{"id":19837,"date":"2018-01-23T12:18:59","date_gmt":"2018-01-23T20:18:59","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=19837"},"modified":"2018-01-23T12:18:59","modified_gmt":"2018-01-23T20:18:59","slug":"convergence-ai-data-hpc","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2018\/01\/23\/convergence-ai-data-hpc\/","title":{"rendered":"Exploring the Convergence of AI, Data and HPC"},"content":{"rendered":"

Artificial intelligence. It’s perhaps the water cooler topic of our decade, or perhaps the rest of this century. And nowhere is that more true than in high performance computing. An insideHPC special report,<\/a> sponsored by Intel, explores one of the most interesting and cutting-edge areas of AI, and that’s the convergence of deep learning, data and HPC. This convergence is making AI technology more accessible to data scientists with no coding background required.<\/p>\n

\"AI-HPC\"<\/a>

Download the Full Report.<\/a><\/p><\/div>\n

In fact, according to the report, sponsored by Intel, HPC and the artificial intelligence communities are\u00a0converging as they are both running similar types of data and compute intensive workloads on HPC hardware, including supercomputers, institutional clusters or the cloud.<\/p>\n

\n

“The emerging AI community on HPC infrastructure is critical to achieving the vision of AI: machines that don\u2019t just crunch numbers, but help us make better and more informed complex decisions.”\u00a0\u2013 Pradeep Dubey, Intel Fellow and Director of Parallel Computing Lab<\/span><\/p>\n<\/blockquote>\n

The insideHPC guide begins by giving an overview of the HPC landscape. Understanding\u00a0 popular deep learning terms and value propositions are key.\u00a0 The report goes in-depth on the ins and outs of topics such as training, inferencing, scalability and more. Inferencing is top of mind for many deep learning experts, as this is the operation that makes data derived models valuable because they can work to predict the future and perform recognition tasks better than humans.<\/p>\n

The report also covers recent breakthroughs in these areas.\u00a0For example, a breakthrough in deep learning training outlined in the special report is a result of work by collaborators with the Intel<\/span>\u00a0<\/span>Parallel Computing Center that works to bring deep learning training to everyone, regardless of whether you run a leadership class supercomputer or a simple workstation.\u00a0<\/span><\/p>\n

The AI-HPC report also explores specific customer use case results, as well. These include Boston Children’s Hospital’s efforts to\u00a0to use AI to better diagnose various forms of damage in the human brain, as well as\u00a0 Carnegie Mellon University’s efforts to utilize games and game theory to address the challenges of learning in a limited information environment, among others.<\/p>\n

[clickToTweet tweet=”Research breakthroughs in the AI software ecosystem are being quickly integrated into popular software packages. #AI” quote=”Research breakthroughs in the AI software ecosystem are being quickly integrated into popular software packages. #AI”]<\/p>\n

Continue reading for a breakdown of hot topics in the artificial intelligence software ecosystem, as well as what hardware is out there to support AI software. Software explored includes Intel Nirvana Graph, a scalable intermediate language. Hardware discussed includes Intel Xeon Scalable processors and Intel Xeon Phi CPUs, as well as the Intel Neural Network Processor, FPGAs, Neuromorphic chips,\u00a0Intel Omni-Path Architecture and more.<\/p>\n

This insideHPC special report covers the following information in detail:<\/p>\n

    \n
  1. \u00a0An overview of artificial intelligence in the HPC landscape<\/li>\n
  2. Customer use case results<\/li>\n
  3. The software ecosystem<\/li>\n
  4. Hardware to support the AI software ecosystem<\/li>\n<\/ol>\n

    Download the full white paper, “insideHPC Special Report: AI-HPC is Happening Now,”<\/a> courtesy of Intel, to learn more about the potential of AI and the convergence of AI-HPC.\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

    The demand for performant and scalable AI solutions has stimulated a convergence of science, algorithm development, and affordable technologies to create a software ecosystem designed to support the data scientist. A special insideHPC report explores how HPC and the data driven AI communities are converging as they are arguably running the same types of data and compute intensive workloads on HPC hardware, be it on a leadership class supercomputer, small institutional cluster, or in the cloud.<\/p>\n","protected":false},"author":10516,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[269,180,210,67,84,60,58],"tags":[324,638,284,277,96],"acf":[],"yoast_head":"\nDeep Learning: Exploring the Convergence of AI, Data and HPC<\/title>\n<meta name=\"description\" content=\"A special report explores how HPC and the data driven AI communities are converging as they are running similar data and compute workloads on HPC hardware.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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