We propose Tangram, a general-purpose high-level language that achieves high performance across architectures. In Tangram, a program is written by synthesizing elemental pieces of code snippets, called codelets. A codelet can have multiple semantic-preserving implementations to enable automated algorithm and implementation selection. An implementation of a codelet can be written with tunable knobs to allow architecture-specific parameterization. The Tangram compiler produces highly optimized code by choosing and composing architecture-friendly codelets, and then tuning the knobs for the target architecture. We demonstrate that Tangram’s synthesized programs are comparable in performance to existing well-optimized codes on both CPUs and GPUs. The language is defined in a concise and maintainable way to improve debuggability and to enable progressive improvement. This strategy allows users to extend their applications and achieves higher performance on existing architectures and new architectures.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose Tangram, a general-purpose high-level language that achieves high performance across architectures. In Tangram, a program is written by synthesizing elemental pieces of code snippets, called codelets. A codelet can have multiple semantic-preserving implementations to enable automated algorithm and implementation selection. An implementation of a codelet can be written with tunable knobs to allow […]<\/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":[1782,95,20,1793,378],"class_list":["post-13395","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-high-level-languages","tag-nvidia","tag-opencl","tag-tesla-c2050"],"views":2399,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13395","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=13395"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13395\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13395"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13395"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13395"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}