3 ms·

I’m curious if pgvector is using it. It’s actually pretty fast for us without even indexing.

• sakras 3y ago
  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.")
  • ashvardanian 3y ago
    Yes, the improvements for float32 aren't very dramatic, but it can be 3x NumPy/SciPy: https://ashvardanian.com/posts/simsimd-faster-scipy/ https://ashvardanian.com/posts/simsimd-faster-scipy/ C11 is also a bit tricky, as its support is optional, as far as I remember.
    • sakras 3y ago
      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..
      • menaerus 3y ago
        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/distance_impl.h https://github.com/scipy/scipy/blob/main/scipy/spatial/src/d...
  • teaearlgraycold 3y ago
    It doesn’t support f16 or i8 at all yet (at least not in the released version).