{"id":10648,"date":"2013-10-04T23:32:02","date_gmt":"2013-10-04T20:32:02","guid":{"rendered":"http:\/\/hgpu.org\/?p=10648"},"modified":"2013-10-04T23:32:02","modified_gmt":"2013-10-04T20:32:02","slug":"advanced-optimization-techniques-for-sparse-grids-on-modern-heterogeneous-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10648","title":{"rendered":"Advanced Optimization Techniques for Sparse Grids on Modern Heterogeneous Systems"},"content":{"rendered":"<p>GPU based heterogeneous systems provide a peak performance in the order of TFlop\/s and an advantageous ratio between performance and energy consumption. However, reaching high performance on GPUs is often a difficult task. This thesis proposes advanced optimization techniques that allow for efficiently porting a set of sparse grid algorithms to GPUs. The performance obtained on GPUs is improved using an auto-tuning strategy whereas full utilization of the heterogeneous system is ensured using different load balancing schemes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPU based heterogeneous systems provide a peak performance in the order of TFlop\/s and an advantageous ratio between performance and energy consumption. However, reaching high performance on GPUs is often a difficult task. This thesis proposes advanced optimization techniques that allow for efficiently porting a set of sparse grid algorithms to GPUs. The performance obtained [&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":[11,89,3],"tags":[1782,14,452,20,379,1505,390],"class_list":["post-10648","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-nvidia-quadro-6000","tag-thesis"],"views":2271,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10648","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=10648"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10648\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10648"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10648"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10648"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}