Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

It’s Time to Open Up the GPU

gpuopen.com

Post reply on HN
Print view View original

31–40 of 95 posts

Re: It’s Time to Open Up the GPU

#31

Post by [deleted] »

pjmlp wrote: post #29
cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

Maybe if Apple and Khronos cared to define OpenCL native support for C++ and Fortran since day 1, this would have turned out to be different.

[deleted]

Re: It’s Time to Open Up the GPU

#32

Post by mjhirn »

cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

We are developing the Rust Machine Intelligence Framework Leaf[1] for which we created the portable High-Performance Computation Framework Collenchyma [2], which abstracts over CUDA, OpenCL and commen host CPU. Although we are ready to bring Machine Learning to OpenCL supported devices, the OpenCL ecosystem lacks the fundamental kernels, tooling and library that already exist for the CUDA ecosystem (e.g. cuDNN[3]). But we looking forward to support OpenCL, maybe SPIR-V accelerates the process.

[1]: https://github.com/autumnai/leaf

[2]: https://github.com/autumnai/collenchyma

[3]: https://developer.nvidia.com/cudnn

Re: It’s Time to Open Up the GPU

#33

Post by gnarbarian »

cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

I wish we could have hardware vendors compete on hardware with completely open software stacks from drivers on up. AMD is much farther down this path than Nvidia is. The downside of this is it gives the hardware manufacturers less flexibility in implementing new features because the APIs are design by committee.

On one side we have design by committee (Khronos) giving us tools like OpenCL, Vulkan, and OpenGL on the other side we have things like Cuda and DirectX. Design by committee APIs will always have a few characteristics. The standards won't move as fast. The APIs will be more hardware agnostic. And they will either be slower and less feature rich (openCL vs cuda) or lower level and more difficult to use (OpenGL/Vulcan vs DirectX).

Whenever we have a clear market leader they either push their own standard or Embrace Extend and Extinguish the open standards. The leader used to be 3dFx pushing glide, and Nvidia/ATI were the underdogs pushing standards that could run on eachothers hardware.

The underdogs will try to push the open standards (because they must) and the leaders will push proprietary ones (because they can) and they can move faster than the committee. There's always going to be resistance toward spending money on research your competitor can take advantage of.

I think Vulcan and SpirV have the potential to put a real dent in proprietary software stacks. It can provide real benefits over any proprietary stack at the moment for heterogeneous computing. (nvidia's blindspot is they will naturally always favor the GPU). I hope it doesn't end up like OpenCL did (feature poor and less performant than cuda). We're a long ways off from having open hardware and open drivers that can compete against closed stacks due to the performance arms race. But GOOD open standards get code written in them and they can win out over market leaders (like what happened with 3dfx). I think Vulkan might be the starting point for this. If it gains traction and can maintain leadership in performance open stacks will have a chance at competing.

The biggest issue is that closed stacks can always benefit from innovations in open ones but not the reverse. Because of this, I don't think we'll ever see competitive open drivers from market leaders. But we can have competitive open APIs that can compete across vendors, If we can at least get back to that stage I would be happy.

Re: It’s Time to Open Up the GPU

#34

Post by Gracana »

gravypod wrote: post #24
cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

That is and will continue to be one of the biggest concerns for the GPU market for some time now. I assume that after a few years, factories will be buying computers, cameras, and GPUs instead of people for inspection. This will be a large market, and that's what NVIDIA understood when they started making ties with different companies. AMD has a lot of catch up to do, but in my opinion they can do it.

> factories will be buying computers, cameras, and GPUs instead of people for inspection

There's a lot of this stuff on the market already. Even self-contained machine vision devices can be pretty powerful, like this[1] integrated camera+light device with built-in web server for setup and configuration and monitoring. With the built-in software, you can measure parts and verify shapes and dimensions and read labels.

[1] http://www.microscan.com/en-us/Products/Machine-Vision-Syste...

Re: It’s Time to Open Up the GPU

#35

Post by wtallis »

robbies wrote: post #22
wtallis wrote: post #20

Earlier quoted context omitted.

OpenCL is basically a framework to make it more convenient to run shaders for non-graphics purposes. It's still a high-level cross-vendor hardware abstraction layer. This sounds like it's more about officially documenting and supporting the kinds of things people have been reverse-engineering: https://news.ycombinator.com/item?id=10605156

Yes to your OpenCL stuff, no to your reverse-engineering stuff. The post you linked is about generating your own native shaders on GCN. GPUOpen could possibly expose that, but that's a difficult problem to expose to developers considering the variety of architectures exposed at any time by the IHVs (including just inside one IHV at any time). If they did go down that path, they would open up their internal bytecode-t…

Yes, it may be a difficult problem to handle exposing hardware-specific functionality in a non-portable way, but if GPUOpen is to have any substance at all it's obvious that they're going to actually do some of that. This can't just be an announcement of a batch of OpenGL vendor extensions.

And at the moment, it seems like GPU architectures within one company aren't really that much more diverse than Intel's CPU architectures over a similar time period. They're not overhauling the ISA every year.

Re: It’s Time to Open Up the GPU

#36

Post by ethbro »

robbies wrote: post #23
cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

I can only speculate on this aspect (from inside the industry), but it's been plain to see that AMD has been hemorrhaging engineers (though they still have plenty of talent). This relates to your request about getting built-in support: I'm not sure AMD has the manpower to do that work, and they might be hoping the OSS community helps them with that...

I'd be interested in what the ROI on more deeply embedding with OSS devs is vs employing someone to contribute.

From what I've seen, for years the OSS GPU driver crowd's response to "opened" specifications / docs seems to have been "That's nice, but these aren't the docs we need."

Re: It’s Time to Open Up the GPU

#37

Post by ino »

sccxy wrote: post #3
FussyZeus wrote: post #2

Speaking of opening GPU's, is this page making anyone else's scrolling chug hardcore? I don't think I've ever had a page stutter this hard.

5MB website... 90% of this are useless super large images. Also webpagetest.com counted 23 javascript requests.

The thumbnails are 1920×700px in case you use a 20K monitor.

Re: It’s Time to Open Up the GPU

#38

Post by hngiszmo »

cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

Isn't it beautiful how OpenSource is the joker card of the underdogs? Google played it with Android against Apple, too.

In 2007 I did my diploma work based on AMD GPU cause it was better. In 2011/12 AMD was the standard for Bitcoin mining, yielding twice the performance than NVIDIA. Now, buying my laptop I ended buying NVIDIA. What happened to AMD?

Re: It’s Time to Open Up the GPU

#40

Post by Symmetry »

hngiszmo wrote: post #38
cs702 wrote: post #21

This is an AMD project motivated by the fact that NVIDIA has become the de facto standard for GPU computation in deep learning and machine learning applications. TensorFlow, Torch, Theano, CNTK, CAFFE, and every other deep learning framework out there works out of the box with NVIDIA hardware via the CUDA stack, but not with AMD hardware, e.g., via the OpenCL stack. Which is a shame, because the entire NVIDIA hardwar…

Isn't it beautiful how OpenSource is the joker card of the underdogs? Google played it with Android against Apple, too. In 2007 I did my diploma work based on AMD GPU cause it was better. In 2011/12 AMD was the standard for Bitcoin mining, yielding twice the performance than NVIDIA. Now, buying my laptop I ended buying NVIDIA. What happened to AMD?

AMD is still better for bitcoin mining but that's a matter of integer versus floating point processing power.
Post reply on HN