{"id":6970,"date":"2012-01-19T16:59:53","date_gmt":"2012-01-19T14:59:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=6970"},"modified":"2012-01-19T16:59:53","modified_gmt":"2012-01-19T14:59:53","slug":"platform-characterization-for-domain-specific-computing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6970","title":{"rendered":"Platform Characterization for Domain-Specific Computing"},"content":{"rendered":"<p>We believe that by adapting architectures to fit the requirements of a given application domain, we can significantly improve the efficiency of computation. To validate the idea for our application domain, we evaluate a wide spectrum of commodity computing platforms to quantify the potential benefits of heterogeneity and customization for the domain-specific applications. In particular, we choose medical imaging as the application domain for investigation, and study the application performance and energy efficiency across a diverse set of commodity hardware platforms, such as general-purpose multi-core CPUs, massive parallel many-core GPUs, low-power mobile CPUs and fine-grain customizable FPGAs. This study leads to a number of interesting observations that can be used to guide further development of domain-specific architectures.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We believe that by adapting architectures to fit the requirements of a given application domain, we can significantly improve the efficiency of computation. To validate the idea for our application domain, we evaluate a wide spectrum of commodity computing platforms to quantify the potential benefits of heterogeneity and customization for the domain-specific applications. In particular, [&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":[11,89,3],"tags":[1782,14,377,452,20,199],"class_list":["post-6970","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-fpga","tag-heterogeneous-systems","tag-nvidia","tag-tesla-c1060"],"views":1896,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6970","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=6970"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6970\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6970"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6970"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6970"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}