{"id":6215,"date":"2011-11-08T18:42:06","date_gmt":"2011-11-08T16:42:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=6215"},"modified":"2011-11-08T18:42:06","modified_gmt":"2011-11-08T16:42:06","slug":"patus-a-code-generation-and-auto-tuning-framework-for-parallel-stencil-computations","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6215","title":{"rendered":"PATUS: A Code Generation and Auto-Tuning Framework For Parallel Stencil Computations"},"content":{"rendered":"<p>PATUS is a code generation and auto-tuning framework for stencil computations targeted at modern multi- and many-core processors, such as multicore CPUs and graphics processing units. Its ultimate goals are to provide a means towards productivity and performance on current and future multi- and many-core platforms. The framework generates the code for a compute kernel from a specification of the stencil operation and a Strategy: a description of the parallelization and optimization methods to be applied. We leverage the auto-tuning methodology to find the optimal hardware architecture-specific and Strategy-specific parameter configuration.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>PATUS is a code generation and auto-tuning framework for stencil computations targeted at modern multi- and many-core processors, such as multicore CPUs and graphics processing units. Its ultimate goals are to provide a means towards productivity and performance on current and future multi- and many-core platforms. The framework generates the code for a compute kernel [&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":[215,1782,14,20,298,176,378],"class_list":["post-6215","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-nvidia","tag-optimization","tag-package","tag-tesla-c2050"],"views":2235,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6215","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=6215"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6215\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6215"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6215"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6215"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}