{"id":5966,"date":"2011-10-21T13:38:57","date_gmt":"2011-10-21T10:38:57","guid":{"rendered":"http:\/\/hgpu.org\/?p=5966"},"modified":"2015-08-15T23:13:50","modified_gmt":"2015-08-15T20:13:50","slug":"openmp-for-accelerators","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5966","title":{"rendered":"OpenMP for Accelerators"},"content":{"rendered":"<p>OpenMP [14] is the dominant programming model for shared-memory parallelism in C, C++ and Fortran due to its easy-to-use directive-based style, portability and broad support by compiler vendors. Compute-intensive application regions are increasingly being accelerated using devices such as GPUs and DSPs, and a programming model with similar characteristics is needed here. This paper presents extensions to OpenMP that provide such a programming model. Our results demonstrate that a high-level programming model can provide accelerated performance comparable to that of hand-coded implementations in CUDA.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>OpenMP [14] is the dominant programming model for shared-memory parallelism in C, C++ and Fortran due to its easy-to-use directive-based style, portability and broad support by compiler vendors. Compute-intensive application regions are increasingly being accelerated using devices such as GPUs and DSPs, and a programming model with similar characteristics is needed here. This paper presents [&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":[1782,14,809,989,20,252,378],"class_list":["post-5966","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-dsp","tag-fortran","tag-nvidia","tag-openmp","tag-tesla-c2050"],"views":3560,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5966","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=5966"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5966\/revisions"}],"predecessor-version":[{"id":14431,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5966\/revisions\/14431"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5966"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5966"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5966"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}