{"id":24154,"date":"2020-11-29T15:05:29","date_gmt":"2020-11-29T13:05:29","guid":{"rendered":"https:\/\/hgpu.org\/?p=24154"},"modified":"2020-11-29T15:05:29","modified_gmt":"2020-11-29T13:05:29","slug":"bootcmatchg-an-adaptive-algebraic-multigrid-linear-solver-for-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=24154","title":{"rendered":"BootCMatchG: An adaptive Algebraic MultiGrid linear solver for GPUs"},"content":{"rendered":"<p>Sparse solvers are one of the building blocks of any technology for reliableand  high-performance  scientific  and  engineering  computing.   In  this  paperwe present a software package which implements an efficient multigrid sparsesolver  running  on  Graphics  Processing  Units.   The  package  is  a  branch  ofa  wider  initiative  of  software  development  for  sparse  Linear  Algebra  com-putations on emergent HPC architectures involving a large research groupworking in many application projects over the last ten years.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sparse solvers are one of the building blocks of any technology for reliableand high-performance scientific and engineering computing. In this paperwe present a software package which implements an efficient multigrid sparsesolver running on Graphics Processing Units. The package is a branch ofa wider initiative of software development for sparse Linear Algebra com-putations on emergent HPC [&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":[11,89,3],"tags":[1782,14,1558,20,176,2072],"class_list":["post-24154","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-multigrid","tag-nvidia","tag-package","tag-sparse-linear-iterative-solver"],"views":1653,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/24154","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=24154"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/24154\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=24154"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=24154"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=24154"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}