{"id":13504,"date":"2015-02-22T15:59:48","date_gmt":"2015-02-22T13:59:48","guid":{"rendered":"http:\/\/hgpu.org\/?p=13504"},"modified":"2015-02-22T15:59:48","modified_gmt":"2015-02-22T13:59:48","slug":"rsvdpack-subroutines-for-computing-partial-singular-value-decompositions-via-randomized-sampling-on-single-core-multi-core-and-gpu-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13504","title":{"rendered":"RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures"},"content":{"rendered":"<p>This document describes an implementation in C of a set of randomized algorithms for computing partial Singular Value Decompositions (SVDs). The techniques largely follow the prescriptions in the article &quot;Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions,&quot; N. Halko, P.G. Martinsson, J. Tropp, SIAM Review, 53(2), 2011, pp. 217-288, but with some modifications to improve performance. The codes implement a number of low rank SVD computing routines for three different sets of hardware: (1) single core CPU, (2) multi core CPU, and (3) massively multicore GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This document describes an implementation in C of a set of randomized algorithms for computing partial Singular Value Decompositions (SVDs). The techniques largely follow the prescriptions in the article &quot;Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions,&quot; N. Halko, P.G. Martinsson, J. Tropp, SIAM Review, 53(2), 2011, pp. 217-288, but with some [&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":[36,89,157,3],"tags":[1787,14,569,597,1796,128,628,20,176,1017],"class_list":["post-13504","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-mathematics","category-paper","tag-algorithms","tag-cuda","tag-cula","tag-mathematical-software","tag-mathematics","tag-matrix-decomposition","tag-numerical-analysis","tag-nvidia","tag-package","tag-tesla-m2070"],"views":2676,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13504","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=13504"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13504\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13504"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13504"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13504"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}