{"id":2894,"date":"2011-02-18T17:47:44","date_gmt":"2011-02-18T17:47:44","guid":{"rendered":"http:\/\/hgpu.org\/?p=2894"},"modified":"2011-02-18T17:47:44","modified_gmt":"2011-02-18T17:47:44","slug":"power-aware-performance-of-mixed-precision-linear-solvers-for-fpgas-and-gpgpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2894","title":{"rendered":"Power-aware Performance of Mixed Precision Linear Solvers for FPGAs and GPGPUs"},"content":{"rendered":"<p>Power has emerged as a significant constraint to high performance systems. We propose modeling power-based performance (performance\/watt) and clock-based performance for GPGPUs and FPGAs. Based on the modeling, we perform a case-study with mixed precision linear solvers for a Xilinx XC5VLX330T FPGA and NVIDIA Tesla C1060 GPU. In the case-study,  the FPGA shows power- and clock-based performance better than the GPGPU while the GPGPU shows better time-based performance.  <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Power has emerged as a significant constraint to high performance systems. We propose modeling power-based performance (performance\/watt) and clock-based performance for GPGPUs and FPGAs. Based on the modeling, we perform a case-study with mixed precision linear solvers for a Xilinx XC5VLX330T FPGA and NVIDIA Tesla C1060 GPU. In the case-study, the FPGA shows power- and [&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":[11,3],"tags":[1782,344,377,37,625,20,67,199],"class_list":["post-2894","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-energy-efficient-computing","tag-fpga","tag-linear-algebra","tag-mixed-precision","tag-nvidia","tag-performance","tag-tesla-c1060"],"views":1980,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2894","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=2894"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2894\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2894"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2894"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2894"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}