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BrookGPU: General Purpose Programming on GPUs
An anonymous reader writes "
BrookGPU is a compiler and runtime system that provides an easy, C-like programming environment (read: No GPU programming experience needed) for today's GPUs. A shader program running on the NVIDIA GeForce FX 5900 Ultra achieves over 20 GFLOPS, roughly equivalent to a 10 GHz Pentium 4. Combine this with the increased memory bandwidth, 25.3 GB/sec peak compared to the Pentium 4's 5.96 GB/sec peak, and you've got a seriously fast compute engine but programming them has been a real pain. BrookGPU adds simple data parallel language additions to C which allow programmers to specify certain parts of their code to run on the GPU. The compiler and runtime takes care of the rest. Here is the Project Page and Sourceforge page."
"graphics-realted" things include things like floating point mathmatics, linear algebra, and vector operations. If you are doing anything computationally intensive, this might be usefull. You don't have to actually use the hardware to do anything graphical if you are just interested in turning numbers.
Definately possible - general purpose CPUs have to do everything where graphics cards can specialize and do what little they can, faster.
Also, good point about comparing GHz to GHz - AMD CPUs do more per cycle than Intel, but are also clocked much lower. You could look at a subset of instructions (ie: FLoating-point OPerations (FLOPS)) but this only gives you a piece of the overall performance picture.
Without having read the article, my guess is they extrapolated (educated, math-based guess) how fast a 10GHz P4 would perform and compared the results that way.
I'd LOVE to see this tech built into a SETI or Folding@Home client (steroids version). (Imagine the kids - "Mom, I need the Radeon 9800XT to find a cure for Grandma's cancer!")
This looks like a straightforward and clean extension that experienced C/C++ programmers won't find difficult to learn, but it isn't entirely clear to me whether just using this language, without any knowledge of GPU architecture, will lead to big improvements in performance. Granted, you don't need to know the details, but you've got to have an idea of what it is that you're trying to do and in a general way how the special constructs of the language allow you to do that. As with other such language extensions, you can nominally write in the language but not really use the extensions (how many "C++" programs have you seen that were really C programs with // comments and a few couts?) or use them in unintended ways that prevent the intended optimization. It seems to me that if the project really is aiming at programmers who are not familiar with GPUs, they need at least to provide a brief introduction to the special properties of GPU architecture and some guidelines as to how to use the features of the language to take advantage of them. At present I don't find this either on the web sites or in the distribution.
WARNING: Lots of conjecture involved.
:)
That said, if you can fit your data sets and your program on to the video memory (128MB isn't uncommon on high-end), and you're doing lengthy calculations on these sets while being only interested in the results (again, not uncommon in HPC), then the relative slowness of reading these results back becomes a nonissue.
Does that help?