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?
There's a higher level of abstraction
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#42Chris Latner of Apple's Swift and Tesla fame is running a company entirely predicated on this, but at the deterministic language design level rather than the inference level. https://www.modular.com/mojo If a beam search, initiative plan and execute phase is more effective than having better tooling in a deterministic programming language then this will clearly take the lead.
Thanks for the link! I am not familiar with the company but reminds me of the whole formal methods debate in distributed systems. Sure, writing TLA+ specs is the 'correct' deterministic way to build a Raft implementation, but in reality everyone just writes messy Go/Java and patches bugs as they pop up because its faster.
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#43Optimization 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.
But we might end up with "work on my infrastructure" optimization that would be hard to reproduce. Like that research that evolved an FPGA where some unconnected parts where crucial for the the expected behaviour. https://www.eetimes.com/whatever-happened-to-evolvable-hardw...
Adding a few diverse hardware environments available for testing during the duration would mitigate this. Many companies wouldn't have any issues having infrastructure specific optimizations either. (Part of) Deepseek's big advantage over their chinese competitors was their intelligent use of the hardware, after all.
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I can't tell whether you're trying to convince humans, parody someone who might be, or give superficial sentiment for automated traders' webscrapers to be influenced by
or they left the /s off and it's a remark about how the fine article sounds more like hype-machine emesis than legitimate, substantive research
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#49I 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?
There's a higher level of abstraction https://www.modular.com/mojo
So if CUDA could be ported to Mojo w/ AI then it would be basically available for any GPU/accelerator vendor. Seems like the right kind of approach towards making CUDA a non-issue.
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#50Calling beam search 'AI' is doing a lot of heavy lifting here. This is just superoptimization with a very expensive heuristic function.
That's correct - however as other commenters have noted. Doing this by hand is extremely challenging for human engineers working on tensor kernels. The expense calculation might be expense of improvement = (time taken per optimization step * cost of unit time ) / ( speedup - 1) The expensive heuristic function is saving wall time well also being cheaper in cost of unit time. And as the paper shows the speed up provid…
Usually the rate of overall improvement for this type of optimization is less than Moore law rate of improvement, thus not worth the company investment. 17x micro-benchmarks don't count. Real improvements come from architectural changes, for example: MoE, speculative multi-token prediction, etc.