Rewrite the four open-core distance functions (cosine / euclidean / manhattan /
dot-product) from object-accumulating `reduce` to single-pass allocation-free
indexed loops. cosine's per-element `{dotProduct,normA,normB}` object was the
hot-path GC lever.
MEASURED (tests/benchmarks/distance-microbench.mjs, dim=384, N=20000, median of
41): cosine 44.3ms -> 7.4ms (~6x), euclidean 9.2ms -> 6.6ms (~1.4x); the built
cosineDistance drops ~44ms -> ~9ms. Numerically identical (same ops, same order)
so recall is unchanged; full suite green (1753/1753).
Also drop the unfounded perf JSDoc ("faster than GPU", "Node.js 23.11+") and the
`new Function(distanceFn.toString())` eval in calculateDistancesBatch — with the
functions now tight loops, the batch is a thin JIT-inlined map (no worker, no
stringify/reconstruct).
Evidence-revised scope: the Float32Array resident-storage half of the original
Fork X is DROPPED. The same microbench shows Float32Array is ~1.7x SLOWER for
this compute (V8 widens f32 -> f64 on every element read), so it would regress
the hot path for a RAM win the open-core JS path does not need — billion-scale
vector RAM is the native provider's SIMD/mmap/quantized domain. The resident
representation stays number[]; no type-chain or cache changes.
David Snelling
b5bc73fb17