{"id":5941,"date":"2011-10-18T16:08:29","date_gmt":"2011-10-18T13:08:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=5941"},"modified":"2011-10-18T16:08:29","modified_gmt":"2011-10-18T13:08:29","slug":"design-and-performance-of-the-op2-library-for-unstructured-mesh-applications","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5941","title":{"rendered":"Design and Performance of the OP2 Library for Unstructured Mesh Applications"},"content":{"rendered":"<p>OP2 is an &quot;active&quot; library framework for the solution of unstructured mesh applications. It aims to decouple the scientific specification of an application from its parallel implementation to achieve code longevity and near-optimal performance by re-targeting the back-end to different multi-core\/many-core hardware. This paper presents the design of the OP2 code generation and compiler framework which, given an application written using the OP2 API, generates efficient code for state-of-the-art hardware (e.g. GPUs and multi-core CPUs). Through a representative unstructured mesh application we demonstrate the capabilities of the compiler framework to utilize the same OP2 hardware specific run-time support functionalities. Performance results show that the impact due to this sharing of basic functionalities is negligible.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>OP2 is an &quot;active&quot; library framework for the solution of unstructured mesh applications. It aims to decouple the scientific specification of an application from its parallel implementation to achieve code longevity and near-optimal performance by re-targeting the back-end to different multi-core\/many-core hardware. This paper presents the design of the OP2 code generation and compiler framework [&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":[11,89,90,3],"tags":[215,955,1782,14,20,1015,1793,252,176,378],"class_list":["post-5941","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-code-generation","tag-compilers","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-460","tag-opencl","tag-openmp","tag-package","tag-tesla-c2050"],"views":1829,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5941","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=5941"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5941\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5941"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5941"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5941"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}