{"id":1978,"date":"2010-12-12T14:16:29","date_gmt":"2010-12-12T14:16:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=1978"},"modified":"2010-12-12T14:16:29","modified_gmt":"2010-12-12T14:16:29","slug":"cuda-scalable-parallel-programming-for-high-performance-scientific-computing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1978","title":{"rendered":"CUDA: Scalable parallel programming for high-performance scientific computing"},"content":{"rendered":"<p>Graphics processing units (GPUs) originally designed for computer video cards have emerged as the most powerful chip in a high-performance workstation. Unlike multicore CPU architectures, which currently ship with two or four cores, GPU architectures are &#8220;manycore&#8221; with hundreds of cores capable of running thousands of threads in parallel. NVIDIA&#8217;s CUDA is a co-evolved hardware-software architecture that enables high-performance computing developers to harness the tremendous computational power and memory bandwidth of the GPU in a familiar programming environment &#8211; the C programming language. We describe the CUDA programming model and motivate its use in the biomedical imaging community.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Graphics processing units (GPUs) originally designed for computer video cards have emerged as the most powerful chip in a high-performance workstation. Unlike multicore CPU architectures, which currently ship with two or four cores, GPU architectures are &#8220;manycore&#8221; with hundreds of cores capable of running thousands of threads in parallel. NVIDIA&#8217;s CUDA is a co-evolved hardware-software [&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":[1782,14,95,20],"class_list":["post-1978","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-high-level-languages","tag-nvidia"],"views":2184,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1978","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=1978"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1978\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1978"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1978"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1978"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}