{"id":15182,"date":"2015-12-31T00:55:13","date_gmt":"2015-12-30T22:55:13","guid":{"rendered":"http:\/\/hgpu.org\/?p=15182"},"modified":"2015-12-31T00:55:13","modified_gmt":"2015-12-30T22:55:13","slug":"a-comparison-of-the-performance-of-hpc-accelerators","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15182","title":{"rendered":"A Comparison of the performance of HPC Accelerators"},"content":{"rendered":"<p>This project aims to port the scientific application GADGET-3 to multiple accelerators, research on the performance achieved and compare the porting\/optimisations on the given accelerators with different architectures. In this project, the most time-consuming functions of GADGET-3 was identified based on the profiling. Partial functions in GADGET-3 were ported to the accelerator NVIDIA K40 card using CUDA C and to AMD FirePro W9100 Card using OpenCL. The CUDA version GADGET-3 functions were profiled and analysed with different task assignment on the GPGPU device for the given test cases. Detailed comparisons were also conducted between the CUDA C version GADGET-3 on NVIDIA cards and the OpenCL version code on AMD card. For the given test cases, NVIDIA K40 delivered better performance compared with AMD FirePro W9100.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This project aims to port the scientific application GADGET-3 to multiple accelerators, research on the performance achieved and compare the porting\/optimisations on the given accelerators with different architectures. In this project, the most time-consuming functions of GADGET-3 was identified based on the profiling. Partial functions in GADGET-3 were ported to the accelerator NVIDIA K40 card [&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":[11,90,3],"tags":[1655,7,1782,20,1793,67,1543,390],"class_list":["post-15182","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-amd-firepro-w9100","tag-ati","tag-computer-science","tag-nvidia","tag-opencl","tag-performance","tag-tesla-k40","tag-thesis"],"views":2980,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15182","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=15182"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15182\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15182"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15182"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15182"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}