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Comment by xvilka | Hacker News Reader

NVIDIA is the major obstacle to FOSS for a decades already. So they keep their spirit even now. Mainline Tensorflow, for example, still doesn't support OpenCL.

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And OpenCL was recently deprecated by OSX, wasn’t it?

Edit: yes. Discussion on hn: https://news.ycombinator.com/item?id=17231593

So what? High-end GPU are used in servers and desktops, which almost exclusively run Linux in this space. The really relevant thing here is hardware (Nvidia vs AMD) and software - Nvidia drivers + CUDA (+ Nvidia's OpenCL) vs AMD drivers + OpenCL.

So far Nvidia does excellent technical work with their hardware + their out of the box CUDA toolkit + support in every major deep learning library. The drawback is that they keep everything closed and they charge a huge premium. AMD has great hardware at good prices but the software side is non-existent and the Linux support goes from bad to worse.

Nvidia is the one having absolute control here and they choose to squeeze the market because they can.

And Nvidia isn’t the only company to do so. Intel had a similar monopoly and squeezed it for all it was worth, while providing the best available scientific computing libraries. I can’t blame them, but I would certainly prefer it if there was a way to generically dispatch optimal code for heterogeneous hardware.

The advantages of MKL (or does that mean something else?) are over-sold. Indeed, I understand the relevant primitives in this context were driven by Pabst's free implementations in libxsmm. I don't know whether "dispatch optimal code for heterogeneous hardware" means chose the SIMD instructions or offload appropriately, but what's missing in whichever case it is?

Intel's free software release seems to me to be rather in their favour compared with other vendors.

I was speaking regarding different GPUs. I’d like a framework which generates the fastest code for performing a given task for whatever GPU you have.

MKL’s fft isn’t a huge improvement on fftw with avx512, but its blas is ~3x as fast as openblas currently. And before these open source projects caught up, it was by far the best.

And I agree, they’ve been better about it lately. I was comparing Intel then and Nvidia now.

I don't know a lot about GPU compilation/offload, but I'd expect normally to dispatch to library kernels the way linear algebra frameworks do, given we can't even compile GEMM effectively on CPUs. Sufficiently smart tools always welcome, though.

For a free avx512 BLAS, use the current release of BLIS. OpenBLAS recently gained skx (but not knl) gemm support, but I don't know how good it is as I don't have the hardware.

And Google never supported it on Android, creating their Renderscript dialect instead.

Khronos failed to appreciate that most devs want to use something else other than C for GPGPU.

When they realized the mistake and came up with SPIR it was too late.

Also SYSCL is no better than CUDA with their community edition.

Could AMD open-sourcing their (hopefully incoming) stack of learning libraries be a competitive edge for them? They are far behind but this could be an important step.

AMD already did. For example, their 3x3 convolution written in HSAIL is licensed under MIT and available here: https://github.com/ROCmSoftwarePlatform/MIOpen/blob/master/s...

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