Still handicaps them compared to Nvidia where you can just buy anything recent and expect it to work. Suspect it also means they get virtually no open source contributions from the community because nobody can run or test it on personal hardware.
Each assembly language from each generation of cards changes. PTX recompiles the "pseudo-assembly" instructions into the new assembly code each generation.
---------
AMD has no such technology. When AMD's assembly language changes (ex: from Vega into RDNA), its a big compiler change. AMD managed to keep the ISA mostly compatible from 7xxx GCN 1.0 series in the late 00s all the way to Vega 7nm in the late 10s... but RDNA's ISA change was pretty massive.
I think its only natural that RDNA was going to have compiler issues.
---------
AMD focused on Vulkan / DirectX support for its RDNA cards, while its compute team focused on continuing "CDNA" (which won large supercomputer contracts). So that's just how the business ended up.
This makes absolutely no sense to me, and I have a Ph.D:
"* RDNA -- consumer cards. A new ISA with new compilers / everything. I don't think its reasonable to expect AMD's compilers to work on RDNA, when such large changes have been made to the architecture. (32-wide instead of 64-wide. 1024 registers. Etc. etc.) * CDNA -- based off of Vega's ISA. Despite being "legacy ISA", its pretty modern in terms of capabilities. MI100 is competitive against the A100. CDNA is likely going to run Frontier and El Capitan supercomputers. ROCm focused on CDNA. They've had compilers emit RDNA code, but its not "official" and still buggy. But if you went for CDNA, that HIP / ROCm stuff works enough for the Oak Ridge National Labs. Yeah, CDNA is expensive ($5k for MI50 / Radeon VII, and $9k for MI100). But that's the price of full-speed scientific-oriented double-precision floating point GPUs these days.
I neither know nor care what RDNA, CDNA, A100, MI50, Radeon VII, MI100, or all the other AMD acronyms are. Yes, I could figure it out, but I want plug-and-play, stability, and backwards-compatibility. I ran into a whole different minefield with AMD. I'd need to run old ROCm, downgrade my kernel, and use a different card to drive monitors than for ROCm. It was a mess.
NVidia gave me plug-and-play. I bought a random NVidia card with the highest "compute level," and was confident everything would work. It does. I'm happy.
Intel has historically had great open source drivers, and if it give better plug-and-play and open source, I'll buy Intel next time. I'm skeptical, though. The past few year, Intel has a hard time tying their own shoelaces. I can't imagine this will be different.
Its right there in the ROCm introduction.
https://github.com/RadeonOpenCompute/ROCm#Hardware-and-Softw...
> ROCm officially supports AMD GPUs that use following chips:
> GFX9 GPUs
> "Vega 10" chips, such as on the AMD Radeon RX Vega 64 and Radeon Instinct MI25
> "Vega 7nm" chips, such as on the Radeon Instinct MI50, Radeon Instinct MI60 or AMD Radeon VII, Radeon Pro VII
> CDNA GPUs
> MI100 chips such as on the AMD Instinct™ MI100
--------
The documentation of ROCm is pretty clear that it works on a limited range of hardware, with "unofficial" support at best on other sets of hardware.
(1) There are a million different ROCm pages and introductions
(2) Even that page is out-of-date, and e.g. claims unofficial support for "GFX8 GPUs: Polaris 11 chips, such as on the AMD Radeon RX 570 and Radeon Pro WX 4100," although those were randomly disabled after ROCm 3.5.1.
... if you have a Ph.D in AMD productology, you might be able to figure it out. If it's merely in computer science, math, or engineering, you're SOL.
There are now unofficial guides to downgrading to 3.5.1, only 3.5.1 doesn't work with many modern frameworks, and you land in a version incompatibility mess.
These aren't old cards either.
Half-decent engineer time is worth $350/hour, all in (benefits, overhead, etc.). Once you've spent a week futzing with AMD's mess, you're behind by the cost of ten NVidia A4000 cards which Just Work.
As a footnote, I suspect in the long term, small purchases will be worth more than the supercomputing megacontracts. GPGPU is wildly underutilized right now. That's mostly a gap of software, standards, and support. If we can get that right, every computer have many teraflops of computing power, even for stupid video chat filters and whatnot.
It seems pretty simple to me if we're talking about compute. The MI-cards are AMD's line of compute GPUs. Buy an MI-card if you want to use ROCm with full support. That's MI25, MI50, or MI100.
> As a footnote, I suspect in the long term, small purchases will be worth more than the supercomputing megacontracts. GPGPU is wildly underutilized right now. That's mostly a gap of software, standards, and support. If we can get that right, every computer have many teraflops of computing power, even for stupid video chat filters and whatnot.
I think you're right, but the #1 use of these devices is running video games (aka: DirectX and Vulkan). Compute capabilities are quite secondary at the moment.
For tasks which require that much GPU, I'm using cloud machines. My dev desktop would like a working GPU 24/7, but it doesn't need to be nearly that big.
If I had my druthers, I would have bought an NVidia 3050, since it has adequate compute, and will run once available <$300. Of course, anything from the NVidia consumer line is impossible to buy right now, except at scalper prices.
I just did a web search. The only card from that series I can find for sale, new, was the MI100, which runs $13k. The MI50 doesn't exist, and the MI25 can only be bought used on eBay. Corporate won't do eBay. Even the MI100 would require an exception, since it's an unauthorized vendor (Amazon doesn't have it).
Combine that with poor software support, and an unknown EOL, and it's a pretty bad deal.
> I think you're right, but the #1 use of these devices is running video games (aka: DirectX and Vulkan). Compute capabilities are quite secondary at the moment.
Companies should maximize shareholder value. Right now:
- NVidia is building an insurmountable moat. I already bought an NVidia card, and our software already has CUDA dependencies. I started with ROCm. I dropped it. I'm building developer tools, and if they pick up, they'll carry a lot of people.
- It will be years before I'm interested in trying ROCm again. I was oversold, and AMD underdelivered.
- Broad adoption is limited by lack of standards and mature software.
It's fine to day compute capabilities are secondary right now, but I think that will limit AMD in the long term. And I think lack of standards is to NVidia's advantage right now, but it will hinder long-term adoption.
If I were NVidia, I'd make:
- A reference CUDA open-source implementation which makes CUDA coda 100% compatible with Xe and Radeon
- License it under GPL with a CLA, so any Intel and AMD enhancements are open and flow back
- Have nominal optimizations in the open-source reference implementation, while keeping the high-performance proprietary optimizations NVidia proprietary (and only for NVidia GPUs)
This would encourage broad adoption of GPGPU, since any code I wrote would work on any customer machine, Intel, AMD, or NVidia. On the other hand, it would create an unlevel playing field for NVidia, since as the copyright holder, only NVidia could have proprietary optimizations. HPC would go to NVidia, as would markets like video editing or CAD.