{"id":9875,"date":"2013-07-10T23:31:18","date_gmt":"2013-07-10T20:31:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=9875"},"modified":"2013-07-10T23:31:18","modified_gmt":"2013-07-10T20:31:18","slug":"meshfreegfem-in-hardware-efficiency-prospective","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9875","title":{"rendered":"Meshfree\/GFEM in hardware-efficiency prospective"},"content":{"rendered":"<p>A fundamental trend of processor architecture evolving towards exaflops is fast increasing floating point performance (so-called &quot;free&quot; flops) accompanied by much slowly increasing memory and network bandwidth. In order to fully enjoy the &quot;free&quot; flops, a numerical algorithm of PDEs should request more flops per byte or increase arithmetic intensity. A meshfree\/GFEM approximation can be the class of the algorithm. It is shown in a GFEM without extra dof that the kind of approximation takes advantages of the high performance of manycore GPUs by a high accuracy of approximation; the &quot;expensive&quot; method is found to be reversely hardware-efficient on the emerging architecture of manycore.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A fundamental trend of processor architecture evolving towards exaflops is fast increasing floating point performance (so-called &quot;free&quot; flops) accompanied by much slowly increasing memory and network bandwidth. In order to fully enjoy the &quot;free&quot; flops, a numerical algorithm of PDEs should request more flops per byte or increase arithmetic intensity. A meshfree\/GFEM approximation can be [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,20,251,550,551],"class_list":["post-9875","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-nvidia","tag-nvidia-geforce-gtx-285","tag-partial-differential-equations","tag-pdes"],"views":2004,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9875","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=9875"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9875\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9875"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9875"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9875"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}