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Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

blogs.nvidia.com

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

#241

Post by fluffything »

wegs wrote: post #122
bigdict wrote: post #52

Earlier quoted context omitted.

Have you heard about CuPy?

Yes. I won't bet my business on a one-vendor solution with a medium-sized community which might disappear at some point. If CuPy supported NVidia and AMD, and was folded into Numpy, I'd buy the biggest, beefiest GPU I could find overnight.

What technology would you bet your business on then?

Today, you can write numpy code, and that runs on pretty much all CPUs from all vendors, with different levels of quality.

A one line change allows you to run all numpy code you write on nvidia GPUs, which at least today, are probably the only GPUs you want to buy anyways.

In practice, you would probably be also running your whole software stack on CPUs, at least for debugging purposes. So if you change your mind about using nvidia hardware at some point, you can just revert that one line change and go back to exclusively targeting CPUs. Or who knows, maybe some other GPU vendors might provide their numpy implementation by then, and you can just go from CuPy to ROCmPy or similar.

Either way, if you are building a numpy stack today, I don't see what you lose today from using CuPy when running your products on hardware for which that's available.

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

#242

Post by [deleted] »

tbenst wrote: post #157

Important to remember the half-precision tensorcore misrepresentations where the 8x improvement over fp32 claimed on Imagenet with tensorcores (V100) was actually only 1.2-2x [1,2]. Furthermore, there are major precision issues with network architectures like variational autoencoders and many others. We use V100s for Richardson-Lucy like deconvolutions for example, where we have near-exact photon counts up to 10,000…

[deleted]

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

#243

Post by l33tman »

Keyframe wrote: post #200
krapht wrote: post #188

Earlier quoted context omitted.

I am confused. If you used to work in DSP then you know the standard practice involves MATLAB, where the default is double precision. This is also the default datatype in NumPy. Most engineers don't like working in fixed-point unless they have to for other reasons, like moving it onto an FPGA or something.

Why not? Because of interop with other libs/tools? It's not _that_ hard, but I can see the problem if whole workflow isn't like that.

You know a cool thing you can do to help fixed point interop between operations in a complex system where you absolutely don't want to accidentally overflow anywhere? You can tack on some bits to the number to control an overall scale of the fixed point number. Let's call it an exponent ;)

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

#244

Post by Keyframe »

l33tman wrote: post #243
Keyframe wrote: post #200

Earlier quoted context omitted.

Why not? Because of interop with other libs/tools? It's not _that_ hard, but I can see the problem if whole workflow isn't like that.

You know a cool thing you can do to help fixed point interop between operations in a complex system where you absolutely don't want to accidentally overflow anywhere? You can tack on some bits to the number to control an overall scale of the fixed point number. Let's call it an exponent ;)

Agreed. Let's standardize it :))

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

#245

Post by blattimwind »

baybal2 wrote: post #195
blattimwind wrote: post #39

Earlier quoted context omitted.

Tesla is famous in the US for inventing polyphase AC and induction motors, but this is really one of these stories were a bunch of people invented the same thing very closely to each other due to a precipitating reaching of understanding. Note that Tesla's designs were IIRC two-phase which is largely inferior to three-phase. The push for three-phase and associated designs and inventions (three phase transformers on a…

Ironically, that's what they put on the PCB. Those yellow things on both side of the chip package are actual micro transformers made of some material with very tricky magnetic properties. I wonder, was the name Ampere a reference to its titanic current consumption? Those only reason to put those on the PCB would be to provide current above 1kA

You're looking at an SXM module, so that PCB is the whole thing. TDP is 400 W. I assume these run at a sub-1 V core voltage due to their relatively low clocks, so yeah, you are looking at a core supply current that may well exceed 500 A at full load and has to be provided by those VRMs crammed on that board.

Worth pointing out that that's not really new. Gaming cards have been running at about a Volt for a bunch of years now and all of those chuck 250+ W, so the currents are rather substantial.

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

#246

Post by Teknoman117 »

fluffything wrote: post #194
adev_ wrote: post #154

Earlier quoted context omitted.

> That you can't compile CUDA to AMD GPUs isn't NVIDIA's fault, it's AMD, for deciding to pursue OpenCL first, then HSA, and now HIP. Using a branded & under patent concurrent proprietary technology and copying its API for your own implementation is Maddness that will lead you for sure in front of a court. It seems that even Google understood that the hard way ( https://en.wikipedia.org/wiki/Google_v._Oracle_America…

How come? There is a CUDA C++ and CUDA C toolchains available under a MIT license, large part s of which are contributed by NVIDIA. How can they sue you for using something that they give you with a license that says "we allow you to do whatever you want with it" ?

the MIT license doesn't have an express patent grant. If Nvidia has a patent on some technology used by the open source code, they could sue you for patent infringement if you use it in a way that displeases them. What they can't do is sue you for copyright infringement.

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

#247

Post by fluffything »

barbegal wrote: post #30
krapht wrote: post #13

Earlier quoted context omitted.

Physics simulations. There's a rule of thumb that to get an n-bit accurate result after a long chain of calculations, intermediate results should be stored with 2n bits. Often using the full dynamic range of a float is necessary because the magnitude of different physical phenomena varies so wildly. I guess people do store intermediate results in floats in order to take advantage of GPU acceleration. However, once yo…

I've always argued that if you are getting close to having to worry about underflow, overflow etc. then you have an ill-conditioned problem and just increasing the size of your intermediate results won't help you a huge amount because you need more precision from your inputs. There are very few fields where you need more than the 7 decimal digits afforded by floats. Maybe the only exceptions are in astrophysics.

When solving linear systems of equations (which arise pretty much everywhere) Krylov subspace methods are usually quite effective because the Krylov subspace is orthonormal.

If your floating-point precision isn't high enough, you'll end up instead with a "subspace" that isn't spanned by orthogonal vectors, and the consequences of this are pretty drastic (requiring n-times the number of iterations to solve the system, you'll never find a solution, etc.). These all happen even if your system of equations has a good condition number, you just need to make the precision low enough.

This is why most people use double precision, and some people use quad precision. For many systems, e.g., if you are using CG as your solver, quad precision can cut the number of iterations by a large factor (2x-4x). These problems are still bandwidth bound, and using quad precision duplicates your memory bandwidth requirements, but if it reduces the number of iterations by 4x, you just halved your time to solution.

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

#248

Post by m463 »

chrischen wrote: post #193
ska wrote: post #158

Earlier quoted context omitted.

> but this is really one of these stories were a bunch of people invented the same thing very closely to each other due to a precipitating reaching of understanding. This is by far the dominant case of invention. Truly independent work is incredibly rare.

And yet we grant patents so liberally, giving a windfall to the first person who files.

Remember the long view - patents cause people to hurry to publish and share their ideas publically. Why shouldn't they be granted liberally.

In a few years, the temporary monopoly falls away and the benefit passes to everyone.

I think they should work to make them even cheaper and easier to file.

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

#249

Post by pcwalton »

fermienrico wrote: post #137
fluffything wrote: post #94

Earlier quoted context omitted.

> They make good hardware, but there's a lot of lock-in, and not a lot of transparency. This sounds like you'd like NVIDIA to open-source all their software. I see this type of request a lot, but I don't see it happening. NVIDIA's main competitive advantage over AMD and Intel is its software stack. AMD could release a 2x powerful GPGPU tomorrow for half the price and most current NVIDIA users wouldn't care because wh…

People who are into RISC-V and other side projects/open stacks obviously have not worked on mission critical problems. When you have a Jet engine hoisted up for a test rig, and something fails in your DSP library, you don't hesitate to call Matlab engineering support to help on within next 30 mins. Try that with some python library. People give a lot of flak to Matlab for being closed source but there is a reason the…

> People who are into RISC-V and other side projects/open stacks obviously have not worked on mission critical problems.

"People who are into RISC-V" nowadays includes folks like Chris Lattner, who has worked on more mission-critical problems than most everyone here.

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

#250

Post by btashton »

krapht wrote: post #188
btashton wrote: post #167

Earlier quoted context omitted.

I thought for this the standard practice is fixed-point. Requires more planning and mental gymnastics but usually much faster and gives you full control of the precision. Maybe this has changed from my DSP days.

I am confused. If you used to work in DSP then you know the standard practice involves MATLAB, where the default is double precision. This is also the default datatype in NumPy. Most engineers don't like working in fixed-point unless they have to for other reasons, like moving it onto an FPGA or something.

most of it was FPGA work but in some cases DSP processors. Even when you have floating point support it is much slower than if you use fixed point. As for MATLAB, modeled plenty of filters in it for fixed-point math.
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