{"id":29273,"date":"2024-07-07T13:51:04","date_gmt":"2024-07-07T10:51:04","guid":{"rendered":"https:\/\/hgpu.org\/?p=29273"},"modified":"2024-07-07T13:51:04","modified_gmt":"2024-07-07T10:51:04","slug":"psctoolkit-solving-sparse-linear-systems-with-a-large-number-of-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=29273","title":{"rendered":"PSCToolkit: solving sparse linear systems with a large number of GPUs"},"content":{"rendered":"<p>In this chapter, we describe the Parallel Sparse Computation Toolkit (PSCToolkit), a suite of libraries for solving large-scale linear algebra problems in an HPC environment. In particular, we focus on the tools provided for the solution of symmetric and positive-definite linear systems using up to 8192 GPUs on the EuroHPC-JU Leonardo supercomputer. PSCToolkit is an ongoing mathematical software project aimed at exploiting the extreme computational speed of current supercomputers for relevant problems in Computational and Data Science.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this chapter, we describe the Parallel Sparse Computation Toolkit (PSCToolkit), a suite of libraries for solving large-scale linear algebra problems in an HPC environment. In particular, we focus on the tools provided for the solution of symmetric and positive-definite linear systems using up to 8192 GPUs on the EuroHPC-JU Leonardo supercomputer. PSCToolkit is an [&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":[89,157,3],"tags":[14,37,597,1796,628,20,2066,176,2012],"class_list":["post-29273","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-mathematics","category-paper","tag-cuda","tag-linear-algebra","tag-mathematical-software","tag-mathematics","tag-numerical-analysis","tag-nvidia","tag-nvidia-a100","tag-package","tag-sparse"],"views":2570,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29273","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=29273"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29273\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29273"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29273"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29273"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}