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.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy.
Here to describe the inside story on building AI Bridges are Dr. Nick Nystrom, Interim Director of Research, and Paola Buitrago, Director of AI and Big Data, both at Pittsburgh Supercomputing Center. The discussion is moderated by Dana Gardner, principal analyst, at Interarbor Solutions.
Here are some excerpts:
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?
 |
| Nystrom |
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.
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.
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?
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.]
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: What other big data analytics benefits have you gained from this platform?
 |
| Buitrago |
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.
Explore the New Path to
Computing
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
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.
Explore the New Path to
Computing Solutions
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.
We're
always looking at the best available technology for performance, for
price, and then architecting that into a solution that will serve
research.
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.
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.
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.
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?
Explore the New Path to
Computing
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
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.
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.
Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.
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