Beating GCC 12 – 118x Speedup for Jensen Shannon D. Via AVX-512FP16

What it does

SIMD-accelerated distances, dot products, matrix ops, geospatial & geometric kernels for 16 numeric types — from 6-bit floats to 64-bit complex — across x86, Arm, RISC-V, and WASM, with bindings for Python, Rust, C, C++, Swift, JS, and Go 📐 - Release Beating GCC 12 - 118x Speedup for Jensen Shannon Divergence via AVX-512FP16 · ashvardanian/NumKong

Divergence functions are a bit more complex than the Cosine Similarity, primarily because they have to compute logarithms, which are relatively slow when using LibC's logf . So, aside from minor patches, in this PR, I've rewritten the Jensen Shannon distances leveraging several optimizations, mainly focusing on AVX-512 and AVX-512FP16 extensions, which resulted in 4.6x improvement over the auto-vectorized single-precision variant and a whopping 118x improvement over the half-precision code produced by GCCfrom github.com

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