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2012-10-29 09:30:01
An exclusive, behind-the-scenes look at the US bid to build a radical new
machine, capable of solving some of the most complex questions in science
today. Its secret: video game technology.
The sound of 20 quadrillion calculations happening every second is dangerously
loud. Anyone spending more than 15 minutes in the same room with the Titan
supercomputer must wear earplugs or risk permanent hearing damage. The din in
the room will not come from the computer's 40,000 whirring processors, but from
the fans and water pipes cooling them. If the dull roar surrounding Titan were
to fall silent, those tens of thousands of processors doing those thousands of
trillions of calculations would melt right down into their racks.
Titan is expected to become the world's most powerful supercomputer when it
comes fully online at the US Oak Ridge National Laboratory, near Tennessee, in
late 2012 or early 2013. But on this afternoon in mid-October, Titan isn't
technically Titan yet. It's still a less-powerful supercomputer called Jaguar,
which the US Department of Energy (DoE) has operated and continuously upgraded
since 2005. Supercomputing power is measured in Flops (floating point
operations per second), and Jaguar was the first civilian supercomputer to
break the "petaflop barrier" of one quadrillion operations per second (a
quadrillion is a one followed by 15 zeroes). In June 2010 it was the fastest
supercomputer on Earth.
Gallery: Building a speed machine
But high-performance computing records don't last long: a Chinese machine
pushed Jaguar into second place just six months later. Then in October 2011,
the supercomputer design firm Cray announced that it would transform Jaguar
into a new machine that could retake the number-one spot, with an estimated
peak performance of 20 petaflops.
Cray's blue-jacketed technicians have been pacing up and down Jaguar's
catacomb-like aisles for months, opening its 200 monolithic black cabinets and
sliding out its processor blades like enormous safe-deposit boxes. Jaguar's
brain surgery takes place on spartan worktables that wouldn't look out of place
in a hobbyist's garage. A technician fits a paperback-sized ingot of metal and
silicon into an empty space in the blade and fastens it into place with a
battery-powered screwdriver. The ingot contains a graphics processing unit, or
GPU. Cray has installed one of these GPUs alongside every one of Jaguar's
18,688 CPU chips. It's this "hybrid architecture" that will turn Jaguar into
Titan, packing an order of magnitude more computing horsepower into the same
amount of physical space.
Turbo-charged
GPU-accelerated supercomputers burst onto the world stage in 2010, when China's
Tianhe-1A machine overtook Jaguar as the fastest supercomputer on earth. "It
came out of nowhere," says Wu-chun Feng, a high-performance computing expert at
Virginia Tech. "China didn't even have a high-performance computing program."
Instead of relying solely on expensive, highly customized, multicore
microprocessors, Tianhe-1A got a speed bump by using "off the shelf" GPUs made
by Nvidia, whose chips power the displays of video-game consoles and consumer
laptops. Titan takes the same approach using the same chip design that powers
the ultra-high resolution Retina display on Apple s Macbook Pro. These
intricate squares of silicon will provide 90% of Titan's peak supercomputing
performance.
So, what do video-game graphics have in common with high-end scientific
computing? Simulation. "About ten years ago, we observed that the chips we
designed for gaming were starting to look more like general purpose processors
for simulating physics," says Sumit Gupta, Nvidia's senior director of high
performance GPU computing. "When you'd shoot a tree in a video game and it
would fall, you'd want it to look natural, so the simulations became more and
more complex."
At the same time, redrawing every pixel on an HD laptop screen 60 times per
second also requires so-called parallel computation. "This is why GPUs are
designed to run hundreds of calculations at the same time very efficiently,"
says Steve Scott, Tesla chief technology officer at Nvidia. "It turns out that
this is very similar to the way high performance scientific computing is done,
where you're simulating the climate, or the interactions between drug
molecules, or the airflow over a wing."
But where video game physics only have to look real enough to a distracted
teenager, supercomputer simulations have to be scientifically accurate down to
the level of individual atoms - which is why Titan needs tens of thousands of
GPUs all working together on the same problem, not to mention enough Random
Access Memory (RAM) to hold the entire simulation in memory at once. (Titan has
710 terabytes of RAM, about as much as a stack of iPads 7km high.)
But supercomputers have been getting along without GPUs for decades. A CPU chip
- the same general-purpose silicon "brain" inside your laptop, your smartphone,
and every computer at Google or Facebook - can run high-performance scientific
calculations, too, if you chain enough of them together. The current fastest
supercomputer, IBM's "Sequoia" system at Lawrence Livermore National Laboratory
in California, contains over 98,000 CPUs, each with 18 cores.
What GPUs offer that CPUs can't is a blast of relatively cheap,
energy-efficient horsepower. Scaling up the Jaguar supercomputer from 1.75
petaflops to 20 could have been done by adding more cabinets stuffed full of
CPUs. But those take up space, and more importantly, suck up power.
Off-the-shelf GPUs, meanwhile, aren't designed to act self-sufficiently like
normal chips - they're add-ons "that accelerate a CPU like a turbo engine,"
says Gupta - so they consume much less energy than a CPU would to do the same
amount of calculating. By bolting a GPU onto each one of the 18,688 AMD Opteron
CPU chips already in Jaguar, the DoE was able to create a next-generation
supercomputer without scrapping the one they already had - or blowing up their
electric bill.
Bigger is better
The new machine, like any supercomputer, is all about speed: "time to
solution," as Jack Wells, director of science for Oak Ridge s computing
facility, puts it. "It's about solving problems that are so important that you
can't wait," he says. "If you can afford to wait, you're not doing
supercomputing." Competition among research projects for "core hours" on Titan
is intense. Of the 79 new-project proposals received by Oak Ridge's selection
panel, only 19 will run on Titan in 2013.
Winning proposals will apply Titan's computational might to problems in areas
such as astrophysics (simulating Type-1A supernovae and core collapses),
biology (modeling human skin and blood flow at a molecular level), earth
science (global climate simulations and seismic hazard analysis of the San
Andreas fault in California), and chemistry (optimizing biofuels and engine
combustion turbulence). According to Buddy Bland, project director of the Oak
Ridge computing facility, Titan will typically run four or five of these
supercomputing "jobs" at once.
But some jobs are so complex that they'll take over Titan entirely. The
Princeton Plasma Physics Laboratory, for example, will use all of Titan's
computing cores to help design components for the International Thermonuclear
Experimental Reactor (Iter), a prototype nuclear fusion project in France.
"Their goal is to have this reactor online by 2017," Bland says. "It'll use
magnetic fields to circulate plasma through a big donut-shaped reactor at 100
million degrees Fahrenheit. How do you contain that kind of energy? That's what
they need Titan to help them figure out."
As fast as Titan is, these simulations can still take days, weeks, or even
months to complete. And the very idea of "fast" has a different meaning to
computational scientists than it does to users of consumer apps like Photoshop
or Final Cut Pro. "It's not so much about running our applications and
calculations faster - we want to run them bigger," says Tom Evans, a scientist
at Oak Ridge who uses the supercomputer to model nuclear reactor systems.
"Maybe that means adding four times more spatial resolution in our simulations,
or replacing approximations with more accurate physics. Of course we always
like to go faster. But it's less interesting to do the same science faster than
it is to do something new that you couldn't even do before."
In other words, bigger is better - and not just for the scientific bragging
rights. Having a top-ranked supercomputer on American soil "demonstrates global
competitiveness and attracts brainpower," says Jack Wells. Take Jeremy Smith,
director of Oak Ridge's Center for Molecular Biophysics, who used to work at
the University of Heidelberg in Germany. "I found out that Oak Ridge would have
this nice toy to play with," he says, "so I nipped across the pond." (Smith's
research on biofuels began on Jaguar and will continue on Titan.)
Power play
Many of the smart people that Titan attracts will use the supercomputer to
chart the future of supercomputing itself. So-called petascale machines like
Titan and Sequoia can accomplish amazing feats of simulation, like screening
millions of potential drug compounds against a target molecule in a single day.
But researchers like Jeremy Smith want to do even more.
They envisage an "exascale" computer - a thousand times more powerful than
Titan and able to do one quintillion calculations per second (a quintillion is
a one with 18 zeroes after it). A machine like this "would have enough
computing power to screen tens of millions of drug compounds against all known
living protein classes," Smith says. "That means we'll be able to predict if
the drug will work and what all the side effects will be - not only
generically, but for individual people, based on their own genetic sequences.
This is amazing potential."
The trouble with building an exascale machine, however, is the amount of energy
required to get there. "If we just scaled up what we're doing today, it would
take a couple of nuclear power plants to power," says Buddy Bland. But Wu Feng,
who curates an annual list of the world's most energy-efficient supercomputers,
is less pessimistic. "The trends indicate that we'll be able to get to the
exascale for 50 megawatts," he says. That's about half as much power as Apple
and Google s data centers in North Carolina are estimated to use.
But government-funded scientific institutions don't have tech companies
bottomless bank accounts. The DoE wants an exascale computer by 2020 that can
run on 20 megawatts of electricity or less. Reaching that goal will require
entirely new chip designs that draw even less power than the GPU-accelerated
systems like Titan do.
Mobile devices, most of which use chip designs from the UK firm Arm, could
offer a way forward. "You've probably noticed that when you put a smartphone in
your pocket it doesn't burn through your pants," says Jack Wells. "The same
design principle is going to be used in high-performance computing to get to
the exascale." Jack Dongarra, a computer scientist at the University of
Tennessee whose Top500 list ranks the world's fastest supercomputers, ran
benchmarking software on an iPad 2 and found that the tablet was equivalent to
some of the fastest supercomputers of the mid-1990s. "That's incredible
computing power in your hand," he says. "The Arm processor is clearly capable."
Still, simply lashing together thousands of low-power processors - whether they
come from smartphones, gaming consoles, or laptops - does not a supercomputer
make. Passing data between all those chips creates bandwidth bottlenecks that
limit the total speed of the system. "It's like having two hemispheres of your
brain on opposite sides of the room connected by a wire," says Feng. An
exascale computer will have to speed up its entire internal network - perhaps
by using fibre optic connections between racks of chips, accelerators on every
piece of silicon, or both.
Meanwhile, says Buddy Bland, jockeying for the title of "world's fastest
supercomputer" will continue, and no single interconnect design or chip
architecture is "best." "Whoever has the biggest budget is likely to be in the
top spot," he says wryly. "But a healthy diversity in architectures is a
wonderful thing because certain applications can run well on one, and others
well on another."
What's indisputable is that supercomputing has become the "third pillar" of
doing science, alongside theory and experimentation. The best way to grasp the
power of Titan, says Bronson Messer, a computational astrophysicist at Oak
Ridge, is not to compare it to a Formula 1 racing car or a turbocharged engine,
but to the Large Hadron Collider. "Titan is like the particle accelerator, and
the simulations and applications that we run on Titan are like the detectors
that discovered the Higgs boson," Messer says. "The size or power of these
machines isn't what pushes science forward. It's the people using them, who
know what to look for."