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I tried ROCm. I bought a supported card (RX570/RX580 series). Within 12 months, AMD dropped support. Newer versions of ROCm didn't work with the card. Older ver

I tried ROCm. I bought a supported card (RX570/RX580 series). Within 12 months, AMD dropped support. Newer versions of ROCm didn't work with the card. Older versions didn't actually work either, since all other tooling assumed newer versions. Dependency hell. When things kinda started working in one context, where I could use old tooling (not the one I wanted to use ROCm in), it CUPy was slower than CPU, and then hard crashed my computer randomly. I read a web page that the card can either act as HIP or a graphics card, but not both at the same time. I have no idea if that's right, but if it is, it's dumb. AMD had no support. Card maker said this didn't fall under warranty. I got burned over and over. I bought NVidia. It just worked. I'm working on a potentially major piece of infrastructure, and AMD is accumulating debt. If it worked out-of-the-gate, I imagine we would have kept support. Within 6 more months, we'll be NVidia-specific. AMD will be that much further in the hole for support. I'd love for ROCm to win, since I think open is critical here. On the other hand, I can't imagine it will. AMD would need to run this as a loss leader for a while, and engineer this at a level to get this competitive with NVidia. A half-baked product like ROCm seems like a money hole for everyone involved. Customers get burned, and I can't imagine AMD comes out positive. In the meantime, NVidia is minting gold here.

belval 5y ago
Yeah Nvidia is so far ahead at this point I wouldn't really risk it on anything else. The problem is that all that troubleshooting adds up fast and the whole DL golden years were built on CUDA. TensorFlow and PyTorch both "support" ROCm and HIP but you run into weird issues very often. A lot of public repository for recent architectures also come with their own CUDA kernels that you need to compile to the vendor lock-in is very strong in my opinion. And even if AMD's offering was not an absolute dumpster fire, Google, Microsoft and Amazon all have their own accelerator that are maturing and will be more cost effective on the long run.
sorenjan 5y ago
Khrono's SYCL is the open alternative to CUDA. It's what Intel is using for their oneAPI, there's a CUDA backend and even a HIP backend.
- synergy20 5y ago
  Nice try, still no way to compare with CUDA, Nvidia is so much ahead and it's hopeless to catch up.
- pjmlp 5y ago
  It is not a real alternative, as it lacks the polyglot ecosystem and tooling from CUDA. It it is the usual Khronos defines the base stuff and hopes for the best regarding their partners.
jjoonathan 5y ago
Yep. This is the same lesson that I learned in 2013 and again in 2015 with OpenCL, both for professional apps and for programming. On the professional app side, there was a lot of "support" for OpenCL that had caveats severe enough to make it unusable. Sure, blender supports OpenCL! You just have to make a custom build and then the GPU target is slower than the CPU target. Sure, Adobe apps support OpenCL -- but any GPU render contexts are solid black boxes and forum posts indicate it has been that way for a year and nobody cared to fix it. Same thing with programming and debugging: there were slides suggesting feature parity with CUDA on a bunch of fronts that just weren't implemented or locked up the computer if you tried to actually use them. It got so bad that I walked away from my sunk costs, sold my AMD cards, ate the ebay tax, ate the nvidia tax, and bought cards that actually worked. Now I'm in "twice bitten, once shy" mode with AMD. I hate paying the green tax as much as the next guy and I desperately want to have a second source of professional GPUs, but I'm not going to be the guinea pig. Not again. Not for the 3rd time. I want to see someone else successfully using AMD cards for common ML workflows and for blender before I even consider risking it again.
- gspr 5y ago
  I'm not nearly as invested as you, but for my first "real" GPU compute project of any significant size and impact, I shocked my colleagues and picked OpenCL. All our hardware is nVidia, but I thought I'd make an effort to fight that vendor lock-in. And I find OpenCL quite pleasant! But… my god. OpenCL is a second-class citizen (at best!) on all three of the major platforms. The situation is dire. But the solution can't be to leave the world to CUDA.
jacquesm 5y ago
Intel has done pretty much the same thing with their Xeon-phi series. Promising on paper but without the long term support and dedication from the mothership such efforts are doomed to fail. NVidia really gets it: people care about results, not necessarily how they get there and if that is done in the most 'pure' way possible, if it 'just works' that's good enough.
AnthonyMouse 5y ago
> AMD would need to run this as a loss leader for a while, and engineer this at a level to get this competitive with NVidia. Their problem has been that as little as five years ago they were a dying company. They didn't have the resources to do this right. That's no longer the case, but once you have the money there is still a lag between then and when the release funded by that money comes out. And even then they're fighting an uphill battle against the perceptions created during their dark age. Probably the biggest thing they have working for them is Nvidia's behavior. Proprietary everything and single vendor lock in makes everybody chafe, so as soon as they can produce something usable, everyone will want to use it.
- blip54321 5y ago
  I'd take "usable." If AMD was half the performance of NVidia, but open, stable, compatible, and robust, that would reach that bar for me, and I think for a lot of people. I think that will be an increasingly hard bar to clear, as software becomes coupled to CUDA, though. AMD will be chasing a losing race. They won once with Intel, but this one feels harder....

belval 5y ago

Yeah Nvidia is so far ahead at this point I wouldn't really risk it on anything else. The problem is that all that troubleshooting adds up fast and the whole DL golden years were built on CUDA. TensorFlow and PyTorch both "support" ROCm and HIP but you run into weird issues very often. A lot of public repository for recent architectures also come with their own CUDA kernels that you need to compile to the vendor lock-in is very strong in my opinion. And even if AMD's offering was not an absolute dumpster fire, Google, Microsoft and Amazon all have their own accelerator that are maturing and will be more cost effective on the long run.

sorenjan 5y ago

Khrono's SYCL is the open alternative to CUDA. It's what Intel is using for their oneAPI, there's a CUDA backend and even a HIP backend.

synergy20 5y ago
Nice try, still no way to compare with CUDA, Nvidia is so much ahead and it's hopeless to catch up.
pjmlp 5y ago
It is not a real alternative, as it lacks the polyglot ecosystem and tooling from CUDA. It it is the usual Khronos defines the base stuff and hopes for the best regarding their partners.

jjoonathan 5y ago

Yep. This is the same lesson that I learned in 2013 and again in 2015 with OpenCL, both for professional apps and for programming. On the professional app side, there was a lot of "support" for OpenCL that had caveats severe enough to make it unusable. Sure, blender supports OpenCL! You just have to make a custom build and then the GPU target is slower than the CPU target. Sure, Adobe apps support OpenCL -- but any GPU render contexts are solid black boxes and forum posts indicate it has been that way for a year and nobody cared to fix it. Same thing with programming and debugging: there were slides suggesting feature parity with CUDA on a bunch of fronts that just weren't implemented or locked up the computer if you tried to actually use them. It got so bad that I walked away from my sunk costs, sold my AMD cards, ate the ebay tax, ate the nvidia tax, and bought cards that actually worked. Now I'm in "twice bitten, once shy" mode with AMD. I hate paying the green tax as much as the next guy and I desperately want to have a second source of professional GPUs, but I'm not going to be the guinea pig. Not again. Not for the 3rd time. I want to see someone else successfully using AMD cards for common ML workflows and for blender before I even consider risking it again.

gspr 5y ago
I'm not nearly as invested as you, but for my first "real" GPU compute project of any significant size and impact, I shocked my colleagues and picked OpenCL. All our hardware is nVidia, but I thought I'd make an effort to fight that vendor lock-in. And I find OpenCL quite pleasant! But… my god. OpenCL is a second-class citizen (at best!) on all three of the major platforms. The situation is dire. But the solution can't be to leave the world to CUDA.

jacquesm 5y ago

Intel has done pretty much the same thing with their Xeon-phi series. Promising on paper but without the long term support and dedication from the mothership such efforts are doomed to fail. NVidia really gets it: people care about results, not necessarily how they get there and if that is done in the most 'pure' way possible, if it 'just works' that's good enough.

AnthonyMouse 5y ago

> AMD would need to run this as a loss leader for a while, and engineer this at a level to get this competitive with NVidia. Their problem has been that as little as five years ago they were a dying company. They didn't have the resources to do this right. That's no longer the case, but once you have the money there is still a lag between then and when the release funded by that money comes out. And even then they're fighting an uphill battle against the perceptions created during their dark age. Probably the biggest thing they have working for them is Nvidia's behavior. Proprietary everything and single vendor lock in makes everybody chafe, so as soon as they can produce something usable, everyone will want to use it.

blip54321 5y ago
I'd take "usable." If AMD was half the performance of NVidia, but open, stable, compatible, and robust, that would reach that bar for me, and I think for a lot of people. I think that will be an increasingly hard bar to clear, as software becomes coupled to CUDA, though. AMD will be chasing a losing race. They won once with Intel, but this one feels harder....