nvidia-geforce-gtx-480"],"views":2310,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13860","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=13860"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13860\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13860"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13860"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13860"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}