{"id":5247,"date":"2011-08-22T15:19:00","date_gmt":"2011-08-22T12:19:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=5247"},"modified":"2011-08-22T15:19:00","modified_gmt":"2011-08-22T12:19:00","slug":"improving-programmability-of-heterogeneous-many-core-systems-via-explicit-platform-descriptions","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5247","title":{"rendered":"Improving programmability of heterogeneous many-core systems via explicit platform descriptions"},"content":{"rendered":"<p>In this paper we present ongoing work towards a programming framework for heterogeneous hardware- and software environments. Our framework aims at improving programmability and portability for heterogeneous many-core systems via a Platform Description Language (PDL) for expressing architectural patterns and platform information. We developed a prototypical code generator that takes as input an annotated serial task-based program and outputs, parametrized via PDL descriptors, code for a specific target heterogeneous computing system. By varying the target PDL descriptor, code for different target configurations can be generated without the need to modify the input program. We utilize a simple task-based programming model for demonstration of our approach and present preliminary results indicating its applicability on a state-of-the-art heterogeneous system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present ongoing work towards a programming framework for heterogeneous hardware- and software environments. Our framework aims at improving programmability and portability for heterogeneous many-core systems via a Platform Description Language (PDL) for expressing architectural patterns and platform information. We developed a prototypical code generator that takes as input an annotated serial [&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":[11,89,90,3],"tags":[215,1782,14,452,20,251,379,1793,252,119],"class_list":["post-5247","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-code-generation","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-nvidia-geforce-gtx-480","tag-opencl","tag-openmp","tag-presentation"],"views":2045,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5247","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=5247"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5247\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5247"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5247"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5247"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}