deep in our department. Uh and she is currently
the program manager for the A. S. C
. R. Program the advanced Scientific Computing research program
in the Department of Energy's Office of Science. Let
me tell you a little bit about Dr Nowell's career
because she has had an interesting career. Her dissertation work was
done here in the digital libraries research lab and her
work involved the design of one of the first information
visualization user interfaces for the envision project. This was
a joint project funded by NSF sponsored by them to
which was one of the early digital library projects for
CS literature. Most of you have used the MTL
and the Tripoli Computer Society deals and this was a
precursor project. Um This all happened in the mid
90s and the goal of this project was to explore
innovative ways of searching and viewing information. So if
you think about it, that's A little bit more
than 15 years ago. Think about how we take
for granted the deals that we use today. Dr
Noel. Previous to that served as a chief scientist
in the information analytics group at the Pacific Northwest National
Lab. Some of us noticed by the acronym P
. N. N. L. And then she
moved to be a program director in data data analysis
visualization in the office of cyber infrastructure at NSS.
Before she joined them mentioned, she also has had
experience as a faculty member. Early in her career
she was a faculty member at Lynchburg College where she
headed both the theater department and then subsequently was Sheriff
Computer. So probably unique in our country, Held
the chairmanship of those two are highly accomplished. The
mother clearly has interdisciplinary talents and interests and we bought
our career service in to the scientific community serving in
various positions in government institutions. We will be presenting
her as a department with a C. S.
F. B. T. Distinguished Alumni Award later
today. I'm very, very proud and happy to
have you with us. Please help me welcome dr
back to C. S. F. P.
T. Thank you so much for the warm welcome
. Those kind words, those of you in the
back please let me know if at any point you
can't hear, give me some kind of signal that
I need to be louder. One advantage to the
theatrical background as I can probably adjust and fill the
ballroom if I need to, but I'm trying not
to deafen people up front if I don't have to
when I was here, no one would have ever
guessed. Certainly not me that I would wind up
Running programmes in an office of supercomputing or two or
do many of the things that I have done and
I want to start with a thank you to the
faculty members here who taught me so well and prepared
me to go places that I never expected to go
and to be doing. The things that I'm doing
now, that we're not within my wildest dreams.
When I was a student here at Virginia Tech,
I'm going to talk a little bit about the Office
of Science, the Office of Advantage Scientific Computing research
that we usually call Oscar. I'm going to ground
some of the discussion about data in a discussion of
climate change and explain to you why that matters to
the Office of Science. And I hope on a
more personal level, why it matters to me and
should matter to every human being on the planet.
And then I'll talk a little bit about the research
that lies ahead and the work that I hope you
will do with us. I say this and having
conversed with Barbara about what to present here, I
no longer do my own research. It's probably a
decade since I was a hands on researcher in visualization
and the cognitive issues associated with information visualization and scientific
visualization. My work these days is not in designing
interfaces, but in designing and leading large programs of
research for the country. And now, particularly for
the Office of Science, the Office of Science is
not well known to many faculty and graduate students,
but we are one of the nation's largest funders of
research in the physical sciences. We operate a number
of scientific user facilities, 31 to be exact across
all of the Office of Science and the users of
those facilities fan not only this country, but other
continents. We support people in universities and industry in
wide ranges of research and national labs and using the
machines. Over 27 0 researchers who have doctorates or
are graduate students and postdocs have sort of support of
one kind from the Office of Science. Within the
Office of Science. The goals my office are to
support leadership and computational science and super cute computing,
both through operation of machines and through development of the
software that runs on those machines. The office operates
to leadership class supercomputing facilities at Oak Ridge National Lab
, Argonne National Lab. Those are among the fastest
machines in the world. And we operate a very
practical capacity machine at the National Energy Research Supercomputing Center
Nurse along with Energy Sciences Network S. Net,
which connects all of those machines to our national labs
and occasionally in university or two. Along the way
the computer science program, he is focused on two
things Right now, we focused inward at providing the
operating systems, runtime systems and programming environments and tools
for the supercomputers that we operate. And then we
focus on the science requirements of the other offices within
the Office of Science, helping them make use of
those systems to carry out their computational science and analyze
the data that comes in increasing volumes from the user
facilities that they operate. This is within the context
of an office that has a number of components.
We have a networking and collaboration program at an applied
mathematics program, both of which fund lines of research
that look a lot to me like things I saw
in the computer science department here that we put them
in separate programs and that's probably better for their budgets
. And we have a program called Side Ac that
Scientific Discovery through advanced computing that operates in partnership with
the other offices and enables domain scientists to get funding
from other offices to work with the computer science and
math folks that we support transitioning software that we develop
into operational use for science. This is a summary
of the budget fantasy That we had last summer when
we had some illusions that our budget would go up
about$15,000 in the coming year. Instead, we
are Given a baseline of the 2012 budget which was
around$440 million dollars from which thank you. Sequestration
. We are down somewhat and expect to be down
at least for the coming year. Those who don't
know they sequestration is a 10 year sentence, Not
a single one. So we continue to hope that
flat will not remain the new up someday there will
be budget increases again. But meanwhile we move on
. Simulation modeling and simulation are critical to the mission
of the Office of Science. If you think about
energy and the things that relate to energy that will
give you some insight into what we care about and
why we care about ways of producing energy, things
like combustion and nuclear power plants. Of necessity because
of our partnership with the National Nuclear Security Administration.
We care about maintenance and monitoring of the nation's nuclear
weapons stockpile, which is managed largely and monitored largely
through modeling and simulations on supercomputers. Today we care
about climate change and the environment, not just for
the personal and obvious social reasons, but because ways
of producing and consuming our energy resources impact climate change
tremendously and impact the environment we all live in in
many other ways. But also because through manipulation of
environmental environmental resources we may be able to achieve carbon
sequestration and even mitigate some of the impact of nuclear
waste. We need um a stream that's X.
Escape because it will take three eggs a flops to
completely model the U. S. Power grid.
Also one of our major concerns. And it will
take an extra scale machine to allow us to do
climate modeling in a scale of roughly 10 m.
Which is our ambition. When we look at our
exa scale computing initiative, this is our path to
the future. This will be a partnership across the
government and our national labs, the computing industry,
our labs. Everyone, if we were to build
an extra scale system from today's components, It would
consume over 200 MW of power which is completely unacceptable
in terms of its hazards to the people in the
building Who had operated. And also because that would
cost us$200 million dollars a year. Which is
more than the entire research budget for us. So
we have an ambitious program of research to produce a
machine That consumes only 20 MW of power and yet
runs at Exxon flops And fits within 500 cabinets while
also being programmable and understandable despite billion way concurrency.
Most people don't live in a world where they think
about exa flops a lot. One way to think
about exit flops is on the order of a million
flops per person on the planet. Think about how
well we control the population of the planet and whether
we really want processors at war with each other on
a supercomputer or do we need new ways to conceptualize
management our systems at that scale. But it all
comes back to energy efficient computing Right now. one
Petaflop uses three megawatts here,$3 million a year
for the power. We are sometimes asked why why
? Instead of building an exa scale machine, don't
we build 10 machines that are 100 peta flops.
You don't solve the power problem right. You still
have a power bill that's larger than our research budget
. So we know that we have to change the
way computing has done and that has tremendous implications across
all of science. This is an old slide that
was put together by our advisory committee. I want
to pause on for a minute despite a couple of
errors in it because it really tells the story and
presents the challenges of what we're trying to do.
We want on the order of 1000 fold increase in
system performance. Well, we had a two pet
slot machines so they chose 500 to put here.
But power can increase by only a factor three memory
band On the nose will increase by only a factor
of 16 Storage capacity Will increase by only a factor
of 20 and I. O. Bandwidth By only
a factor of 100. Within these numbers comes a
story in which flops are free. We no longer
have to worry about the computational cost of a floating
point operation. What we have to worry about and
what we have to minimize his data movement. Mm
. P A message passing interface is something that my
management says we need to put a stake through its
heart because it's so dependent on moving data around and
you can't afford to do it. It's difficult enough
to move data and expensive enough to move data across
the chip. Moving it across. A supercomputer is
prohibitively expensive in terms of data getting it off the
machine is daunting for a variety of reasons. Basically
if you listen to computational scientist this architecture he is
hostile to science and it's up to us in the
computer science research program to develop the software stack that
will let scientific codes run in this environment. That
at the hardware level doesn't look very friendly to scientists
. Besides the challenges of data movement, we're looking
at a machine with so many parts that hardware failures
will be almost continuous because of reduced scale and running
hardware at reduced voltages at threshold voltage levels. We'll
be looking at soft errors that are not detected in
hardware so that we have a machine that I'm not
deterministic anymore. That gives us a host of challenges
in the program ability and in uncertainty, quantification programming
the scale of billion way concurrency is mind boggling to
most people. You think of the challenges of getting
someone to understand hundredweight concurrency to think about coordinating computation
at that level and then think about billion Waken currency
. And you will quickly realize we do not have
the intellectual constructs to think about codes effectively at that
. Imagine trying to debug a code on a non
deterministic machine with that level of concurrency and think about
the kinds of tools and technologies that we must develop
so that these machines are easy to use. Yes
, easy to use. No longer requiring only hero
programmers. And how do we find that level of
concurrency within a scientific application? This is a non
trivial. From the scientist perspective, they have adapted
to move into new machines thus far by simply scaling
up the size of the problems that they attempted.
But because this machine will have a smaller memory footprint
relative to the size of computation, that's not going
to be the way that we achieve billion away concurrency
anymore. And the challenge that we face is that
scientists much to completely reformulate their approaches to doing their
physics, their chemistry, their biological modelling, their
stockpile management. It is a huge challenge that we
have begun to address. But we are at the
very beginning of this great adventure. The implications of
a solution to our exa scale challenges will reach all
the way to your desktop. Because if we are
fully successful, you will be looking at desktop systems
that are at terrorists came And this department will likely
own at least one penny scale system 10 to 15
years from now. So you might begin thinking about
how you would use such a resource. I would
also suggest to you and I don't think I have
to go on this rant with this department, but
I do with some that it is unethical to graduate
someone with a degree in computer science who doesn't understand
parallel programming in a world where you can no longer
purchase a unit processor machine. I'm sad to say
there are many universities that need that ran and then
like Mhm. Mhm. At the same time that
we are addressing the challenges of Exa Scale, we
are mindful. Exa Scale systems present their own big
data challenges. When you talk about building way,
concurrency and the data that results the rest of the
Office of Science and building their new user facilities,
alert us to their own challenges with data intensive science
. And what the popular press has name big data
. We're looking at genomics data at our joint genomics
Institute on the scale of 10 petabytes By 2021 and
hired high energy physics with the large Hadron collider.
That's just one of the instruments for ACP. They're
talking about 15 petabytes of data per year but they
don't just throw the data the way they keep it
for years and continue to compare and contrast. So
that number increases. We operate light sources that produce
300 terabytes of data per day increasing continuously. And
climate data, which is our largest growing data collection
at most of our facilities will be in the range
of multiple exabytes within a decade. As I will
talk through some climate examples here. I'll do that
with precaution. I'm not an expert in modeling and
simulation and I'm not a climate scientist and I didn't
even stay at a Holiday Inn Express last but I
know some experts in this area pretty well. Could
provide me with a lot of interesting material. And
one of the things I'm going to show you is
that when we talk about big data and data intensive
science big is not necessarily the important word. It's
not just petabytes or exabytes that we have to worry
. It's data with 10,000 dimensions that we have to
worry. It's complex systems that interact with one in
many ways. So this graph is showing you the
various options for how climate might evolve and what we
believe natural forces would produce in terms of normal climate
variability. That's the blue band and then there's what
we believe would happen under continued forcing because our energy
production methods and that's the pink on the black is
the observed reality. There are those who deny that
climate change is occurring and they deny that human behavior
is causing energy production methods or causing. But recent
articles in Science magazine in The New york Times point
out that the most pessimistic of the climate models are
the ones that are more accurately forecast recent trends in
climate around the world. So why does deal we
care? Let me give you just a few examples
. We care about energy production. 80 of US
. refinery capacity is less than 1.5 m above sea
level, 80%. We have a great many power
plants along the coast as well as our concerns for
refinery capacity. So that brings us to a deep
and abiding interest in sea level rise. We know
, for example, that just just melting the Greenland
ice sheet would raise sea levels by about 20 ft
. The east arctic ice sheet, if melted entirely
, Could raise sea level by as much as 200
ft. Is that likely within this century, happily
. No, there are some studies that say no
, it's not likely that sea level will rise more
than 6.5 ft or so in this century. That's
enough to put the tidal basin in the Washington mall
underwater. Maybe someone in Washington attention, but we're
not counting on that. We know that in the
last interglacial period sea level was 4-6 m above where
it is now. We may be lucky if that's
as far as it goes. I'm not going to
spend a whole lot of time on this, giving
you lots of pointers into reports because I hope you
okay. Another reason to care. I have a
degree from the University of New Orleans. So I
look at this particular slide pumps out 2400 miles of
highway Along the Gulf Coast, less than four ft
above sea level. And we thought Katrina and rita
did a number on New Orleans. Sea level rise
made it worse. We're looking not only at sea
level rise, but other implications of global change,
changing patterns of precipitation, both in terms of flood
and drought. More severe hurricanes, possibly more tornadoes
. We're also looking at implications for the food supply
, water energy, an impact on human health and
behavior. by 2100, it is expected that temperatures
will rise any quarter from 2 to 11.5 degrees Fahrenheit
on average two 11.5 degrees. And remember my earlier
comment that the more pessimistic bottles are the ones that
are tending to be the most now. Yeah,
very difficult things for us to manage in terms of
rising temperatures and impact on health. Mhm. This
would more than double the days over 90° for a
large part of the United States by the end of
this century. As shown in Iskra. It also
has implications for food production as some of our major
crops like corn and soy will cease to parliament When
the temperature rises by two or 3°, there goes
corn and soy at a time when population is increasing
dramatically And we already expect to need a 50 increase
in the food supply before the end of the century
. In terms of availability of water, I think
most people have seen that texas has been in persistent
and severe drought for some time. The Mississippi waterway
was so affected by drought this summer. Transportation on
it stopped for a period of time because the water
levels were too low. And yet if you're in
the Northeast, it is likely to get cooler and
wetter with implications for flooding. While the Southwest is
looking at worsening drought and decreasing snowpack in the mountains
, worsening drought situation for the summer. Meanwhile,
like the brilliant population that we are, everyone in
the United States wants to move to California, mhm
. To the southwest to join the wars for water
and the rationing and the burns longs and the brownouts
. Because we're so smart about these things, right
? Um This is an indication of population movement.
As you see the rural areas of the country where
drought will be less likely losing population while the worst
hit areas in terms of drought and diminishing water supply
will be gaining population at a tremendous rate and the
areas most likely to be affected by sea level rise
, like florida remains a magnet for people who want
to move there. One of the things that's likely
to happen with climate change is that we become a
warmer wetter planet in some places. Dryer by others
. So you look in this slide at the increase
in severe weather events and heavy precipitation events and look
at what happens in the Northeast around new york city
and boston, Where we have a 67 increase in
heavy precipitation. What's talked about now is 100 year
flood is likely to become more 10 years. For
those are the individualization share my pain at this image
, which is showing you the dark blue is water
around the tip of Manhattan and quiet this building chill
man and the pale blue is flood around the bill
. This is famous 100 year flood zone for the
city. If you think about what happened during Hurricane
Sandy, the flooding of the subway system, the
flooding in the rural areas around new york city,
this is what we're and having a tough increasing frequency
. Consider also that some of those buildings flooded are
likely to be power facilities. We hope there are
Nuclear generators Plus five in that context, Current Energy
Secretary chu observes. We seek solutions. We don't
see, dare I say this. Just scientific papers
. We are looking for people who want to make
a difference, who want not to admire problems but
but to make a difference, we need help,
we need a lot to help us with the modeling
and simulation to help us understand the implications on health
, communicate with policy makers find solutions to mitigate problems
the climate change bring to society in terms of data
science, this is a list of just you are
the kinds of data that are involved understanding climate science
and understanding its implications for society. We have data
sources like mobile stations collecting observational data, high resolution
weather stations, you've got full blown snow and weather
stations satellite imagery, weather radar, you've got ocean
current observations and air current observations, measurements of gas
in the atmosphere and radiation in the atmosphere. You've
got to deal with cyanosis, snow, hydrology potential
for avalanches. You've got to understand the results from
seismic probes. You've also got to understand things like
urban infrastructure and population patterns to really assess what is
happening in an era. At the Supercomputing conference in
New Orleans a few years ago, the focus was
on climate science and climate data. And I remember
one of our leading climate scientists standing in front of
an audience and talking about a great surprise. He
and his colleagues had one night had completed a simulation
and sea level rise and published a paper and the
group of coastal city urban planners came and said,
can we get your sea level rise data for our
region because we want to figure out where we can
put power plants and where we can locate new housing
developments and have people be safe. And the climate
scientists pause and thought about the problems that request presented
to first was the fact that they hadn't kept the
data once they had done their scientists. Then there
was the small matter of the resolution of the models
not being anywhere near precise enough to provide the kind
of guidance that file this one. And there's the
further challenge of something we're looking at very closely as
we look at policy implications and that's that data collected
in oneness are rarely captured with sufficient metadata and other
forms of provenance to be understandable by people in another
district. If you read the America competes reauthorization 2010
will find in it. I believe it's on page
11 in section 13 A mandate to science agencies to
begin discussion of policy around preservation of public access to
data resulting from science that we support. In february
of this year, the Office of Science and Technology
Policy changed that recommendation to a mandate. Not only
that we should discuss policy, but we should have
an implementation plan for it within six months. The
issues there are vast the implications are also fine for
thousands of you interested in data analysis and visualization,
this particular image. Mhm. You chose results from
one multi marble cement ensembles on climate modeling and this
shows clear evidence of global warming, atmospheric warming that
results from human productivity of energy production and consumer energy
. The upper air layers of the atmosphere that would
normally be warmed by the sun are not. So
it's at the lower mid range where you see the
effects of greenhouse gases, see evidence of what some
of us think it's pretty conclusive. Yes. So
the challenge is data driven science requires high throughput very
fast. I o. We've only recently within the
last year learned that are super computers not only have
an output bottleneck as we've known all along, but
for many of our largest codes that use the largest
numbers and I'm meaning billions of cycles on supercomputers,
their input back. If you do graphs and their
input and output, the input bars go off the
graph and the output bars are invisible because they do
relatively small amounts of out in comparison to the amount
of data they input over the rod part of that
I suspect results from our current checkpoint restart model of
saving the machine states period so that when you have
a hard fault input the day again and restart and
I could speculate that system was experiencing a lot of
false during that. But it's also increasingly the case
that models are fed by experimental and observational data as
a starting and that we need to compare and cross
analyzed between simulation data and observation. Experimental data.
Typically our analytic methods require large memory footprint with the
excess feel architecture trying to provide that. So we
know that for a variety of systems, both light
sources climate systems simulations, we need to find better
ways to deal with streaming data. Our current methods
of analysing streaming data are tied up to hypothesis.
They're biased in favor of proving or disproving a particular
hypothesis. But that means that as we throw away
a very, very high percentage of data from the
experiment, we also throw away the opportunity to stuff
to discover the unexpected that is less and less acceptable
as experiments are expensive to run and many of them
will never be run again. Our scientists spend way
too much time studying machine architectures trying to learn enough
computer science to work on large scale machines that are
difficult for senior computer scientists understand and they're pulled away
from their physics chemistry, their combustion science, we
need to change that. We need to change the
amount of time that they have to invest in managing
data. And we need to give them better resources
to understand their data and to extract from that date
. The high consequence science that is important to our
nation's future. We need ways to integrate data,
we need ways to capture providence. Yeah. We
need ways to make data usable. Not only by
those who originally produced it but by others who need
to use that data in combinations not originally conceived to
solve problems, not within the thoughts of the scientists
who created the data. Part of that is capturing
day semantics and developing others were metadata within and across
communities and helping people make sense of that metadata Human
understand as well as a machine understand they need to
be able to understand what's been done to the data
. How has it been selected? How has it
been normalized? How has it been downsized, compressed
reformulate? The transformations are numerous and of those trivial
things from multi scale of physics. Which scale does
it happen? To be honest. We looked at
scales that range from nano scale to planetary scales and
time ranges from nanoseconds. Two centuries. We've been
holding workshops with other offices bes as basic energy sciences
. Right now, the model has been for scientists
to take their data home on a thumb drive.
Taking home a petabytes of data is somewhat more of
a challenge. Most of us think that trying to
get a bite to a laptop is called. So
we have to help them find new models for doing
science and doing science with collaborations that spend not only
multiple institutions but sometimes multiple nations and multiple. This
research challenges just a few album captured here how we
manage move and analyzed data, how we transform data
into knowledge. Because I would argue that if what
comes off of a supercomputer believed inside you might as
well not have turned the large hot water heater on
. We need ways to define metadata and methods to
generate it automatically and capture it. We need new
scientific work clothes systems sophisticated about the challenges of science
and about the machine architectures they run a we need
to understand the strengths and weaknesses of standards and we
need to understand how to better communicate the scientific findings
, not only to the scientists to the public and
to policymakers and politicians. This okay. Mhm.
We know that because of the changing supercomputer architecture we
have to start from zero and build a whole new
generation. Those on technologies. My office has supported
development visit and power view to most large widely used
scientific visualization and analysis tools and they're not going to
run. And the excess scale environment without major reformulation
. I've listened to ballrooms full of mathematicians, listen
to destructions of this architecture, go away and working
and come back and look at us baffled and angry
and say the solvers are not going to run.
Mr sure no more land. We have a problem
we have to reformulate the solvers. We need new
data structures and traversal out is that minimize data moves
. Resilient in the face of hard and soft,
false. We have a lot of work and I
hope you'll join us. Thank you so much.
Any questions I have just a little sense at the
time there is a problem. Yeah, interesting is
trying to present. I'm thinking about training students to
participate in this. Well if you're here to take
a position the need for new degrees um additional science
darling. Right. one of the things that my
office operators right now, yes. Yeah. Is
a computational science graduate. All right. That support
diverse disciplines. And one of the things that requires
in exchange for years of graduate school support is that
they take forces a variety of courses in computer science
and math. And they also do internships at the
national lab working on real science problems in resource environment
. We're delighted six senator csg fellas go on to
careers lands. What I'm here over from industry is
that we don't turn out nearly enough people to understand
why what we don't turn up. Nearly enough people
to understand parallel program. I sat in a meeting
of the Council on Competitiveness working with the interagency,
working high end computing that on our co chair.
And they were telling us these industry leaders from companies
like GE were telling us that degrees may become irrelevant
if the people involved. Those degrees don't have the
skills that industry needs right now. The biggest barrier
to their use of modeling and simulation is the inability
to hire know how to do parallel programming here.
My land. Mm You we need people do not
have a clinton scale. We need people who have
the interdisciplinary understanding to work with scientists understand the science
and to work within these large scale environments to solve
the important problems whether their problems of climate change or
homeland security. Where data challenges are at least as
a we are starving in the national lab and part
for people with the skills that we need. And
then few people that we have, particularly with data
science skills are being sucked up by industry so fast
that I wonder how we're going to achieve the scientific
solution. But life form will find we depend.
Yes, we need more of this. Mhm.
Yeah. Yeah. Higher the nation. Lot of
your slides mm offering now exactly those slides. Most
of those images come from the global plant. Yeah
. Global change dot gov site. They're not my
creations. I'm an artist from some of those choices
. Lightning wins. As I said, I'm one
of the co chairs of the Interagency Working Group on
high end Computing. That's part of the national coordination
office for networking and Research and Development. We have
regular meetings on multiple topics that bring together laughter,
no different parts of the D. O. D
. S. L. Any agency that is doing
work in a particular area is likely to have a
representative meetings. When you talk about data, quickly
add anyone from veterans affairs to NIH to Homeland Security
. So yes there is a lot inter agency coordination
and discuss. one of the things under discussion in
the hit group right now is developing a new federal
plan for high end computing, Observing to our embarrassment
that the one currently on the website has 2004 and
is aimed at developing scale systems. We got there
other questions. Yes. Mhm. Yeah. Actually
thank you for a great time. Great. So
you see quantum computing helping out with your mission and
your order is your office looking at quantum computing at
all parts of the Office of Science. Look at
quantum computing. For the most part computing research is
done by other agencies. I Arka has an interest
in it and DARPA has an interest in it.
I don't know what the NSL still does, but
again, remember that we come together for discussions regularly
as a mission agency. We don't have the luxury
of doing science for the sake of science or computer
science. For the sake of computer science. We
have to keep our eye on the goal and the
goal for us is supporting the science that's essential to
the nation. With respect to energy. We have
to be able to run the nuclear weapons stockpile simulation
codes to support the annual certifications to the President and
the Congress. We have to be able to do
the research and combustion that we must support to reach
some of the nation's goals With regard to reducing emissions
. We have to support the climate research and related
modeling and simulation and that means that we are somewhat
conservative in our choices of architectures because we have millions
and millions and millions of lines of science code that
must continue to run and we can't afford to rewrite
it all from scratch for a new architecture. It's
hard enough to think about an exact scale architecture and
how those legacy codes will continue to run those of
you in software engineering. Do the quick math.
What is it still around$100$110 per line of
good quality code at millions and millions and millions of
lines of code. We can't we can't afford to
rewrite it. And in the case of some of
those codes, the scientists who would be needed to
validate the codes are no longer with us. We
don't have a lot of nuclear weapons desires around.
So messing with the codes that they have validated is
not a good career. Other questions. Yes.
So the question is basically Okay. Region solving the
problem. Getting complications but solve the problem. Is
that right? So in terms of solving problems happens
lots of industry. Thanks solutions of islam. Yeah
. You comment on um he has some kind of
values have encouraged candy and in operations that winds and
daniel. Mhm. Certainly. And our excess health
program funds people in industry along with people in academia
and the national labs. I can't think of any
of the programs of research that we operate that are
willing to fund industry partners in collaboration. In fact
, for our latest X stacked solicitation that's focused on
environment and execution models. I believe Intel is one
of the leading organizations. They're not a sub awardee
. They're the primary on that award. Similarly in
some of our visualization codes we have small companies maintain
those codes over and we happily support. We also
are able to fund people in other countries and we
we want to fund the best minds to solve our
important problems wherever those minds are. Oh, let
me remember, toss out another carrot for the faculty
in the room. We don't have two weeks in
the summer restriction of funding for faculty. We will
have the loop on you for the academic year and
we will happily support research As well as 10ure track
and your graduate students. Also, Can I send
you a sovereign song and get you away from NSF
? Mm Shit again. Thank our speaker for coming
today, remind you all that there is a need
to speaker section help for 5 15 and target.
I'm sorry Mcbride, one of six this afternoon.
Graduate students and undergraduates are all invited. Okay.