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HOW SCIENTISTS AND SUPERCOMPUTERS COULD MAKE OCEANS DRINKABLE


REMOVING SALT FROM SEAWATER IS AN ENORMOUS CHALLENGE. RESEARCHERS MAY HAVE THE
ANSWER—BUT IT WILL REQUIRE A WHOLE LOT OF PROCESSING POWER.

RENE CHUN

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Aleksandr Noy has big plans for a very small tool. A senior research scientist
at Lawrence Livermore National Laboratory, Noy has devoted a significant part of
his career to perfecting the liquid alchemy known as desalination—removing salt
from seawater. His stock-in-trade is the carbon nanotube. In 2006, Noy had the
audacity to embrace a radical theory: Maybe nanotubes—cylinders so tiny, they
can be seen only with an electron microscope—could act as desalination filters.
It depended on just how wide the tubes were. The opening needed to be big enough
to let water molecules flow through but small enough to block the larger salt
particles that make seawater undrinkable. Put enough carbon nanotubes together
and you potentially have the world’s most efficient machine for making clean
water.

JUST HOW TINY ARE CARBON NANOTUBES?


WIDTH OF A SPIDER'S THREAD

4,000 nanometers




50 CARBON NANOTUBES

0.8 nanometers each

Most of his colleagues at the lab dismissed the idea as sci-fi. “It was hard to
imagine water going through such very small tubes,” says Noy. But if the
nanotube theory was correct, the benefit would be incalculable. Many of the
world’s regions are currently in the midst of a potable water shortage, with 1.2
billion people—about one-sixth of the global population—living in areas of water
scarcity. Desalination can help, but the infrastructure in place today requires
massive amounts of energy (and therefore money) to heat seawater or to force it
through complex filters. If nanotube filters worked, they could greatly reduce
the world’s water woes.

ALEKSANDR NOY


SENIOR RESEARCH SCIENTIST, LAWRENCE LIVERMORE NATIONAL LABORATORY

Noy’s team set up a simple filtration experiment and let it run overnight. In
the morning, two assistants noticed a puddle on the lab floor; water had slipped
through the nanotubes so rapidly that the small reservoir meant to catch the
liquid had overflowed. Researchers would later confirm that the flow rate of
water through carbon nanotubes is six times higher than it is through the
filters used in today’s desalination plants.

That puddle may have been small, but it was one of the biggest discoveries of
Noy’s career. “The experiment was exciting,” he recalls, “because nobody knew
what to expect.” Now that everyone does, a huge challenge remains—one that might
be possible to surmount with enough computing power.

Luckily, scientists are on the verge of a breakthrough called exascale computing
(which in Google’s case is likely to come from a throng of machines connected in
the cloud). Exascale will dwarf today’s most powerful supercomputers. This kind
of extreme processing power will be a huge asset to researchers figuring out how
to make nanotubes work as large-scale water filters. These tubes—and the
billions of molecules that flow through them—are far too small to study in
detail, and physically testing different variations is difficult and
time-consuming. Exascale computer modeling will put those tiny tubes into
sharper focus, which will dramatically speed up nanotube desalination research.
In fact, the technology will help tackle a number of today’s thorniest
environmental problems.


THE PROMISE OF EXASCALE POWER

VASTLY INCREASED SPEED COULD HELP SURMOUNT ONCE-IMPOSSIBLE CHALLENGES AND LEAD
TO BIG BREAKTHROUGHS.

 * DRUG DISCOVERY
   
   Imagine sifting through a trillion possible drug combinations to find a
   treatment perfectly tailored for each individual.

 * WEATHER FORECASTING
   
   Meteorologists could crunch reams of data to give as many as four weeks’
   notice to people in the path of severe weather.

 * LANGUAGE TRANSLATION
   
   Real-time language translation may become a commonplace feature on
   smartphones.

For those not versed in the jargon of Silicon Valley, exascale refers to the
horsepower offered by the next generation of supercomputers. An exascale machine
will have the ability to crunch a quintillion (a billion billion) calculations
per second. That’s nearly 11 times more powerful than China’s Sunway TaihuLight,
the fastest computer in use today. Think of exascale as the processing power of
roughly 50 million tethered laptops.

A worldwide race is on to build the first exascale machine, which will enable
scientists to revisit everything from theoretical physics to long-term weather
forecasts. But research like Noy’s quest to understand nanotubes will likely be
some of the first projects to realize the advantages of increased computing
capabilities.

GEORGE DAHL


RESEARCH SCIENTIST, GOOGLE BRAIN TEAM

“A jump in computation power will be a huge benefit to materials science, drug
discovery, and chemistry,” says George Dahl, a research scientist on the Google
Brain team. All of these areas of research, Dahl explains, require building
computer models of molecules—an activity that demands a lot of processing power.
“These are very slow computations,” says Dahl, “for each and every molecule or
material we want to analyze.”

But there’s more, he adds. If you apply machine learning—which also benefits
from advances in computing power—to molecular simulations, you get a double
whammy of increased power. “You can use machine learning in conjunction with
materials science to find all new materials.”

These are exactly the type of advances that will lead to a better, less
expensive salt water filter. And that’s not the only way exascale-level
computing might help the planet’s water challenges.

Because exascale computing will also be exceptional at processing significant
quantities of data, it could help with projects like the work being done, in
part, by Google engineers Noel Gorelick, cofounder of the Earth Engine platform,
and Tyler Erickson, a senior developer advocate focusing on water-related
analyses for the platform. The cloud-based platform analyzes environmental data
on a global scale. A recent ambitious effort, led by Gorelick and the European
Commission’s Joint Research Centre, sought to create high-resolution maps of
surface water around the world. By looking at 30-plus years of satellite images
using the Earth Engine data, the team mapped (and measured) the evolution of
Earth’s bodies of water over the decades, revealing vanished lakes and dried-up
rivers as well as the formation of new water bodies. It would have taken three
years just to download the necessary data if it had been done all at once.
That’s quite an archive, Erickson says, but exascale will allow the team to
collect even more information—at a vastly quicker speed—to produce even more
accurate maps.

“There are other data sources that we could be looking at if we had more
processing power,” Erickson says. An exascale machine, he points out, has the
potential to tap into the world’s most undervalued resource: citizen scientists.
Imagine if the water-mapping project were opened to, say, anyone flying a drone
who shoots HD video. “That would be a pretty spectacular amount of data,” he
says. High school kids piloting DJI Phantoms over rivers and estuaries might
upload video to the Google Cloud, where, thanks to exascale power, it could be
filed, geo-referenced against Google’s base map of the world, analyzed, and
distilled into digital cartography. This democratization of science in action
could aid agricultural planning, prepare regions for disasters, or even help
monitor ecological changes. (To spur similar projects at other organizations, in
2014 Google announced that it is donating a petabyte of cloud storage for
climate data as well as 50 million hours of computing with the Google Earth
Engine platform.)

Dahl, for his part, is quick to add that leaps in processing power won’t solve
every computing challenge. But, he says, the biggest benefits may come from uses
we have yet to imagine. He makes an analogy to the invention of the microscope—a
device that led to lifesaving new discoveries. “Maybe there will be something
that we’ve never considered doing that suddenly will become practical,” he says.
“Maybe it will allow us to build something like the microscope—a completely new
tool that, in turn, enables completely new discoveries.”

ONLY 3 PERCENT OF THE WATER ON EARTH IS FRESH AND DRINKABLE

AND WE CAN ONLY ACCESS A TINY FRACTION OF THAT.

Total amount of fresh water. Unfortunately, almost all of it is trapped in
glaciers, polar ice caps, and deep underground.

High-performance computing is measured in FLOPS. This metric can be applied to
any machine, from a laptop to the world’s fastest supercomputer. More FLOPS
equal more speed; more speed equals higher resolution, or the ability to see
things in finer detail; higher resolution equals more accurate
computer-simulation images and predictions. This is especially valuable to
places like the National Oceanic and Atmospheric Administration, which uses
computers to predict weather patterns, changes in climate, and disruptions in
the oceans and along the coasts.

> Exaflop systems can perform 1018 (a billion billion) calculations per second.

NOAA expects to use exascale systems in the 2020s. “It will give us the ability
to provide more accurate warnings of severe weather at finer scales and longer
lead times that will provide much better protection of lives and property,” says
Brian D. Gross, deputy director of high performance computing and communications
at the agency. Scientists could help build up resilience in anticipation of
extreme climate events, such as a devastating hurricane, enabling an entire
region to limit the damage and death toll.

To convey the scale of that computing power, Gross explains that the department
used teraflops systems in the 2000s (a trillion calculations per second) that
could accurately track large weather features roughly the size of a state; today
the systems use petaflops (a quadrillion calculations per second) and can
accurately track weather features the size of a county. Exascale computing will
allow NOAA to zoom in much closer for more detail—for instance, accurately
mapping thunderstorms as small as a city. This resolution provides more
information, which reveals a lot more about how storms of all sizes will behave
and evolve. “Higher-resolution models more accurately depict larger-scale
weather systems like hurricanes, improving the prediction of rainfall and storm
tracks,” says Gross. Put another way: A few years from now, weathercasters will
have little excuse if they mess up the five-day forecast. And we’ll know more
about exactly where and when that next superstorm will hit.


EXASCALE COMPUTING CAN HELP SOLVE FRESH WATER SHORTAGES

FASTER SUPERCOMPUTERS WILL AID RESEARCHERS WHO ARE STUDYING DESALINATION AND
DEPOLLUTION FILTERS TO BOOST THE AMOUNT OF DRINKABLE WATER IN THE WORLD.



Fresh water access is a challenge around the world. From the depleted aquifers
beneath Saudi Arabia to the baked soil of Brazil to America’s breadbasket, where
drought has spread across the Great Plains like cracks in pavement, mass
dehydration is looming. A 2012 U.S. intelligence report concluded that fresh
water shortages will even impact national security. The demand for fresh water
is expected to be 40 percent higher than the global supply by 2030.

RAMYA TUNUGUNTLA


POSTDOCTORAL RESEARCHER, LAWRENCE LIVERMORE NATIONAL LABORATORY

Rising temperatures, less rainfall, more people, pollution, and poverty—the
challenges underlying the demand can seem, on the surface, insurmountable. But
Aleksandr Noy remains convinced that an exascale machine will help him create a
nanotube membrane that filters water and saves lives. “With so much computer
power, we can run a quick simulation before we go to the lab,” he says. “That’s
really helpful because it will allow us to focus our energy on the experiments
that make sense.” And there is still a lot to figure out: The precise
measurements required for water transport through the nanotubes has yet to be
established, and no one knows the best membrane material in which to embed a
bunch of nanotubes or how they should be arranged. “In many of the nanotube
modeling studies using simulations, there is still discrepancy in the numbers,”
says Ramya Tunuguntla, a postdoctoral researcher working with Noy. “That’s a
challenge we must overcome.” Like Noy, she thinks a more robust supercomputer
will take their research to the next level: “With exascale, we could run longer
simulations to collect more data.”

In 2023, a new computer will be installed at Livermore Lab. With four to six
times the number-crunching power of the current system, this machine, dubbed
Sierra, is likely the last step before exascale will enable the ogling of all
those gorgeous high-def images that come with a quintillion FLOPS. In fact,
exascale may have already arrived elsewhere by then. One top researcher at
Livermore says that while the first exascale machines will start showing up in
the U.S. around 2020, China—the prohibitive favorite in this race—claims it will
deliver a prototype either later this year or early next year that some have
called the “super-supercomputer.”

Costas Bekas, a two-time Gordon Bell Prize winner and an exascale expert at the
IBM Research lab in Zurich, points out that exascale isn’t an end—computing
power will continue to grow. He foresees a day when computer modeling allows us
to examine the universe at not just the molecular level but at the atomic level.

“Exascale means that we can finally crack—in an acceptable amount of time and
energy spent—things that are very complex, like how carbon nanotubes work,”
Bekas says. “Exaflops will not save the planet. We have too many problems.
However, it will definitely make the Earth a much better place to live.”

Back at Lawrence Livermore, Aleksandr Noy and Ramya Tunuguntla load another
nanotube membrane into a test cell, flip a switch, and collect more data. Soon
they—along with exascale computing—may change the lives of billions.

RENE CHUN is a writer based in New York. His work has appeared in publications
ranging from The New York Times and The Atlantic to Wired and Esquire.

Animations by Justin Poulsen
Illustrations by Matthew Hollister

In this story: sustainability , Google Cloud , innovation


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