{"id":8592,"date":"2020-05-14T16:40:52","date_gmt":"2020-05-14T20:40:52","guid":{"rendered":"http:\/\/local.brightwhiz\/?p=8592"},"modified":"2020-05-14T16:40:52","modified_gmt":"2020-05-14T20:40:52","slug":"nvidia-cuda-toolkit-11","status":"publish","type":"post","link":"http:\/\/local.brightwhiz\/nvidia-cuda-toolkit-11\/","title":{"rendered":"NVIDIA Announces CUDA Toolkit 11"},"content":{"rendered":"\n<p>NVIDIA has announced CUDA Toolkit 11 for general availability which introduces support for the new NVIDIA A100 based on the <a href=\"http:\/\/local.brightwhiz\/tag\/nvidia\">NVIDIA<\/a> Ampere architecture, Arm server processors, performance-optimized <a href=\"http:\/\/local.brightwhiz\/tag\/libraries\">libraries<\/a>, and new developer tools with improvements for A100.<\/p>\n\n\n\n<p>This new release comes with new a host of new features and improvements. These include the ability to develop for the NVIDIA Ampere <a href=\"http:\/\/local.brightwhiz\/tag\/gpu\">GPU<\/a> architecture such as the new NVIDIA A100 GPU and multi-GPU systems based on A100 such as DGX A100 and HGX A10.<\/p>\n\n\n\n<p>CUDA Toolkit 11 also supports the new third-generation Tensor Cores to accelerate mixed-precision matrix operations on different data types, including TF32 and Bfloat16.<\/p>\n\n\n\n<p>Multi-Instance GPU virtualization and GPU partitioning capabilities for improved GPU utilization as well as library performance optimizations for linear algebra, FFTs, matrix multiplication, JPEG decoding, and more are now supported.<\/p>\n\n\n\n<p>Other features include programming and API improvements for task graphs, asynchronous data movement, fine-grained synchronization, L2 cache residency control, and enhancements to the <a href=\"http:\/\/local.brightwhiz\/tag\/nsight\">Nsight<\/a> developer tools family for tracing, profiling, <a href=\"http:\/\/local.brightwhiz\/tag\/debugging\">debugging<\/a>, and roofline analysis.<\/p>\n\n\n\n<p>CUDA C++ also gets a boost with compiler performance and usability improvements, new link-time optimization capabilities, support for new host compilers, and language standards including <a href=\"http:\/\/local.brightwhiz\/tag\/c\">C++17<\/a>.<\/p>\n\n\n\n<p>Also on the same Parallel C++ <a href=\"http:\/\/local.brightwhiz\/tag\/stl\">STL<\/a> support using libcu++ and integration of CUB as a CUDA C++ core library in the Toolkit has been added.<\/p>\n\n\n\n<p>You can find out more about NVIDIA CUDA Toolkit 11 from the official CUDA page <a href=\"https:\/\/devblogs.nvidia.com\/cuda-11-features-revealed\/\" target=\"_blank\" rel=\"noreferrer noopener\">here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>NVIDIA has announced CUDA Toolkit 11 for general availability which introduces support for the new NVIDIA A100 based on the NVIDIA Ampere architecture, Arm server processors, performance-optimized libraries, and new&#8230;<\/p>\n","protected":false},"author":1,"featured_media":8593,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[25,13,16,18],"tags":[33,111,164,172,184,270,280,350,408,412,433,449,563],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>CUDA Toolkit 11 From NVIDIA is now Available for Download<\/title>\n<meta name=\"description\" content=\"NVIDIA has announced the availability of CUDA Toolkit 11 for general availability with new features and performance improvements\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/local.brightwhiz\/nvidia-cuda-toolkit-11\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CUDA Toolkit 11 From NVIDIA is now Available for Download\" \/>\n<meta property=\"og:description\" content=\"NVIDIA has announced the availability of CUDA Toolkit 11 for general availability with new features and performance improvements\" \/>\n<meta property=\"og:url\" content=\"http:\/\/local.brightwhiz\/nvidia-cuda-toolkit-11\/\" \/>\n<meta property=\"og:site_name\" content=\"Brightwhiz.com\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/brightwhiz\/\" \/>\n<meta property=\"article:published_time\" content=\"2020-05-14T20:40:52+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/local.brightwhiz\/wp-content\/uploads\/2020\/05\/CUDA-Toolkit-11.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"630\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Michael Bright\" 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