{"id":17984,"date":"2018-02-09T09:16:19","date_gmt":"2018-02-09T07:16:19","guid":{"rendered":"https:\/\/hgpu.org\/?p=17984"},"modified":"2018-02-09T09:16:19","modified_gmt":"2018-02-09T07:16:19","slug":"ikra-cpp-a-c-cuda-dsl-for-object-oriented-programming-with-structure-of-arrays-layout","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=17984","title":{"rendered":"Ikra-Cpp: A C++\/CUDA DSL for Object-Oriented Programming with Structure-of-Arrays Layout"},"content":{"rendered":"<p>Structure of Arrays (SOA) is a well-studied data layout technique for SIMD architectures. Previous work has shown that it can speed up applications in high-performance computing by several factors compared to a traditional Array of Structures (AOS) layout. However, most programmers are used to AOS-style programming, which is more readable and easier to maintain. We present Ikra-Cpp, an embedded DSL for object-oriented programming in C++\/CUDA. Ikra-Cpp&#8217;s notation is very close to standard AOS-style C++ code, but data is layed out as SOA. This gives programmers the performance benefit of SOA and the expressiveness of AOS-style object-oriented programming at the same time. Ikra-Cpp is well integrated with C++ and lets programmers use C++ notation and syntax for classes, fields, member functions, constructors and instance creation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Structure of Arrays (SOA) is a well-studied data layout technique for SIMD architectures. Previous work has shown that it can speed up applications in high-performance computing by several factors compared to a traditional Array of Structures (AOS) layout. However, most programmers are used to AOS-style programming, which is more readable and easier to maintain. We [&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":[1782,14,94,1651,20,176],"class_list":["post-17984","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-data-structures-and-algorithms","tag-dsl","tag-nvidia","tag-package"],"views":2603,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17984","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=17984"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17984\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17984"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17984"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}