{"id":7513,"date":"2012-04-28T23:54:04","date_gmt":"2012-04-28T20:54:04","guid":{"rendered":"http:\/\/hgpu.org\/?p=7513"},"modified":"2012-04-28T23:54:04","modified_gmt":"2012-04-28T20:54:04","slug":"solving-stochastic-differential-equations-using-general-purpose-graphics-processing-unit","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7513","title":{"rendered":"Solving Stochastic Differential Equations Using General Purpose Graphics Processing Unit"},"content":{"rendered":"<p>Stochastic Differential Equations are important in many models of various physical or artificial phenomena. To get meaningful results it is desirable to solve the initial value numerical integration problem for a sufficiently large ensemble of realizations. Each element of the ensemble has the same form, thus exposing inherent data-parallelism. We implemented a cross-platform library written in C++ and CUDA that exploits data-parallelism by integrating all realizations in parallel using CUDA, and then computing the properties of the solution using CUDA again. This offers a great speed up over a sequential approach while keeping the overall algorithm for arriving at results essentially the same.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stochastic Differential Equations are important in many models of various physical or artificial phenomena. To get meaningful results it is desirable to solve the initial value numerical integration problem for a sufficiently large ensemble of realizations. Each element of the ensemble has the same form, thus exposing inherent data-parallelism. We implemented a cross-platform library written [&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":[36,11,89,3],"tags":[1787,1782,14,810,20,379,176,199,1226],"class_list":["post-7513","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-differential-equations","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-package","tag-tesla-c1060","tag-tesla-c2075"],"views":2062,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7513","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=7513"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7513\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7513"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7513"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7513"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}