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AMD Demonstrates "Teraflop In a Box"
UncleFluffy writes "AMD gave a sneak preview of their upcoming R600 GPU. The demo system was a single PC with two R600 cards running streaming computing tasks at just over 1 Teraflop. Though a prototype, this beats Intel to ubiquitous Teraflop machines by approximately 5 years." Ars has an article exploring why it's hard to program such GPUs for anything other than graphics applications.
GPGPU is hard because we're still in the very early days of this particular revolution. As I think about it, and from what we know of AMD's plans in particular, I think this is kind of like the evolution of FPU.
See, in the early days FPU was a seperate chip (anyone remember buying an 80387 to plug into their mobo?). Writing code to use FPU was also a complete pain in the ass, because you had to use assembly, with all the memory management and interrupt handling headaches inherent. FPUs from different vendors weren't guaranteed to have completely compatible instruction sets. Because it was such a pain in the ass, only highly special purpose applications made use of FPU code. (And, it's not that computer scientists hadn't thought up appropriate abstractions to make writing floating point easy. Compilers just weren't spitting out FPU code).
Then, things began to improve. The FPU was brought on die, but as an optional component (think 486SX vs 486DX). Languages evolved to support FPUs, hiding all the difficulty under suitible abstractions so programmer could write code that just worked. More applications began to make use of floating point capabilities, but very few required a FPU to work.
Finally, FPU was brought on die as a bog standard part of the CPU. At that point, FPU capabilities could be taken for granted and an explosion of applications requiring an FPU to achieve decent performance ensued (see, for istance, most games). And writing FPU code is now no longer any more difficult than declaring type float. The compiler handles all the tricky parts.
I think GPGPU will follow a similar trajectory. Right now, we're in phase one. Use a GPU for general purpose computation is such an incredible pain that only the most specialized applications are going to use GPGPU capabilities. High level languages haven't really evolved to take advantage of these capabilities yet. And yes, it's not as though computer scientists don't have appropriate abstractions that would make coding for GPGPU vastly easier. Eventually, GPGPU will become an optional part of the CPU. Eventually high level languages (in addition to the C family, perhaps FORTRAN or Matlab or other languages used in scientific computing) will be extended to use GPGPU capabilities. Standards will emerge, or where hardware manufacturers fail to standardize, high level abstraction will sweep the details under the rug. When this happens, many more applications will begin to take advantage of GPGPU capabilities. Even further down the road, GPGPU capabilities will become bog standard, at which point will see an explosion of applications that need these capabilities for decent performance.
Granted, the curve for GPGPU is steeper because this isn't just a matter of different instructions, but a change in memory management as well. But I think this kind of transition can and will eventually happen.