{"id":4995,"date":"2011-08-03T13:40:03","date_gmt":"2011-08-03T10:40:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=4995"},"modified":"2011-08-03T13:40:03","modified_gmt":"2011-08-03T10:40:03","slug":"a-variant-of-mersenne-twister-suitable-for-graphic-processors","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4995","title":{"rendered":"A Variant of Mersenne Twister Suitable for Graphic Processors"},"content":{"rendered":"<p>The author proposes pseudorandom number generators suitable to execute on a graphic processor. They generate pseudorandom numbers in device memory on graphic processors. Each generator uses shared memory on graphic processors as its internal state space, and uses constant memory as a look-up table for a linear transformation. Output formats of the generator are 32-bit integers and single precision floating point numbers obeying the IEEE 754 format. A 64-bit integer version and double precision floating point version are also available. The author also proposes a parameter generator for these generators. The parameter generator provides us with independent streams of pseudorandom numbers, which can be generated in parallel.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The author proposes pseudorandom number generators suitable to execute on a graphic processor. They generate pseudorandom numbers in device memory on graphic processors. Each generator uses shared memory on graphic processors as its internal state space, and uses constant memory as a look-up table for a linear transformation. Output formats of the generator are 32-bit [&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":[1787,1782,14,597,20,530,253,176,203],"class_list":["post-4995","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-mathematical-software","tag-nvidia","tag-nvidia-geforce-gt-120","tag-nvidia-geforce-gtx-260","tag-package","tag-pseudo-random-number-generators"],"views":2272,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4995","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=4995"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4995\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4995"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4995"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4995"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}