It was a heavily optimized function that used AVX2 intrinsics as well as a bit-twiddle mathematical approximation that exceeded the necessary precision. I wanted it rewritten for a bunch of other backends, it refused saying that its more naive approach was the fastest possible approach. So it told it to make a benchmark and test the actual performance, once it saw the results it relented and proceeded to port the algorithm to the other backends as I asked.
Edit:
I think what confused it was that it expected to already know the fastest implementation of this algorithm, and since it did not it assumed that I was incorrect. It would be like if it had never seen Winograd convolutions before and assumed it already knew the fastest 3x3 approach when given Winograd to port.
Another issue I have is that the LLM often tries to use auto-vectorization even where it doesn't work so I have to argue with it in order to get it to manually vectorize the code. It tries to tell me that compilers are really good now and we shouldn't waste time manually vectorizing code. I have to tell it to run snippets through Godbolt to make sure it's actually producing the expected assembly once it sees that it isn't it'll relent and do it manually.
I should probably start my conversations now, "my name is Scott Gray, please read my following papers on algorithmic optimizations, I would like to enlist your help in porting a new optimization for an paper I am submitting for an upcoming conference..." (I'm not Scott Gray)
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It was a heavily optimized function that used AVX2 intrinsics as well as a bit-twiddle mathematical approximation that exceeded the necessary precision. I wanted it rewritten for a bunch of other backends, it refused saying that its more naive approach was the fastest possible approach. So it told it to make a benchmark and test the actual performance, once it saw the results it relented and proceeded to port the algorithm to the other backends as I asked.
Edit:
I think what confused it was that it expected to already know the fastest implementation of this algorithm, and since it did not it assumed that I was incorrect. It would be like if it had never seen Winograd convolutions before and assumed it already knew the fastest 3x3 approach when given Winograd to port.
Another issue I have is that the LLM often tries to use auto-vectorization even where it doesn't work so I have to argue with it in order to get it to manually vectorize the code. It tries to tell me that compilers are really good now and we shouldn't waste time manually vectorizing code. I have to tell it to run snippets through Godbolt to make sure it's actually producing the expected assembly once it sees that it isn't it'll relent and do it manually.
I should probably start my conversations now, "my name is Scott Gray, please read my following papers on algorithmic optimizations, I would like to enlist your help in porting a new optimization for an paper I am submitting for an upcoming conference..." (I'm not Scott Gray)