{"id":12833,"date":"2014-09-25T23:31:59","date_gmt":"2014-09-25T20:31:59","guid":{"rendered":"http:\/\/hgpu.org\/?p=12833"},"modified":"2014-09-25T23:31:59","modified_gmt":"2014-09-25T20:31:59","slug":"scalability-analysis-of-parallel-algorithms-on-gpu-clusters","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12833","title":{"rendered":"Scalability Analysis of Parallel Algorithms on GPU Clusters"},"content":{"rendered":"<p>Scalability is an important concept in the domain of parallel computing. Since Graphics Processing Unit (GPU) clusters are and will be widely utilized in high performance computing platforms, we investigate the factors influencing the scalability for combinations of parallel algorithms (PA) and GPU clusters (GC).We present a scalability model for combination PA-GC and then validate it by executing some well-known benchmarks on certain GPU cluster (i.e., TH-1A). Experimental results show that the proposed model can well predict the scalability of various PA-GC combinations. In particular, we have discussed the influencing factors for scalability and pointed out the corresponding techniques to improve the scalability of PA-GC combinations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Scalability is an important concept in the domain of parallel computing. Since Graphics Processing Unit (GPU) clusters are and will be widely utilized in high performance computing platforms, we investigate the factors influencing the scalability for combinations of parallel algorithms (PA) and GPU clusters (GC).We present a scalability model for combination PA-GC and then validate [&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":[36,11,89,3],"tags":[1787,451,1782,14,106,20,931],"class_list":["post-12833","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-benchmarking","tag-computer-science","tag-cuda","tag-gpu-cluster","tag-nvidia","tag-tesla-m2050"],"views":2273,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12833","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=12833"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12833\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12833"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12833"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}