{"id":16375,"date":"2016-08-04T00:20:46","date_gmt":"2016-08-03T21:20:46","guid":{"rendered":"http:\/\/hgpu.org\/?p=16375"},"modified":"2016-08-04T00:20:46","modified_gmt":"2016-08-03T21:20:46","slug":"programming-embedded-manycore-refinement-and-optimizing-compilation-of-a-parallel-action-language-for-hierarchical-state-machines","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16375","title":{"rendered":"Programming Embedded Manycore: Refinement and Optimizing Compilation of a Parallel Action Language for Hierarchical State Machines"},"content":{"rendered":"<p>Modeling languages propose convenient abstractions and transformations to handle the com- plexity of today&#8217;s embedded systems. Based on the formalism of Hierarchical State Machine, they enable the expression of hierarchical control parallelism. However, they face two importants challenges when it comes to model data-intensive applications: no unified approach that also accounts for data-parallel actions; and no effective code optimization and generation flows. In this thesis, we propose a modeling language extended with parallel action semantics and hierarchical indexed-state machines suitable for computationally intensive applications. Together with its formal semantics, we present an optimizing model compiler aiming for the generation of efficient data-parallel implementations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modeling languages propose convenient abstractions and transformations to handle the com- plexity of today&#8217;s embedded systems. Based on the formalism of Hierarchical State Machine, they enable the expression of hierarchical control parallelism. However, they face two importants challenges when it comes to model data-intensive applications: no unified approach that also accounts for data-parallel actions; and [&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":[11,90,3],"tags":[955,1782,1793,390],"class_list":["post-16375","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-compilers","tag-computer-science","tag-opencl","tag-thesis"],"views":2146,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16375","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=16375"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16375\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16375"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16375"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}