Why NVIDIA Thinks CUDA for C and Brook+ Are Viable Alternatives

While OpenCL is a high level API, it does require the programmer to perform certain tasks that don't have much to do with the parallel algorithm being implemented. OpenCL devices in the system need to be found and set up to properly handle the task at hand. This requires a lot of overhead like creation of a context, device selection, creating the command cue(s), management of buffers for supplying and collecting data on the OpenCL device, and dynamically compiling OpenCL kernels within the program. This is all in addition to writing kernels (data parallel functions) and actually using them in a program that does useful work.

The overhead and management work required is similar to what goes on with OpenGL. This makes sense considering the fact that both use GPUs, they can share data with eachother, and that the same standards body that manages OpenGL is now managing OpenCL. But the fact remains that this type of overhead is cumbersome and can be a real headache for anyone who is more interested in the algorithm. Like scientists working on HPC code who know the theory much better than the programming most of the time.

Both Brook+ and CUDA for C hide the complexity of setting up the hardware by allowing the driver to handle the details. This allows developers to write a kernel, use it, and forget about what's actually going on in the hardware for the most part. Going with something like this as a first move for both NVIDIA and AMD was a good move, as it allows developers to get familiar with the type of programming they will be doing in the future for data parallel problems without tacking higher levels of complexity than necessary.

NVIDIA, for one, believes a language extension as opposed to an API like OpenCL has major benefits and will always have a place in GPU computing (and especially in the HPC space where scientists don't want to be programmers any more than they need to). When asked if they would submit their language to a standards body, NVIDIA said that was highly unlikely as there are other language efforts out there and NVIDIA has been advancing CUDA for C much more rapidly than a standards body would.

On the down side, putting more control in the hands of the developer can result in better, faster code. There is a bit of a "black box" feeling to these solutions: you put code in and get results out, but you can't be sure what goes on in the middle to make it happen. OpenCL gives you better ability to fine tune the software and make sure that exactly what you want to happen happens. Despite NVIDIA's assertions that scientists interested in coding for HPC solutions will have a better experience with CUDA, the cost/benefit of ultra-fine tuning code for HPC machines leans heavily in favor of spending the time and money on optimizing. This means that OpenCL will likely be the choice for performance sensitive HPC applications. CUDA for C and Brook+ will likely have more of a place in just trying out ideas before settling on a final direction.

So there you have it. OpenCL will enable applications in the consumer space to take advantage of data parallel hardware, while Brook+ and CUDA may still have a place in the industry as well (but not on the consumer side of things). That is, until some other more popular standard data parallel language extensions come along and pushes both CUDA for C and Brook+ out of the market.

Open, Closed, Proprietary ... Sorting out the Confusion OpenCL Extending OpenGL
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  • v12v12 - Wednesday, January 07, 2009 - link

    Testing123, ignore plz Reply
  • corporategoon - Tuesday, January 06, 2009 - link

    Did this article go through an editor? Reply
  • chizow - Friday, January 02, 2009 - link

    Kind of surprising you didn't directly address this given the amount of FUD being thrown around with regards to PhysX, particularly from AMD and its supporters. You indirectly answered what I had already suspected however, that given Nvidia has stated they plan CUDA to be fully portable to both OpenCL and DX11 there should also be no portability issues for AMD and Brook+:

    quote:

    AMD could make an investment in the CUDA for C language and create either their own compiler (nothing is stopping them). But then you still have the same problem of interoperability as if NVIDIA implemented Brook+. If NVIDIA or AMD want to make their solution work with the other guy, they would need to write a wrapper to translate CAL to PTX or PTX to CAL.


    I'm guessing the unfinished thought from the first sentence should read something like "or write a CUDA to Brook+ wrapper" as thats essentially what the last part suggests. Since both vendors will write wrappers for their code to OpenCL, perhaps this wrapper could pull double duty, although it would double the amount of transcoding needed. Less than efficient for sure, but certainly better than a complete impasse due to incompatibility.


    Reply
  • ltcommanderdata - Friday, January 02, 2009 - link

    Are you suggesting that hardware PhysX acceleration will come to AMD GPUs as soon as nVidia and AMD enable hardware OpenCL support? Because I don't think it's that simple.

    nVidia seems to have rebranded the meaning of CUDA. Maybe it's all just marketing speak, but CUDA before seemed to mean using nVidia GPUs for GPGPUs operation in general. But now since OpenCL, CUDA seems to more specifically related to the GPGPU interface to nVidia GPUs with languages being separate on top, namely OpenCL, DX11 and C for CUDA. If PhysX is written in C for CUDA, which it no doubt is seeing there wasn't anything else available up to now, then adding support for the OpenCL language in the CUDA interface layer won't help get PhysX supported on AMD GPUs. PhysX will still be written in nVidia's proprietary language which AMD GPUs can't understand. To support AMD GPUs, either nVidia will have to rewrite PhysX from C for CUDA to OpenCL, which would be awfully generous of them or AMD will have to make a C for CUDA to CAL translator and hope PhysX doesn't have any nVidia hardware specific optimizations, which it no doubt has, to mess things up.
    Reply
  • apanloco - Friday, January 02, 2009 - link


    Anyone knows if multiple applications can take advantage of OpenCL at the same time? I think OpenGL is exclusive to one application, but if OpenCL is used by regular applications this could be a problem?
    Reply
  • yyrkoon - Thursday, January 01, 2009 - link

    "With R580 AMD (then ATI) actually published part of their ISA and called the initiative CTM (for Close to Metal). Before we had a beta version of CUDA, we had folding@home GPU accelerated on R520 and R580"

    I also read an interview through gamedev.net where ATI was emulating Direct 3D 10 calls in hardware on one of their x1900xtx's ( Direct 3D 9 hardware )long before I heard about folding@home on the GPU. I remember being so impressed with the technology, that I could not wait until Vista + Directx 10 titles became available. Too bad that there are so few ( if any ) titles that currently take advantage of this technology in the ways I had hoped. Hopefully that will change soon.
    Reply
  • ltcommanderdata - Thursday, January 01, 2009 - link

    http://www.tgdaily.com/content/view/38764/140/">http://www.tgdaily.com/content/view/38764/140/

    It's interesting that you mentioned that AMD and nVidia look to be continuing to push their proprietary GPGPU solutions, but AMD has actually made statements they are abandoning their proprietary CTM GPGPU implementation and are moving fully to OpenCL. Admittedly, its probably just a realization that CTM isn't taking off as fast as CUDA and it's in their best interest to push OpenCL. In comparison, nVidia will continue to develop their own CUDA implementation alongside OpenCL.

    I wonder if you can get a statement from nVidia whether they will move PhysX to OpenCL? Right now I believe PhysX is written in C for CUDA and of course requires nVidia GPUs for hardware acceleration. If they moved to OpenCL, then AMD GPUs would support it as well. Although perhaps nVidia prefers to keep PhysX to themselves as a product differentiator.

    It'd also be interesting if you could ask AMD whether older GPUs like the X1600, X1800, and X1900 will be supported in OpenCL? You already pointed out in your article that the RV530, R520, and R580 had GPGPU folding@home clients so they are certainly capable of GPGPU operation. It'd probably be in ATI's own interest to have as large an OpenCL base as possible and ATI's original FireStream dedicated GPGPU card was R580 based as well. Apple could probably help them as well seeing the number of X1600 and X1900 used in various iMac, MacBook Pro, and Mac Pro generations that could use support for OpenCL in Snow Leopard.

    And I agree with melgross that it's strange Apple got no mention in the article seeing that they pretty much developed OpenCL, then submitted it to Khronos, and was no doubt a major driving force behind the quick ratification in order to get it ready for Snow Leopard. And I believe Apple's Aaftab Munshi was the chair of the OpenCL working group.
    Reply
  • danger22 - Thursday, January 01, 2009 - link

    i am looking forward to the day when I can run my finite element simulations on my GPU. come on Ansys its time for a GPGPU Multiphysics! Reply
  • Amiga500 - Thursday, January 01, 2009 - link

    Same boat, same boat... with both CFD and FEA.

    Have you heard of FEAST-GPU (from Dortmund university)?

    Its a GPU accelerated FE package - unfortunately it isn't out in the public domain yet.



    Anyhow - from my own digging, I'm not sure if the CPU is a major bottleneck for FE simulations - a lot of what I see tends to point towards the hard-drive and I/O performance.
    Reply
  • Sheep100 - Sunday, January 04, 2009 - link

    If you provide enough RAM to the analysis you definitely end up CPU limited for single core runs. We have 24 - 32 GB per node for Abaqus and Nastran analyses. The nodes get RAM - bandwidth limited when stepping up the number of cores used or the number of concurrent runs on a node. We are looking forward to the core i7/Nehalem Xeon systems coming soon that will provide a big improvement here. (These codes run slower on Opteron cores.)

    GPGPU versions of Abaqus, Nastran & Ansys would be very interesting given the large memory bandwidth available on the high end cards. I suspect that re-writing & validating the various solver algorithms to target OpenCL would be a long process. I'm also unsure how possible it is to get data parallelism out of them since the scaling rate of Abaqus, for example, on multi-core systems, even with good bandwidth, is not anywhere near linear. Although this might just highlight the deficiency of the current method of extracting parallelism.
    Reply

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