Read updates highlighting what’s hot in high performance computing, including wrap ups from SC16 and news about our HPC and OpenACC training event.

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TITLE GOES HEREHPC TOP 5 STORIES
WEEKLY INSIGHTS INTO THE WORLD OF
HIGH PERFORMANCE COMPUTING

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Missed Supercomputing 16?
Catch up on what you missed…

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Another way to catch up on
the news, follow #HPCMeetsAI
and #SC16 on Twitter

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Here are the “Top Five” stories
highlighting what’s hot in High
Performance Computing.

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The Power of the GPU in Scientific Computing, Data Centers, and Deep Learning
The importance of such chips for developing and training new AI algorithms quickly cannot be understated,
according to some AI researchers. "Instead of months, it could be days," Nvidia CEO Jen-Hsun Huang said in a
November earnings call, discussing the time required to train a computer to do a new task. "It's essentially
like having a time machine."
While Nvidia is primarily associated with video cards
that help gamers play the latest first-person shooters at
the highest resolution possible, the company has also
been focusing on adapting its graphics processing unit
chips, or GPUs, to serious scientific computation and
data center number crunching.
"In the last 10 years, we’ve actually brought our GPU
technology outside of graphics, made it more general
purpose," says Ian Buck, vice president and general
manager of Nvidia's accelerated computing business unit.
LEARN MORE

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Microsoft and Cray Collaborate, with NVIDIA Tesla P100s
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Cray believes that its work with Microsoft and CSCS could
have solved this problem by applying supercomputing
architectures to accelerate the training process.
The three worked together to scale the Microsoft
Cognitive Toolkit on a Cray XC50 supercomputer at CSCS
nicknamed “Piz Daint”.
According to the supercomputer manufacturer, deep
learning problems share algorithmic similarities with
applications that are traditionally run on a massively
parallel supercomputer. So by optimizing inter-node
communication using the Cray XC Aries network and a high
performance MPI library, each training job is said to be
able to leverage more compute resources and therefore
reduce the amount of time required to train them.

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NVIDIA Training at CU: Deep Learning ad OpenACC Programming
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NVIDIA, the pioneer of GPU-accelerated
computing, and CU Boulder's Research Computing
are pleased to host a two-day training event
Wednesday, Jan. 11, and Thursday, Jan. 12, with a
focus on high-performance computing, deep
learning and OpenACC programming.
Days one and two will consist of hands-on tutorials
on OpenACC and deep learning, respectively. On
the second day, in lieu of attending the deep
learning session, select participants may engage in
an OpenACC hackathon.
Why attend?
NVIDIA GPUs are the world’s fastest and most
efficient accelerators. This workshop will teach
attendees how to accelerate applications across a
diverse set of domains using OpenACC and
demonstrate use of GPUs for deep learning.

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GPUs & Deep Learning in the Spotlight for NVIDIA at SC16
Deep learning is the fastest-growing field in artificial
intelligence, helping computers make sense of infinite
amounts of data in the form of images, sound, and
text. Using multiple levels of neural networks,
computers now have the capacity to see, learn, and
react to complex situations as well or better than
humans. This is leading to a profoundly different way
of thinking about your data, your technology, and the
products and services you deliver.
In this video from SC16, Roy Kim from NVIDIA describes how the company is bringing in a new age of AI with
accelerated computing for Deep Learning applications. “Come join NVIDIA at SC16 to learn how AI
supercomputing is breaking open a world of limitless possibilities. This is an era of multigenerational
discoveries taking place in a single lifetime. See how other leaders in the field are advancing computational
science across domains, get free hands-on training with the newest GPU-accelerated solutions, and connect
with NVIDIA experts.”
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HPE Apollo 6500 for Deep Learning Contains NVIDIA Tesla K80s
In this video from SC16, Greg Schmidt from
Hewlett Packard Enterprise describes how
the HPE Apollo 6500 high density GPU server
is ideal for Deep Learning applications.
“With up to eight high performance NVIDIA
GPUs designed for maximum transfer
bandwidth, the HPE Apollo 6500 is purpose-
built for HPC and deep learning applications.
Its high ratio of GPUs to CPUs, dense 4U form
factor and efficient design enable
organizations to run deep learning
recommendation algorithms faster and more
efficiently, significantly reducing model
training time and accelerating the delivery of
real-time results, all while controlling
costs.”
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10. Stay tuned for weekly
HPC Top 5 Stories