{"id":17760,"date":"2017-11-12T16:08:19","date_gmt":"2017-11-12T14:08:19","guid":{"rendered":"https:\/\/hgpu.org\/?p=17760"},"modified":"2017-11-12T16:08:19","modified_gmt":"2017-11-12T14:08:19","slug":"scalable-and-massively-parallel-monte-carlo-photon-transport-simulations-for-heterogeneous-computing-platforms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=17760","title":{"rendered":"Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms"},"content":{"rendered":"<p>We present a highly scalable Monte Carlo (MC) 3D photon transport simulation platform designed for heterogeneous computing systems. By developing a massively parallel MC algorithm using the OpenCL framework, this research extends our existing GPU-accelerated MC technique to a highly-scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware. Furthermore, multiple thread-level and device-level load-balancing strategies have been developed to obtain efficient simulations using multiple CPUs and GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a highly scalable Monte Carlo (MC) 3D photon transport simulation platform designed for heterogeneous computing systems. By developing a massively parallel MC algorithm using the OpenCL framework, this research extends our existing GPU-accelerated MC technique to a highly-scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel [&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":[36,90,3,12],"tags":[1787,1922,1962,7,98,452,20,1972,1957,1092,1897,1767,1793,176,1783],"class_list":["post-17760","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-opencl","category-paper","category-physics","tag-algorithms","tag-amd-r9-nano","tag-amd-radeon-rx-480","tag-ati","tag-computational-physics","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-1050-ti","tag-nvidia-geforce-gtx-1080-ti","tag-nvidia-geforce-gtx-590","tag-nvidia-geforce-gtx-980-ti","tag-nvidia-geforce-gtx-titan-x","tag-opencl","tag-package","tag-physics"],"views":6006,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17760","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=17760"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17760\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17760"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17760"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17760"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}