{"id":1531,"date":"2010-11-20T09:01:40","date_gmt":"2010-11-20T09:01:40","guid":{"rendered":"http:\/\/hgpu.org\/?p=1531"},"modified":"2010-11-20T09:01:40","modified_gmt":"2010-11-20T09:01:40","slug":"sblock-a-framework-for-efficient-stencil-based-pde-solvers-on-multi-core-platforms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1531","title":{"rendered":"SBLOCK: A Framework for Efficient Stencil-Based PDE Solvers on Multi-core Platforms"},"content":{"rendered":"<p>We present a new software framework for the implementation of applications that use stencil computations on block-structured grids to solve partial differential equations. A key feature of the framework is the extensive use of automatic source code generation which is used to achieve high performance on a range of leading multi-core processors. Results are presented for a simple model stencil running on Intel and AMD CPUs as well as the NVIDIA GT200 GPU. The generality of the frame- work is demonstrated through the implementation of a complete application consisting of many different stencil computations, taken from the field of computational fluid dynamics.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a new software framework for the implementation of applications that use stencil computations on block-structured grids to solve partial differential equations. A key feature of the framework is the extensive use of automatic source code generation which is used to achieve high performance on a range of leading multi-core processors. Results are presented [&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":[11,89,3],"tags":[1782,14,20,550,551],"class_list":["post-1531","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-partial-differential-equations","tag-pdes"],"views":2211,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1531","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=1531"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1531\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1531"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1531"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1531"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}