{"id":7355,"date":"2012-03-27T12:34:37","date_gmt":"2012-03-27T09:34:37","guid":{"rendered":"http:\/\/hgpu.org\/?p=7355"},"modified":"2012-03-27T12:34:37","modified_gmt":"2012-03-27T09:34:37","slug":"practical-and-theoretical-aspects-of-a-parallel-twig-join-algorithm-for-xml-processing-using-a-gpgpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7355","title":{"rendered":"Practical and Theoretical Aspects of a Parallel Twig Join Algorithm for XML Processing using a GPGPU"},"content":{"rendered":"<p>With an increasing amount of data and demand for fast query processing, the efficiency of database operations continues to be a challenging task. A common approach is to leverage parallel hardware platforms. With the introduction of general-purpose GPU (Graphics Processing Unit) computing, massively parallel hardware has become available within commodity hardware. XML is based on a tree-structured data model. Naturally, the most popular XML querying language (XPath) uses patterns of selection predicates on multiple elements, related by a tree structure. These are often abstracted by twig patterns. Finding all occurrences of such a (XML query) twig pattern in an XML document is a core operation for XML query processing. We present a new algorithm, GPU-Twig, for matching twig patterns in large XML documents, using a GPU. Our algorithm uses the data and task parallelism of the GPU to perform memory-intensive tasks whereas the CPU is used to perform I\/O and resource management. We therefore efficiently exploit both the high-bandwidth GPU memory interface and the lower-bandwidth CPU main memory.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>With an increasing amount of data and demand for fast query processing, the efficiency of database operations continues to be a challenging task. A common approach is to leverage parallel hardware platforms. With the introduction of general-purpose GPU (Graphics Processing Unit) computing, massively parallel hardware has become available within commodity hardware. XML is based on [&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],"tags":[1787,1782,14,667,427,20,379],"class_list":["post-7355","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-databases","tag-join","tag-nvidia","tag-nvidia-geforce-gtx-480"],"views":2370,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7355","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=7355"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7355\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7355"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7355"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7355"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}