{"id":2804,"date":"2011-02-11T12:42:06","date_gmt":"2011-02-11T12:42:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=2804"},"modified":"2011-02-11T12:42:06","modified_gmt":"2011-02-11T12:42:06","slug":"fast-computing-of-scattering-maps-of-nanostructures-using-graphical-processing-units","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2804","title":{"rendered":"Fast computing of scattering maps of nanostructures using graphical processing units"},"content":{"rendered":"<p>Scattering maps from strained or disordered nano-structures around a Bragg reflection can either be computed quickly using approximations and a (Fast) Fourier transform, or using individual atomic positions. In this article we show that it is possible to compute up to 4.10^10 $reflections.atoms\/s using a single graphic card, and we evaluate how this speed depends on number of atoms and points in reciprocal space. An open-source software library (PyNX) allowing easy scattering computations (including grazing incidence conditions) in the Python language is described, with examples of scattering from non-ideal nanostructures.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Scattering maps from strained or disordered nano-structures around a Bragg reflection can either be computed quickly using approximations and a (Fast) Fourier transform, or using individual atomic positions. In this article we show that it is possible to compute up to 4.10^10 $reflections.atoms\/s using a single graphic card, and we evaluate how this speed depends [&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":[196,14,166,359,20,436,176,1783,513],"class_list":["post-2804","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-condensed-matter","tag-cuda","tag-materials-science","tag-mesoscale-and-nanoscale-physics","tag-nvidia","tag-nvidia-geforce-gtx-295","tag-package","tag-physics","tag-python"],"views":2136,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2804","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=2804"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2804\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2804"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2804"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2804"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}