{"id":13882,"date":"2015-04-17T00:11:04","date_gmt":"2015-04-16T21:11:04","guid":{"rendered":"http:\/\/hgpu.org\/?p=13882"},"modified":"2015-04-17T00:11:04","modified_gmt":"2015-04-16T21:11:04","slug":"optimizing-asp-net-with-c-amp-on-the-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13882","title":{"rendered":"Optimizing ASP.NET with C++ AMP on the GPU"},"content":{"rendered":"<p>This whitepaper is intended for Microsoft Windows developers who are considering writing high-performance parallel code in Amazon Web Services (AWS) using the Microsoft C++ Accelerated Massive Parallelism (C++ AMP) library. This paper describes an ASP.NET Model-View-Controller (MVC) web application written in C# that invokes C++ functions running on the graphics processing unit (GPU) for matrix multiplication. Since matrix multiplication is of order N-cubed, multiplying two 1024 x 1024 matrixes requires over one billion multiplications, and is therefore an example of a compute-intensive operation that would be a good candidate for GPU programming. This paper shows how to use AWS Elastic Beanstalk and the AWS Toolkit for Visual Studio to launch a Microsoft Windows Server instance with an NVIDIA GPU in the Amazon Elastic Compute Cloud (Amazon EC2) on AWS.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This whitepaper is intended for Microsoft Windows developers who are considering writing high-performance parallel code in Amazon Web Services (AWS) using the Microsoft C++ Accelerated Massive Parallelism (C++ AMP) library. This paper describes an ASP.NET Model-View-Controller (MVC) web application written in C# that invokes C++ functions running on the graphics processing unit (GPU) for matrix [&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":[11,3],"tags":[1641,750,1782,324,20,1732,176],"class_list":["post-13882","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-c-amp","tag-cloud","tag-computer-science","tag-matrix-multiplication","tag-nvidia","tag-nvidia-grid-k520","tag-package"],"views":3569,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13882","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=13882"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13882\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13882"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13882"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13882"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}