This episode of the Khronos Fusion Energy podcast
features Jack Dungara, one of the foundational
architects of modern high -performance scientific
computing and the 2021 winner of the Turing Award,
the highest honor in computer science, described
as the Nobel Prize of Computing. Jack is internationally
recognized for his pioneering work in numerical
linear algebra and high -performance computing.
including the development of software libraries
that underpin much of today's scientific computing
ecosystem and form the mathematical backbone
of tools such as MATLAB. His career spans decades
at the intersection of mathematics, computer
science, and large -scale scientific simulations,
with appointments at institutions including Argonne
National Laboratory, the Los Alamos National
Lab, and Oak Ridge National Laboratories. as
well as longstanding leadership roles. In this
conversation, Jack shares his personal and professional
journey from early academic challenges to becoming
one of the world's influential figures in computational
science, while offering deep insight into how
computation has evolved from supporting infrastructure
into a true scientific instrument. We explore
the limits of modern supercomputing design, why
most systems fail to achieve their theoretical
performance, and why co -design, bringing together
applications, algorithms, and hardware architecture,
is essential for the next generation of scientific
breakthroughs. As a scientific advisor at Kronos
Fusion Energy, Jack plays a critical role in
shaping how large -scale physics -based simulations
are designed, validated, and trusted. especially
in the pursuit of a proper digital twin for fusion
energy systems. His perspective directly informs
Chronos' approach to verification, validation,
uncertainty quantification, and the responsible
integration of AI with first principles numerical
methods. Together, we discuss the future of high
-performance computing, the complementary role
of AI, the energy costs of next -generation supercomputers,
and what it will take to build computing systems
capable of supporting predictive, safety -critical
simulations for fusion energy and beyond. This
episode is both a masterclass in scientific computing
and a deeply human story about curiosity, resilience,
and the people behind the code. Here's Jack Dungara.
All right, so let's see. Let me go back quite
a bit. quite a ways here so um this my story
is something like this my grandparents so all
of my grandparents as well as my father were
born in sicily so um italian is my heritage my
grandparents come from two small villages in
the middle of sicily one's called villa rosa
and the other is called santa catarina And I
had an opportunity to go visit those places a
number of years ago. And they're isolated. It's
mountainous. It's rocky. And I'm sure it was
a very poor existence. My grandfather, my father's
father, worked in a sulfur mine in Sicily. And
I guess he was looking for a little bit better.
And what they did is they decided to leave Sicily.
And that was back in 1929. My grandfather was
42 years old. And he took the family and went
to the U .S. and arrived at Ellis Island. He
had $25 in his pocket. I'm sure he was filled
with a lot of hope for a new life. My father
was 10 years old at that point when he made the
journey. So they immigrated, they settled in
Chicago, they knew some people there and they
made their way to Chicago and settled in in the
Italian neighborhood. My parents and my father
met my mother in Chicago and she has a similar
background. My grandparents on that side of the
family also come from Sicily, of course. So my
parents had a very basic education and they actually
didn't finish high school. So they did have an
understanding about education and college and
knew those things were important. They also understood
and modeled the importance of hard work. So I
went to school. all through elementary school
and high school, I sort of struggled with reading
and spelling. I think I accelerated in math and
science, but I was really having a hard time
with reading, spelling, and everything that went
along with that. And as an adult, I learned I
have dyslexia. So that was probably one reason
why I was not quite at the same level as my other
peers. And I wanted to be a high school teacher.
So that was my ambition when I graduated high
school and decided I would go to college. So
I was the first person in my family to go to
college. I have two younger brothers, and my
younger brothers are electricians. And they,
you know, they have a trade. They went to college
maybe for a year or two and decided it wasn't
for them. And they decided to drop out and get
a career as electricians. And they formed a company
and were very successful in that. And I went
on to college. I went to a place in Chicago where
they trained the teachers for the Chicago public
school system. And I did okay. I was probably
an average student. I was still struggling with
reading and spelling and writing. But I did excel
in math and science. And I guess I stood out
and my professors at college. encouraged me to
apply to a program. And the program was to spend
a semester with a scientist. So I was in the
Chicago area and we have a Department of Energy
laboratory in Chicago called Argonne National
Lab. And I applied for this program and they
accepted about 50 students from around the country.
And I was one of those students. So I got to
spend roughly 16 weeks. working at Argonne National
Lab. Transformational in the sense that, you
know, I discovered things that I had never really
seen before. I was working with scientists. I
was engaging in a form of research. And I found
my passion in some sense. I found that that's
really something I wanted to do. So I went on
to... to get a master's degree. After the master's
degree, I worked at that place, Argonne National
Lab. They hired me full -time. I wasn't ready
for a PhD. And over the course of working at
Argonne, we had many visitors and those visitors
came. I interacted with them and they were encouraging
me to go back to school. So I had an interest
in linear algebra. And I took up with one of
the visitors from the University of New Mexico.
And I was encouraged by Argonne National Lab
to go to school. They paid me a little bit to
do that. And I went and got a Ph .D. at the University
of New Mexico. And while I was there, I was working
at Los Alamos National Lab, which is close to
Albuquerque. and had just a really door -opening
experience with, again, another set of scientists
working on high -performance computing. I was
doing linear algebra, so I was trying to implement
and design things that would run fast on those
computers that we had at Los Alamos. And we had
really some of the fastest computers at that
time. were in use at Los Alamos and I got to
really get a first hand first exposure to those
to those computers. So that was sort of my path
to where I ended up. That was my lead into what
I would call my experience with with numerical
computing and also high performance computing.
That's pretty cool, Jack. Are these programs
still available for people to like apply to out
of high school and undergrad? Oh, yeah. Yeah,
absolutely. And I I I encourage students to do
that. I take in students. So I worked for many
years at Argonne National Lab and I would have
students interacting with me from that program.
And when I moved to Tennessee, I I worked. at
Oak Ridge National Lab, another Department of
Energy laboratory, and also took in students
in working with that. So I view it as a very
engaging, very interesting way to open new minds
and to expose people to some of the ideas that
we're doing and having them sort of participate
and contribute to the overall cause. So it's
a very good exposure there is um uh you know
a relationship that gets formed and you know
i've i've been in contact with some of the students
i helped mentor uh for many years very cool um
i kind of wanted to tell you my my car license
plate says math lab because like the program
math lab has been so central in like everything
i've done in life but i know Behind, I'd love
to, I'll send you a picture someday, but behind
that is a stack of mathematical computing, like
baseline applications that you designed, right,
Jack? Like how did you guys do that? How did
you guys put that together? So I'm stunned that
your license plate says MATLAB. I've been pulled
over twice where they thought they said MATLAB.
I see. yeah yeah i'm breaking bad right well
that's that's so interesting so um so just to
go back a little bit uh where i was telling the
story um my phd advisor at new mexico um uh was
his name is cleve moeller and cleve is the author
of matlab so um so i have a very close relationship
to um to the math works also to matlab i was
at ground zero when cleve was creating it matlab
uses the software that we've written over the
years and it's integral to to how matlab does
its computation so that's that's cool that you
have a matlab matlab license plate my early license
plate in new mexico on my car now we're talking
back in the late 70s, was LINPACK. So LINPACK
was the project that myself and Cleve Moeller
and a couple other people, Pete Stewart and Jim
Bunch, worked on. It's a package of linear algebra
software for solving systems of linear equations.
And that LINPACK became the basis of MATLAB.
Later, it was transitioned to a much more updated
version. called LAPAC, which I was responsible
for. And so every time I touch MATLAB, I know
I'm using my software and hopefully it's working
correctly. You've been using it here for the
last, I've been using it for the last decade
and trying to apply it to fusion energy and it's
been going great. Yeah, I'll show it to you sometimes.
And I feel Like, I should not have this license
plate, so if either of you want it, I will transfer
ownership. And I absolutely love that we both
express our love for things through license plates.
That's terrific. That's really great. That's
a rare thing to have in common. So where are
we going with simulations, do you think, in terms
of large systems that use supercomputing, parallel
computing, you know? nanotechnology when it comes
to microchips and data collection. Where are
we, Jack, and where are we going, you think?
Well, just at the beginnings in some sense, you
know, we treat computing, many people treat computing
as infrastructure. And, you know, it's the thing
behind the scenes that lets us solve equations
faster. And, you know, the shift to seeing computing
as a scientific instrument. is happening gradually.
And there's a number of things that could realize.
First, you know, computing isn't neutral. The
algorithms, the discretization, the numerical
stability, these things, these are choices that
shape what we observe. And in some sense, the
code and the algorithms are... part of the instrument
so if we solve something that's unstable or frowning
errors dominate you're not measuring the phenomenon
you thought you were measuring you're measuring
artifacts of the method and that's um that's
exactly the same thing as a miscalibrated i don't
know sensor in some sense and you know the biggest
scientific payoffs come when computation enables
new questions not just faster answers, when simulation
starts to replace or complement experiments in
the early design stages. And when it becomes
predictive and not merely descriptive, that's
when it feels like an instrument. So a telescope
just doesn't confirm what we already know. It
lets you see what you couldn't see before. And
large -scale computing began to do that for complex
systems. So once you accept the instrument framing,
the priorities change. You start thinking about
calibration, about error bars, about reproducibility,
about controlling experiments and software. You
build test suites that sort of help you understand
and calibrate runs. You preserve. reference solutions,
so you can do checking. You design algorithms
to behave reliably under stress, and you become
very cautious about claiming results without
really understanding the numerical sensitivities.
So from the instrument mind sense, it also forces
us to think. Not just ask, did it run, but ask,
you know, did it measure something real? And
with AI, you know, that's really complementing
what we do today in terms of the numerics. It
allows us to really go beyond that. AI is really
a very important tool that we will use in helping
us solve problems. It's not going to replace
the numerical methods. It's going to be used
in conjunction with those with those methods
and do something. I think that'll really help
us transform The way in which we approach and
tackle some of the most challenging problems
that we have I like that you said telescopes
because I was reading an article about in video
being heavily involved in the Vera Rubin Observatory
and I was well, what is the big What is the big
data processing component of that? Is it putting
thousands of pictures together? What are they
doing there that requires the supercomputing?
Because that's a really reliable application,
it seems. Oh, yeah, it's tremendous. Tremendous
capabilities are being given to us. You know,
when you think about astronomy and think about
looking at changes and how those changes are
affected over. over time, we can scoop up all
of this data and quickly analyze and see differences
between things and understand things at a very
fine level that we couldn't really do before.
So it allows us to really carry out these experiments
in a way that, in a much faster way, in a more
inductive way than we could have from before.
But we have to be cautious. So when we use AI
to help us navigate through this data, one thing
AI is good at is predicting the next thing based
on all of the information that it's seen before.
But if it sees something that it hasn't seen
before, it has a hard time figuring out what
it should do next. And that's where we're often
led into an area where it starts to... hallucinate
and perhaps get into trouble. So, you know, building
things in conjunction with the standard way in
which we model stuff really helps us in getting
past some of those difficulties. So applying
the physical laws which may help us predict those
maybe uncertain things or rare events allows
us to work in conjunction with of the AI. So
I think it's going to play an important role.
We see that in many areas. Astronomy is one area,
but there are many more areas where this will
help us and drive us forward and put us on a
much better footing, get us to solutions or getting
us to a point where we can ask questions at a
deeper level and hopefully come up with new answers
to the problems. What do you think we're going
to solve with high -performance computing in
the next decade that nobody thinks, it's not
in anybody's purview? Like, what is that? Yeah,
what is that indeed? So if we had some of those
answers, we would know. I hope it's communication
with animals. That's what I'm rooting for, and
nobody's talking about this. That's my secret
wish. okay well okay that's uh that's something
that perhaps is tractable with the use of high
performance computing and uh you know trying
to understand better how those patterns can fit
together and how we can match those patterns
to something that we might be able to express
and hopefully from that exchange information
with animals. So yes, so perhaps that's a possible
solution to some of these problems. But there
are so many important issues that we need to
address from that standpoint. um that the high
performance computing allow us to really uh to
get to so high performance computing you know
today we think of it as a way to um to come up
with the solution faster and come up with the
solution with greater fidelity and come up with
a way of optimizing the solutions that we have.
So we really use high performance computing to
do those three things. So when I say optimizing,
we think about running multiple versions of an
application, an ensemble calculation where we
make small changes to the parameters and look
at what the outcome is. And from that, we can
perhaps choose a design of something which is
optimal for the case that we have and that's
used in many cases it's used for optimizing something
to make it safer for instance a car so we're
gonna we're gonna make many runs of this model
of a car crashing into a wall and we adjust the
parameters so that the car survives the crash
in a way that allows the human in the car to
walk away. And through that, we can come up with
better methods. So that's just one way in which
these are being used for optimizing and making
things much more safe and perhaps making us more
productive. Is there, like, a dream theory for
a supercomputer that we have not built yet? Like,
there are a few, I would think. What is, like,
the one that if we, other than money, what is
the holdup in building that one? Right. So, you
know, so today we have this really, I think,
unfortunate situation. So when we buy a supercomputer.
We have a certain amount of money and we have
a certain target performance that we would like
to see. So those are the two poles in the tent,
if you will. And those poles will dictate the
kind of computer that we get. So we make a request
for proposals. We go out to vendors and say,
we have this much money and we want this much
performance. And the vendors cobble together
a machine. And what they do is they get a machine
which... uses commodity parts, commodity parts
meaning standard off -the -shelf processors,
standard off -the -shelf interconnect networks,
and put them together in a box. And that box
has a price tag where it matches what we have
in terms of the funds available, and it'll match
the peak performance of what we're asking for.
So those two things are good. But unfortunately,
that peak performance is going to be hard to
realize in many applications. So, you know, our
dirty secret in terms of high performance computing
or scientific computing is that we typically
achieve less than or equal to 10 % of the theoretical
peak performance of these computers. That is
the high performance computing. The supercomputers
have a rated peak performance, and our applications,
when implemented on them, have achieved only
a small fraction of that potential. And the reason
for that is the machines are basically misdesigned.
They're misdesigned because they're designed
to give us high performance. High performance
means, in this context, floating point performance,
how many flops we get. floating point operations
per second. And the reality is that on our computers
today, we are over provisioned for flops. So
the flop count is very high. But what's missing
is the part where we move data to the part of
the computer where we can do the operations.
So data movement is very expensive on our computers.
So we are not building systems which have enough.
bandwidth to meet the demands of the applications.
And the result is that we achieve only 10 % of
that peak performance in our computing systems.
So that's a problem which is very hard to solve.
We've invested in floating point operations,
but we've not done this similar investment in
terms of data movement, which perhaps is more
expensive. And the result is this mismatch. So
if I was designing a computer, I would design
it with co -design in mind. So co -design is
where we get the application people together
with the architects, together with the algorithms
people, together with the software designers
and build a system. which matches the needs of
the applications rather than matches the needs
of the money that we have and the expectation
for this large peak performance. So building
the system based on that would certainly give
us a much better return. And what's important
in scientific computing is the scientist's time.
It's not the time on the computer. So we really
don't care about how much computing time it takes
compared to how much time we can save by having
something which is much simpler to use and which
provides us with a solution in a much better
fashion. So this co -design is something that
we really need to follow, and we are not doing
it in terms of scientific computing. And today,
you know, with AI and with the AI companies,
having this tremendous capital that they can
they can spend on hardware to satisfy the applications
that are being driven by ai we see a situation
where the ai uh super kings are are able to design
their hardware to do the applications that they
need so they are doing co -design they are designing
their own hardware so you take a look at amazon
they have their own hardware design google has
their own systems the tpus microsoft has their
own systems they're all designing equipment hardware
to match the needs of their of their application
putting them in a much better position but they
have the resources to do that you know we see
companies that are on the four or five trillion
dollars of uh of capital of the of their market
capitalization So they have tremendous resources
that they can invest in the hardware to get them
to a place where they can actually design their
own hardware. In the scientific area, we don't
have quite the same set of resources. So we're
in some sense stuck. And there's a new model
that's being formed. It's a public -private partnership
that's being encouraged by the government. So
the president had an executive order in November.
which said that there would be this partnership
between the Department of Energy and companies
to let companies like Cisco deploy an AI system
on government land. The government would supply
the power, the electric power, and Cisco is going
to supply the hardware and have that in existence
to help. meet some of the needs of the Department
of Energy need for high performance computing.
And Cisco would then also be selling AI services
to the rest of the community. So it's a new kind
of relationship that's being formed. This is
a private public partnership. It's being exploited
in Europe. They have these AI gigafactories,
which are similar. in terms of the ability to
solve large complex problems that are being used
both for ai purposes solely as well as for meeting
the needs of the scientific community right i
like that um so basically build for for a purpose
and then not be limited by buying dollar amounts
and you can build teams that are capable of great
things yeah um so kind of just backwards engineer
like game it not from the budget but i see i
feel like um i feel like private companies i
guess have more more spend money spending money
to do stuff like that um i'm i would be surprised
if uh nvidia wasn't on top of such things Well,
NVIDIA is supplying the hardware, of course,
for that. Ultimately, they're going to be using
accelerators, GPUs to really push. So there's
not a big difference between the kind of computers
we use in the scientific area and the kind of
computers that are needed for AI. And that's
what we're seeing. is this balancing act between
between them the government doesn't have enough
funding to buy the kind of computers that those
commercial companies can invest in and the result
is this kind of partnership yeah um you you are
kind of a regulations guy if I got that right,
but how do you feel AI is being regulated now
versus like, what should we do? Should we let
her lose? Should we put like sport leashes on
these things? I don't know. Well, AI is such
a tremendous tool and as a tool that can be used
for good and bad things as many. uh many things
that we create that has both pluses and minuses
and we need some guard rails or controls so that
we don't we don't depart in a substantial way
but you know it needs to it needs to evolve it
needs to be we need to understand what the capabilities
are what the limitations are we need to understand
better you know where we can get in trouble and
how those issues could be resolved and what we
can do to, I won't say control, but to put some
regulation, some kind of check and balance on
how these tools get used and deployed. Without
that, you know, I think we'll just have chaos
ultimately. So we do need some oversight in these
things. I'm assuming that when you were back
in Los Alamos back when you were working on nuclear
simulations, that's what that's what I feel like
you'd be where you don't have to tell me. But
I feel how what is our accuracy now when it comes
to simulations of large. Complex multivariant
systems. Right, so so we have. uh you know we
have a number of things simulation the physics
is grounded and and you know we have conservative
laws the conservation laws that is we have known
constraints we have tested hypothesis and you
know we have to we have to put everything together
ai gives us speed and pattern extraction and
powerful approximations uh when we need to explore
you know large design spaces um you know we have
a number of things that ai can help with in terms
of surrogate models that approximate expensive
simulations for rapid exploration we have reduced
order models that capture the domain behavior
we have inverse problems that we need to solve
And we can extract parameters and AI can accelerate
and search some of these things and get the correct
fittings. We have control in real time decision
support where we need fast prediction. We have
guided simulation campaigns where we decide what
to do based on the current. uh situation and
the uncertainty or the expected information gain
so you know there are many things that that go
into this and and somehow we we have to put them
together and come up with solutions that are
meaningful in the long run and allow us to have
a have faith in the methods that we that we're
using to solve these problems. So there are really
many areas and many things that we need to refine.
And we really need to fall back on our principles
of investigation and looking at how things fit
together and understanding when we really can
get into trouble. We need some way to help us
navigate when we get a solution to a problem,
that that solution is, in fact, something we
can believe in or trust. So, you know, there
are many, many things that we have to do along
the way to get to that point. Really, too far
off. What would say 99 .5 % reliability? What
is it like? if we were to pick a low -hanging
fruit application, what would it be? Sorry, for
what? For what purpose? Yeah, so let's say the
telescope that we spoke of before, what would
that... What's the, if you were to simulate something
like that or the next generation of the VR laboratory
or something like that, the telescope, what is
our level of simulation accuracy? Would it be
like 99 .5 %? Is it at 95 %? Where are we? How
accurately can we predict these, like a digital
twin predict an actual, a real model? So it's
all going to depend on the number of... a number
of issues which are complicated which relate
to the um to the problem itself so you know i
like to think of it in terms of a framework so
there's verification so we need to understand
the the mathematics and the implications that
we have uh are we solving the correct set of
equations are the discretizations implemented
as intended uh do we have some kind of convergence
uh under refinement and you know where are we
stable uh under some perturbations so you know
there are a number of things that uh that come
along that really give us confidence in the solutions
are we getting what we expect in terms of the
order of accuracy verification is where you know
we use manufacturing solutions uh analytic benchmarks
and code to code comparisons to get there and
you know beyond that there's validation you know
it's about the physics are these the right equations
and models for this kind of regime that we're
looking at do the results match the experiments
and the data that we have are the boundary conditions
and the materials modeling realistic you know
validation is hard because the experiments have
uncertainty too and you know, the whole regime
where we're looking at may be unacceptable. And
then comes uncertainty quantification, where
even if the model is right, what's the uncertainty
from the parameters, the initial conditions,
the discretizations that were chosen, and the
subgrid that we have. So, you know, modern simulations,
this pipeline needs error bars in the same way
experiments need. uh physical experiments need
error bars so you know there's um there's a number
of things that come into the uh analysis that
would lead to giving us that number 99 .5 percent
uh effectiveness and it really depends on a number
of things along the way so i can't really quite
quantify it for that telescope but um you know
there's there's things that people have to put
together to help us in getting to that solution.
So people often underestimate how often we lose
track of the truth because the runs can't be
reproduced or because of tiny changes in the
version or the compilers or the numerical results
have shifted. So if the simulation that we're
doing is going to replace the experiment in the
early design, then that simulation pipeline must
behave like an instrument. It has to be calibrated,
documented, and we need some way to audit what's
going on. Right. Yeah. And there are lots of
applications that are built in that kind of keep
it honest. you know my math lab simulations that
i built but um so in terms of like just if we
had a magical supercomputer 25 years from now
what is like the big thing that we would have
solved for humanity if we got it today how would
that translate into a solution 25 years from
now right so you know there's so many ways these
computers are used it's hard to pinpoint one
thing okay so if we're going to design something
we would want to design a drug we want to be
able to have drug discovery so it's being you
know we're using that today for you know coming
up with new drugs that can effectively combat
something. So if we had a wish, we would wish
that we can come up with drugs that would effectively
combat cancer. So cancer is something which has
many forms, and it's not going to be one drug.
It's going to be multiple drugs that go after
those multiple forms of cancer. So in 25 years,
if we had the right stuff, maybe we would be
in a better position to to detect, treat, and
to overcome, you know, a disease like that. But
we can point to many diseases, cancer being one
that perhaps is the most maybe widespread and
affects many, many people here. I hear 2026 is
the year of singularity. Where are you on this?
How optimistic are you? I'm not a believer. Well,
I don't say believer. There seems to be a limit
to everything. And are we going to get to a point
where we can get past that limit? I don't think
it's going to happen in 2026. And I'm not sure
it's going to happen in my lifetime anyway. So
I don't think I'm going to benefit from that
singularity. um you know maybe my my predictive
capabilities are incorrect but uh i'm not holding
out for that uh for that point oh no oh no okay
that's too bad um what do you think about quantum
computing jack like what is it going to do to
all of the uh high performance computing from
now is it going to 10 exit 100 exit yeah i think
you know quantum computing is a wonderful research
area so you know if i was younger i might i might
be thinking about going into that area it's very
ripe there's a lot of there's a lot of interesting
research problems and a number of things that
need to be explored and pushed on to understand
better. Today, I think quantum computing is overhyped.
So we have a number of companies that are making
quantum computing computers and have overblown
the expectations, if you will, for these computers.
And I'm afraid what's going to happen is we're
going to go through a quantum winter where things
don't pan out, people lose interest, and those
companies will go away. And we'll survive from
that. We've seen a quantum winter happen with
AI, and we've recovered nicely, I would say,
from that. And today, AI is viewed with a tremendous
amount of respect and is being used to help.
solve some real problems. One of the issues with
quantum computing is we have only a very few,
a handful of algorithms which can benefit from
quantum computing. And they could be very important
methods, but there's only a handful. So we're
not gonna replace our conventional computing
with quantum computing. That's not gonna happen.
What we might do in the near future, and people
are doing it today. is to augment our computing
systems with a quantum device. So I think of
it almost like the way we think of GPU. So a
GPU is something we connect to a conventional
computer and benefit from it by offloading parts
of the computation that can be done by that device,
by that GPU, much faster. So in a quantum situation,
we might offload. the the part of the computation
that could be done by the quantum computing much
much faster and then get back to our our conventional
thing so i see a think of a computing system
of the future as composed of our conventional
digital computers our our accelerators in the
form of gpus perhaps a quantum computer maybe
an optical computer optical computing is quite
intriguing you know we shine light which is digitized
into some into some media and the results is
maybe a multiplication takes place. And it happens
really fast. So we end up with something which
can do something very quickly. Or maybe neuromorphic
computer, a computer that acts like our brains
in terms of how it does things, in terms of spiky
information. and networks that are involved in
that neuromorphic computer. Or maybe going back
to analog computing could be part of this ensemble
of computing devices that we deploy, each one
having its own characteristic and its own ability
to solve something much better than the others
can, and it's used when it's appropriate to.
to use that. And we might build a computing device
which has multiple sets of these things to help
meet the needs of the problem that we're trying
to solve. So that could be a computing system
of the future. And as these things go, they get
integrated into other parts to make them run
really fast. So we're seeing this integration
happen today between what I would call the conventional
computer and the GPU. So they're now being made
as a as a chip in itself and that chip has the
ability to do both of those components and it's
doing it much better because the communication
happens in a shorter span of of distance and
that that translates into a lot faster and a
lot less energy being exploited in terms of coming
up with the solution so it's all going to amount
to energy and how much energy is expended in
it you know our computers today You know, I think
about our big supercomputers that we use in scientific
computing. They have energy needs on the order
of, we're talking about megawatts, tens of megawatts
of power. So the big computer we have here at
Oak Ridge National Lab has an energy requirement
of something on the order of 30 megawatts. So
30 million watts. And think of a megawatt, one
megawatt. If I use one megawatt at my home here
in... in Tennessee for a year, I'll get a bill
from the electric company for a million dollars.
So a megawatt year is a million dollars. So that
computer at Oak Ridge costs roughly $30 million
just to turn it on for the year. Now that computer
costs $600 million, to put it in perspective.
But the computers that are being built today
for AI are going to gigawatts of power. So now
we're talking about tremendous amounts of energy
necessary to fire up these computers and a lot
of cooling that has to take place. And we're
talking about billions of dollars, really, to
put that machine, just to operate the machine
for a year period of time. The actual hardware
is going to cost at least a billion dollars.
for those AI computers. So you see the need for
this private public kind of arrangement where
the public with its tremendous resources in terms
of the companies like Amazon, Google, Microsoft
can invest in these machines and allow them to
be used in the scientific context. Yeah, they're
doing a lot. I heard 10 gigawatt data centers
being built in Tennessee, I think, near Oak Ridge.
Google is building one with a nuclear fission
company. The guys over in Memphis are building
something for Elon Musk with his company that
are on that order today. Yeah, yeah. We're hoping
to use Fusion Energy to run some of these things
within the next decade or so. We're hoping we're
a viable option. But I hear you. In terms of
quantum computing, though, I've plugged into
Microsoft's quantum computer through their API.
It's called Quixit. And then I've used Google
DeepMind. So I've plugged into some of these.
run quantum algorithms for like, you know, fuel
systems and kind of test test things. We bought
10 hours for the whole year in 2024 and we bought
10 hours and we used about eight of it and we
did so much work. It was a tremendous amount
of work. It was. 10 15 years worth of work but
what you're saying is that we could have a quantum
computing like a quantum setup that's commercially
available that i could have downstairs in the
warehouse uh that my company could then use is
is what you're saying like this is this is possible
is what you're saying Well, I would say that,
yes, it's possible. I would also say that, you
know, think about the cloud as you're using that
Microsoft system. That's often the cloud and
you're using it remotely. And that's a fine way
to use it rather than to have to provide that
whole infrastructure in your basement. Using
it someplace else is certainly an attractive
arrangement. Yeah, because I mean, for us, the
big application is plasma control. And that has
these mitigation algorithms to keep them away
from hitting the walls and like, and there's
just so much in it that if there was something
that was scalable and commercially available
and like a unique setup, I would like to dedicate
one such setup to like each generator just to
keep that thing just so precisely running. So
that would be our goal. And something like that
takes such a tremendous amount of computing and
so many parallel things running at the same time
and keeping it in sync. I don't know. I guess
nothing would be possible without some of the
things that you have built. That's crazy. I was
writing, I was helping my... buddy here in la
make a movie on um on basically a quantum computer
uh crafting the code to santoshi nakamoto's bitcoin
uh he has about uh apparently the legend says
you know uh 20 of all bitcoin is kind of in his
wallet and and we can see it we know that we
can see it um it's worth about 70 billion dollars
in today's today's money and so can a quantum
computer crack it and this is this is this is
the theme of the movie script right jack and
so i'm uh so so we we theorize that it's possible
and we kind of you know without giving away the
ending you know hopefully um what do you think
about that can we can we crack this bitcoin thing
can we make it go away with the quantum with
a couple of people well okay so let me say that
quantum computing is good at solving problems
which are very hard and in computing we characterize
those problems as being non -polynomial time
to solution so we think about np non -polynomial
time and quantum computing is good at making
inroads into NP problems. So what you're saying
is you're going to crack a code, and that's something
which is an NP problem. And we have a computer
that potentially does that. So using that computer
does make sense if you have the right algorithm
and that it's going to be able to uh uh to uh
to be run and to be implemented and and to do
all the things it needs to do to crack the code
i'm not an expert on on on cryptography uh but
you know sure if we're dreaming if we're making
writing a science fiction script i would say
that's a good thing to go on to go after is exactly
that use a quantum computer to crack the code
and to unleash those bitcoins and to produce
billions of dollars that that'd be great right
like yeah it's a small robin hood group of people
that's right yeah um so i don't know how far
are we from that for a willing group of like
12 12 really smart folks would you say we're
like a decade away from that or so cracking cracking
bitcoin is a little bit more difficult uh it's
not like a uh public private key encryption uh
it's uh it's slightly different than that i'm
not an expert so i don't know all the ins and
outs but um i think you're right you know a quantum
computer has this incredible ability to uh come
up with solutions uh in a very rapid way we gotta
we gotta implement something so there has to
be an algorithm uh quantum algorithm and then
an implementation there has to be some software
that runs there has to be some way to verify
the solution you know with quantum computing
you don't get one solution you get an approximation
to a solution and you're going to be getting
the number you have to make a number of tries
at the solution because you're getting a statistical
distribution for where the solution might be
so you know we have to take all of those things
into account and you know as we know quantum
computing are very sensitive so that um you know
a truck going by will disrupt the quantum computer
and we have to start all over again and you know
there are many things which are getting in the
way and many things which are quite different
than what we think of this in terms of our standard
conventional computational resources so computers
make mistakes that happens all the time and we
hope it doesn't happen when we're doing the computation
and we hope we hope that there's some way to
mitigate those those failures so you know one
of the big problems with quantum computing is
being able to do fault tolerant, that is to check
and to correct errors when they happen and do
it at the gate level, do it at the very smallest
level inside of the computer. So that's one of
the big challenges, I would say, to making quantum
computing a reality today is having fault tolerance
at the gate level. And, you know, I think that's
an interesting, again, research area to be involved
in. There's a number of, you know, people looking
at that, of course, and hopefully we'll overcome
that and quantum computing could become a real
reality. Yeah, I'm hopeful. I'm hopeful for that.
I mean, the big task would be like something
like sweeping the whole internet and making it
all. like taking all the lies away or like, you
know, some like huge thing would be game changer.
I was at a talk once in Boston and someone said
the way to find out if somebody successfully
built a quantum computer in their basement is
when everybody's bank account goes to zero. and
we're like oh and then the next day the banks
reset it and they get new encryption and whatever
and they try to or they pay off the people that
did this what however this works out but but
that was going to be the way that people let
the world know that we have a quantum computer
um what are other criminal ways jack can we apply
quantum computing to just for crime since we're
on topic wow yeah so i've never been faced with
that question how can we use uh computing to
uh uh to create a to do a crime yeah so i i i'm
not uh yeah i'm not i haven't thought about it
i'm sure there i'm sure there are ways uh you
know we could um we could sabotage uh things
and and make things of a fail uh you know we're
looking at uh so so okay so where does computing
affects everybody in terms of weather forecasting.
So if we were able to mispredict intentionally
the weather, we can cause chaos at some level.
And that chaos may translate into some criminal
activity to do something nefarious. We could
write a script, I'm sure, around that premise.
I was going to say like steal from the Louvre,
but that happened a few months ago. It feels
like it's not fiction anymore. And it didn't
require any sort of sophisticated computing or...
No. I think it was a ladder. A ladder and a big
saw to cut through glass. That was what they
needed. Yeah. He stole stolen stuff. That's pretty
cool. So if you were 25 today, you would get
into quantum computing. Is that where we're at?
Well, if I was 25 today, I would consider quantum.
I would consider going into AI. AI is where all
of the action is today and trying to use it to
help solve some of the problems that we have.
So those are the two big areas, I would say.
You know, I tell my students, you know, the most
important thing to do is to... follow your passion
you know pick a problem that you can't stop thinking
about and you know coming up with coming up with
ways to overcome some of those dead ends that
you encounter confusing results that happen papers
that have been rejected codes that won't run
you know that you have to overcome passion is
what keeps you learning and when nobody is really
giving you a pat on the back. It's, you know,
spend time early, sampling broadly, go to courses,
seminars, and do things. You know, aim high is
another thing I recommend. Don't optimize for
incremental wins. You know, choose questions
that matter scientifically, from a social standpoint,
or from an architectural standpoint you know
define success in a way that still would be meaningful
if you only get halfway let's say aim to work
aim for work that changes how the field measures
progress i also tell people in research you should
expect to fail failure isn't an accident in science
it's the default output from exploration most
times we fail you know they don't survive Our
ideas don't survive. Our hypotheses don't survive.
And the trick is to fail cheaply and informatively.
Design experiments that can teach you something
even when something doesn't work. And you'll
be in a better situation. And the final thing
I tell people, my students, is to build a network
of colleagues. Now, your career will be shaped
by how much by people as by ideas. Build relationships
before you need them. Give talks in seminars.
Ask thoughtful questions. Share code and credit.
Be reliable. Follow through on what you're doing.
A good network isn't transactional. It's a community
where it makes you smarter and it builds collaborations
and complements your skills. So all of those
things, I think, go into helping whatever area
people go into in making it really an important
step. That's awesome. Wow. Did your parents know
that you were this genius child when you were
8 or 10 years old? When did they know what they
had running around their house? Well, they never
did. So here's the story I sometimes tell. So
I mentioned earlier that my brothers have a trait.
You know, they were electricians. And my mother
knew exactly what they did. I would try to explain
to my mother, you know, what I did. And she never
really got it. And she was always, you know,
a bit concerned that I wasn't doing very well
financially. You know, she was, I'm a teacher.
Okay, how much do I teach? I teach one course
a year. you don't teach very much and i said
you know what you know she asked what do i do
well i think i come up with a problem i write
a proposal 10 pages and i send it into an agency
and if they like it they they send me millions
of dollars so i can carry out those ideas and
that was a that was a confusing uh confusing
story. You know, she couldn't understand any
part of that story. You know, sitting around
thinking about things, the government giving
you money so that you can carry out what you
wanted to do. That didn't make any sense at all.
And, you know, she would visit us and, you know,
we had a slightly different way of existing from
what she learned and how she carried out her
life. An example of that is my wife. You know,
we had three kids. my wife would um whenever
we had a birthday from one of them my wife would
make a cake she would she would and she would
ask the kids you know what kind of cake do you
want oh give me a dinosaur cake or give me a
uh give me a you know an animal of some kind
you know give me something in the shape of that
so my my wife would go off and and make this
cake and my mother was shocked at this you know
she she thought it was a a sign that we didn't
have enough money to go out and buy a cake from
the bakery because that's that's the mode that
she would have she would have done she wouldn't
go out and make it she would she would go out
and buy it so there was this you know quite uh
so she never really did understand what i what
i did and um even when i you know received this
award she uh she wasn't quite sure uh what it
was her her comment when it appeared when my
name appeared in my picture appeared in the new
york times i tried to explain what this was about
and she says oh you got a nice sweater on in
the picture so that was her that was her take
on the whole on the whole thing oh it was sort
of funny oh no jack i i that that was so that
hit me in a funny way because my mom uh she sees
me working on fusion energy she she's just like
i have why don't you have a regular job like
how do you get a paycheck and health insurance
and she doesn't know and i tell her you know
because i'm kind of like that kid like i'm an
only child and i'm very communicative so like
i tell her everything and she's just kind of
like whatever but last year my cousin saw some
me doing something and then my aunt called her
and said you know if if what priyanka is doing
works out it could change the world like real
things She comes to me about six months ago and
she goes, by the way, I spoke to your aunt and
she's saying that you did that. My God, I tell
you every day. I tell you every day. Maybe you
should have had someone else explain it to her.
That's right. But wow, this was awesome. Thank
you so much, Jack. My pleasure.