From the outside, Virginia Tech's Math Emporium is distinctly unimpressive. Tucked into a gray shopping center just across the road from the university's main campus, it's a computer lab for the school's 8,000 math students that never closes. But when Wu-chun Feng looks at its 550 Apple computers, he sees a supercomputer that's begging to be unleashed.

Feng is part of a team of Virgina Tech researchers that is working to turn places like the Math Emporium into a new kind of supercomputer that's based on the same technology that Google built to power its search engine. They call their project Moon – short for MapReduce On Opportunistic Environments – and according to Feng, they think that they just might have found a way to unleash a massive amount of data analytics power that's just lying dormant on the millions of desktops running in companies and universities.

Project Moon started five years ago, but just last week, the academic paper that christened it was named one of the most important distributed supercomputing papers in the past two decades. And now, Virginia Tech is looking into the possibility of turning it into the basis of a commercial product. "We're going through technology transfer and trying to figure out how much more we might need to do to package it if people want to license it or to spin off a company off of it," says Feng, an associate professor at the university.

The project builds on Hadoop, the open source version of Google's MapReduce platform, and it's just one of many efforts to apply the platform to more than just web services. Long used by such companies as Yahoo, Twitter, and Facebook, Hadoop lets you crunch enormous amounts of data across a sea of cheap computers, and some of the biggest names in tech – from IBM to Oracle to EMC – are now hoping to make some money from it.

With Project Moon, Wu-chun Feng and the other researchers designed a way to turn Macs into nodes on a supercomputer, with each machine helping to solve complex data analysis problems whenever it's not being used. Think of Moon as a kind of Seti@Home project that can do much more complex problem-solving.

One of the great things about Hadoop is that it keeps chugging along even if one of those computers stops working. But the trick for Feng's team was to make Hadoop work in a place like the Math Emporium, where computers are coming in and out of use all the time.

While researching their original paper, Feng and his fellow researchers set up a prototypical Moon environment, modeled on the Math Emporium, that ran nearly 70 Apple systems. They set up a server that could communicate with the Macs as if they were part of a single, large supercomputer. The hard part is making the computers look like one big machine, rather than a flickering collection of processors that are sometimes available, sometimes not.

But the researchers say they've found a way to stitch together a supercomputer out of "a bunch of cycle-stealing jobs," Feng explains. "Basically, if the cycles are idle, we use them. If somebody gets to the computer terminal and starts working, then we evict ourselves and migrate to other idle resources."

Now they're looking at testing it in the Emporium itself, although it's not clear whether or not that will actually happen, Feng says.

With some more work, the 550 Math Emporium desktops could be transformed into a 6.6 teraflop supercomputer, capable of 6.6 trillion mathematical operations per second, but there may be even more unharnessed computing capacity. Figure out a way to harness their graphics chips – which just happen to be well suited for supercomputer work – and you would have a 264 teraflop system, Feng reckons.

Supercomputer geeks have gone after these spare computing cycles in the past, but they've not always been successful. SETI@Home works because it was really easy to break up the space radio telescope data it needs to analyze into discrete chunks of data and scan through them one at a time. But that's not how most supercomputing problems work.

The computing power is out there. The trick for Feng's team is to tweak its software so that it can get enough performance out of a network of desktops to do some real computing. That's a tough management challenge. But if they can pull it off, it could give companies a low-cost way to do supercomputing without having to use services such as Amazon's Elastic Compute Cloud. And that would make organizations such as the Math Emporium much more productive.

"They said, 'Gee if you can make use of these resources, it's a significant return on investment for us.' And it would probably be a significant return on investment for any company that has PCs on every single person's desk," Feng says. "If you can actually do coordinated cycle stealing this would be a really cool enterprise cloud kind of thing, where you don't have to go off to the public infrastructure like Amazon."