Transcript of a discussion on how high-performance computing and memory-driven architectures democratize the benefits from advanced research and business analytics.

1. Inside Story on HPC’s Role in the
Bridges Research Project at Pittsburgh
Supercomputing Center
Transcript of a discussion on how high-performance computing and memory-driven
architectures democratize the benefits from advanced research and business analytics.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript.
Sponsor: Hewlett Packard Enterprise.
Dana Gardner: Welcome to the next edition of the BriefingsDirect Voice of the Customer
podcast series. I'm Dana Gardner, Principle Analyst at Interarbor Solutions, your host and
moderator for this ongoing discussion on digital transformation success. Stay with us now to
learn how agile businesses are fending off disruption -- in favor of innovation.
Our next high-performance computing (HPC) success story interview examines how Pittsburgh
Supercomputing Center (PSC) has developed a research computing capability, Bridges, and
how that's providing new levels of analytics, insights, and efficiencies.
We'll now learn how advances in IT infrastructure and memory-driven architectures are
combining to meet the new requirements for artificial intelligence (AI), big data analytics, and
deep machine learning.
Here to describe the inside story on building Bridges is Dr. Nick
Nystrom, Interim Director of Research at Pittsburgh Supercomputing
Center. Welcome.
Dr. Nick Nystrom: Good morning, Dana, I’m pleased to be here.
Gardner: We're also here with Paola Buitrago, Director of AI and Big
Data at Pittsburgh Supercomputing Center. Welcome.
Paola Buitrago: Thank you, Dana. It’s a pleasure to be here.
Gardner: Let's begin with what makes Bridges unique. What is it
about Bridges that is possible now that wasn't possible a year or two ago?
Equal opportunity HPC
Nystrom: Bridges allows people who have never used HPC before to use it for the first time.
These are people in business, social sciences, different kinds of biology and other physical
sciences, and people who are applying machine learning to traditional fields. They're using the
same languages and frameworks that they've been using on their laptops and now that is
scaling up to a supercomputer. They are bringing big data and AI together in ways that they just
haven't done before.
Nystrom

2. Gardner: It almost sounds like the democratization of HPC. Is that one way to think about it?
Nystrom: It very much is. We have users who are applying tools like R and Python and scaling
them up to very large memory -- up to 12 terabytes of random access memory (RAM) -- and
that enables them to gain answers to problems they've never been able to answer before.
Gardner: There is a user experience aspect, but I have to imagine there are also underlying
infrastructure improvements that also contribute to user democratization.
Nystrom: Yes, democratization comes from
two things. First, we stay closely in touch with
the user community and we look at this
opportunity from their perspective first. What
are the applications that they need to run?
What do they need to do? And from there, we
began to work with hardware vendors to
understand what we had to build, and, what we
came up with is a very heterogeneous system.
We have three tiers of nodes having memories ranging from 128 gigabytes to 3 terabytes, to 12
terabytes of RAM. That's all coupled on the same very-high-performance fabric. We were the
first installation in the world with the Intel Omni-Path interconnect, and we designed that in a
custom topology that we developed at PSC expressly to make big data available as a service to
all of the compute nodes with equally high bandwidth, low latency, and to let these new things
become possible.
Gardner: What other big data analytics benefits have you gained from this platform?
Bridges’ new world
Buitrago: A platform like Bridges enables that which was not
available before. There's a use case that was recently described by
Tuomas Sandholm, [Professor and Director of the Electronic
Marketplaces Lab at Carnegie Mellon University. It involves strategic
machine learning using Bridges HPC to play and win at Heads-Up,
No-limit Texas Hold'em poker as a capabilities benchmark.]
This is a perfect example of something that could not have been
done without a supercomputer. A supercomputer enables massive
and complex models that can actually give an accurate answer.
Right now, we are collecting a lot of data. There's a convergence of
having great capabilities right in the compute and storage -- and also
having the big data to answer really important questions. Having a system like Bridges allows us
to, for example, analyze all that there is on the Internet, and put the right pieces together to
answer big societal or healthcare-related questions.
Gardner: The Bridges platform has been operating for some months now. Tell us some other
examples or use cases that demonstrate its potential.
Dissecting disease through data
Buitrago
We stay in touch with the user
community and we look at this from
their perspective. What are the
applications that they need to run?
What we came up with is a very
heterogeneous system.

3. Nystrom: Paola mentioned use cases for healthcare. One example is a National Institutes of
Health (NIH) Center of Excellence in the Big Data to Knowledge program called the Center for
Causal Discovery.
They are using Bridges to combine very large data in genomics, such as lung-imaging data and
brain magnetic resonance imaging (MRI) data, to come up with real cause-and-effect
relationships among those very large data sets. That was never possible before because the
algorithms were not scaled. Such scaling is now possible thanks very large memory
architectures and because the data is available.
At CMU and the University of Pittsburgh, we have those resources now and people are making
discoveries that will improve health. There are many others. One of these is on the Common
Crawl data set, which is a very large web-scale data set that Paola has been working with.
Buitrago: Common Crawl is a data set that collects all the information on the Internet. The data
is currently available on the Amazon Web Services (AWS) cloud in S3. They host these data
sets for free. But, if you want to actually analyze the data, to search or create any index, you
have to use their computing capabilities, which is a good option. However, given the scale and
the size of the data, this is something that requires a huge investment.
So we are working on actually offering the same data set, putting it together with the computing
capabilities of Bridges. This would allow the academic community at large to do such things as
build natural language processing models, or better analyze the data -- and they can do it fast,
and they can do it free of charge. So that's an important example of what we are doing and how
we want to support big data as a whole.
Gardner: So far we’ve spoken about technical requirements in HPC, but economics plays a
role here. Many times we've seen in the evolution of technology that as things become
commercially available off-the-shelf technologies, they can be deployed in new ways that just
weren’t economically feasible before. Is there an economics story here to Bridges?
Low-cost access to research
Nystrom: Yes, with Bridges we have designed the system to be extremely cost-effective.
That's part of why we designed the interconnect topology the way we did. It was the most cost-
effective way to build that for the size of data analytics we had to do on Bridges. That is a win
that has been emulated in other places.
So, what we offer is available to research communities at no charge -- and that's for anyone
doing open research. It's also available to the industrial sector at essentially a very attractive
rate because it’s a cost-recovery rate. So, we do work with the private sector. We are looking to
do even more of that in future.
Explore the New Path to
High Performance
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4. Also, the future systems we are looking at will
leverage lots of developing technologies. We're
always looking at the best available technology
for performance, for price, and then architecting
that into a solution that will serve research.
Gardner: We’ve heard a lot recently from Hewlett
Packard Enterprise (HPE) recently about their advances in large-scale memory processing and
memory-driven architectures. How does that fit into your plans?
Nystrom: Large, memory-intensive architectures are a cornerstone of Bridges. We're doing a
tremendous amount of large-scale genome sequence assembly on Bridges. That's individual
genomes, and it’s also metagenomes with important applications such as looking at the gut
microbiome of diabetic patients versus normal patients -- and understanding how the different
bacteria are affected by and may affect the progression of diabetes. That has tremendous
medical implications. We’ve been following memory technology for a very long time, and we’ve
also been following various kinds of accelerators for AI and deep learning.
Gardner: Can you tell us about the underlying platforms that support Bridges that are currently
commercially available? What might be coming next in terms of HPE Gen10 servers, for
example, or with other HPE advances in the efficiency and cost reduction in storage? What are
you using now and what do you expect to be using in the future?
Ever-expanding memory, storage
Nystrom: First of all, I think the acquisition of SGI by HPE was very strategic. Prior to Bridges,
we had a system called Blacklight, which was the world’s largest shared-memory resource. It’s
what taught us, and we learned how productive that can be for new communities in terms of
human productivity. We can’t scale smart humans, and so that’s essential.
In terms of storage, there are tremendous opportunities now for integrating storage-class
memory, increasing degrees of flash solid-state drives (SSDs), and other stages. We’ve always
architected our own storage systems, but now we are working with HPE to think about what we
might do for our next round of this.
Gardner: For those out there listening and reading this information, if they hadn’t thought that
HPC and big data analytics had a role in their businesses, why should they think otherwise?
Nystrom: From my perspective, AI is permeating all aspects of computing. The way we see AI
as important in an HPC machine is that it is being applied to applications that were traditionally
HPC only -- things like weather and protein folding. Those were apps that people used to run on
just big iron.
Now, they are integrating AI to help them find rare events, to do longer-term simulations in less
time. And they’ll be doing this across other industries as well. These will be enterprise workloads
where AI has a key impact. It won’t necessarily
turn companies into AI companies, but they will
use AI as an empowering tool to make what they
already do, better.
Gardner: An example, Nick?
Nystrom: A good example of the way AI is
permeating other fields is what people are doing
at the Institute for Precision Medicine, [a joint effort between the University of Pittsburgh and the
University of Pittsburgh Medical Center], and the Carnegie Mellon University Machine Learning
and Computational Biology Departments.
We're always looking at the best
available technology for performance,
for price, and then architecting that
into a solution that will serve research.
These will be enterprise workloads
where AI has a key impact. They will
use AI as an empowering tool to make
what they already do, better.

5. They are working together on a project called Big Data for Better Health. Their objective is to
apply state of the art machine learning techniques, including deep learning, to integrated
genomic patient medical records, imaging data, and other things, and to really move toward
realizing true personalized medicine.
Gardner: We’ve also heard a lot recently about hybrid IT. Traditionally HPC required an on-
premises approach. Now, to what degree does HPC-as-a-service make sense in order to take
advantage of various cloud models?
Nystrom: That’s a very good question. One of the things that Bridges makes available through
the democratizing of HPC is big data-as-a-service and HPC-as-a-service. And it does that in
many cases by what we call gateways. These are web portals for specific domains.
At the Center for Causal Discovery, which I mentioned, they have the Causal Web. It’s a portal,
it can run in any browser, and it lets people who are not experts with supercomputers access
Bridges without even knowing they are doing it. They run applications with a supercomputer as
the back-end.
Another example is Galaxy Project and Community Hub, which are primarily for bioinformatic
workflows, but also other things. The main Galaxy instance is hosted elsewhere, but people can
run very large memory genome assemblies on Bridges transparently -- again without even
knowing. They don’t have to log in, they don’t have to understand Linux; they just run it through
a web browser, and they can use HPC-as-a-service. It becomes very cloud-like at that point.
Super-cloud supercomputing
Buitrago: Depending on the use case, an environment like the cloud can make sense. HPC
can be used for an initial stage, if you want to explore different AI models, for example. You can
fine-tune your AI and benefit from having the data
close. You can reduce the time to start by having a
supercomputer available for only a week or two.
You can find the right parameters, you get the
model, and then when you are actually generating
inferences you can go to the cloud and scale there.
It supports high peaks in user demand. So, cloud
and traditional HPC are complimentary among
different use cases, for what’s called for in different
environments and across different solutions.
Gardner: Before we sign off, a quick look to the future. Bridges has been here for over a year,
let's look to a year out. What do you expect to come next?
Nystrom: Bridges has been a great success. It's very heavily subscribed, fully subscribed, in
fact. It seems to work; people like it. So we are looking to build on that. We're looking to extend
that to a much more powerful engine where we’ve taken all of the lessons we've learned
improving Bridges. We’d like to extend that by orders of magnitude, to deliver a lot more
capability -- and that would be across both the research community and industry.
Explore the New Path to
High Performance
Computing Solutions
Cloud and traditional HPC are
complimentary among different use
cases, for what’s called for in
different environments and across
different solutions.

6. Gardner: And using cloud models, what should look for in the future when it comes to a richer
portfolio of big data-as-a-service offerings?
Buitrago: We are currently working on a project to make data more available to the general
public and to researchers. We are trying to democratize data and let people do searches and
inquiries and processing that they wouldn’t be able to do without us.
We are integrating big data sets that go from web crawls to genomic data. We want to offer
them paired with the tools to properly process them. And we want to provide this to people who
haven’t done this in the past, so they can explore their questions and try to answer them. That’s
something we are really interested in and we look forward to moving into a production stage.
Gardner: I'm afraid we’ll have to leave it there. We've been examining how the Pittsburgh
Supercomputing Center has developed a research capability, Bridges, and how that's providing
new levels of analytics, insights and efficiencies. And we've learned how advances in IT
infrastructure and HPC architectures are combining to meet new requirements -- for such uses
as AI and big data deep learning.
So please join me in thanking our guests, Dr. Nick Nystrom, Interim Director of Research at the
Pittsburgh Supercomputing Center. Thank you.
Nystrom: Thank you.
Dana Gardner: We've also been here with Paola Buitrago, Director of AI and Big Data at the
Pittsburgh Supercomputing Center. Thank you.
Buitrago: Thanks, Dana.
Gardner: And thanks also to our audience for joining this BriefingsDirect Voice of the Consumer
digital transformation success story. I’m Dana Gardner, Principal Analyst at Interarbor Solutions,
your host for this ongoing series of Hewlett Packard Enterprise-sponsored interviews. Thanks
again for listening. Please feel free to pass this along in your IT community, and do come back
next time.
Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript.
Sponsor: Hewlett Packard Enterprise.
Transcript of a discussion on how high-performance computing and memory-driven
architectures democratize the benefits from advanced research and business analytics.
Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.
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