Cray set to benefit from boom in big data
Print Edition
“Big data” means big computers, and good news for Cray Inc.
The pioneer of supercomputers in the 1970s stood on the brink of obscurity 20 years ago but is now surging back to prominence. Its shares have almost doubled over the past 12 months.
The explosion of data — measuring weather, traffic, health and countless other areas — coupled with a desire to tease meaning out of it, demands greater computing power than is accessible via standard machines.
“The assumption was that supercomputers were cliche five years ago. People thought, ‘I can run my simulation on my laptop’,” said Barry Bolding, a Cray vice president, at the company’s Seattle headquarters last week. “That may have been true, so long as the data associated wasn’t growing as well. But raw data is being created in exabytes as we sit here. More data means bigger computer, bigger computer means more data.”
Cray’s strengths
Experts estimate that 2.5 exabytes — or 2.5 billion gigabytes — of data are now generated every day, and the world's capacity to store that data is doubling every 40 months, which all plays to Cray’s strengths.
A basic Cray cabinet costs US$500,000 and up and is roughly the size of a refrigerator. Major customers can group 200 or more into massive supercomputers worth hundreds of millions of dollars, such as “Titan” based at the US Department of Energy’s Oak Ridge National Laboratory.
Titan, completed by Cray last year, is the world’s third-fastest supercomputer, takes up the size of a basketball court and can perform more than 20,000 trillion calculations a second.
To be sure, most companies will never need that scale, or can process what they need through multiple machines running in tandem on a high-speed network or in the cloud, which for many projects works out cheaper and more power-efficient.
What makes supercomputers different is that they can make a huge number of interconnected calculations at the same time, rather than a consecutive list of unconnected calculations, which makes them good for running complex simulations and mining unrelated data.
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