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SC17 Session Preview: Dr. Pradeep Dubey on AI & The Virtuous Cycle of Compute

Deep Learning was recently scaled to obtain 15PF performance on the Cori supercomputer at NERSC. Cori Phase II features over 9600 KNL processors. It can significantly impact how we do computing and what computing can do for us. In this talk, I will discuss some of the application-level opportunities and system-level challenges that lie at the heart of this intersection of traditional high performance computing with emerging data-intensive computing.

NVIDIA P100 GPUs come to Google Cloud Platform

Today the good folks at the Google Compute Platform announced the availability of NVIDIA GPUs in the Cloud for multiple geographies. Cloud GPUs can accelerate workloads such as machine learning training and inference, geophysical data processing, simulation, seismic analysis, molecular modeling, genomics and many more high performance compute use cases. “Today, we’re happy to make some massively parallel announcements for Cloud GPUs. First, Google Cloud Platform (GCP) gets another performance boost with the public launch of NVIDIA P100 GPUs in beta.

Preferred Networks in Japan Deploys 4.7 Petaflop Supercomputer for Deep Learning

Today Preferred Networks announced the launch of a private supercomputer designed to facilitate research and development of deep learning, including autonomous driving and cancer diagnosis. The new 4.7 Petaflop machine is one of the most powerful to be developed by the private sector in Japan and is equipped with NTT Com and NTTPC’s GPU platform, and contains 1,024 NVIDIA Tesla P100 GPUs.

NASA Perspectives on Deep Learning

Nikunj Oza from NASA Ames gave this talk at the HPC User Forum. “This talk will give a broad overview of work at NASA in the space of data sciences, data mining, machine learning, and related areas at NASA. This will include work within the Data Sciences Group at NASA Ames, together with other groups at NASA and university and industry partners. We will delineate our thoughts on the roles of NASA, academia, and industry in advancing machine learning to help with NASA problems.”

GPUs Accelerate Population Distribution Mapping Around the Globe

With the Earth’s population at 7 billion and growing, understanding population distribution is essential to meeting societal needs for infrastructure, resources and vital services. This article highlights how NVIDIA GPU-powered AI is accelerating mapping and analysis of population distribution around the globe. “If there is a disaster anywhere in the world,” said Bhaduri, “as soon as we have imaging we can create very useful information for responders, empowering recovery in a matter of hours rather than days.”

The AI Revolution: Unleashing Broad and Deep Innovation

For the AI revolution to move into the mainstream, cost and complexity must be reduced, so smaller organizations can afford to develop, train and deploy powerful deep learning applications. It’s a tough challenge. The following guest article from Intel explores how businesses can optimize AI applications and integrate them with their traditional workloads. 

CCIX Project to link ARM Processors and FPGAs for HPC

Today ARM, Xilinx, Cadence, and Taiwan Semiconductor announced plans to produce the first test chip for the Cache Coherent Interconnect for Accelerators (CCIX) project. CCIX (pronounced “C6”) aims to prove that many-core ARM processors linked to FPGAs have a home in HPC. “The test chip will not only demonstrate how the latest Arm technology with coherent multichip accelerators can scale across the data center, but reinforces our commitment to solving the challenge of accessing data quickly and easily.”

Video: Characterization and Benchmarking of Deep Learning

 Natalia Vassilieva from HP Labs gave this talk at the HPC User Forum in Milwaukee. “Our Deep Learning Cookbook is based on a massive collection of performance results for various deep learning workloads on different hardware/software stacks, and analytical performance models. This combination enables us to estimate the performance of a given workload and to recommend an optimal hardware/software stack for that workload. Additionally, we use the Cookbook to detect bottlenecks in existing hardware and to guide the design of future systems for artificial intelligence and deep learning.”

New OrionX Survey: Insights in Artificial Intelligence

In this Radio Free HPC podcast, Dan Olds and Shahin Khan from OrionX describe their new AI Survey. “OrionX Research has completed one the most comprehensive surveys to date of Artificial Intelligence, Machine Learning, and Deep Learning. With over 300 respondents in North America, representing 13 industries, our model indicates a confidence level of 95% and a margin of error of 6%. Covering 144 questions/data points, it provides a comprehensive view of what customers are doing and planning to do with AI/ML/DL.”

Call for Participation: GTC 2018 in San Jose

The GPU Technology Conference (GTC 2018) has issued their Call for Participation. The event takes place March 26-29 in San Jose, California. “Don’t miss this unique opportunity to participate in the world’s most important GPU event, NVIDIA’s GPU Technology Conference (GTC 2018). Sign up to present a talk, poster, or lab on how GPUs power the most dynamic areas in computing today—including AI and deep learning, big data analytics, healthcare, smart cities, IoT, HPC, VR, and more.”