James Lujan: Trinity is our first advanced technology system.
It provides the NNSA as well as Lawrence Livermore, Los Alamos and Sandia National Laboratories
the ability to do large-scale nuclear weapons simulation but it also gives us the ability
to look at what we think are technologies that are forward looking.
Manuel Vigil: There's a lot of advancements in processors or computing nodes or everything
else, but what a system like Trinity does is allow you to put all of that together at scale,
how do these technologies work together or do they work at all?
Lujan: It's one of the key elements in the overall Stockpile Stewardship Program because
it allows us to do simulations at large scale.
But not only can we do larger problems that we haven't been able to do before, we can
do them more quickly.
Trinity is a top ten machine as it relates to the amount of operations per second that
we can do.
However it's not just the speed of Trinity that's important, but how efficiently that
we can use that speed.
Vigil: Ultimately the most rewarding time is when you see scientists run applications
on the system and get some real results that help them define new things in their area
of science that we've never seen before.
Garrett Kenyon: We have gotten results from a project that seeks to implement a new kind
of neural network called a sparse prediction machine a sparse prediction machine differs
a bit from the sort of deep neural networks that you might have been reading about in
the popular press in that it's more biologically realistic.
And one result of this is we can train a sparse prediction machine in much the way a biological
system learns.
We also hope to apply these algorithms to large-scale computer simulations, simulations
of the weather for example, so that we can learn maybe more about the underlying processes
that govern weather dynamics and also make better predictions about what the, what the
future weather might be.
Los Alamos has a principal responsibility for guaranteeing the integrity of some components
of the United States stockpile.
And we have to do this entirely without testing.
So part of the way we do this is through simulating, through large scale computer simulations and
we believe a sparse prediction machine might be able to contribute to the analysis and
even acceleration of, of simulations of this type.
Karissa Sanbonmatsu: This model is a lot larger than most models.
And so we are simulating what's called chromatin.
Which is basically a little chunk of your, of one of your chromosomes.
We were able to do a much larger portion of DNA with this.
Trinity is one of the only computers in the world that can fit this problem onto the computer and fit it in memory.
The DNA is packed in the cells by wrapping around these spools and we were able to simulate
many many of the spools together with the DNA on this platform.
It was really thrilling to see it on the screen.
And just to see all the different spools interacting and so forth.
There are many different applications.
The main ones are in embryo development, understanding how the
embryo develops and developmental diseases, and also in cancer.
This gene we're simulating is called GATA4 and it's related to gastrointestinal disease
and gastrointestinal development.
And so if we can understand you know the shape of this gene how the dynamics of it how it
responds to drugs we may have a chance to find cures for those kinds of cancer.
Brian Albright: VPIC is one of the highest performing particle-in-cell plasma codes in the world.
Being able to run VPIC on Trinity enabled us to do was to run problems at unprecedented
sizes and unprecedented scale.
And this was really something that let us you know make discoveries at the kind of the
forefront of mix and turbulence in plasmas that nobody else in the world has been able to do.
What we're trying to do is we're trying to look at the way plasmas will mix with one another.
We see this happen in astrophysical plasmas we see this occur in the Earth's magnetosphere.
We also see this happen on Earth as well, in plasmas for instance in inertial fusion experiments.
One of the advantages for us going to this platform is that it kind of presages what
machines of the future may look like.
The design changes we made to the code today are ones that we anticipate using in the future.
The Lab has kind of a broad national security mission that involves nuclear weapons and
worrying about our energy security, it involves protecting our space assets.
The problems that we're working on as part of this open science project are really helping
with that whole spectrum of different application areas for the Lab’s national security mission.
Vigil: It's working with great people across a multitude of organizations that make this work.
Lujan: We've been able to solve some problems within the stockpile that we've never been
able to solve before.
So that's exciting in and of itself, having a unique capability and being able to do new
things with it as as always enjoyable.