Intel to Develop Discrete GPUs, Hires AMD's Raja Koduri as Chief Architect

Who knows, perhaps Intel will be developing more general purpose massively parallel compute processors, but intends to integrate some of the knowledge and experience accrued from the field of graphics processors.

But as I said in another comment, the truth is Intel doesn't seem to be knowing what it's doing, which is why it's pushing in 5 or 6 different directions with many-core accelerators, FPGAs, custom ASICs, neuromorphic CPUs, quantum computers, graphcores, and so on.

By the time Intel figures out which one of these is "ideal" for machine learning, and behind which arrows to "put more wood," Nvidia will have an insurmountable advantage in the machine learning chip market, backed by an even stronger software ecosystem that Intel can't build because it doesn't yet know "which ML chips will win out".

If I would describe Intel is a sentence these days is "Intel doesn't have a vision." It's mostly re-iterating on its chips and rent-seeking these days by rebranding weak chips with strong chip brands, and adding names like "Silver" and "Gold" to Xeons (and charging more for them, because come on - it says Gold on them!), as well as essentially bringing the DLC nickle-and-diming strategy from games to its chips and motherboards.

Meanwhile, it's wasting billions every year on failed R&D projects and acquisitions because it lacks that vision on what it really needs to do to be successful. Steve Jobs didn't need to build 5 different smartphones to see which one would "win out" in the market.

Effectively, this argument is much like saying that your personal workloads don't use AVX and demanding that Intel tape out a whole different die without it. You would very rightly be laughed out of town for even suggesting it.

Much like the economics of cryptomining cards that lack display outputs, this comes down to whether there is actually enough of a market to justify taping out a whole specialty product just for this one niche, vs the economies of scale that come from mass production. AFter all that is the logic behind using a GPU in the first place, instead of a custom ASIC for your task (like Google's Tensor Processing Unit). On the whole it is probably cheaper if you just suck it up and accept that you're not going to use every last feature of the card on every single workload. It's simply too expensive to tape out a different product for every workload.

This only gets more complicated when you consider that many types of GPGPU computation actually do use things like the texture units, since it allows you to coalesce memory requests with 2D/3D locality rather than simple 1D locality. I would also not be surprised if delta compression were active in CUDA mode, since it is a very generic way to increase bandwidth.

The GPGPU community absolutely does use the whole buffalo here, there is very little hardware that is purely display-specific. If you want hardware that is more "compute-oriented" than the consumer stuff, that's why there's GP100 and GV100 parts. If you want even more compute-oriented than that, you're better off looking at something like that's essentially fixed-function hardware dedicated to your particular task, rather than a general-purpose GPU unit.

So, it doesn't really make any economic sense.

I think the recent Intel + AMD custom chip was probably the last thing Raja did before he got pulled RTG got the reins put back on and now he’s hoping ship to peruse what he’s wanted all along. To work with more independence.

I know AMD has access to some resources, but If Intel decided he was a strategic hire, the game was over before it started. It’s not just that they are richer, it’s worth spending more to them because you could argue it ties into their most important long term IP battles in related areas like massively parallel computing, ML, etc.

I am short AMD, long NVDA. Keep an eye open on ER tomorrow.

[1] https://software.intel.com/en-us/articles/intel-sdm

half precision is FP16.

The GP102 and 104 Cards which include the consumer cards and Tesla’s like the P4 and P40 are inference focused and support Int 16 and Int 8 dot products while the first generation Pascal GP100 doesn’t.

If anyone artificially locked down FP16 support it’s AMD as consumer VEGA doesn’t support it for compute.

2) NVIDIA already has a competing solution, Pascal has had unified memory support form day one, 49 bit address space, AST and paging they already partner with SSD makers to have an addon card which is mapped to VRAM.

I'd love to see the Phi approach taken further. I'm not a huge fan of having different ISAs, one for my CPU, one for the compute engines of the GPU (to say nothing about the blobs on my GPU, network controller, ME). I'd prefer a more general approach where I could easily spread the various workloads running on my CPU to other, perhaps more specialized but still binary-compatible, cores.

Heck... Even my phone has 8 cores (4 fast, 4 power-efficient, running the same ISA).

No, this physically is not present on consumer chips. You can't subdivide the ALUs like that even on Tesla P5000 cards. Of course you can promote FP8 to FP32 without an issue, on any card, but you don't gain any performance either.

At the time Pascal was designed it didn't make any sense to waste die space on FP16 support let alone FP8, since games are purely FP32. This is changing now that Vega has FP16 capability ("Rapid Packed Math") and titles may be using this capability where appropriate. I would not be surprised to see it in Volta gaming cards at all.

It's funny, everything old is new again. Someone comes up with this idea about once every 10 years. Using FP16 or FP24 used to be big back in the DX9 days.

> 2) A licensed / clone of AMD SSG technology to give massive on-GPU memory: NVidia's 12 GB memory is not sufficient for anything beyond thumbnail or VGA sized images.

You're looking for NVIDIA GPUDirect Peer-to-Peer, which has existed since like 2011.

https://developer.nvidia.com/gpudirect

AMD's product is actually purely marketing hype, it's simply a card that contains a PLX chip to interface a NVMe SSD. It is the same technology that is used for multi-GPU cards like the Titan Z or 295x2, and it offers no performance advantages vs a regular NVMe SSD sitting in the next PCIe slot over.

This is something that people didn't know they wanted until AMD told them they wanted it. But you can do this on any GeForce card even, no need to shell out $7000 for some crazy custom card that doesn't even run CUDA.

The bigger problem is that there really isn't much of a use-case for it. NVMe runs at 4 GB/s, which is painfully short of the ~500 GB/s that the GPU normally runs at. That is even significantly less bandwidth than host memory can provide (a 3.0x16 PCIe bus limits you to 16 GB/s of transfers regardless of whether that's coming from NVMe or host memory).

How many machine learning strategies is Intel going to try? Does it even know what it's doing? Spending billions of dollars left and right on totally different machine learning technologies kind of looks like it doesn't, and it's just hoping it will get lucky with one of them.

And even if you think that's not a terrible strategy to "see what works", there's still the issue that they need to have great software support for all of these platforms if they want developer adoption. The more different machine learning strategies it adopts, the harder that's going to be for Intel to achieve.

Even if my precompiled CUDA application could run on Intel GPUs at 50% of the throughput, I'd be happy if I could later tweak and recompile it to get the full benefits from their hardware.

An alternative would be for them to (again magically) modify all major DL frameworks to support their GPUs.

I don't even know which option is more realistic.

I feel like non-competes are similar to parents these days. Everyone has tons of patents and everyone is infringing on everyone else so they just agree to pay licensing fees to one another and never go to war.

I’m certain AMD has hired their share of Intel people by now, its a no win.

Why on earth would you think Intel extending their near-monopoly is a thing to celebrate?

https://en.wikipedia.org/wiki/Larrabee_(microarchitecture)

The case is probably that the Intel graphics team just decided they'd rather play against the big boys at nVidia and actually put enough cores on a chip to be a competitor, but in order to do that, they'd need to go off-chip for power and heat dissipation reasons. Hiring the guy from AMD helps you sell the new solution, since presumably that's what this guy's good at.

The market's rife for being disrupted as it has been incredibly stagnant with nVidia and AMD's tit-for-tat for the past, well, decade.

Microsoft's Mixed Reality platform has the stated goal of running on integrated graphics and even a mid tier card in a year or two should do fine for usable vr/ar.

[1] https://www.intel.com/content/www/us/en/foundry/emib.html

Keep in mind Intel currently builds GPUs - just of the integrated variety. What's new here is that Intel is deciding to build discrete (standalone, like those you'd plug into a PCIe port) GPUs.

A GPU on the CPU die, non-discrete, is often referred to as an "integrated GPU" or "integrated graphics." They're typically not very powerful, though they run common non-gaming applications just fine.