SANDRA RIVERA: Hello, and welcome to Google Next 2021.
My name is Sandra Rivera, Head of the Data Center and AI Group
at Intel.
It's a privilege to participate in this event with Dr.
Geraldine Van der Auwera from the Broad Institute of MIT
and Harvard to share how we're working together
to create innovative solutions for the health care industry.
Google Cloud and Intel have collaborated together
for more than 10 years, driving consistent improvements
for our customers.
Google was an early adopter of Xeon scalable processors
for their cloud services, and it was
in no small part because of the speed of integration
of new technology, acceleration engines,
and application optimizations that are
pervasive on Xeon platforms.
When we partner with organizations
like the Broad Institute on their infrastructure
and solution requirements, our goal is to help them optimize
and get the most out of their cloud workloads
so that they can focus on solving
complex problems in life sciences and genomics research.
One way we've done this is through our Intel Select
Solutions program, where we collaborate closely on workload
requirements to build platforms with verified hardware
and software stacks that are optimized
across compute, storage, and networking resources.
Built on Xeon scalable processors,
Intel Select Solutions help ensure
partners like the Broad Institute
get the performance they require when they need it most.
And with that, I'd like to welcome Dr. Van der
Auwera to share more about the solution, including
their challenges, how they worked on finding
a solution together with Google Cloud and Intel,
and the great results they're delivering in health care.
GERALDINE VAN DER AUWERA: Thank you so much
for that introduction.
I'm delighted to have this opportunity
to talk about the work we've been doing together to advance
biomedical research for all.
Our mission at the Broad Institute
is to uncover the molecular basis of major human diseases
to inform prevention, detection, treatments, and cures,
develop effective new approaches to diagnostics
and therapeutics, and disseminate discoveries, tools,
methods, and data openly to the entire scientific community.
In the course of fulfilling this mission,
we face many challenges.
And the one I want to talk about today concerns
the deluge of data that we've been experiencing
in recent years.
Extraordinary improvements in data generation technologies
and plummeting costs over the past decade
have led to a boom in the number of data sets
are being produced.
It gives us an unprecedented opportunity
to fuel scientific breakthroughs and deliver
on the promise of precision medicine.
Yet, all that data is only useful
if we can analyze it at scale collaboratively.
The most immediate problem we face
is that the computations we need to run on all this data
are complex, to say the least.
Every byte of data has to go through massive pipelines
designed to squeeze out the useful information
from the raw signal.
The longer it takes for those pipelines to run,
the more time we have to wait between generating the data
and deriving actionable insights.
And then there's the question of cost.
Even when the cost of a single operation is small,
these costs for a job can add up to huge sums of money
when we're looking at cohorts of hundreds of thousands
of human genomes.
Every extra cent that we spend on a pipeline that's
not as efficient as it can be turns
into a limitation on the scope of the research we
can afford to undertake.
But that's not all.
Frankly, there's so much work to do
to maximize the use of these data sets
and not enough brains in the game yet.
We have to broaden the range of researchers who can effectively
contribute to this work.
It can't just be a handful of big established powerhouses.
There are lots of really smart people throughout the world who
could contribute so much more if only they had the right tools.
So how do we overcome these challenges together?
For the past five years or so, we've
partnered with Intel through the Intel-Broad Center
for Genomic Data Engineering.
It's been a wonderful collaboration
that has produced robust solutions for accelerating
our tools and pipelines.
These have helped us take control of the flood of data,
scale up our analyses, and make it cheaper
to run them on the cloud.
And as a complement to that work,
we've been building a platform called Terra in partnership
with Microsoft and Verily that enables
us to put the power that these optimized tools deliver
into the hands of just about any researcher on the planet.
So let's talk about tools and pipelines.
We use many different tools, but none
so much as the Genome Analysis Toolkit,
or GATK, which we develop in-house and make
available to the research community
as an open source package.
It's probably the world's most widely used toolkit
for identifying variants and genome sequencing data.
We run it in the form of automated pipelines
that will take a massive pile of raw sequence data
and put it through several layers of transformation
to extract the genomic variants.
And those variants are what researchers ultimately care
about for understanding human genetics,
diagnosing diseases, and eventually designing
strategies for treating or even eliminating those diseases.
But all that processing is a lot of work.
And there are some classic bottlenecks
that, for a long time, really restricted our ability
to scale up our analyses.
Thankfully, our Intel collaborators
developed optimized versions of some
of the key algorithms involved, which now run faster
on chips equipped with features like AVX-512.
We also see a general acceleration
of processing with the latest generations of CPUs.
For example, we recently performed some benchmarking
that showed faster execution times
on the second-generation Xeon processors,
again, with some really significant effects
on classic bottlenecks like genome alignments
and variant calling.
And it's important to note that this is not
just a case of buying a shorter runtime by doing
the work in fancy hardware that costs more.
When we ran the benchmarks on Google Cloud,
comparing N1 and N2 instances, we
see the N2 instances with the Xeon CPUs
outperforming the N1s on both runtime and cost.
So the pipelines were faster and cheaper to run,
and that's admittedly a pretty good combination.
If you want to learn more about that,
check out the blog post linked at the bottom
of the slide, which outlines the benchmarking
protocol and our findings.
So now let's talk about the cloud.
I mentioned it's imperative that we
get more brains into the game, and the cloud is an obvious way
to increase access to large data sets
and put more computational power into the hands of researchers
across the world.
But you can't just throw life scientists and physicians
at the Google Cloud console.
That's just asking too much of them.
When you're specialized at that level,
learning to use the Cloud as is would be a whole 'nother job.
And that's why we partnered with Microsoft and Verily
to develop a platform called Terra that
sits on top of the Cloud infrastructure,
packages all the relevant functionality
in a form that's more immediately
usable by researchers in the life sciences.
The Terra platform provides applications and interfaces
that are tailored to the needs and expectations of life
scientists, and it connects together the data, tools,
and computing resources needed to advance biomedical research.
Importantly, since we're talking about human biomedical data,
the entire platform operates according
to stringent standards of security under the FedRAMP
program.
So a credentialed researcher can easily
access the data sets they need and apply the tools they
want securely and at scale.
And they can share their work at any stage,
either just with their collaborators
or with the world, in a form that
makes their analysis completely reproducible and extensible.
Yet, it's not only about the researchers.
Terra brings together a multitude
of people and organizations with different roles to play.
Funders, data generators, even patients
who wish to donate their data to research--
they want more researchers to be able to make
use of their precious data.
Meanwhile, tool developers want to make
their tools available to the research
community in a way that just works out of the box--
containerized tools, example data,
and preset configurations, and minimal amount
of support burden.
For example, all the optimized GATK tools and pipelines
I mentioned earlier are available in Terra for anyone
to use.
That means any researcher in the world
can take advantage of the Intel team's optimization work
to run scalable analysis pipelines that
are fast, cost-effective, and scientifically excellent.
And that's where the impact of our collaborative problem
solving is truly maximized.
Let me give you one more example.
We recently announced a new partnership with the Centers
for Disease Control and a company
called Theiagen Genomics to enable every public health
lab in the United States to use Terra to access, analyze,
and share data about SARS-CoV-2.
That means each of these dozens of labs,
which are typically small and don't always
have dedicated bioinformaticians on staff,
will now be able to run optimized pipelines that
are set up to work out of the box, efficiently, and at scale.
And they'll be able to share and compare their data much faster
without the confounding factors that come from using
different sets of tools.
We had already been working in this way
with a subset of states with very encouraging results.
So we're excited to work with the CDC
to roll this out across the country,
particularly as we head into what could be a difficult fall
and winter season for the fight against COVID.
Finally, I want to emphasize that Terra is not
a walled garden.
It's a hub in an ecosystem of platforms, portals, and data
repositories that are interconnected
through open source standards that we like to think
of as a data biosphere.
And it's in that same spirit of connectedness
that we recently launched the Eric and Wendy Schmidt
Center, which will bring together
a global and collaborative network across academia
and industry to promote interdisciplinary research
between data and life sciences in order
to transform biology and ultimately improve
human health.
If you'd like to join us in the brave new world of genomics
in the Cloud, please visit Terra.bio
and take the Intel-accelerated GATK pipelines for a spin,
or browse the preloaded workspace in the Terra showcase
to find other use cases that might interest you.
You can also pick up a copy of my O'Reilly book, "Genomics
in the Cloud," if you'd like a step-by-step, hands-on
introduction to this fascinating and fast-expanding field.
And with that, I thank you for your attention.
I wish you a great rest of the conference.
Thank you.
SANDRA RIVERA: Thank you, Dr. Van der Auwera.
This is such a great example of how we can apply technology
to solve some of the world's most complex problems.
Thank you for choosing Intel and Google
on such an important project I'd also
like to thank Thomas Kurian and his GCP leadership team
for their continued support of our strategic alliance
and for the opportunity to speak at Google Next 2021.
I'm looking forward to doing more amazing things together.
Have a great event, everybody.