In this paper we present a chronological review of five distinct data structures commonly found in literature and ray tracing systems: Bounding Volume Hierarchies (BVH), Octrees, Uniform Grids, KD-Trees, and Bounding Interval Hierarchies (BIH). This review is then followed by an extensive comparative study of six different ray traversal algorithms implemented on a modern Kepler CUDA GPU architecture, to point out pros and cons regarding performance and memory consumption of such structures. We show that a GPU KD-Tree ray traversal based on ropes achieved the best performance results. It surpasses the BVH, often used as primary structure on state-of-the-art ray tracers. A carefully well implemented ropes based KD-Tree CUDA traversal can improve performance on a 12-39% approximate range. This suggests that, for critic real time applications, the ropes based KD-Tree traversal is a more adequate option on GPU. However, this structure consumes at least 4x more memory space than BVHs and BIHs. This disadvantage can be a limiting factor on memory limited architectures.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper we present a chronological review of five distinct data structures commonly found in literature and ray tracing systems: Bounding Volume Hierarchies (BVH), Octrees, Uniform Grids, KD-Trees, and Bounding Interval Hierarchies (BIH). This review is then followed by an extensive comparative study of six different ray traversal algorithms implemented on a modern Kepler […]<\/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":[89,3],"tags":[14,412,20,1601,1611],"class_list":["post-12224","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","tag-cuda","tag-kd-tree","tag-nvidia","tag-nvidia-geforce-gtx-780-ti","tag-raytraycing"],"views":3773,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12224","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=12224"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12224\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12224"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12224"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12224"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}