{"id":5505,"date":"2011-09-08T14:23:48","date_gmt":"2011-09-08T11:23:48","guid":{"rendered":"http:\/\/hgpu.org\/?p=5505"},"modified":"2011-09-08T14:23:48","modified_gmt":"2011-09-08T11:23:48","slug":"a-fast-gpu-implementation-for-solving-sparse-ill-posed-linear-equation-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5505","title":{"rendered":"A Fast GPU Implementation for Solving Sparse Ill-Posed Linear Equation Systems"},"content":{"rendered":"<p>Image reconstruction, a very compute-intense process in general, can often be reduced to large linear equation systems represented as sparse under-determined matrices. Solvers for these equation systems (not restricted to image reconstruction) spend most of their time in sparse matrix-vector multiplications (SpMV). In this paper we will present a GPU-accelerated scheme for a Conjugate Gradient (CG) solver, with focus on the SpMV. We will discuss and quantify the optimizations employed to achieve a soft-real time constraint as well as alternative solutions relying on FPGAs, the Cell Broadband Engine, a highly optimized SSE-based software implementation, and other GPU SpMV implementations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Image reconstruction, a very compute-intense process in general, can often be reduced to large linear equation systems represented as sparse under-determined matrices. Solvers for these equation systems (not restricted to image reconstruction) spend most of their time in sparse matrix-vector multiplications (SpMV). In this paper we will present a GPU-accelerated scheme for a Conjugate Gradient [&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":[89,33,3],"tags":[545,580,14,377,1786,512,20,357,234,298,421],"class_list":["post-5505","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","tag-cell-processor","tag-conjugate-gradient-solver","tag-cuda","tag-fpga","tag-image-processing","tag-image-reconstruction","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-nvidia-geforce-gtx-280","tag-optimization","tag-sparse-matrix"],"views":2177,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5505","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=5505"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5505\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5505"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5505"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5505"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}