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- 🪄 Fix CodeRabbit comments on this PR
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Review comments at @cpp/src/neighbors/detail/hnsw.hpp:
- Around line 2844-2852: Update the automatic ACE fallback path near
`cagra_ace_selected` so builds triggered by insufficient memory use a unique
temporary `build_dir` instead of the default `/tmp/ace_build`. Preserve any
explicitly supplied `ace_params` directory, and ensure the generated directory
is unique across existing and concurrent builds before publishing the index.
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📒 Files selected for processing (15)
cpp/CMakeLists.txtcpp/src/neighbors/detail/cagra/ace_external_plan.hppcpp/src/neighbors/detail/hnsw.hppcpp/src/neighbors/detail/hnsw/external_build.cuhcpp/src/neighbors/detail/hnsw/external_format.hppcpp/src/neighbors/detail/hnsw/external_translate.cucpp/src/neighbors/detail/hnsw/external_translate.hppcpp/src/neighbors/detail/hnsw/external_workspace.hppcpp/src/neighbors/detail/hnsw/index_impl.hppcpp/src/neighbors/detail/hnsw/serialize_layout.hppcpp/tests/CMakeLists.txtcpp/tests/neighbors/ann_hnsw_ace.cuhcpp/tests/neighbors/hnsw_external_io.cuexamples/cpp/src/hnsw_ace_example.cupython/cuvs/cuvs/tests/test_hnsw_ace.py
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A
hnsw::buildusing CAGRA-ACE on-disk builds a CAGRA index on disk and useshnsw::from_cagrato build an HNSW index from the ACE intermediate files. This patch adds support to directly create an HNSW index during ACE builds. It streams partitioned graph data into HNSW format with transactional publication, KvikIO-based I/O, and GPU hierarchy support. This speeds up the end-to-end runtime by removing a disk read of the full CAGRA graph. An exemplary BIGANN-100M build on an L40S GPU shows 8.14% end-to-end speedup with direct POSIX I/O on a consumer NVMe.The direct CAGRA-ACE disk path does not produce a byte-identical HNSW index. Mainline assigns hierarchy levels with
hnswlib’s stateful RNG after ACE reordering; the fused path uses deterministic per-ID levels and partitioned hierarchy construction. This changes maximum levels, internal IDs, and neighbor lists while preserving a validhnswliblayout. The maximum absolute recall difference was 0.0087 for three exemplary datasets: