I wonder if this type of work can be applied towards translating kernels between GPU vendors, e.g. CUDA → AMD. Does anyone know if that's possible or whether that kind of problem is AGI-complete?
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#23AI has told me that Biden was preparing for his upcoming debate with Trump. It told me that in May 2025.
AI has told me its not raining in my city and that in fact there was 0% chance of it that day. As I was looking out my open front door watching a heavy downpour.
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#25Optimization work sounds like it might be a really good fit for coding agents. If you can provide a robust test which "proves" the implementation works the actual work of increasing its performance is the kind of thing a coding agent could run in a loop, testing each optimization to see if the tests still pass and it runs faster.
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#26Was in a startup where we were trying to do this (our tagline was "using AI to make AI run faster and more efficiently"). But we ran out of funding at the end of '22 :(
We were just a little early, I think.
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#27I wonder if this type of work can be applied towards translating kernels between GPU vendors, e.g. CUDA → AMD. Does anyone know if that's possible or whether that kind of problem is AGI-complete?
It seems like it could be possible now with a bit of work. I don't think that it would require AGI. Didn't AMD have (or fund) something like this and then decide not to pursue it further recently? It was called HIP. There's also ZLUDA https://www.blopig.com/blog/2024/03/an-open-source-cuda-for-...
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#30Earlier quoted context omitted.
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I'd argue the logic goes the other way, if all it takes to get high performant kernels is to rent a GPU farm, that seems to undercut the years and millions of engineering hours required to build the NVIDIA SW infrastructure. High hopes for smaller players now
The fact that nobody cared to optimize kernels for these hardware platforms proves Nvidia's CUDA moat, especially now that squeezing performance has become so important for serving inference.
Hardware ISA is broken => nobody knows how to program the hardware => unoptimized kernels => nobody will use your hardware.
Also, bad baselines present opportunities for LLMs to optimize for.
Indeed, the kernel that achieved a 17X speedup seems to be a conv1d, which AWS could not care less about optimizing.