{"id":6815,"date":"2012-01-03T16:33:52","date_gmt":"2012-01-03T14:33:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=6815"},"modified":"2012-01-03T16:33:52","modified_gmt":"2012-01-03T14:33:52","slug":"gpgpu-accelerated-texture-based-radiosity","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6815","title":{"rendered":"GPGPU Accelerated Texture-Based Radiosity"},"content":{"rendered":"<p>Radiosity is a popular global illumination algorithm capable of achieving photorealistic rendering results. However, its use in interactive environments is limited by its computational complexity. This paper presents a GPGPU-based implementation of the gathering radiosity approach using texture-based discretisation and the OpenCL framework. Hemicubes are rendered to a texture array and processed by OpenCL kernels in parallel to compute the output radiance of the patches. Results show that even with the high synchronisation overhead of the OpenGL-OpenCL interoperability, the proposed method is an order of magnitude faster than a CPU-based implementation, and that it approaches interactive speeds. Investigation of the influence of different parameters shows that an increase in hemicube size results in a linear increase in computation time, while an increase in the number of layers in the texture array dimensions results in a logarithmic decrease in computation time.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Radiosity is a popular global illumination algorithm capable of achieving photorealistic rendering results. However, its use in interactive environments is limited by its computational complexity. This paper presents a GPGPU-based implementation of the gathering radiosity approach using texture-based discretisation and the OpenCL framework. Hemicubes are rendered to a texture array and processed by OpenCL kernels [&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":[1787,659,1782,20,1088,1793,182,144],"class_list":["post-6815","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-opencl","category-paper","tag-algorithms","tag-computational-complexity","tag-computer-science","tag-nvidia","tag-nvidia-geforce-gtx-550-ti","tag-opencl","tag-opengl","tag-rendering"],"views":1980,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6815","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=6815"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6815\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6815"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6815"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}