We present a parallel Preconditioned Bi-Conjugate Gradient Stabilized(BICGstab) solver for the Poisson problem. Given a real, nosymmetric and positive definite coefficient matrix, the parallized Preconditioned BICGstab – solver is able to find a solution for that system by exploiting the massive compute power of todays GPUs. Comparing sequential CPU implementations and that algorithm.we achieve a speed up from 8 to 10 depending on the dimension of the coefficient matrix. Additionally the concept of preconditioners to decrease the time to find a solution is evaluated using the AINV method.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present a parallel Preconditioned Bi-Conjugate Gradient Stabilized(BICGstab) solver for the Poisson problem. Given a real, nosymmetric and positive definite coefficient matrix, the parallized Preconditioned BICGstab – solver is able to find a solution for that system by exploiting the massive compute power of todays GPUs. Comparing sequential CPU implementations and that algorithm.we achieve a […]<\/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":[36,11,89,3],"tags":[1787,1782,14,20,1392,421],"class_list":["post-8682","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gt-610-m","tag-sparse-matrix"],"views":3042,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8682","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=8682"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8682\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8682"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8682"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8682"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}