{"id":10628,"date":"2013-10-02T23:45:06","date_gmt":"2013-10-02T20:45:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=10628"},"modified":"2013-10-02T23:45:06","modified_gmt":"2013-10-02T20:45:06","slug":"a-graphics-processor-based-intranuclear-cascade-and-evaporation-simulation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10628","title":{"rendered":"A graphics processor-based intranuclear cascade and evaporation simulation"},"content":{"rendered":"<p>Monte Carlo simulations of the transport of protons in human tissue have been deployed on graphics processing units (GPUs) with impressive results. To provide a more complete treatment of non-elastic nuclear interactions in these simulations, we developed a fast intranuclear cascade-evaporation simulation for the GPU. This can be used to model non-elastic proton collisions on any therapeutically relevant nuclei at incident energies between 20 and 250 MeV. Predictions are in good agreement with Geant4.9.6p2. It takes approximately 2 s to calculate $1times 10^6$ 200 MeV proton-$^{16}$O interactions on a NVIDIA GTX680 GPU. A speed-up factor of $sim$20 relative to one Intel i7-3820 core processor thread was achieved.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Monte Carlo simulations of the transport of protons in human tissue have been deployed on graphics processing units (GPUs) with impressive results. To provide a more complete treatment of non-elastic nuclear interactions in these simulations, we developed a fast intranuclear cascade-evaporation simulation for the GPU. This can be used to model non-elastic proton collisions on [&hellip;]<\/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":[89,3,12],"tags":[98,14,72,20,1306,1783],"class_list":["post-10628","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-computational-physics","tag-cuda","tag-monte-carlo-simulation","tag-nvidia","tag-nvidia-geforce-gtx-680","tag-physics"],"views":2277,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10628","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=10628"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10628\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10628"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10628"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10628"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}