Profiling the build showed 90% of all distance evaluations are in back-link
pruning (40.8M of 44.9M at 10K): every overflow re-runs the diversity
heuristic pairwise over ~max_conn candidates.
The bulk build now inserts in batches: plan every node's neighbours against
the graph as it stood when the batch began (read-only, so plans are
independent), link, then prune each overflowing list once. A node gaining
several back-links in a batch is pruned once rather than once per link, so
this is faster even single-threaded (10K: 1676 -> 1074 ms). With `parallel`,
planning and pruning use rayon (10K: 388 ms; 100K: ~21 s -> 5.9 s on 16
cores). Batches start at one node and are capped at 1/16 of the linked graph
and 512 nodes; a node that raises the top layer gets a batch to itself. The
result is deterministic and identical with or without the feature (one code
path; test compares two builds byte for byte).
Parallelising within a single insert was tried first: 1.45x on 16 cores, tasks
too small. Incremental insert() stays sequential.
Recall on clustered data is unchanged or slightly better; uniform random data
dips slightly (10K, ef=64: 0.474 -> 0.444).
clawhdf5-agent's `parallel` feature now passes through to the index.
Co-Authored-By: Claude Fable 5.1 <[email protected]>
