{"id":10864,"date":"2013-11-08T00:30:43","date_gmt":"2013-11-07T22:30:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=10864"},"modified":"2013-11-08T00:30:43","modified_gmt":"2013-11-07T22:30:43","slug":"automatic-synthesis-of-heterogeneous-cpu-gpu-embedded-applications-from-a-uml-profile","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10864","title":{"rendered":"Automatic Synthesis of Heterogeneous CPU-GPU Embedded Applications from a UML Profile"},"content":{"rendered":"<p>Modern embedded systems present an ever increasing complexity and model-driven engineering has been shown to be helpful in mitigating it. In our previous works we exploited the power of model-driven engineering to develop a round-trip approach for aiding the evaluation and assessment of extra-functional properties preservation from models to code. In addition, we showed how the round-trip approach could be employed to evaluate different deployment strategies, and the focus was on homogeneous CPUbased platforms. Due to the fact that the assortment of target-platforms in the embedded domain is inevitably shifting to heterogeneous solutions, our goal is to broaden the scope of the round-trip approach towards mixed CPU-GPU configurations. In this work we focus on the modelling of heterogeneous deployment and the enhancement of the current automatic code generator to synthesize code targeting such heterogeneous configurations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern embedded systems present an ever increasing complexity and model-driven engineering has been shown to be helpful in mitigating it. In our previous works we exploited the power of model-driven engineering to develop a round-trip approach for aiding the evaluation and assessment of extra-functional properties preservation from models to code. In addition, we showed how [&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":[215,1782,14,452,20],"class_list":["post-10864","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-code-generation","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia"],"views":1894,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10864","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=10864"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10864\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10864"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10864"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10864"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}