Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Nvidia CEO Introduces Nvidia Ampere Architecture, Nvidia A100 GPU

blogs.nvidia.com

Post reply on HN
Print view View original

161–170 of 347 posts

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

#161

Post by paulmd »

santoshalper wrote: post #124
dijit wrote: post #18

Earlier quoted context omitted.

I don't like that people downvoted you for asking a question. If someone thinks the question is stupid or not doesn't mean that a downvote is warranted. (nor an upvote, answer the question and move on.) To answer though; it's just a coincidence, as you might already know Nvidia uses famous scientists (especially in the field of electricity) as the names of their microarchitectures. * Volta (Alessandro Volta, inventor…

Also, that's not a dumb question! That's a very specific name, and two products coming out in a window of time both using the same name is enough to cause some confusion. I found the question and answer to be very useful.

I was actually surprised that NVIDIA went through with it, it seems like a straightforward trademark case. Two types of computer processor that share a marketing name.

Obviously if you are "in the know" they are not really the same type of processor but it is closer than you usually see companies get with their trademarks

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

#162

Post by Sebb767 »

the_pwner224 wrote: post #149
tyingq wrote: post #22

Earlier quoted context omitted.

I suspect some of it is driven by trying to keep the gaming/ai and desktop/server markets from overlapping. Market segmentation. If it were more open, that would be harder.

I wanted to do GPU PCI passthrough in a VM (run Linux host, then for gaming run a Windows VM with the GPU passed through to get good performance). Nvidia disabled this for their consumer GPUs; the Nvidia drivers in the Windows VM will block this from working. It was a purely software thing; there was no reason for this aside from nvidia wanting companies to pay more for the Quadro/etc. GPUs. In addition to that, ther…

GPU passthrough is also doable pretty easily on NVidia nowadays. See here: https://wiki.archlinux.org/index.php/PCI_passthrough_via_OVM...

/r/VFIO on Reddit is also pretty helpful.

That being said, I fully support you buying and using AMD. But no need to throw out perfectly fine hardware in case you still have NVidia lying arround.

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

#163

Post by dadarepublic »

stagger87 wrote: post #125
dadarepublic wrote: post #112

Earlier quoted context omitted.

Thank you for calling out the downvote issue. I've refrained from asking questions for this exact reason. People should be encouraged to ask questions, even if from a position of lesser knowledge of the matter at hand. Those that do answer are probably not only helping the person asking the question, but those who may not ask the question even if it is one in their mind. How many others refrain from enriching the dia…

Commenting about voting is discouraged in the HN guidelines. Also, questions that could be answered with a quick internet search don't make good discussion.

Thanks for reminding me as I had forgotten. Still it seems a bit weird not to be able to discuss. Again, thanks for the reminder (I suspect others who read this thread may also be reminded).

You are correct in saying some questions are better than others. But again that's no reason to downvote as it actively discourages people from participation. I would think the best course would be to ignore and move on as the original comment on this thread mentioned. Downvoting can be a hostile action. Ignoring is neutral.

As per the guidelines themselves: >Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something.

Case in point, my comment was downvoted, even though the subsequent comment was an illuminating reminder for myself and possibly others. Those downvotes seem somewhat arbitrary at best, hostile at worst.

I've no more to say so I will refrain from any further comments on voting dynamic.

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

#164

Post by amelius »

adev_ wrote: post #121
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…

> AMD could release a 2x powerful GPGPU tomorrow for half the price and most current NVIDIA users wouldn't care because what good is that if you can't program it?. Correction: Nobody will be able to use the AMD hardware (outside of computer graphics) because everybody has been locked-in with CUDA on Nvidia. They can not even change even if they want to: it is pure madness to reprogram an entire GPGPU software stack e…

> Nobody will be able to use the AMD hardware (outside of computer graphics) because everybody has been locked-in with CUDA on Nvidia.

But numpy can be ported. So can pytorch.

I don't think the lock-in is that big of an issue. GPUs do only simple things, but do them fast.

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

#165

Post by fluffything »

adev_ wrote: post #152
fluffything wrote: post #146

Earlier quoted context omitted.

> Correction: Nobody will be able to use the AMD hardware (outside of computer graphics) because everybody has been locked-in with CUDA on Nvidia. NVIDA open-sourced their CUDA implementation to the LLVM project 5 years ago, which is why clang can compile CUDA today, and why Intel and PGI have clang forks compiling CUDA to multi-threaded and vectorized x86-64 using OpenMP. That you can't compile CUDA to AMD GPUs isn'…

> Do you work for AMD I do not. And I use NVidia hardware regularly for GPGPU. But I hate fanboyism. > NVIDA open-source their CUDA implementation to the LLVM project 5 years ago Correction: Google developped an internal CUDA implementation for their own need based on LLVM that Nvidia barely supported it for their own need afterwards. Nothing is "stable" nor "branded" in this work.... Consequently, 99% of public Open…

> Correction: Google developped an internal CUDA implementation for their own need based on LLVM that Nvidia barely supported it for their own need afterwards.

This is widely inaccurate.

While Google did developed a PTX backend for LLVM, the student that worked on that as part of a GSOC got later hired by NVIDIA, and ended up contributing the current NVPTX backend that clang uses today. The PTX backend that Google contributed was removed some time later.

> Nothing is "stable" nor "branded" in this work.

This is false. The NV part of the backend name (NVPTX) literally brands this backend as NVIDIAs PTX backend, in strong contrast with the other PTX backend that LLVM used to have (it actually had both for a while).

> OpenACC backend targets directly the nvidiaptx (proprietary) format.

This is false. Source: I've used the PGI compiler on some Fortran code, and you can mix OpenACC with CUDA Fortran just fine, and compile to x86-64 using OpenMP to just target x86 CPUs. No NVIDIA hardware involved.

> That's wrong again. > > Most of the work done for the parallel STL and by the C++ committee originate from work from HPX and the STELLAR Group

This is also widely inaccurate. The Parallel STL work actually originated with the GCC parallel STL, the Intel TBB, and NVIDIA Thrust libraries [0]. The author of Thrust was the Editor of the Parallelism TS, and is the chair of the Parallelism SG. The members of the STELLAR group that worked on HPX started collaborating more actively with ISO once they started working at NVIDIA after their PhDs. One of them chairs the C++ library evolution working group. The Concurrency working group is also chaired by NVIDIA (by the other nvidia author of the original parallelism TS.

AMD is nowhere to be found in this type of work.

[0] http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2013/n372...

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

#166

Post by andromeduck »

xnyan wrote: post #51
ethbro wrote: post #40

Earlier quoted context omitted.

That's why Intel realized that fab technology was the true differentiator. The only way to outcompete in a sea of clones is to secure exclusive access to a valuable resource they can't. Intel with fabs. Dell with lean supply chains. The surviving hard drive and memory companies with scale. I think IBM and Sun show what happens when you try to fight a stand-up brawl in a commodity space.

>That's why Intel realized that fab technology was the true differentiator. But now the situation is completely reversed. Intel has faced all kinds of problems, costs, and delays due ultimately to the fact that they made a bad choice on their chip architecture but were forced to make it work because they invested so much in the fab. What TSMC is fabbing for nvidia is working out really well, and if it was not nvidia…

I think it's the other way around. The architecture was being limited by their fabs ability to yeild large chips and in the absence of any CPU perf pressure from AMD the natural push would lean more towards increacong graphics performance in order to push more pixels. As in I think Intel probably had the same yeild issues as everyone else ~10-32 nm but only Intel had the high margins small chip volume to make it profitable to ramp until Apple and TSMC happened.

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

#167

Post by btashton »

krapht wrote: post #13
raverbashing wrote: post #9

Earlier quoted context omitted.

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

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 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.

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

#168

Post by zelon88 »

wmf wrote: post #159
zelon88 wrote: post #91

I always thought that a real time scalable architecture would be beneficial. It's refreshing to see someone working on it, and exciting to see that it's nVidia. I always pictured a CPU with variable bit-width. Like a 256-bit ALU that could partition itself down into 16 or 32 bit ALU's as the workload allowed.

That's been around since MMX and AltiVec. It took a while for GPUs to adopt subword SIMD though.

SIMD works great for doing the same thing to multiple pieces of data, but it doesn't do the scaling up that I described.

I'm no chip engineer, so maybe what I'm envisioning isn't possible. In essence, instead of making 4x 64-bit cores you make 128x 2-bit cores and then some architecture on the die to select groups of cores to build a processor of the required size, execute some instructions with that processor, and then disassemble the processor back into a pool of resources.

So SIMD might be able to calculate two 16-bit sums on a 32 bit processor in one cycle, but the hypothetical CPU I'm describing will be able to calculate a single 128 bit sum and eight 16 bit sums in one cycle, at the same time.

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

#169

Post by not2b »

fluffything wrote: post #94
wegs wrote: post #23

Earlier quoted context omitted.

Yeah. That's been my general problem with adopting NVidia for anything. They make good hardware, but there's a lot of lock-in, and not a lot of transparency. That introduces business risk. I'm not in a position where I need GPGPU, but if there wasn't that risk, and generally there were mature, open standards, I'd definitely use it. The major breakpoint would be when libraries like Numpy do it natively, and better yet…

> 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…

It would suffice for NVIDIA to open-source enough specifications and perhaps some subset of core software to enable others to build high quality open source (or even proprietary) software that targets NVIDIA's architecture. They can't hire every programmer in the world; if other programmers can build high-performance software that takes advantage of their platform, that increases the value of their hardware.

Your comparison to Intel isn't valid: most software that runs on Intel processors isn't built with icc, and customers have a choice: they can use icc, gcc, clang, or a number of other compilers. The NVIDIA world isn't equivalent.

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

#170

Post by paulmd »

wegs wrote: post #23
xvilka wrote: post #16

Probably even more closed than ever. They tend to become more and more restrictive with every new hardware generation. I wonder where their promised open source announcement they preannounced before.

Yeah. That's been my general problem with adopting NVidia for anything. They make good hardware, but there's a lot of lock-in, and not a lot of transparency. That introduces business risk. I'm not in a position where I need GPGPU, but if there wasn't that risk, and generally there were mature, open standards, I'd definitely use it. The major breakpoint would be when libraries like Numpy do it natively, and better yet…

NVIDIA has no ethical or moral responsibility to give their competitors the benefit of software they have paid to develop in-house. It is probably a safe bet that you yourself do not develop your projects under the Affero GPL, and so on some level you agree with this.

What you see as "ecosystem lock-in" is properly viewed as software that you pay a premium for as part of your purchase price, above and beyond the pricing of the competitor's hardware. NVIDIA costs more than AMD because they have to employ people to write all that software, and you are "buying" that software when you purchase NVIDIA's product.

Analogously - Amiga has no moral responsibility to let you run AmigaOS on anything except their hardware. This sort of "hardware exclusivity" used to be very common and widely accepted. Today, Apple has no moral responsibility to let you run OS X on anything except their hardware (the existence of underground hackintoshing is irrelevant here). The software is part of what you are buying when you buy the product.

Post reply on HN