Scientific data is mostly multi-valued, e.g., coordinates, velocities, moments or feature components, and it comes in large quantities. The data layout of such containers has an enormous impact on the achieved performance, however, layout optimization is very time-consuming and error-prone because container access syntax in standard programming languages is not sufficiently abstract. This means that changing the data layout of a container necessitates syntax changes in all parts of the code where the container is used. Object oriented languages allow to solve this problem by hiding the data layout behind a class interface, however, the additional coding effort is enormous in comparison to a simple structure. A clever coding pattern, previously presented by the author, significantly reduces the code overhead, however, it relies heavily on advanced C++ features, a language that is not supported on most accelerators. This paper develops a concise macro based solution that requires only support for structures and unions and can therefore be utilized in OpenCL, a widely supported programming language for parallel processors. This enables the development of high performance code without an a-priori commitment to a certain layout and includes the possibility to optimize it subsequently. This feature is used to identify the best data layouts for different processing patterns of multi-valued containers on a multi-GPU system.<\/p>\n","protected":false},"excerpt":{"rendered":"
Scientific data is mostly multi-valued, e.g., coordinates, velocities, moments or feature components, and it comes in large quantities. The data layout of such containers has an enormous impact on the achieved performance, however, layout optimization is very time-consuming and error-prone because container access syntax in standard programming languages is not sufficiently abstract. This means that […]<\/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":[11,90,3],"tags":[1782,273,20,436,1793],"class_list":["post-12165","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-memory-model","tag-nvidia","tag-nvidia-geforce-gtx-295","tag-opencl"],"views":2419,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12165","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=12165"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12165\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12165"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12165"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12165"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}