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Comment by TTPrograms | Hacker News Reader

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.

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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-...

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