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AMD Turns To Ex-Nvidia Star To Jump-start Fusion

By Damon Poeter
May 27, 2010    8:27 PM ET

Page 2 of 3

The poaching of Hegde from Santa Clara, Calif.-based Nvidia signals that AMD is gearing up to move aggressively on the GPU and heterogeneous computing fronts after years on the sidelines, said Jon Peddie, principal analyst at Jon Peddie Research.

While ATI Technologies was an important first-mover in GPU computing beginning in the late 1990s with a promising parallel programming initiative at Stanford University, after ATI’s acquisition by AMD in 2006 that effort tailed off, Peddie said.

Nvidia stepped in, investing millions of dollars in developing its proprietary CUDA architecture and programming language while also evangelizing GPU computing and winning over HPC system builders with its Tesla Preferred Program and other loyalty-building initiatives in the custom systems channel.

“ATI, or I should say AMD, took their eye off the ball of GPU computing. But Nvidia didn’t. And when Nvidia commits to something, they put their all into it,” Peddie said. “So today, CUDA is by far the most widely used and robust programming platform for parallel computing that exists in the world.

“So now AMD has its house in order. They’ve completed their smart fab strategy, the Intel lawsuit is behind them and now they’re ready to get really rolling on GPU computing.”

A major difference between the approaches by Nvidia and AMD to GPU computing is that the former has developed its proprietary CUDA framework, while the latter says it’s committed only to open standards like the OpenCL heterogeneous programming language that can work on any vendor’s hardware. Nvidia GPUs also support OpenCL, but CUDA programs will only run on Nvidia hardware.

“Our strategy is to embrace open standards all the way through. We’re comfortable that we’ll have to win with our hardware. But even with that philosophy, you have to deal with the reality that GPU programming is still relatively new,” Hegde said.

“If you look at a GPU and a CPU, and you want heterogeneous computing, quite frankly, it’s not as easy to program a GPU as a CPU, though much progress has been made. So we’ve got some plans in place to simplify that process and kind of hide the complexity from the developer.”

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