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In Depth

Building Titan: The ‘world’s fastest’ supercomputer

About the author

John Pavlus is an award-winning filmmaker and writer focusing on science, technology and design topics. His work has appeared in Scientific American, Technology Review, Wired, National Public Radio, Nature Publishing Group, The New York Times Magazine, Fast Company, and elsewhere. He lives in Brooklyn, NY. 

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.

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.


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."

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