{"id":8282,"date":"2012-09-27T17:08:06","date_gmt":"2012-09-27T14:08:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=8282"},"modified":"2012-09-27T17:08:06","modified_gmt":"2012-09-27T14:08:06","slug":"increasing-the-performance-of-alltoall-variant-of-self-organizing-migration-algorithm-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8282","title":{"rendered":"Increasing the performance of AllToAll variant of self-organizing migration algorithm using CUDA"},"content":{"rendered":"<p>Modern graphics processing units offer general purpose parallel computing capabilities. Thus they have become a relatively low cost alternative for applications requiring extensive parallel computations. Evolutionary algorithms are especially well suited for parallel SIMD architecture. This paper deals with the modification of AllToAll variation of self-organizing migration algorithm, which has high computational demand for one round of algorithm, using the CUDA framework. The main goal is to speedup performance of the algorithm in comparison to CPU implementations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern graphics processing units offer general purpose parallel computing capabilities. Thus they have become a relatively low cost alternative for applications requiring extensive parallel computations. Evolutionary algorithms are especially well suited for parallel SIMD architecture. This paper deals with the modification of AllToAll variation of self-organizing migration algorithm, which has high computational demand for one [&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":[1787,1782,14,20,298],"class_list":["post-8282","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-nvidia","tag-optimization"],"views":1769,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8282","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=8282"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8282\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8282"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8282"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8282"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}