Technology / Computers

Built-in cooling pipes will keep IMB's new Blue Waters super-computer running smoothly.

Courtesy NCSA

Last October China’s Tianhe-1A took 
the title of the world’s most powerful supercomputer, capable of 2.5 petaflops, meaning it can perform 2.5 quadrillion operations per second. It may not hold the top spot for long, as IBM says that its 20-
petaflop giant Sequoia will come online next year.

Looking ahead, engineers have set their sights even higher, on computers a thousand times as fast as Tianhe-1A that could model the global climate with unprecedented accuracy, simulate molecular interactions, and track terrorist activity. Such machines would operate in the realm called the exa­scale, performing a quintillion (that’s a 1 with 18 zeroes after it) calculations per second.

The biggest hurdle to super-supercomputing is energy. Today’s supercomputers consume more than 5 megawatts of power. Exascale computers built on the same principles would devour 100 to 500 megawatts—about the same as a small city. At current prices, the electric bill alone for just one machine could top $500 million per year, says Richard Murphy, computer architect at Sandia National Laboratories.

To avoid that undesirable future, Murphy is leading one of four teams developing energy-efficient supercomputers for the Ubiquitous High-Performance Computing program organized by the military’s experimental research division, the Defense Advanced Research Projects Agency, or Darpa. Ultimately the agency hopes to bring serious computing power out of giant facilities and into field operations, perhaps tucked into fighter jets or even in Special Forces soldiers’ backpacks.

The program, which kicked off last year, challenges scientists to construct a petaflop computer by 2018 that consumes no more than 57 kilowatts of electricity—in other words, it must be 40 percent as fast as today’s reigning champ, while consuming just 1 percent as much power.

The teams that survive the initial design, simulation, and prototype-building phases may earn a chance to build a full-scale supercomputer for Darpa. Making the cut will demand a total rethink of computer design. Nearly everything a conventional computer does involves schlepping data between memory chips and the processor (or processors, depending on the machine). The processor carries out the programming code for jobs such as sorting email and making spreadsheet calculations by drawing on data stored in memory. The energy required for this exchange is manageable when the task is small—a processor needs to fetch less data from memory. Supercomputers, however, power through much larger volumes of data—for example, while modeling a merger of two black holes—and the energy demand can become overwhelming. “It’s all about data movement,” Murphy says.

The competitors will share one basic strategy to make this back and forth more efficient. This technique, called distributed architecture, shortens the distance data must travel by outfitting each processor with its own set of memory chips.They will also incorporate similar designs for monitoring energy usage.