{"id":6154,"date":"2011-11-03T17:54:38","date_gmt":"2011-11-03T15:54:38","guid":{"rendered":"http:\/\/hgpu.org\/?p=6154"},"modified":"2011-11-03T17:54:38","modified_gmt":"2011-11-03T15:54:38","slug":"performance-portability-of-a-gpu-enabled-factorization-with-the-dague-framework","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6154","title":{"rendered":"Performance Portability of a GPU Enabled Factorization with the DAGuE Framework"},"content":{"rendered":"<p>Performance portability is a major challenge faced today by developers on heterogeneous high performance computers, consisting of an interconnect, memory with nonuniform access, many-cores and accelerators like GPUs. Recent studies have successfully demonstrated that dense linear algebra operations can be efficiently handled by runtime systems using a DAG representation. In this work, we present the GPU subsystem of the DAGuE runtime, and assess, on the Cholesky factorization test case, the minimal efforts required by a programmer to enable GPU acceleration in the DAGuE framework. The performance achieved by this unchanged code, on a variety of heterogeneous and distributed many cores and GPU resources, demonstrates the desired performance portability.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Performance portability is a major challenge faced today by developers on heterogeneous high performance computers, consisting of an interconnect, memory with nonuniform access, many-cores and accelerators like GPUs. Recent studies have successfully demonstrated that dense linear algebra operations can be efficiently handled by runtime systems using a DAG representation. In this work, we present the [&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":[11,3],"tags":[1782,288,106,452,37,20,67,199,378,244],"class_list":["post-6154","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-factorization","tag-gpu-cluster","tag-heterogeneous-systems","tag-linear-algebra","tag-nvidia","tag-performance","tag-tesla-c1060","tag-tesla-c2050","tag-tesla-s1070"],"views":2044,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6154","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=6154"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6154\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6154"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6154"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6154"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}