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As far as I'm aware pg_vector just uses the compiler's autovectorization on float32. I think specifically for Euclidean distance you won't really beat GCC (the README even admits it: "GCC handles single-precision float but might not be the best choice for int8 and _Float16 arrays, which has been part of the C language since 2011.")

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Yes, the improvements for float32 aren't very dramatic, but it can be 3x NumPy/SciPy: https://ashvardanian.com/posts/simsimd-faster-scipy/

C11 is also a bit tricky, as its support is optional, as far as I remember.

I also notice you use FMA in your AVX2 L2 distance calculation. I don't think pg_vector enables that, so SimSIMD might be slightly faster.

Also interesting that it beats NumPy/SciPy by so much! I wonder what they're doing..

Not using SIMD at all as it seems. Thus relying on auto-vectorization of I'm not reading it indirectly.

https://github.com/scipy/scipy/blob/main/scipy/spatial/src/d...

It doesn’t support f16 or i8 at all yet (at least not in the released version).

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