{"id":5223,"date":"2011-08-19T19:41:24","date_gmt":"2011-08-19T16:41:24","guid":{"rendered":"http:\/\/hgpu.org\/?p=5223"},"modified":"2011-08-19T19:41:24","modified_gmt":"2011-08-19T16:41:24","slug":"extending-abstract-gpu-apis-to-shared-memory","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5223","title":{"rendered":"Extending abstract GPU APIs to shared memory"},"content":{"rendered":"<p>Parallel programming is used extensively for general-purpose computations. However, performance of parallel APIs varies for a given problem and a given architecture. This gives rise to the need for having an abstract way to express the parallel problems. This poster presents a new approach through which programmers can access these APIs without having to focus on the technical or platform-specific details. Our earlier approach of Abstract Application Programming Interface (API) targeted for Graphical Processing Unit (GPU) programming is extended to shared memory using OpenMP.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Parallel programming is used extensively for general-purpose computations. However, performance of parallel APIs varies for a given problem and a given architecture. This gives rise to the need for having an abstract way to express the parallel problems. This poster presents a new approach through which programmers can access these APIs without having to focus [&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":[36,11,89,90,3],"tags":[1787,1782,14,20,1793,252,119,660,70],"class_list":["post-5223","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-opencl","tag-openmp","tag-presentation","tag-programming-languages","tag-programming-techniques"],"views":1999,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5223","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=5223"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5223\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}