There's more extreme rainfall, heavy flooding, forest fires.

There's the radio signature, [INAUDIBLE]..

Being able to predict these extreme events more accurately

is kind of the big challenge that we're facing right now.

There's 100 terabytes of climate data every day

from satellites, from observations, from models.

So climate data is a big data problem.

We need things that are fast that

can sift through all of that data rapidly and accurately.

And deep learning is almost perfectly poised for problems

in climate science.

THORSTEN KURTH: A lot of NERSC users are using TensorFlow.

It's one of the more popular frameworks.

We use TensorFlow to iterate quickly

over the different models, different layer parameters.

For this particular climate project,

to create the deep learning model,

we started from segmentation models,

which have proven to be successful,

for example, our satellite imagery segmentation tasks.

And then we use TensorFlow to enhance the models

until we found a set of models to perform well

enough for this specific task.

But for the volume of the data, complexity of the data,

the network required 14 teraflops.

So if you want to do this on your workstation,

it would take months to train.

MIKE HOUSTON: To really tackle these problems requires

the largest computational resources that

are available on the planet.

So systems like the Summit supercomputer,

it's two tennis courts in total size.

I mean, this thing is state-of-the-art.

It's a million times faster than your common laptop.

3.3 exaflops.

Just imagine what you do at your workstation,

but now imagine having 27,000 times that power.

We can do that now.

THORSTEN KURTH: We were surprised how good it actually

scales.

1,000 nodes, then 2,000 nodes.

5,000 nodes.

MIKE HOUSTON: This was the first time

anybody's ever run an AI application at this scale.

Instead of having the climate scientists figure out

how to write high tune code, they

could express things in a very natural way in Python,

in TensorFlow, and get all the high performance code

that most HPC people are used to within TensorFlow.

KARTHIK KASHINATH: We're now entering the space where

AI can actually contribute to the predictions

of these extreme weather events.

MIKE HOUSTON: When you combine traditional HPC with AI,

you can tackle things we never thought that we could tackle.

Fusion reactor research, understanding diseases

like Alzheimer's, cancer, right?

That's incredible.

THORSTEN KURTH: We've shown that with the hyperactivity

framework such as TensorFlow, you can get to massive scale,

and you can get awesome performance

and accomplish your goals.

KARTHIK KASHINATH: Genetics, neuroscience, cosmology,

high energy physics, that is immensely exciting for me.