For derivative sellers like Wachovia, assessing risk and pricing isn’t magic; the software that modeled its derivatives, grinding out the numbers, was complicated and needed to run thousands of what-if scenarios to determine end-of-day prices and to calculate the risk position for the derivatives portfolio. Locked into large, multiprocessor Unix boxes, the risk position calculation could take as long as nine hours. And throwing upgraded hardware at the problem wasn’t going to help much. "It would have cut the time from nine hours to four and a half hours," says Mark Cates, chief technology officer for Wachovia’s Corporate and Investment Banking group. "We needed it to run in under an hour."

The solution wasn’t pricey hardware; it was cheaper hardware. Wachovia linked hundreds of already-deployed desktop computers into a grid, taking advantage of every machine with available processing time. The results were stunning. A job that used to take all day or overnight could now be completed in under an hour, allowing Wachovia to make exponentially faster risk and pricing decisions.

Cates says that the grid solution cost Wachovia a fraction of what it would have cost to upgrade the large Unix environment?an upgrade that wouldn’t have produced anything like the same performance benefit. "We’re seeing ten- to twentyfold processing increases at 25 percent of the cost," he says.

Wachovia isn’t bleeding edge. Thanks to improvements in both hardware and software, numerous companies have begun taking advantage of grid tools. Business users, particularly in the financial services industry, are seeing the benefits of grid in faster responses, reduced time to market for new products, and lower prices per unit of computing horsepower. There are still hurdles to vault before grid goes mainstream (right now, many apps simply don’t make the transition), but grid is no longer just a tool for techies decoding the genome or designing airplane wings.

The Difference Between a Grid and a Cluster

The technology behind grid isn’t new. Its roots lie in early distributed computing projects that date back to the 1980s, where scientists would connect multiple workstations to let complex math problems or software compilations take advantage of idle CPUs, dramatically shortening processing times. For years, vendors and IT departments eyed this opportunity to dramatically increase processing power by employing existing resources. But only recently have the tools arrived to put general business applications to work on a grid.