{"id":6090,"date":"2011-10-28T16:03:26","date_gmt":"2011-10-28T13:03:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=6090"},"modified":"2011-10-28T16:03:26","modified_gmt":"2011-10-28T13:03:26","slug":"implementing-domain-specific-languages-for-heterogeneous-parallel-computing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6090","title":{"rendered":"Implementing Domain-Specific Languages for Heterogeneous Parallel Computing"},"content":{"rendered":"<p>Domain-specific languages offer a solution to the performance and the productivity issues in heterogeneous computing systems. The Delite compiler framework simplifies the process of building embedded parallel DSLs. DSL developers can implement domain-specific operations by extending the DSL framework, which provides static optimizations and code generation for heterogeneous hardware. The Delite runtime automatically schedules and executes DSL operations on heterogeneous hardware.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Domain-specific languages offer a solution to the performance and the productivity issues in heterogeneous computing systems. The Delite compiler framework simplifies the process of building embedded parallel DSLs. DSL developers can implement domain-specific operations by extending the DSL framework, which provides static optimizations and code generation for heterogeneous hardware. The Delite runtime automatically schedules and [&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":[11,89,3],"tags":[215,1782,14,452,1025,20,298,378],"class_list":["post-6090","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-machine-learning","tag-nvidia","tag-optimization","tag-tesla-c2050"],"views":1736,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6090","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=6090"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6090\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6090"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6090"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6090"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}