(cheerful music)
- Hi, I am Kanchan Mehrota,
HPC customer engineering manager at Microsoft.
I lead a team of engineers
who are working with our HPC AI customers
on some of the most complex scenarios every day.
Today, my colleague Hugo Meiland and I
will be presenting on the topic of weather prediction
and climate change and how Azure is helping this community,
not just by supporting the scale and performance it needs
for accurate and timely forecast to be able
to plan our present and future,
but also by being part of this community and mission itself.
We'll conclude this presentation with Brian,
sharing some ongoing work showing the performance of CESM,
the Community Earth System Model on Azure
in partnership with NCAR,
who are working on providing researchers access to it
via the cloud enabling scannable collaborative science
in the global scientific community.
We are all aware of the importance of weather
and climate prediction,
weather and climate prediction is important
for a number of reasons.
The first main reason is that it saves lives.
The more warning people have about natural disasters,
the better they can prepare.
There is also an increasing global demand
for weather services as weather events become
more volatile and costly.
According to NOAA and CEI study,
the average number of billion dollar weather events
per year, between 2008 and 2015
doubled compared to the prior 35 years.
Weather forecasting is not new,
but the increasing impact from these disasters
make it critically important to deliver greater accuracy
and timeliness of workers.
Weather and climate describe the same thing,
the state of the atmosphere,
but at different timescales,
these predictions and results are really important
to mitigate the first effects of climate change
and help decision makers prioritize environmental issues
based on scientific evidence.
Climate change is not just an environmental problem,
but it is also transforming the way we think about security
in this fragile and increasingly interdependent world.
There are a few key things that are needed
for effective weather and climate prediction.
Time to forecast, having an extra week's heads-up
that extreme weather is coming can be huge.
Collaboration and reach out,
crowdsourcing is a way to accelerate scientific advancements
from the research community to produce the most accurate
and reliable operation modeling systems in the world.
Hence the ability to collaborate and share data, models
and forecast with all stakeholders is key.
Accuracy, it is crucial that we continue to collect data
and improve models,
increasing their accuracy to refining our knowledge
of climate and weather.
So how is Azure supporting these requirements?
Modern forecasting has gotten to where
disasters can be prepared for,
it isn't perfect, but it is pretty good.
Several technological trends, such as miniaturization, IOT,
artificial intelligence, big data,
big compute and analytics
that are transforming all sectors of the economy
are also feeling advances in weather prediction
and decision support.
Our focus in this talk will be on
supercomputing capabilities on Azure,
which is one of the key enablers
for fast and timely forecasting
without the necessity of spending upfront
investing in the high performance computing infrastructure,
giving access to architectures and systems,
which may not be readily available in-house.
I would like to now share some examples
of how we have been working with our customers
to help achieve more.
With two tropical storms headed for the Gulf Coast,
a US Navy team of leading engineers and scientists
partnered with Microsoft to successfully deploy
an enhanced weather model
capable of rapidly scaling weather and ocean pattern's
predictions on demand.
The successful COAPMS model running in Azure
further generated many positive outcomes
in modeling weather and ocean patterns
associated with tropical storms, Laura,
later elevated to hurricane Laura,
category four and Marco.
Another interesting scenario is where we are working
with meteomatics,
helping them build their weather as a service.
Meteomatics provides weather simulation
as a service in Azure,
with exceptional resolution, and quality of service.
The weather model of Europe is being calculated 24/7
in less than an hour with 30K cores,
providing the customers instant update
of the current weather conditions.
As will be shared later in the presentation,
you will see we have demonstrated Wharf on Azure
at impressive skills.
We've been working with many, many customers and partners
where they are running Wharf on Azure
and meteomatics is one of them.
The Army Engineer Research and Development Center
is working with Microsoft to improve climate modeling
and natural disaster resilience planning
using predictive analytics
powered cloud-based tools and AI.
After a successful feasibility study
testing weather CSTORM-MS
could be run in commercial cloud
through a cooperative research and development agreement
between ERDC and Microsoft.
The second phase of the project will demonstrate
how well Azure can run CSTORM-MSs,
entire North Atlantic Coast, CSTORM study,
which features a sea level rise value
not previously simulated.
Last example that I wanna share is non use
to the weather community
where the UK Met Office is working with Microsoft,
deliver advanced supercomputing capabilities
for weather and climate research.
The partnership will also include innovation services
to support the Met Office in exploiting future technologies
such as AI plus commercialization opportunities.
This partnership showcases Microsoft-Azure's unique ability
to deliver mission critical supercomputing
integrated with cloud computing at the largest scales.
Microsoft remains the only public cloud provider
giving customers access to the full spectrum
of HPC capabilities, spanning dedicated supercomputers,
InfiniBand HPC clusters with sector leading
real-world application performance and scalability
and a plethora of virtual machine types
with low latency networking.
Apart from these scenarios,
Azure is also supporting
a large number of sustainability scenarios
across wind farm design and optimization,
aircraft emissions reduction,
renewable energy simulation and modeling,
risk and disaster planning, to name a few.
These are a handful of examples
that I'm showing here on this slide.
I would also like to briefly mention
our AI for earth program.
The goal for which is to put Azure cloud to work
for conservation and sustainability.
It's a grants program that gives both money
and Azure resources away to organizations
all over the world,
working at the intersection of AI
and environmental sustainability.
It's also an education program
with all kinds of educational sessions,
engaging with our grantees.
We also build a bunch of ML tools
to help conservation scientists be more efficient
at what they do.
Our key effort and focus currently
is also on our planetary computer platform
built for analyzing large geospatial datasets in Azure.
With this glimpse into our capabilities,
I will hand over to Hugo Meiland,
who will deep dive into some of the real world results,
capabilities and programs Azure offers today
to support our most complex weather
and climate requirements.
Thank you.
- All right, thank you Kanchan.
One of the main goals for the Azure cloud environment
has been and is
to be the cloud for high performance computing.
This is not a new focus.
It has been for a long time.
Here supporting these workloads is a hardware architecture
that supports tightly coupled applications.
Let me start with a well-known example
of such an application and its capability on Azure.
Here, we tested a Wharf,
a large 371 million grid simulation of hurricane Maria
from 2017.
We did this benchmark on a range of setups
ranging from a single node with a 120 cores,
all the way up to a production work size at 256 nodes,
with over 20K processes,
which fills that's roughly 90% efficiency,
but we did not stop there
by pushing it all the way to 80K processes on 672 nodes,
it shows that Azure can do serious large
tightly coupled jobs.
Having the capability to run jobs is one thing,
but can it also be done effectively
and in a price efficient way?
In a joint effort with CIRES, NOAA, and PSL,
exactly disburse research,
the results have two important conclusions.
Azure can deliver a configuration
that is the most cost efficient, but not only that,
it is also the fastest solution
of the configurations tested.
So what does the Azure HVC fleet look like today?
Within the CPU based nodes, there are two main series.
DHB nodes are optimized for memory bandwidth
and have several generations of the AMD Zen architecture.
The HC nodes are optimized for dense compute
and based on the Intel Xeon architecture,
most important here is that all of the HV and HC series
come with amphibia InfiniBand
to deliver a faster interconnect,
especially for tight coupled applications.
For GPU intensive work loads,
we have the NC and ND series,
where today I will focus on the ND series.
The latest generation of this node
is designed around the Nvidia A100
and has a configuration of Maximo A8-A100 GPUs
either with 40 gigabytes or 80 gigabytes GPU memory
for each GPU.
Not only this,
it also has an eight way InfiniBand connectivity
allowing each of the A100 GPU's
it's own EDR InfiniBands connection.
This is combined with a full and feeling interconnect
between the GPU's.
Now that we have demonstrated our HPC fleet
and how performance tightly coupled jobs can run on them.
I would like to show you an example of how to use them.
A typical HPC workflows tends to look something
like represented here with the user submitting a job
or having scheduled jobs run on a fixed time slots.
After the run,
the end results are accessed by users
for visualization and analysis.
The step in between that can be taken
is unique to the cloud,
orchestration to only spin up the nodes,
if and when a job needs to be run,
and shutting them down afterwards.
For short living clusters,
this setup is not too complicated.
However, for longer living HPC environments,
it's important to integrate them
into the enterprise cloud environments.
With the approved networking, security, and integration
with active directory for authentication and authorization.
For this, I would like to present
the AsherHPC OnDemad Platform,
which was recently released in opensource.
Using this platform is one of the ways we try to provide you
with a supercomputing environment,
as you would experience it in your own well-known workflows.
It allows to be used in connection
with your own active directory authentication,
the open PBS scheduler and the only mounds portal,
which allows secure web based connection,
even over the internet.
Through this platform,
you have the capability to run your jobs,
how to scale the compute nodes using cycle clouds.
Monetary environments have access
to a list of parallel file system
with HSM to block and analyze the results
with GPU accelerated visualization nodes.
With this, I would like to hand over to Brian Dobbins.
- [Brian] Hi everyone, I'm Brian Dobbins.
I'm a software engineer at NCAR,
the National Center for Atmospheric Research.
I'm gonna talk to you briefly today about our work
on porting the CESM climate model to Azure.
CESM, the Community Earth System Model
is a sophisticated open source climate model
used by research teams worldwide.
At NCAR, we've been working with Azure
to port this model to their cloud,
enabling simple to use
high-performance on-demand cloud-based modeling
by this global research community.
These two factors, simple to use and high performance
are key aspects of our work.
Porting CESM to new platforms
is often a significant challenge to climate scientists,
but the cloud enables us to pre-configure the software.
So it's ready to use, freeing scientists
from having to wrestle with system level complexities.
In terms of performance,
these models are computationally expensive
and very latency sensitive,
often requiring large clusters or supercomputers.
And I'll show you shortly how Azure's cloud platform
with InfiniBand networking enable scalable science.
Typically the atmosphere component of climate model's
the most computational expensive part.
So it's a natural place to examine the scalability.
This chart shows the performance
of a one degree atmospheric model.
As we scale up the number of nodes from 1 to 32
on both Cheyenne, the NCAR supercomputer
and the Azure HBV three nodes.
The rate here is in simulated years per day,
a common metric in the climate science community
showing how many model years we can simulate
in a real days time.
As you can see,
Azure scales incredibly well and offers performance
surpassing our current in-house supercomputer
on the simulations,
achieving just over 30 simulated years per day,
a 105% improvement on just 32 nodes.
This case for people familiar with CESM
is an F2000climo compset
running the spectral element version of cam
at an any 30 resolution or roughly one degree.
We stopped at 32 nodes in this chart,
but have actually pushed out a bit further in other tests
achieving over 35 simulated years per day,
this is a great result.
It shows researchers can perform long-term simulations
of a climate in a short period of time,
really enabling burst like usage in the cloud.
Let's take a look at one more example.
This is a B1850 case, also at one degree resolution.
This is a very common configuration
used in modern climate research.
It is a fully coupled run,
combining atmosphere model, an ocean model,
a land model, a glacier model,
an ice model, a wave model, and a river runoff model.
It's very complex, and load balancing this is a challenge.
So for the moment we simply ran identical configurations
on both Cheyenne and Azure to see how it fared.
As you can see, the results are again very impressive
with the newer hardware
and Azure offering better performance than Cheyenne
in each of these three configurations.
On roughly 2000 cores,
the Azure system was scaling well
and performing over 35% faster than our supercomputer.
Better yet, these numbers for Azure
represent a low watermark.
With tuning, we expect we'll be able to run even faster
in the near future.
This run essentially shows that the workhorse configuration
of CESM is not only viable on Azure,
but highly performance scalable.
Basically Azure is a viable platform
for running CESM simulations across a variety of scales
and making this easily accessible to anyone
helps democratize the use of climate models
for cutting edge research.
So to sum up, we've successfully ported CESM
to the Azure platform and not only that,
but we've exceeded the performance
of the current NCAR supercomputer,
doubling performance on the atmosphere runs,
and an improvement of a minimum of 35% on fully coupled runs
with this number likely to improve rapidly.
Clearly this is a viable platform for climate modeling.
So now we're working on enabling others to easily spin up
this environment to do their own science.
If you're interested in this, get in touch with me.
My email address is below.
We're also planning some higher end runs
to push the Azure platform further
in tune for really scalable science.
Finally, I should just add my thanks to the Azure team
and in particular, Charles Perry,
for his enormous help on this project,
it wouldn't have come together without him.
I hope you've enjoyed this,
and thanks very much for your time.
(gentle music)