Comment by ms013 | Hacker News Reader

Comment by ms013 | Hacker News Reader

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Exactly! Plus, even before CUDA existed, people in numerical computing had already noticed that GPUs were ideal hardware for certain classes of problems and were doing awkward things like embedding numerical calculations as shaders and other tricks to use the graphics APIs as a way to encode computations. I recall colleagues who worked on those pre-CUDA systems meeting some resistance from NVIDIA in terms of opening up the low-level APIs that would make the hardware more directly accessible, versus the encodings that they needed to go through to use graphics primitives for the work.

NVIDIA got lucky by having the right kind of hardware, and created CUDA not due to some insight into the AI world, but based on the needs of scientific computing. Just look at their early papers on CUDA (E.g., Luebke's 2008 paper "CUDA: SCALABLE PARALLEL PROGRAMMING FOR HIGH-PERFORMANCE SCIENTIFIC COMPUTING") - AI was nowhere to be seen. They were focused on traditional high performance computing problems.

HPC techniques are exactly those that ML and deep learning use in implementation. I don't see how DL is much different from this point of view. It relies on fast linear algebra. HPC relies on fast linear algebra. Nvidia perfected the processor that does this job orders of magnitude faster and provided convenient libraries and development environment to make it accessible.

Nobody is arguing that not to be true. The issue is arguing that NVIDIA foresaw their systems being relevant to AI/DL/ML: they simply were lucky. They opened their architecture to HPC people, concerned largely with fluid dynamics, finite element methods, nbody methods, and so on. To claim that they had any insight into the emergence of ML as a core application of their silicon is simply a laughable revision of history: as we were talking about in an ancestor of this thread.

Well, I argue that they did foresaw its business viability. They invested heavily in DL before any one of their competitors. Of course there were researchers everywhere working on that, but there are researchers working on everything. What is the most difficult part is recognizing that it can be taken to mainstream and betting huge resources on that. Of course no one argues that Nvidia CEO is the first man in the world that saw GPU/AI applicability. But of those in positions of power in the tech business community...

But they didn't really bet anything huge on it. In 2014 they had a few researchers work on cuDNN; a few years after deep learning had already taken off in academia. The first chip they made which did anything specific for DL was the P100 last year; but no-one is actually using that chip, everyone is using high end gaming chips.

They really haven't made much forward looking investment in this area, they just got lucky since they had built CUDA beforehand for other reasons, and that was what people standardized on.

AMD's chips would work just fine for DL if everything wasn't already written in CUDA, needing them to invest in a cross-platform CUDA alternative in the form of ROCm/HIP.

I've been following AMD's efforts, and I think what you say obscures the practical points.

In fact, I prefer OpenCL to CUDA. However, that it is not only a matter of "a few people"'s effort is that AMD had a few people on BLAS libraries since forever, and their stuff is almost unusable for DL, let alone a match for CUDA. Add to that that Nvidia provided cuDNN that everyone uses. That it is not a matter of just a few people can be seen from the fact that AMD still does not provide an alternative, even a toy one, that works. Everythinig in AMD/OpenCL world relies on a few 3-rd party open-source efforts. Some of those work OK, some are cool, some are great, some are garbage, but there is no ecosystem anywhere near Nvidia's.

They have HIP (and they have OpenCL) but these are only general purpose compilers. They have nothing when it comes to the libraries.

It's pointless to keep arguing about who did what, but I will mention that the ROCm folks have significant headcount and are porting all the major libraries to HIP: https://rocm.github.io/dl.html

In comparison, NVIDIA had all this effort done for them by the library developers.

As someone who has several top of the line GPUs from 3 years ago that are not even properly supported by ROCm, and someone who invested time in OpenCL 2.0 only to be told by AMD that OpenCL 1.2 is what I'm going to get on ROCm because they don't care, I tend to have a cynical view on this.

I'll just say that there is a reason the projects you linked have only a handful of stars on GitHub, despite having significant headcount and supposed megacorp backing.

People have been burned by AMD so many times that rarely anyone cares any more. I do, but I'm not very optimistic...

Personally, I'm waiting for these projects to get upstreamed, I have no interest in running an AMD fork, and thankfully they seem to be trying to get it upstreamed, but that will also depend on the libraries caring enough.

I think everyone in the deep learning space is interested in AMD succeeding so that we're not all locked into NVIDIA or Google's TPUs, but everyone wants someone else to take the leap first and pay that cost.

I'm certainly not going to buy any AMD GPUs before these versions have been upstreamed and a few people have kicked the tires, the lost productivity just isn't worth it.

When Nvidia was targeting HPC the push was for better DP power in GPUs.

Now that Deep Learning is big it's a push toward Half-Precision actually.

Most of the "incredible power" of a Tesla V100 is 100% unusable by a Physicist for example

Also I love your MCMC sampler

Thanks :)

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