The multigrid method has proved to be effective for a large class of numerical methods. In this study, a strategy based on Full Approximation Storage (FAS) scheme is implemented together with Full Multigrid Algorithm (FMG) to accelerate convergence of steady state solutions of the two-dimensional compressible Euler equations on Graphics Processing Unit (GPU). The Beam and Warming linearization scheme in curvilinear coordinates is used to discretize the governing equation. The second-order central and the fourth-order compact finite-difference schemes are applied for spatial discretization. A high-performance GPU-implemented block-tridiagonal solver based on Block Cyclic Reduction (BCR) algorithm is utilized. The proposed BCR solver is applied to finite-difference discretization on structured grids via Alternating Direction Implicit (ADI) scheme. Attention is directed towards the computational performance of the V-cycle and W-cycle multigrid strategies in two and three grid levels using the NVIDIA GTX480 graphics card. Speedups between 2x-5.2x are achieved in comparison to the Intel Core i7-920 2.67GHz CPU for different grid sizes.<\/p>\n","protected":false},"excerpt":{"rendered":"
The multigrid method has proved to be effective for a large class of numerical methods. In this study, a strategy based on Full Approximation Storage (FAS) scheme is implemented together with Full Multigrid Algorithm (FMG) to accelerate convergence of steady state solutions of the two-dimensional compressible Euler equations on Graphics Processing Unit (GPU). The Beam […]<\/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":[36,89,104,3],"tags":[1787,14,1795,20,379],"class_list":["post-10923","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-algorithms","tag-cuda","tag-fluid-dynamics","tag-nvidia","tag-nvidia-geforce-gtx-480"],"views":2420,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10923","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=10923"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10923\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10923"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10923"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10923"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}