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Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

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Re: Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

#4
post #2

Extending the tensor Ops to FP64 is an interesting, if not surprising, design choice. Are there many applications sure to leverage this capability? Aside from HPL, of course.

I am suspecting that this is specifically targeted at the HPC market as the lack of FP64 has always been a hindrance to HPC deployment.

You have to remember that the HPC market is $35B today. HPE makes $3B a year alone from that, maybe more with Cray acquisition.

So it's no surprise that NVIDIA wants to position themselves on that market.

Plus, you have too look at the long game with MLX acquisition ( heavy player in the HPC market) and Cumulus. I wouldn't be surprised to see NVIDIA trying to bypass Intel/AMD completely and offer Direct to Interconnect device. Rather than the hybrid CPU + GPU box.

Re: Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

#5
It seems like the TF32 format is similar to BF16 but with 3 more precision bits (in other words, it is FP32 with 13 low-order bits dropped instead of 16).

Full adders in FP units scale with the square of the mantissa bits, so if the number of mantissa bits stay the same, they can reuse the existing units.

Since Nvidia already has existing FP16 units on die, using those units for TF32 calculations probably doesn't cost too much additional die area.

Re: Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

#8
post #3

Oof. Double-precision is only 2.5x better, which is less impressive than 20x for float. I still haven't found anything more cost-effective for double precision data processing (cost + dev time) than a rack full of used Xeons...

The double-precision number probably best represents the generational improvement.

The 20x 32-bit floating point improvement is probably achieved by comparing doing full FP32 calculations on the previous generation vs doing TF32 calculations on Ampere. This would not be an apple-to-apple comparison as the TF32 result is less precise.

That said, it is probably not terribly important for deep learning at least, given the success of BF16.

Re: Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

#9
post #3

Oof. Double-precision is only 2.5x better, which is less impressive than 20x for float. I still haven't found anything more cost-effective for double precision data processing (cost + dev time) than a rack full of used Xeons...

I'm curious: why exactly do you need double precision digits? Not dismissing, just wondering what kind of application needs it.
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