{"id":8139,"date":"2012-09-01T01:05:28","date_gmt":"2012-08-31T22:05:28","guid":{"rendered":"http:\/\/hgpu.org\/?p=8139"},"modified":"2012-09-01T01:05:28","modified_gmt":"2012-08-31T22:05:28","slug":"parallel-gpu-accelerated-recursion-based-generators-of-pseudorandom-numbers","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8139","title":{"rendered":"Parallel GPU-accelerated Recursion-based Generators of Pseudorandom Numbers"},"content":{"rendered":"<p>The aim of the paper is to show how to design fast parallel algorithms for linear congruential and lagged Fibonacci pseudorandom numbers generators. The new algorithms employ the divide-and-conquer approach for solving linear recurrence systems and can be easily implemented on GPU-accelerated hybrid systems using CUDA or OpenCL. Numerical experiments performed on a computer system with modern Fermi GPU show that they achieve good speedup in comparison to the standard CPU-based sequential algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The aim of the paper is to show how to design fast parallel algorithms for linear congruential and lagged Fibonacci pseudorandom numbers generators. The new algorithms employ the divide-and-conquer approach for solving linear recurrence systems and can be easily implemented on GPU-accelerated hybrid systems using CUDA or OpenCL. Numerical experiments performed on a computer system [&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":[36,11,89,90,3],"tags":[1787,1782,14,20,1793,931],"class_list":["post-8139","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-opencl","tag-tesla-m2050"],"views":3803,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8139","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=8139"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8139\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8139"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8139"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8139"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}