{"id":1795,"date":"2010-12-02T16:04:43","date_gmt":"2010-12-02T16:04:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=1795"},"modified":"2010-12-02T16:04:43","modified_gmt":"2010-12-02T16:04:43","slug":"glift-generic-efficient-random-access-gpu-data-structures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1795","title":{"rendered":"Glift: Generic, efficient, random-access GPU data structures"},"content":{"rendered":"<p>This paper presents Glift, an abstraction and generic template library for defining complex, random-access graphics processor (GPU) data structures. Like modern CPU data structure libraries, Glift enables GPU programmers to separate algorithms from data structure definitions; thereby greatly simplifying algorithmic development and enabling reusable and interchangeable data structures. We characterize a large body of previously published GPU data structures in terms of our abstraction and present several new GPU data structures. The structures, a stack, quadtree, and octree, are explained using simple Glift concepts and implemented using reusable Glift components. We also describe two applications of these structures not previously demonstrated on GPUs: adaptive shadow maps and octree 3D paint. Lastly, we show that our example Glift data structures perform comparably to handwritten implementations while requiring only a fraction of the programming effort.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents Glift, an abstraction and generic template library for defining complex, random-access graphics processor (GPU) data structures. Like modern CPU data structure libraries, Glift enables GPU programmers to separate algorithms from data structure definitions; thereby greatly simplifying algorithmic development and enabling reusable and interchangeable data structures. We characterize a large body of previously [&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":[11,3],"tags":[444,1782,94,95,20,301],"class_list":["post-1795","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-cg","tag-computer-science","tag-data-structures-and-algorithms","tag-high-level-languages","tag-nvidia","tag-nvidia-geforce-6800-gt"],"views":2291,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1795","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=1795"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1795\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1795"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1795"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1795"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}