{"id":11733,"date":"2014-03-25T21:21:06","date_gmt":"2014-03-25T19:21:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=11733"},"modified":"2015-08-27T01:19:13","modified_gmt":"2015-08-26T22:19:13","slug":"interpolation-with-radial-basis-functions-on-gpgpus-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11733","title":{"rendered":"Interpolation with Radial Basis Functions on GPGPUs using CUDA"},"content":{"rendered":"<p>This report gives a brief introduction to the interpolation with radial basis functions and it\u2019s application to the deformation of computational grids. The FGP algorithm is quoted as an iterative method for the calculation of the interpolation coefficients. A multipole method is described for the efficient approximation of the required matrix-vector product. Results are presented for different test systems and the results are compared with respect to the theoretical peak performances of the systems. The report concludes with a summary and an outlook to open issues.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This report gives a brief introduction to the interpolation with radial basis functions and it\u2019s application to the deformation of computational grids. The FGP algorithm is quoted as an iterative method for the calculation of the interpolation coefficients. A multipole method is described for the efficient approximation of the required matrix-vector product. Results are presented [&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":[89,157,3],"tags":[14,1796,628,20,1306],"class_list":["post-11733","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-mathematics","category-paper","tag-cuda","tag-mathematics","tag-numerical-analysis","tag-nvidia","tag-nvidia-geforce-gtx-680"],"views":2723,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11733","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=11733"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11733\/revisions"}],"predecessor-version":[{"id":14485,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11733\/revisions\/14485"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11733"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11733"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11733"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}