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No. This is far beyond the capabilities of current AI, and will remain so for the foreseeable future. You could let your model of choice churn on this for month
by colonCapitalDee 11mo ago
No. This is far beyond the capabilities of current AI, and will remain so for the foreseeable future. You could let your model of choice churn on this for months, and you will not get anywhere. It will be able to reach a somewhat working solution quickly, but it will soon reach a point where for every issue it fixes, it introduces one or more issues or regressions. LLMs are simply not capable of scaffolding complexity like a human, and lack the clarity and rigorousness of thought required to execute an *extremely* ambitious project like performant CUDA to ROCm translation.
- measurablefunc 11mo ago[flagged]
- colonCapitalDee 11mo agoWell that's your problem. Here's a tip: just because someone says something doesn't mean you have to listen to them
- measurablefunc 11mo ago[flagged]
- bigyabai 11mo agoThis explains everything.
- impossiblefork 11mo agoI don't think it really is, especially not if it's turned into a system, with multiple prompts, verification, etc. Humans have problems with IMO problems, and this kind of kernel translation is a problem which is easier to humans, where there's more probably actually more data and a problem where the system can get feedback by simply running it and measuring memory use, runtime etc. It'd be a system and no one has developed it, but I think it can be done with present LLMs as a core mechanism. They just need to be trained with RL on this specific problem. Anyone with a good LLM, from Google to Mistral could probably do this, but it'd be a project.