{"id":12398,"date":"2014-06-28T13:25:43","date_gmt":"2014-06-28T10:25:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=12398"},"modified":"2014-06-28T13:25:43","modified_gmt":"2014-06-28T10:25:43","slug":"evaluation-of-dgemm-implementation-on-intel-xeon-phi-coprocessor","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12398","title":{"rendered":"Evaluation of DGEMM Implementation on Intel Xeon Phi Coprocessor"},"content":{"rendered":"<p>In this paper we will present a detailed study of implementing double-precision matrix-matrix multiplication (DGEMM) utilizing the Intel Xeon Phi Coprocessor. We discuss a DGEMM algorithm implementation running &quot;natively&quot; on the coprocessor, minimizing communication with the host CPU. We will run DGEMM across a range of matrix sizes natively as well using Intel Math Kernel Library. Our optimizations were designed to support maximal reuse of on-die cache, which significantly reduces transfer from GDDR. Finally we analyze the improvement of a classic matrix multiplication implementation based on Cauchy algorithm compared to the latest results achieved using the Intel Math Kernel Library DGEMM subroutine.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we will present a detailed study of implementing double-precision matrix-matrix multiplication (DGEMM) utilizing the Intel Xeon Phi Coprocessor. We discuss a DGEMM algorithm implementation running &quot;natively&quot; on the coprocessor, minimizing communication with the host CPU. We will run DGEMM across a range of matrix sizes natively as well using Intel Math Kernel [&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":[36,11,3],"tags":[1787,1782,1483,37,324,67],"class_list":["post-12398","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-computer-science","tag-intel-xeon-phi","tag-linear-algebra","tag-matrix-multiplication","tag-performance"],"views":2323,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12398","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=12398"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12398\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12398"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12398"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12398"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}