{"id":17773,"date":"2017-11-16T09:51:45","date_gmt":"2017-11-16T07:51:45","guid":{"rendered":"https:\/\/hgpu.org\/?p=17773"},"modified":"2017-11-16T09:51:45","modified_gmt":"2017-11-16T07:51:45","slug":"hydra-a-c11-framework-for-data-analysis-in-massively-parallel-platforms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=17773","title":{"rendered":"Hydra: a C++11 framework for data analysis in massively parallel platforms"},"content":{"rendered":"<p>Hydra is a header-only, templated and C++11-compliant framework designed to perform the typical bottleneck calculations found in common HEP data analyses on massively parallel platforms. The framework is implemented on top of the C++11 Standard Library and a variadic version of the Thrust library and is designed to run on Linux systems, using OpenMP, CUDA and TBB enabled devices. This contribution summarizes the main features of Hydra. A basic description of the overall design, functionality and user interface is provided, along with some code examples and measurements of performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hydra is a header-only, templated and C++11-compliant framework designed to perform the typical bottleneck calculations found in common HEP data analyses on massively parallel platforms. The framework is implemented on top of the C++11 Standard Library and a variadic version of the Thrust library and is designed to run on Linux systems, using OpenMP, CUDA [&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":[11,89,3],"tags":[98,1782,14,597,20,252,176,1543],"class_list":["post-17773","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computational-physics","tag-computer-science","tag-cuda","tag-mathematical-software","tag-nvidia","tag-openmp","tag-package","tag-tesla-k40"],"views":6114,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17773","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=17773"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17773\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17773"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17773"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}