{"id":16536,"date":"2016-09-10T14:14:49","date_gmt":"2016-09-10T11:14:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=16536"},"modified":"2016-09-10T14:14:49","modified_gmt":"2016-09-10T11:14:49","slug":"opensbli-a-framework-for-the-automated-derivation-and-parallel-execution-of-finite-difference-solvers-on-a-range-of-computer-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16536","title":{"rendered":"OpenSBLI: A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures"},"content":{"rendered":"<p>Exascale computing will feature novel and potentially disruptive hardware architectures. Exploiting these to their full potential is non-trivial. Numerical modelling frameworks involving finite difference methods are currently limited by the &#8216;static&#8217; nature of the hand-coded discretisation schemes and repeatedly may have to be re-written to run efficiently on new hardware. In contrast, OpenSBLI uses code generation to derive the model&#8217;s code from a high-level specification. Users focus on the equations to solve, whilst not concerning themselves with the detailed implementation. Source-to-source translation is used to tailor the code and enable its execution on a variety of hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Exascale computing will feature novel and potentially disruptive hardware architectures. Exploiting these to their full potential is non-trivial. Numerical modelling frameworks involving finite difference methods are currently limited by the &#8216;static&#8217; nature of the hand-coded discretisation schemes and repeatedly may have to be re-written to run efficiently on new hardware. In contrast, OpenSBLI uses code [&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":[11,89,104,90,3],"tags":[1600,215,1782,14,327,1795,597,242,20,1793,1543],"class_list":["post-16536","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-fluid-dynamics","category-opencl","category-paper","tag-cfd","tag-code-generation","tag-computer-science","tag-cuda","tag-finite-difference","tag-fluid-dynamics","tag-mathematical-software","tag-mpi","tag-nvidia","tag-opencl","tag-tesla-k40"],"views":2162,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16536","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=16536"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16536\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16536"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16536"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16536"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}