{"id":6182,"date":"2011-11-06T23:37:53","date_gmt":"2011-11-06T21:37:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=6182"},"modified":"2011-11-06T23:37:53","modified_gmt":"2011-11-06T21:37:53","slug":"the-conjugate-gradient-solver-accelerated-by-gpu-for-solving-wave-propagation-problems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6182","title":{"rendered":"The conjugate gradient solver accelerated by GPU for solving wave-propagation problems"},"content":{"rendered":"<p>There are several possibilities to speed-up an iterative solver, e.g. by applying an efficient preconditioner to decrease the number of required iterations, or by parallelizing the given algorithm, etc. To acquire maximum performance from a massively parallelized environment, different parts of such a solver must be asynchronously parallelized to avoid expensive cooperation between threads. The aim of this work is to introduce a parallel computing environment, the Graphic Processing Unit (GPU) [1, 2], to accelerate the matrix multiplication of the conjugate gradient solver applied to wave propagation problems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>There are several possibilities to speed-up an iterative solver, e.g. by applying an efficient preconditioner to decrease the number of required iterations, or by parallelizing the given algorithm, etc. To acquire maximum performance from a massively parallelized environment, different parts of such a solver must be asynchronously parallelized to avoid expensive cooperation between threads. The [&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":[36,11,89,3],"tags":[1787,1782,580,14,37,324,20],"class_list":["post-6182","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-conjugate-gradient-solver","tag-cuda","tag-linear-algebra","tag-matrix-multiplication","tag-nvidia"],"views":1904,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6182","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=6182"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6182\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6182"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6182"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6182"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}