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Tera-scale Computing: Intel's Attack of the Cores

The Tera-scale approach is a radical change from Intel's Xeon 5100, which uses two complex processor cores.

But one of the driving forces behind the Tera-scale research is the fact that chip transistor counts, already in the billions, will continue to double over time.

Intel chips will approach 32 billion transistors by the end of the decade, researchers said.

The rising transistors numbers give Intel the option to go with large numbers of smaller cores without radically increasing chip area.

Thus far, Intel and others have used the extra transistors to create more complex chips with larger onboard memory caches.

But while the current approach brings increases in instruction processing or work done per clock, that doesn't mean you're getting a commensurate increase in terms of overall efficiency, said Steve Pawlowski, chief technology officer for Intel's Digital Enterprise Group in Hillsboro, Ore.

"One of the ways to get efficiency is you make the cores simpler and you do a lot of them and put them on the die. That's where Tera-scale is coming in. We're saying, 'Hey, for a certain class of workloads, you can take advantage of this parallelism. You can have extremely efficient architectures because you can use more of [the cores].'"

Shifting toward lots of simple cores—trading two Woodcrest cores for tens of 386-style cores—would greatly increase a chip's parallel processing abilities, and thus offer more performance, analysts agreed. But it brings its own issues.

PointerClick here to read more about Intel's swifter transistors.

"The bigger question is, how do you take advantage of such a system?" said Dean McCarron, principal analyst with Mercury Research, in Cave Creek, Ariz.

"Not everything lends itself to that [many threads]. But, that said, everybody seems to be in agreement that this is the path we're pretty much forced to go down."

But programming for Tera-scale chips will require a completely different approach that uses lots of different threads simultaneously.

That's a concept only a few programmers are currently familiar with, Pawlowski said.

Next Page: Getting to work.



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