Yes, AMD completely overslept here and their ROCm is much inferior. But at least regulators can force NVIDIA to open their CUDA library and at least have some translation layers like ZLUDA.
Even though I think they will play the same card like Microsoft obfuscating and making it very confusing to hinder the portability.
But at least regulators can force NVIDIA to open their CUDA library and at least have some translation layers like ZLUDA.
I don’t believe there’s anything stopping AMD from re-implementing the CUDA APIs; In fact, I’m pretty sure this is exactly what HIP is for, even though it’s not 100% automatic. AMD probably can’t link against the CUDA libraries like cuDNN and cuBLAS, but I don’t know that it would be useful to do that anyway since I’m fairly certain those libraries have GPU-specific optimizations. AMD makes their own replacements for them anyway.
IMO, the biggest annoyance with ROCm is that the consumer GPU support is very poor. On CUDA you can use any reasonably modern NVIDIA GPU and it will “just work.” This means if you’re a student, you have a reasonable chance of experimenting with compute libraries or even GPU programming if you have an NVIDIA card, but less so if you have an AMD card.
Yes, AMD completely overslept here and their ROCm is much inferior. But at least regulators can force NVIDIA to open their CUDA library and at least have some translation layers like ZLUDA.
Even though I think they will play the same card like Microsoft obfuscating and making it very confusing to hinder the portability.
I don’t believe there’s anything stopping AMD from re-implementing the CUDA APIs; In fact, I’m pretty sure this is exactly what HIP is for, even though it’s not 100% automatic. AMD probably can’t link against the CUDA libraries like cuDNN and cuBLAS, but I don’t know that it would be useful to do that anyway since I’m fairly certain those libraries have GPU-specific optimizations. AMD makes their own replacements for them anyway.
IMO, the biggest annoyance with ROCm is that the consumer GPU support is very poor. On CUDA you can use any reasonably modern NVIDIA GPU and it will “just work.” This means if you’re a student, you have a reasonable chance of experimenting with compute libraries or even GPU programming if you have an NVIDIA card, but less so if you have an AMD card.