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How to Think About GPUs

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Re: How to Think About GPUs

#21

Post by Philpax »

tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

There's more in common with other GPU architectures than there are differences, so a CUDA consultant should be able to pivot if/when the other players are a going concern. It's more about the mindset than the specifics.

Re: How to Think About GPUs

#22

Post by dotancohen »

Philpax wrote: post #21
tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

There's more in common with other GPU architectures than there are differences, so a CUDA consultant should be able to pivot if/when the other players are a going concern. It's more about the mindset than the specifics.

I've been hearing that for over a decade. I can't even name off hand any CUDA competitors, none of them are likely to gain enough traction to upset CUDA in the coming decade.

Re: How to Think About GPUs

#23

Post by WithinReason »

tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

What's in this article would apply to most other hardware, just with slightly different constants

Re: How to Think About GPUs

#24

Post by Philpax »

dotancohen wrote: post #22
Philpax wrote: post #21

Earlier quoted context omitted.

There's more in common with other GPU architectures than there are differences, so a CUDA consultant should be able to pivot if/when the other players are a going concern. It's more about the mindset than the specifics.

I've been hearing that for over a decade. I can't even name off hand any CUDA competitors, none of them are likely to gain enough traction to upset CUDA in the coming decade.

Hence the "if" :-)

ROCm is getting some adoption, especially as some of the world's largest public supercomputers have AMD GPUs.

Some of this is also being solved by working at a different abstraction layer; you can sometimes be ignorant to the hardware you're running on with PyTorch. It's still leaky, but it's something.

Re: How to Think About GPUs

#25

Post by amelius »

tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

I mean it is similar to investing time in learning assembly language.

For most IT folks it doesn't make much sense.

Re: How to Think About GPUs

#26

Post by qwertox »

tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

It's a valid point of view, but I don't see the value in sharing it.

There are enough people for who it's worth it, even if just for tinkering, and I'm sure you are aware of that.

It reads a bit like "You shouldn't use it because..."

Learning about Nvidia GPUs will teach you a lot about other GPUs as well, and there are a lot of tutorials about the former, so why not use it if it interests you?

Re: How to Think About GPUs

#27

Post by physicsguy »

Philpax wrote: post #24
dotancohen wrote: post #22

Earlier quoted context omitted.

I've been hearing that for over a decade. I can't even name off hand any CUDA competitors, none of them are likely to gain enough traction to upset CUDA in the coming decade.

Hence the "if" :-) ROCm is getting some adoption, especially as some of the world's largest public supercomputers have AMD GPUs. Some of this is also being solved by working at a different abstraction layer; you can sometimes be ignorant to the hardware you're running on with PyTorch. It's still leaky, but it's something.

I still don't see ROCm as that serious a threat, they're still a long way behind in library support.

I used to use ROCFFT as an example, it was missing core functionality that cuFFT has had since like 2008. It looks like they've finally caught up now, but that's one library among many.

Re: How to Think About GPUs

#28

Post by physicsguy »

tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

It really isn't that hard to pivot. It's worth saying that if you were already writing OpenMP and MPI code then learning CUDA wasn't particularly difficult to get started, and learning to write more performant CUDA code would also help you write faster CPU bound code. It's an evolution of existing models of compute, not a revolution.

Re: How to Think About GPUs

#29

Post by woooooo »

qwertox wrote: post #26
tormeh wrote: post #19

I find it very hard to justify investing time into learning something that's neither open source nor has multiple interchangeable vendors. Being good at using Nvidia chips sounds a lot like being an ABAP consultant or similar to me. I realize there's a lot of money to be made in the field right now, but IIUC historically this kind of thing has not been a great move.

It's a valid point of view, but I don't see the value in sharing it. There are enough people for who it's worth it, even if just for tinkering, and I'm sure you are aware of that. It reads a bit like "You shouldn't use it because..." Learning about Nvidia GPUs will teach you a lot about other GPUs as well, and there are a lot of tutorials about the former, so why not use it if it interests you?

It's a useful bit of caution to remember transferrable fundamentals, I remember when Oracle wizards were in high demand.
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