HPCwire: AMD Sees a Heterogeneous Future

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February 23, 2007

AMD Sees a Heterogeneous Future

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Though not the largest market for either AMD or Intel, high performance computing has become a technology battleground for the two chipmakers. In the past few years, multi-core Opteron processors, HyperTransport technology, and the open standard Torrenza platform have propelled AMD to prominence in the high-end space. But with the introduction of Intel's new Xeon offerings, the game has changed for AMD.

Now, with last year's acquisition of graphics-card maker ATI, AMD appears to be pursuing a rather different path than its rival. To get a sense of how this is going to play out in the HPC landscape, HPCwire talked to Phil Hester, AMD Chief Technology Officer, and Bob Drebin, Chief Technology Officer for the company's new Graphics Products Group, about their vision for high-end processing and how this fits into AMD's overall business strategy.

HPCwire: Can you characterize AMD's high performance computing strategy?

Hester: It's a layered strategy. The top layer is generally what we refer to as accelerated computing. What this is basically trying to recognize is that workloads are getting more diverse, and that applies to both the client and the server space. Below this top layer of accelerated computing, there are at least two other elements.

One of these is the idea of Torrenza and being able to build specialized accelerators. An example of that would be the work done with GPUs and stream computing. There's also work we're doing with companies who use FPGAs for specialized acceleration, whether that would be Java, vector processing or whatever. We're also working with our key OEM customers, like Cray, to help them build custom silicon solutions on our Torrenza platform.

The third layer is really around what we see happening at the processor level itself with regards to the Fusion [CPU-GPU hybrid] processors. For a lot of workloads, the historic idea of more and more homogeneous cores is not the right answer. The better answer is a mix of heterogeneous cores. The first example of that will be a Fusion processor that is primarily aimed at the client space, driven by the fact that, with the advent of Vista, most of the applications that will run on desktop and mobile systems are going to require 3-D graphics.

There's an analogy to this in the early days of the PC. With an 80286-based PC and an open socket next to it for the 80287 math coprocessor, maybe one percent of the people bought the coprocessor. In the 80386 generation, with the 80387 socket, maybe 10 or 20 percent of the people bought the coprocessor. After the introduction of Windows and other emerging applications, the 80486 processor integrated the FP coprocessor because at that point enough of the applications demanded floating point. The economics was such that the incremental die size increase to incorporate floating-point capabilities just made sense. We're kind of to that point now in the client space [with graphics functionality].

With that said, once you put a GPU on-chip, you will be able to exploit the capability of that device to solve more general-purpose problems. That means that over time, you want to expose new native mode user instruction extensions that leverage the capabilities of the GPU. The other thing that you start recognizing is that GPUs, as essentially large vector units, are optimized for parallel execution. If you look at the performance of CPUs versus GPUs for applications that are parallelizable, the GPU has 20 to 50 times better performance. By being able to exploit both sequential and parallel applications on a single chip, it's kind of a perfect map to Amdahl's Law.

Although the original Fusion processor will be targeted for the client space, we also believe that a lot of the characteristics, such as power efficiency and performance per watt, will likely be a good building block for large clusters. As I'm sure you know, some of these clusters are not necessarily limited by an organization's budget, but by the amount of power and cooling that they can get into their data center. So one important metric for us is performance per watt per dollar.

The flip side of that is because every one these client CPUs has this graphics function integrated in it, it will never be designed to be the world's highest performance floating point device or GPU. We still believe that in the desktop and client space, there will continue to be a need for GPUs just as we know them today. And likewise, GPUs are going to continue to evolve to do, for example, true IEEE math. So our stream computing initiatives are the beginning points of figuring out how the GPU itself needs to evolve to become a more generalized element of a high performance technical computing solutions.

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