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It changes alot. It is not legal to make a 'CUDA' driver for an AMD GPU as Nvidia own cuda. You can see there was a open implementation of this that AMD sponsor

by matt-p 3y ago

It changes alot. It is not legal to make a 'CUDA' driver for an AMD GPU as Nvidia own cuda. You can see there was a open implementation of this that AMD sponsored until they got threatened with a lawsuit by Nvidia

dogma1138 3y ago
ZLUDA ate the dust not because they implemented CUDA but because they were misusing complied NVIDIA libraries. If it was a clean room implementation of the API NVIDIA wouldn’t care. Heck that’s exactly what AMD did with HIP. But what you cannot do is essentially intercept calls to and reverse engineer NVIDIA binaries in real time because you can’t be arsed to build your own.
- dheera 3y ago
  > But what you cannot do is essentially intercept calls to and reverse engineer NVIDIA binaries in real time because you can’t be arsed to build your own. And this is precisely what anti-trust ligitation would allow them to do. Preventing someone from reverse-engineering a product with the sole intention of maintaining monopoly status may be seen as anti-competitive. AMD makes really, really good CPUs now, but only after ligitation against Intel allowed them to keep up with evolving x86 standards. It's not about being "arsed" to build your own, the problem is NVIDIA controls the ecosystem-wide standard. NVIDIA can add to CUDA at any point in time and launch a GPU at the same time, the ecosystem would be forced to buy it if they want to stay on the cutting edge, and AMD would never be able to compete or reverse engineer these new standards in time.
  - dogma1138 3y ago
    They didn’t RE the CUDA API, they reused libraries like cudnn that’s a completely different case than say Oracle vs Google. What ZULDA did wasn’t to maintain a compatibility with the CUDA API and provided an open implementation of it but rather use all the CUDA based libraries that NVIDIA provided on top of it. The equivalence again would be that not only Google implemented their own Java compatible API but rather that they used the now Oracle owned JVM to do so and redistributed it. AMD already implemented CUDA essentially one to one in the form of HIP in ROCm. The issue that they face is that they don't have all the equivalent middleware to make copy pasting code actually work and this was what ZULDA did but instead of building a HIPdnn they just reused NVIDIA binaries.
nemothekid 3y ago
>that AMD sponsored The problem currently, as people like Hotz and many others are discovering, it not the lack of CUDA. Most people use PyTorch and don't care what the underlying software is. Infact most CUDA is hand tuned to nvidia hardware anyways and is optimized to make the most on nvidia. The problem is AMD's drivers - the piece that actually sends the code to run on the GPU, tends to be broken. AMD cannot "sponsor" an outsider to fix this. A legal, but broken, AMDCUDA will not be any better than the current situation; so no, having CUDA on AMD wouldn't change anything. The problem is not "CUDA is not AMD", the problem is AMD has not, does not, and for some reason will not invest adequately in GPU compute. CUDA is a mirage; if AMD had a similar platform someone would have done the work already to ensure PyTorch works on it. PyTorch already supports ROCm, people don't use it because the performance is bad and it's buggy. When nvidia had this problem, nvidia hired engineers to work on open source projects and debug issues in open source libraries (not even limited to AI, you will find nvidia engineers debugging issues in a wide range of CUDA projects). When AMD has this issue, they barely acknowledge it.

by matt-p 3y ago

dogma1138 3y ago

ZLUDA ate the dust not because they implemented CUDA but because they were misusing complied NVIDIA libraries. If it was a clean room implementation of the API NVIDIA wouldn’t care. Heck that’s exactly what AMD did with HIP. But what you cannot do is essentially intercept calls to and reverse engineer NVIDIA binaries in real time because you can’t be arsed to build your own.

dheera 3y ago
> But what you cannot do is essentially intercept calls to and reverse engineer NVIDIA binaries in real time because you can’t be arsed to build your own. And this is precisely what anti-trust ligitation would allow them to do. Preventing someone from reverse-engineering a product with the sole intention of maintaining monopoly status may be seen as anti-competitive. AMD makes really, really good CPUs now, but only after ligitation against Intel allowed them to keep up with evolving x86 standards. It's not about being "arsed" to build your own, the problem is NVIDIA controls the ecosystem-wide standard. NVIDIA can add to CUDA at any point in time and launch a GPU at the same time, the ecosystem would be forced to buy it if they want to stay on the cutting edge, and AMD would never be able to compete or reverse engineer these new standards in time.
- dogma1138 3y ago
  They didn’t RE the CUDA API, they reused libraries like cudnn that’s a completely different case than say Oracle vs Google. What ZULDA did wasn’t to maintain a compatibility with the CUDA API and provided an open implementation of it but rather use all the CUDA based libraries that NVIDIA provided on top of it. The equivalence again would be that not only Google implemented their own Java compatible API but rather that they used the now Oracle owned JVM to do so and redistributed it. AMD already implemented CUDA essentially one to one in the form of HIP in ROCm. The issue that they face is that they don't have all the equivalent middleware to make copy pasting code actually work and this was what ZULDA did but instead of building a HIPdnn they just reused NVIDIA binaries.

nemothekid 3y ago

>that AMD sponsored The problem currently, as people like Hotz and many others are discovering, it not the lack of CUDA. Most people use PyTorch and don't care what the underlying software is. Infact most CUDA is hand tuned to nvidia hardware anyways and is optimized to make the most on nvidia. The problem is AMD's drivers - the piece that actually sends the code to run on the GPU, tends to be broken. AMD cannot "sponsor" an outsider to fix this. A legal, but broken, AMDCUDA will not be any better than the current situation; so no, having CUDA on AMD wouldn't change anything. The problem is not "CUDA is not AMD", the problem is AMD has not, does not, and for some reason will not invest adequately in GPU compute. CUDA is a mirage; if AMD had a similar platform someone would have done the work already to ensure PyTorch works on it. PyTorch already supports ROCm, people don't use it because the performance is bad and it's buggy. When nvidia had this problem, nvidia hired engineers to work on open source projects and debug issues in open source libraries (not even limited to AI, you will find nvidia engineers debugging issues in a wide range of CUDA projects). When AMD has this issue, they barely acknowledge it.