{"id":16370,"date":"2016-08-01T23:55:32","date_gmt":"2016-08-01T20:55:32","guid":{"rendered":"http:\/\/hgpu.org\/?p=16370"},"modified":"2016-08-01T23:55:32","modified_gmt":"2016-08-01T20:55:32","slug":"the-antarex-approach-to-autotuning-and-adaptivity-for-energy-efficient-hpc-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16370","title":{"rendered":"The ANTAREX Approach to Autotuning and Adaptivity for Energy Efficient HPC Systems"},"content":{"rendered":"<p>The ANTAREX project aims at expressing the application self-adaptivity through a Domain Specific Language (DSL) and to run-time manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to Exascale. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. We show through a mini-app extracted from one of the project application use cases some initial exploration of application precision tuning by means enabled by the DSL.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ANTAREX project aims at expressing the application self-adaptivity through a Domain Specific Language (DSL) and to run-time manage and autotune applications for green and heterogeneous High Performance Computing (HPC) systems up to Exascale. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application [&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":[955,1782,1651,344,1793],"class_list":["post-16370","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-compilers","tag-computer-science","tag-dsl","tag-energy-efficient-computing","tag-opencl"],"views":1926,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16370","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=16370"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16370\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16370"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16370"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16370"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}