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Unfortunately that support is via ROCm, which doesn't support the last three generations (!) of AMD hardware: https://github.com/ROCm/ROCm.github.io/blob/master

Unfortunately that support is via ROCm, which doesn't support the last three generations (!) of AMD hardware: https://github.com/ROCm/ROCm.github.io/blob/master/hardware.md https://github.com/ROCm/ROCm.github.io/blob/master/hardware....

dragontamer 5y ago
ROCm supports Vega, Vega 7nm, and CDNA just fine. The issue is that AMD has split their compute into two categories: * 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.
- byefruit 5y ago
  That makes a lot more sense, thanks. They could do with making that a lot clearer on the project. 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.
  - dragontamer 5y ago
    NVidia can support anything because they have a PTX-translation layer between cards, and invest heavily on PTX. 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.
  - blagie 5y ago
    I bought an ATI card for deep learning. I'm a big fan of open source. Less than 12 months later, ROCm dropped support. I bought an NVidia, and I'm not looking back. 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.
    - dragontamer 5y ago
      > Yes, I could figure it out, but I want plug-and-play, stability, and backwards-compatibility Its right there in the ROCm introduction. https://github.com/RadeonOpenCompute/ROCm#Hardware-and-Software-Support 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.
- wmf 5y ago
  Hopefully CDNA2 will be similar enough to RDNA2/3 that the same software stack will work with both.
  - dragontamer 5y ago
    I assume the opposite is going on. Hopefully the RDNA3 software stack is good enough that AMD decides that CDNA2 (or CDNA-3) can be based off of the RDNA-instruction set. AMD doesn't want to piss off its $100 million+ customers with a crappy software stack. --------- BTW: AMD is reporting that parts of ROCm 4.3 are working with the 6900 XT GPU (suggesting that RDNA code generation is beginning to work). I know that ROCm 4.0+ has made a lot of github checkins that suggest that AMD is now actively working on the RDNA-code generation. Its not officially written into the ROCm documentation yet, its mostly the discussions with ROCm github issues that are noting these changes. Its not official support and its literally years late. But its clear what AMD's current strategy is.
- paulmd 5y ago
  > ROCm supports Vega, Vega 7nm, and CDNA just fine. yeah, but that's exactly what OP said - Vega is three generations old at this point, and that is the last consumer GPU (apart from VII which is a rebranded compute card) that ROCm supports. On the NVIDIA side, you can run at least basic tensorflow/pytorch/etc on a consumer GPU, and that option is not available on the AMD side, you have to spend $5k to get a GPU that their software actually supports. Not only that but on the AMD side it's a completely standalone compute card - none of the supported compute cards do graphics anymore. Whereas if you buy a 3090 at least you can game on it too.
  - Tostino 5y ago
    I really don't think people appreciate the fact enough that for developers to care to learn about building software for your platform, you need to make it accessible for them to run that software. That means "run on the hardware they will already have". AMD really need to push to get ROCm compiling for RDNA based chips.
    - paulmd 5y ago
      yeah, it's hard to overstate the importance of getting your hardware into people's hands to work with. A $5k buy-in before you can even play with programming on a device and figure out if your program is going to work on GPGPU at all is a big deal! CUDA being accessible on a $100 gaming GPU has enabled a lot of development, and NVIDIA also has made big efforts to get their hardware out to universities, and that has paid big dividends as well. Also, just flat-out using your own developers to write the basic code for libraries and frameworks and so on. There are tons of basic libraries like CuRAND and CUB (Cuda UnBound) and I'm sure tons of others that NVIDIA wrote not because a customer wanted that, but because it was building blocks that others could leverage into products that would sell NVIDIA hardware. In contrast AMD has fallen behind in that area, and has further fragmented their development work across a bunch of "flavor of the week" frameworks and implementations that usually get discarded within a couple years (remember HIP? remember Bolt? etc)
- FeepingCreature 5y ago
  You don't think it's reasonable to expect machine learning to work on new cards? That's exactly the point. ML on AMD is a third-class citizen.
  - dragontamer 5y ago
    AMD's MI100 has those 4x4 BFloat16 and FP16 matrix multiplication instructions you want, with PyTorch and TensorFlow compiling down into them through ROCm. Now don't get me wrong: $9000 is a lot for a development system to try out the software. NVidia's advantage is that you can test out the A100 by writing software for cheaper GeForce cards at first. NVidia also makes it easy with the DGX computer to quickly get a big A100-based computer. AMD you gotta shop around with Dell vs Supermicro (etc. etc.) to find someone to build you that computer.
- slavik81 5y ago
  There's unofficial support in the rocm-4.3.0 math-libs for gfx1030 (6800 / 6800 XT / 6900 XT). rocBLAS also includes gfx1010, gfx1011 and gfx1012 (5000 series). If you encounter any bugs in the {roc,hip}{BLAS,SPARSE,SOLVER,FFT} stack with those cards, file GitHub issues on the corresponding project. I have not seen any problems with those cards in BLAS or SOLVER, though they don't get tested as much as the officially supported cards. FWIW, I finally managed to buy an RX 6800 XT for my personal rig. I'll be following up on any issues found in the dense linear algebra stack on that card. I work for AMD on ROCm, but all opinions are my own.
  - BadInformatics 5y ago
    I've mentioned this on other forums, but it would help to have some kind of easily visible, public tracker for this progress. Even a text file, set of GitHub issues or project board would do. Why? Because as-is, most people still believe support for gfx1000 cards is non-existent in any ROCm library. Of course that's not the case as you've pointed out here, but without any good sign of forward progress, your average user is going to assume close to zero support. Vague comments like https://github.com/RadeonOpenCompute/ROCm/issues/1542 https://github.com/RadeonOpenCompute/ROCm/issues/1542 are better than nothing, but don't inspire that much confidence without some more detail.

dragontamer 5y ago

ROCm supports Vega, Vega 7nm, and CDNA just fine. The issue is that AMD has split their compute into two categories: * 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.

byefruit 5y ago
That makes a lot more sense, thanks. They could do with making that a lot clearer on the project. 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.
- dragontamer 5y ago
  NVidia can support anything because they have a PTX-translation layer between cards, and invest heavily on PTX. 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.
- blagie 5y ago
  I bought an ATI card for deep learning. I'm a big fan of open source. Less than 12 months later, ROCm dropped support. I bought an NVidia, and I'm not looking back. 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.
  - dragontamer 5y ago
    > Yes, I could figure it out, but I want plug-and-play, stability, and backwards-compatibility Its right there in the ROCm introduction. https://github.com/RadeonOpenCompute/ROCm#Hardware-and-Software-Support 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.
wmf 5y ago
Hopefully CDNA2 will be similar enough to RDNA2/3 that the same software stack will work with both.
- dragontamer 5y ago
  I assume the opposite is going on. Hopefully the RDNA3 software stack is good enough that AMD decides that CDNA2 (or CDNA-3) can be based off of the RDNA-instruction set. AMD doesn't want to piss off its $100 million+ customers with a crappy software stack. --------- BTW: AMD is reporting that parts of ROCm 4.3 are working with the 6900 XT GPU (suggesting that RDNA code generation is beginning to work). I know that ROCm 4.0+ has made a lot of github checkins that suggest that AMD is now actively working on the RDNA-code generation. Its not officially written into the ROCm documentation yet, its mostly the discussions with ROCm github issues that are noting these changes. Its not official support and its literally years late. But its clear what AMD's current strategy is.
paulmd 5y ago
> ROCm supports Vega, Vega 7nm, and CDNA just fine. yeah, but that's exactly what OP said - Vega is three generations old at this point, and that is the last consumer GPU (apart from VII which is a rebranded compute card) that ROCm supports. On the NVIDIA side, you can run at least basic tensorflow/pytorch/etc on a consumer GPU, and that option is not available on the AMD side, you have to spend $5k to get a GPU that their software actually supports. Not only that but on the AMD side it's a completely standalone compute card - none of the supported compute cards do graphics anymore. Whereas if you buy a 3090 at least you can game on it too.
- Tostino 5y ago
  I really don't think people appreciate the fact enough that for developers to care to learn about building software for your platform, you need to make it accessible for them to run that software. That means "run on the hardware they will already have". AMD really need to push to get ROCm compiling for RDNA based chips.
  - paulmd 5y ago
    yeah, it's hard to overstate the importance of getting your hardware into people's hands to work with. A $5k buy-in before you can even play with programming on a device and figure out if your program is going to work on GPGPU at all is a big deal! CUDA being accessible on a $100 gaming GPU has enabled a lot of development, and NVIDIA also has made big efforts to get their hardware out to universities, and that has paid big dividends as well. Also, just flat-out using your own developers to write the basic code for libraries and frameworks and so on. There are tons of basic libraries like CuRAND and CUB (Cuda UnBound) and I'm sure tons of others that NVIDIA wrote not because a customer wanted that, but because it was building blocks that others could leverage into products that would sell NVIDIA hardware. In contrast AMD has fallen behind in that area, and has further fragmented their development work across a bunch of "flavor of the week" frameworks and implementations that usually get discarded within a couple years (remember HIP? remember Bolt? etc)
FeepingCreature 5y ago
You don't think it's reasonable to expect machine learning to work on new cards? That's exactly the point. ML on AMD is a third-class citizen.
- dragontamer 5y ago
  AMD's MI100 has those 4x4 BFloat16 and FP16 matrix multiplication instructions you want, with PyTorch and TensorFlow compiling down into them through ROCm. Now don't get me wrong: $9000 is a lot for a development system to try out the software. NVidia's advantage is that you can test out the A100 by writing software for cheaper GeForce cards at first. NVidia also makes it easy with the DGX computer to quickly get a big A100-based computer. AMD you gotta shop around with Dell vs Supermicro (etc. etc.) to find someone to build you that computer.
slavik81 5y ago
There's unofficial support in the rocm-4.3.0 math-libs for gfx1030 (6800 / 6800 XT / 6900 XT). rocBLAS also includes gfx1010, gfx1011 and gfx1012 (5000 series). If you encounter any bugs in the {roc,hip}{BLAS,SPARSE,SOLVER,FFT} stack with those cards, file GitHub issues on the corresponding project. I have not seen any problems with those cards in BLAS or SOLVER, though they don't get tested as much as the officially supported cards. FWIW, I finally managed to buy an RX 6800 XT for my personal rig. I'll be following up on any issues found in the dense linear algebra stack on that card. I work for AMD on ROCm, but all opinions are my own.
- BadInformatics 5y ago
  I've mentioned this on other forums, but it would help to have some kind of easily visible, public tracker for this progress. Even a text file, set of GitHub issues or project board would do. Why? Because as-is, most people still believe support for gfx1000 cards is non-existent in any ROCm library. Of course that's not the case as you've pointed out here, but without any good sign of forward progress, your average user is going to assume close to zero support. Vague comments like https://github.com/RadeonOpenCompute/ROCm/issues/1542 https://github.com/RadeonOpenCompute/ROCm/issues/1542 are better than nothing, but don't inspire that much confidence without some more detail.