{"id":3413,"date":"2011-03-31T19:41:00","date_gmt":"2011-03-31T19:41:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=3413"},"modified":"2011-03-31T19:41:00","modified_gmt":"2011-03-31T19:41:00","slug":"application-of-the-opencl-api-for-implementation-of-the-nipals-algorithm-for-principal-component-analysis-of-large-data-sets","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3413","title":{"rendered":"Application of the OpenCL API for Implementation of the NIPALS Algorithm for Principal Component Analysis of Large Data Sets"},"content":{"rendered":"<p>An implementation of the nonlinear iterative partial least squares algorithm (NIPALS) was used as a test case for use of OpenCL for computation on a general purpose graphics processing unit (GPGPU) cluster using MPI. Timing results are shown along with results of a model of time required per iteration for defined problem sizes. Various steps in optimization of the code are discussed, moving from use of a single GPU, to multiple GPUs on a single node, to multiple GPUs on multiple nodes. Comparison of performance between OpenCL and BLAS implementations, modern CPU architectures and NVidia Tesla and Fermi class GPU systems are given.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>An implementation of the nonlinear iterative partial least squares algorithm (NIPALS) was used as a test case for use of OpenCL for computation on a general purpose graphics processing unit (GPGPU) cluster using MPI. Timing results are shown along with results of a model of time required per iteration for defined problem sizes. Various steps [&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":[11,90,3],"tags":[430,1782,106,242,20,1034,1793,67,199,1035,429],"class_list":["post-3413","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-blas","tag-computer-science","tag-gpu-cluster","tag-mpi","tag-nvidia","tag-nvidia-quadro-fx-580","tag-opencl","tag-performance","tag-tesla-c1060","tag-tesla-s2050","tag-tesla-t10"],"views":2506,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3413","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=3413"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3413\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3413"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3413"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}