Intel has concluded that its latest technology for high-performance computing is worth the effort and expense of building a new brand. Competitors are not so sure.
The Silicon Valley company’s Xeon microprocessors are the most popular calculating engines for supercomputers as well as conventional servers. Intel on Monday is disclosing more about a separate line of chips that it hopes will complement those products, including a new brand name, Xeon Phi.
Those chips are expected to be used on cards that can plug into computers to work alongside ordinary Xeons, or built into new systems. Intel has constructed one supercomputer using the technology already, a test system that ranks 150th on a list of the 500 largest supercomputers being unveiled at an industry conference in Germany Monday.
Intel’s effort is widely seen as a response to GPUs, or graphics processing units–chips that were first designed for rendering images in videogames but have been promoted by companies like Nvidia for use in accelerating supercomputer performance. It’s the latest in a long-term trend toward parallel processing; where a typical Intel chip might have the core circuitry of one to eight processors, GPUs have hundreds of simpler electronic brains.
So Intel began talking about using a large number of processors on a chip, using stripped-down versions of the x86 designs it popularized in PCs. It called the approach MIC, for many integrated cores.
There have been bumps along the way. Intel in 2007 first discussed plans to use that basic approach in a chip called Larrabee that was aimed at graphics applications. It scrapped that plan in December 2009, but said the underlying technology would live on in high-performance computing.
The first Xeon Phi, previously known by the code-name Knight’s Corner, illustrates that strategy. It has “more than 50″ x86 cores–Intel’s not being more specific–and can deliver a trillion scientific calculations a second, or a teraflop, the company says.
John Hengeveld, an Intel director of marketing, is quick to cite differences between an Xeon Phi and GPUs used to speed up supercomputers. The latter, he says, cannot really work independently; the Phi, by contrast, runs an operating system–so it can essentially be used like a separate computer, though the first products are what Intel prefers to call “co-processors” to distinguish from GPU “accelerators.”
“The Xeon Phi brand will last a long time, and we expect there to be lots of different kinds of products,” Hengeveld says.
A key selling point is the claim that existing software can be made to run on a Xeon Phi with little modification. One person who has witnessed that feature first-hand is Robert Harrison, director of the joint institute for computational sciences, which is affiliated with Oak Ridge National Laboratory and the University of Tennessee.
Harrison and a colleague gave a talk last November at a supercomputer conference, stating that many correct “ports” to adapt scientific programs to the MIC technology were completed in less than one day each. Though Oak Ridge uses GPUs in supercomputers, they said many such programs do not run today on GPUs and probably never will because of cost and complexity.
That doesn’t necessarily mean, Harrison adds in an interview, that such basic software ports are running faster on the MIC technology; extra tuning, he says, may be needed to exploit the extra cores. “But that ability to get something running right away, albeit slowly, is a huge boost,” he says.
Nvidia roundly rejects such arguments. The company says its programming technology, called CUDA, is being widely used at universities and at companies to realize big speed-ups from deploying GPUs.
Intel’s decision to use many x86 cores “doesn’t buy them anything in terms of programmability,” says Sumit Gupta, a senior director in charge of Nvidia’s Tesla line of chips.
Gupta predicts that users of either GPUs or the Intel technology will use an emerging technology called OpenACC, in which software known as a compiler does much of the work of realizing faster speeds from more processors. And GPUs simply work faster, he says; Nvidia rates its latest Tesla chip at more than 4.5 teraflops.
A more balanced view is provided by Cray, which has been selling supercomputers since the 1970s. Peter Ungaro, its chief executive, says the company will continue to offer systems with different add-on processors, including GPUs.
But Cray is mainly discussing the combination of Xeon and Xeon Phi chips in a powerful system dubbed Cascade that is due out next year. Ungaro says he is “very excited” about the Xeon Phi.
“I think it has the potential to surpass GPUs, but I wouldn’t make that call yet,” he says.