{"id":9168,"date":"2013-04-15T10:08:32","date_gmt":"2013-04-15T07:08:32","guid":{"rendered":"http:\/\/hgpu.org\/?p=9168"},"modified":"2014-02-18T23:49:36","modified_gmt":"2014-02-18T21:49:36","slug":"a-many-core-machine-model-for-designing-algorithms-with-minimum-parallelism-overheads","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9168","title":{"rendered":"A Many-core Machine Model for Designing Algorithms with Minimum Parallelism Overheads"},"content":{"rendered":"<p>We propose a model of computations which aims at capturing parallelism overheads (such as communication and synchronization costs) of programs written for modern GPU architectures. We establish a Graham-Brent theorem for this model so as to estimate running time of programs running on p streaming multiprocessors. We evaluate the benefits of our model with three applications. In each case, our model is used to optimize a program parameter controlling overhead.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a model of computations which aims at capturing parallelism overheads (such as communication and synchronization costs) of programs written for modern GPU architectures. We establish a Graham-Brent theorem for this model so as to estimate running time of programs running on p streaming multiprocessors. We evaluate the benefits of our model with three [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,89,3],"tags":[1787,14,176],"class_list":["post-9168","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-paper","tag-algorithms","tag-cuda","tag-package"],"views":3668,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9168","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=9168"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9168\/revisions"}],"predecessor-version":[{"id":11431,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9168\/revisions\/11431"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}