{"id":7968,"date":"2012-07-25T12:44:27","date_gmt":"2012-07-25T09:44:27","guid":{"rendered":"http:\/\/hgpu.org\/?p=7968"},"modified":"2012-07-25T12:44:27","modified_gmt":"2012-07-25T09:44:27","slug":"fast-end-to-end-multi-conjugate-ao-simulations-using-graphical-processing-units-and-the-maos-simulation-code","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7968","title":{"rendered":"Fast End-to-End Multi-Conjugate AO Simulations Using Graphical Processing Units and the MAOS Simulation Code"},"content":{"rendered":"<p>The Multi-threaded Adaptive Optics Simulator (MAOS) was developed at TMT to efficiently simulate various kind of AO systems. In particular, it can finish a time step of full end-to-end simulation of an ELT size multi-conjugate AO system in 1 second on 8 contemporary cpu cores. We recently ported it to run on graphical processing units (GPUs) using the Nvidia CUDA technology. A 10 fold speed up is obtained with two GTX 580 GPUs, with each time step taking only 0.1 second. A single GPU can finish 30 iterations of the conjugate gradients (CG) tomography algorithm in 30 ms, or 3 iterations of the fourier domain preconditional CG in 5 ms, which is on the same order as the ~1ms requirement.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Multi-threaded Adaptive Optics Simulator (MAOS) was developed at TMT to efficiently simulate various kind of AO systems. In particular, it can finish a time step of full end-to-end simulation of an ELT size multi-conjugate AO system in 1 second on 8 contemporary cpu cores. We recently ported it to run on graphical processing units [&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":[89,3,12],"tags":[14,20,974,321,176,1783],"class_list":["post-7968","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-580","tag-optics","tag-package","tag-physics"],"views":2638,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7968","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=7968"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7968\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7968"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7968"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7968"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}