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Comment by The_rationalist | Hacker News Reader

Actually AMD gpus are the most cost effective gpus and have supported full FP16/8 since 2 years. But people are locked in the Nvidia proprietary jail and no one seems to care... That is really sad and against the consummer interest, but also, deep learning will never become mainstream if it can only run on 5% of the world hardware (Nvidia).

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> Actually AMD gpus are the most cost effective gpus.

No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus.

> But people are locked in the Nvidia proprietary jail and no one seems to care...

Sounds like you want to blame the users, but this is because Nvidia highly invested on GPGPU and Cuda since more than 10 years ago, while AMD did focus on something else like HSA. It is AMD’s fault.

> No, AMD gpu is zero cost effective because Tensorflow does not support AMD gpus.

Incorrect, see:

https://github.com/ROCmSoftwarePlatform/tensorflow-upstream

Do you know where we can find some benchmarks?

I agree with kbumsik here. AMD only has themselves to blame. They have great hardware and fantastic theoretical benchmarks. Heck, even their SGEMMs are really fast and in line with the 15TFlops of FP32 on the VEGA 64s that we've benchmarked. However, it comes down to software ecosystem and optimizations for common deep learning subroutines.

MIOpen[1] is a step in this direction but still causes the VEGA 64 + MIOpen to be 60% of the performance of a 1080 Ti + CuDNN based on benchmarks we've conducted internally at Lambda. Let that soak in for a second: the VEGA 64 (15TFLOPS theoretical peak) is 0.6x of a 1080 Ti (11.3TFLOPS theoretical peak). MIOpen is very far behind CuDNN.

Lisa Su, if you're reading this, please give the ROCm team more budget!

1. https://github.com/ROCmSoftwarePlatform/MIOpen

Or, maybe, a new team.

If AMD developed CUDA transpilers / DL backends / CUDNN equivalents people would switch over in a heartbeat. It's fully their fault for not investing in the software.

If they did that and had a card that got 2x performance / $ or more I would switch in a heartbeat.

AMD has a CUDA Transpiler (https://github.com/ROCm-Developer-Tools/HIP) and a multitude of other open source libraries through GPUOpen ( https://gpuopen.com/professional-compute/).

The quality and open source nature of their tools has resulted in much of my research group (real-time vision) increasingly and voluntarily moving to work on AMD platforms (we were previously almost exclusively using CUDA).

But the GPU needs to support ROCm, which is quite recent right? e.g. The MBP 2015's Radeon m370x doesn't seem to support it.

less recent than a 1080 or 2080ti

2080 Ti is brand new :/

Here is it: https://github.com/ROCm-Developer-Tools/HIP

Also, AMD does not limit FP performance on consumer cards.

> Also, AMD does not limit FP performance on consumer cards.

I don't understand this meme. The consumer cards are different chips with slow fp64 hardware. In what sense is that "limiting" performance relative to the enterprise cards?

On AMD, if I'm not not mistaken, FP16 is twice of FP32, as it should be

"For their consumer cards, NVIDIA has severely limited FP16 CUDA performance. GTX 1080’s FP16 instruction rate is 1/128th its FP32 instruction rate, or after you factor in vec2 packing, the resulting theoretical performance (in FLOPs) is 1/64th the FP32 rate, or about 138 GFLOPs."

https://www.anandtech.com/show/10325/the-nvidia-geforce-gtx-...

"FP16 performance is 1/64th and FP64 is 1/32th of FP32 performance."

https://blog.inten.to/hardware-for-deep-learning-part-3-gpu-...

I agree with your broader point, but stating that Nvidia comprises a mere 5% of the world's hardware is disingenous in a post where you've only talked about GPUs. In actual fact, Nvidia has the overwhelming majority of the high end GPU market, especially in HPC and ML. And as much as I dislike the proprietary lock-in, they have by far the superior software ecosystem.

Actually nvidia isn't the high end leader if you account console APUs (almost all being from AMD). And most gpus in the world are from igpus, apus or ARM SoCs. Dgpu IS a small market, High end Dgpu is a few percents max.

Deep learning generally doesn't run on AMD GPUs, but it runs on a lot more than only Nvidia GPUs.

If we're only counting model training, it runs on CPUs, Google's TPUs, FPGAs, whatever other secret datacenter ASICs are out there, various DL-specific mobile chips, etc.

Way more than 5% of the world's hardware can run inference with deep neural nets, which is the important thing for mass adoption, and definitely more than only Nvidia GPUs can run training.

I haven't had a great experience using anything else than CUDA, e.g. OpenCL. Then again maybe I just lack the skills :x

Do you own AMD stock?

Why this question ? No I don't. My message is objective and openCL/sysCL allow to run on All accellerators, not just AMD nor Nvidia.

Because you started your comment with an objective falsehood, NVIDIA has the most cost effective GPUs as long as CUDA king and AMD doesn’t invest more on the software front

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