{"id":3600,"date":"2011-04-15T20:37:39","date_gmt":"2011-04-15T20:37:39","guid":{"rendered":"http:\/\/hgpu.org\/?p=3600"},"modified":"2011-04-15T20:37:39","modified_gmt":"2011-04-15T20:37:39","slug":"implementation-of-jacobi-iterative-method-on-graphics-processor-unit","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3600","title":{"rendered":"Implementation of Jacobi iterative method on graphics processor unit"},"content":{"rendered":"<p>CUDA is a new computing architecture introduced by NVIDIA Corporation, aiming at general purpose computation on GPU. The architecture has strong compute power in the compute-intensive applications and data-intensive applications, so in recent years, how the framework is applied to the scientific computing has become a hot research. The iterative method for solving systems of linear equations in engineering and scientific computing has a very far-ranging application. The algorithm provided with high computing intensity and parallelism is very suitable for CUDA architecture. In this paper, Jacobi iterative method is implemented on CUDA-enable GPU. The experimental results show that this iterative method can effectively make use of the CUDA-enable GPU computing power and achieve good performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>CUDA is a new computing architecture introduced by NVIDIA Corporation, aiming at general purpose computation on GPU. The architecture has strong compute power in the compute-intensive applications and data-intensive applications, so in recent years, how the framework is applied to the scientific computing has become a hot research. The iterative method for solving systems of [&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":[11,89,3],"tags":[1782,14,37,20],"class_list":["post-3600","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-linear-algebra","tag-nvidia"],"views":1830,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3600","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=3600"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3600\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3600"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3600"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3600"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}