[ZhaoHaoRu]

Overview

This series integrates FlowANN from our OSDI 2026 paper Disentangling Graph Dependencies for Efficient Billion-Scale GPU Vector Search into cuVS. FlowANN keeps a compressed subset of graph edges on the GPU, stores the remaining edges in CPU memory, and fetches CPU-resident edges asynchronously during search.

The complete series adds the index format, graph-building pipeline, C++/C/Python/Rust interfaces, CPU-GPU request queues, serialization, benchmarks, and tests needed to run FlowANN in cuVS. The integration reuses the CAGRA search cores through a graph provider. FlowANN is exposed as an experimental tiered graph storage mode through cuvs::neighbors::cagra, selected with index_params.graph_storage = graph_storage_kind::tiered. FlowANN is excluded from the default cuVS build.

co-author: Haoru Zhao zhaohaoru@sjtu.edu.cn, Jingkai He hjk020101@sjtu.edu.cn, Jingyao Zeng zengjingyao@sjtu.edu.cn, Shenghan Gao gsh20040816@gmail.com

Role in the series

The branch for step 9 of twelve is zhr/flowann-v3-09-benchmarks, with zhr/flowann-v3-08-cpp-persistence as its immediate dependency. The native baseline is NVIDIA/cuvs commit 3dd62c143c78f3a019eecfee9fbae11cebe0d580.

Steps 1 through 10 form a stack. Steps 11 and 12 both start directly from step 10, so Python and Rust can be reviewed in parallel.

The incremental change contains 7 files, 1614 additions and 1 deletions. Compare with the immediate dependency to review this stage. A comparison with the main branch also includes any unmerged predecessors.

The series follows the split discussed in PR #2932. See the original complete PR #2675 for the full integration background.

Step	Scope
1/12	Refactor CAGRA traversal to accept graph providers
2/12	Add graph storage and the CUDA-copy queue runtime
3/12	Add optional GDRCopy queue transport
4/12	Add single-CTA tiered graph search for dense vectors
5/12	Add multi-CTA tiered graph search
6/12	Add dense index construction with grouping and reordering
7/12	Add VPQ vector compression to tiered construction and search
8/12	Expose owning tiered CAGRA indexes and persistence through C++
9/12	Add native benchmarks and the cuVS-bench backend
10/12	Add C bindings and synchronize raw Rust FFI
11/12	Add Python bindings and lifecycle tests
12/12	Add Rust bindings and native lifecycle tests

Scope of this PR

• Add native executables for FlowANN construction and persisted-index search.
• Add query-count handling, repeated measurements, recall, timing and queue statistics output.
• Add the cuVS-bench backend and tests for command construction, result parsing and measurement validation.

Implementation in cuVS

The construction executable reads vector input and writes a tiered index using the C++ interface. The search executable loads an index, runs warmup and measured rounds and reports search timing and recall against supplied ground truth. The Python benchmark backend invokes the native tools and validates their output. It does not depend on the new Python CAGRA bindings introduced in step 11. The separate benchmark option keeps the executables outside the ordinary build unless requested.

Build and load indexes through the public native interface

FLOWANN_BUILD_BENCHMARK reads base vectors, applies CAGRA and tiered build settings, and writes the resulting owning index. FLOWANN_BENCHMARK loads a saved index through the public CAGRA lifecycle, with internal compatibility importers for supported historical formats. The cuVS-bench backend searches existing indexes; it does not silently rebuild them. The executables are enabled by CUVS_BUILD_FLOWANN_BENCHMARKS rather than the ordinary library build.

Account for the queries actually executed

The search tool supports warmup passes, repeated measured passes, queue settings, fixed seeds and optional CPU exact reranking. query_count=0 uses the available query set, and an oversized request is bounded by that set. Each measured record includes the actual query count, search time, rerank preparation, rerank time, total time, QPS, recall and latency percentiles. QPS uses actual queries divided by accumulated batch time. Warmup, index loading and initial setup are outside measured query throughput.

Reject incomplete benchmark output

The Python backend invokes the native search executable and parses measured-round records. It validates required numeric fields, finite values, positive query counts and QPS, valid recall, consecutive round IDs and the expected number of rounds. It rejects inconsistent query counts and overrides that would invalidate the benchmark semantics. This backend requires the native executable but does not require the new Python CAGRA binding from step 11.

Behavior after this PR

Users can run a native build/save/load/search benchmark and collect structured measurements through cuVS-bench. The local validation uses a bounded synthetic dataset and exact CPU ground truth. Large-dataset throughput and speedup claims require separate experiments.

Review focus

Check actual query counts, warmup exclusion, measured-round accounting, recall parsing and how the backend handles missing or invalid measurements.

Build and usage scope

cmake -S cpp -B build-flowann \
  -DCUVS_ENABLE_FLOWANN_SEARCH=ON \
  -DCUVS_FLOWANN_USE_GDRCOPY=OFF \
  -DCUVS_ENABLE_FLOWANN_BUILD=ON \
  -DCUVS_BUILD_FLOWANN_BENCHMARKS=ON
cmake --build build-flowann --target cuvs NEIGHBORS_FLOWANN_TEST FLOWANN_BENCHMARK FLOWANN_BUILD_BENCHMARK --parallel 8

The example explicitly selects CUDA-copy transport. GDRCopy is available from step 3 and requires its development library and a working runtime driver. The builder option arrives in step 6 and benchmark options in step 9. The example enables only options present in this stage. FlowANN is disabled by default.

Testing and validation

Test results at this branch commit

The counts below come from the saved validation records for this branch commit, bbf098a0f25c177a38a51685efb65e35f4a27d7d. Conditional skips are listed separately.

Check	Result	Conditional skips
Native library and relevant test-target build	Passed	N/A
FlowANN cold launch in a separate process	1 passed, zero failures	0
FlowANN unit and regression tests present in this stage	60 passed, zero failures	0

Test coverage and environment

• Configured CMake and completed the full native library and relevant test-target build at this branch commit.

• All 61 FlowANN tests present in this stage passed. The cold-launch case ran in a separate process. 0 tests were skipped.

• The native benchmark built, saved and loaded 4,096 32-dimensional vectors and searched 64 queries. Three configurations each completed three measured rounds. Summary Recall@10 values were 1.000000, 0.978125, 0.975000, all meeting >= 0.95.

• All 18 cuVS-bench backend tests passed.

• Native validation used CUDA 13.1, SM90 and GCC 13 with multi-GPU and CAGRA/HNSW support. Queue runtime tests used CUDA-copy transport.

Validation results refer to this branch commit. GDRCopy runtime remains untested because the environment has no /dev/gdrdrv. Billion-scale throughput and a measured performance comparison against the unsplit PR were not rerun.

The final native branch also passed a full library build and existing CAGRA/C API regression checks with FlowANN disabled. That check is recorded at the final branch and is separate from this stage's own build and tests.

Performance results

Native benchmark measured during this split

The following native benchmark results were measured on this step 9 branch commit, bbf098a0f25c177a38a51685efb65e35f4a27d7d.

The recorded workflow uses 4,096 vectors with 32 dimensions, 64 queries and exact CPU squared-L2 ground truth at top-k=10. The input contains 16 Gaussian clusters, with queries formed by perturbing dataset rows. Construction uses graph degree 16, intermediate degree 32, VPQ dimension 8 with 8-bit codes, 64 VQ centers, four groups and a 32,768-byte resident graph budget. Search uses CUDA-copy transport, two queues, itopk=128, 128 rerank candidates and a maximum of 64 iterations. Each configuration has one warmup and three measured rounds.

Algorithm	Batch	Requested / actual queries	Median search time per batch (ms)	Median total time per batch (ms)	Median QPS	Median Recall@10
single_cta	1	0 / 64	0.631736	0.650496	1,537.288	1.000000
multi_cta	64	0 / 64	2.131919	2.615642	24,468.180	0.978125
multi_cta	64	96 / 64	1.987810	2.322291	27,558.992	0.975000

The timing columns summarize each metric independently across the three rounds. QPS includes query search and CPU reranking; loading, initial setup and warmup are excluded. query_count=0 selects all available queries, and a request for 96 queries executes the 64 available rows. Every measured round met Recall@10 >= 0.95. These are small-workload measurements of the benchmark workflow, with no unsplit-versus-split speedup claim.

Historical CAGRA API integration check

The following results are retained from the original complete PR #2675. They compare the direct FlowANN entry point with the unified CAGRA API using the same search engine and hierarchical Wiki88M index. They were not rerun at this branch commit and do not measure Python or Rust wrapper overhead.

Batch	itopk / iterations / width / candidates	Direct FlowANN QPS	CAGRA API QPS	Relative QPS	Direct Recall range	CAGRA API Recall range
1	256/32/2/32	2,018.3	2,022.7	+0.21%	0.98655	0.98565 to 0.98675
10,000	256/90/2/64	159,236.5	158,405.9	-0.52%	0.99078 to 0.99079	0.99078 to 0.99079

QPS is the median of process-level medians and includes query transfer and CPU exact reranking. Batch 1 uses 2,000 queries, multi-CTA/team32, two rerank threads, one queue and 128 seeds. One control process and two API processes each have one warmup and one measured round. Batch 10,000 uses 10,000 queries, single-CTA/team8, 90 rerank threads, 44 queues and 200 seeds. Three control processes and four API processes each have one warmup and five measured rounds, with additional large-batch runs alternating API and control. Queue statistics and persistent pollers are enabled in both paths. The representative checks showed no consistent API overhead; they do not establish equivalence for every configuration.

Historical Wiki88M FlowANN versus sharded CAGRA

The corrected September 20/23, 2026 system sweep from the original PR is retained below. It compares one GPU running FlowANN with two GPUs running sharded CAGRA, using the hardware and run environment recorded in that PR. FlowANN uses GDRCopy in this historical sweep; the current split runtime checks use CUDA-copy.

The dataset contains 87,555,327 base vectors, 10,000 queries and 768 dimensions. Both systems use graph degree 32, VPQ 192 x 8-bit codes and 10,000 VQ centers, with Recall@10 evaluated against the first ten ground-truth IDs. Their graphs, trained codebooks, sharding and entry points differ. This is a comparison of the complete systems at specified recall thresholds, rather than an experiment that changes only the search kernel.

For batch 1, the tools issue 10,000 sequential searches with multi-CTA/team32 and two CPU rerank threads. Batch 10,000 uses single-CTA/team8 and 90 rerank threads. FlowANN uses one queue and 128 seeds for batch 1, and 44 queues and 200 seeds for batch 10,000. Its remaining settings include six groups, 24-bit local IDs, a 7,529,758,208-byte resident graph budget, sync_window_scale=10 and sync_drop_threshold=51. Both systems use hashmap_min_bitlen=14.

Each selected configuration has three independent process runs. At each threshold, the table selects the highest median QPS whose minimum measured recall meets that threshold. Reported recall is that minimum; configuration means itopk / max_iterations / search_width / rerank_candidates. QPS includes query transfer, GPU search, candidate preparation and CPU exact reranking. It excludes index loading, initial JIT compilation and recall calculation. Relative QPS is the raw throughput ratio minus one, without GPU-count normalization.

Batch	Recall threshold	FlowANN QPS / actual Recall / configuration	Sharded CAGRA QPS / actual Recall / configuration	FlowANN relative QPS
1	0.90	3,260.3 / 0.94275 / 512/16/2/32	4,390.2 / 0.95577 / 512/16/2/32	-25.7%
1	0.95	2,793.6 / 0.97446 / 512/20/2/32	4,390.2 / 0.95577 / 512/16/2/32	-36.4%
1	0.99	2,184.3 / 0.99036 / 512/28/2/32	2,905.4 / 0.99092 / 512/28/32/32	-24.8%
10,000	0.90	428,998.2 / 0.91498 / 512/28/2/16	418,951.8 / 0.90688 / 64/28/2/32	+2.4%
10,000	0.95	273,877.4 / 0.97549 / 512/28/4/32	271,524.2 / 0.95504 / 512/28/4/32	+0.9%
10,000	0.99	175,850.9 / 0.99085 / 512/28/8/32	Not reached; highest measured Recall: 194,866.9 / 0.97614 / 512/28/8/32	N/A

At batch 1, FlowANN reaches 74.3%, 63.6% and 75.2% of the two-GPU CAGRA QPS at the respective thresholds. At batch 10,000, the median differences are +2.4% and +0.9% at recall targets 0.90 and 0.95. The QPS ranges overlap at 0.95, so that median difference does not establish a stable advantage. CAGRA's failure to reach 0.99 within the measured settings does not imply that other settings cannot reach it. The historical measurements do not establish performance parity between the original integration and these twelve branches.

Preservation of the complete implementation

Combining the Python branch from step 11 and the Rust branch from step 12 reproduces the complete integration at commit 8a774792167759e1eb7d34942d46021730e2debc, except for the internal split documentation in cpp/src/neighbors/detail/flowann/README.md. Incremental patch reconstruction and both application orders of the language patches have been checked. The comparison covers the final combined implementation. Intermediate stages contain their own and preceding functionality, with their build and test results recorded above.

Co-authored-by: Jingkai He hjk020101@sjtu.edu.cn

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• Repository: NVIDIA/cuvs
• Branch: main
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• Commit: 44df703
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Commits

Reviewing files that changed from the base of the PR and between 842c69f and 47443d8.

Files selected for processing: 14

• c/CMakeLists.txt
• cpp/CMakeLists.txt
• cpp/include/cuvs/neighbors/cagra.hpp
• cpp/src/neighbors/detail/cagra/jit_lto_kernels/search_single_cta_jit.cuh
• cpp/src/neighbors/detail/flowann/README.md
• cpp/src/neighbors/detail/flowann/flowann.hpp
• cpp/src/neighbors/detail/flowann/flowann_serialize.hpp
• cpp/src/neighbors/detail/flowann/queue.cuh
• cpp/src/neighbors/flowann_queue.cpp
• cpp/tests/neighbors/ann_cagra/flowann_queue.cu
• cpp/tests/neighbors/ann_cagra/flowann_search.cu
• cpp/tests/neighbors/ann_cagra/flowann_serialize.cu
• fern/pages/cpp_api/cpp-api-neighbors-cagra.md
• fern/pages/neighbors/cagra.md

Files skipped from review as they are similar to previous changes: 1

• cpp/src/neighbors/detail/flowann/README.md

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Summary

Summary by CodeRabbit

• New Features

  • Added experimental tiered graph storage for CAGRA, with configurable memory budgets, graph grouping, and search settings.
  • Added tiered index building and search for dense and VPQ-F16 datasets, plus serialization and restoration with associated datasets.
  • Added FlowANN graph-building and search benchmarks, including a benchmark backend for searching existing FlowANN indexes.

• Documentation

  • Added guidance on enabling tiered storage, configuring builds and searches, and understanding supported operations.

This PR adds optional FlowANN-backed tiered CAGRA indexes. It adds graph construction, grouping, queue-assisted search, serialization, CMake integration, C++ benchmarks, a Python benchmark backend, tests, and documentation.

Changes: FlowANN tiered CAGRA

Layer / File(s)	Summary
Feature and build wiring cpp/CMakeLists.txt, cpp/tests/CMakeLists.txt, examples/cpp/CMakeLists.txt, cpp/include/cuvs/detail/jit_lto/flowann/flowann_fragments.hpp	CMake adds opt-in FlowANN search and build features, dependencies, generated kernels, test registration, and benchmark targets.
Tiered index contract and serialization cpp/include/cuvs/neighbors/cagra.hpp, cpp/src/neighbors/cagra*.cuh, cpp/src/neighbors/cagra_tiered.cu, cpp/src/neighbors/detail/flowann/flowann*.hpp, cpp/tests/neighbors/ann_cagra/*, fern/pages/cpp_api/cpp-api-neighbors-cagra.md, fern/pages/neighbors/cagra.md	CAGRA adds tiered index state, accessors, operation checks, and serialization dispatch. FlowANN adds owning index storage and current and legacy serialization paths.
Graph layout, grouping, and construction cpp/src/neighbors/detail/flowann/build.hpp, grouping.hpp, flowann_build.hpp, cpp/src/neighbors/detail/cagra/cagra_build.cuh, cpp/tests/neighbors/ann_cagra/flowann_build.cu, examples/cpp/src/flowann_build_benchmark.cu	FlowANN adds packed graph layout planning, capacity-aware grouping, and CAGRA-to-tiered build paths. Tests cover layouts, grouping, seeds, dataset types, and graph-degree limits.
Queue runtime and poller lifecycle cpp/src/neighbors/detail/flowann/queue.cuh, graph_state.cuh, cpp/src/neighbors/flowann_queue.cpp, cpp/tests/neighbors/ann_cagra/flowann_queue.cu	FlowANN adds device queues, host pollers, search-context controls, and queue lifecycle tests for reservations, releases, pause behavior, and errors.
CAGRA traversal and FlowANN search cpp/src/neighbors/detail/cagra/jit_lto_kernels/*, cpp/src/neighbors/detail/flowann/*, cpp/src/neighbors/flowann.cu, cpp/tests/neighbors/ann_cagra/flowann_search.cu	CAGRA search cores accept graph providers. FlowANN adds single-CTA and multi-CTA search, packed-edge traversal, dispatch, and filtering.
Benchmark tools and integration examples/cpp/src/flowann_benchmark.cu, python/cuvs_bench/cuvs_bench/backends/*, python/cuvs_bench/cuvs_bench/tests/test_flowann_backend.py	C++ adds FlowANN build and search benchmarks. The Python benchmark package registers a search backend that validates measurements and stores results.

Estimated code review effort: 5 (Critical) | ~120 minutes

Change: Feature

Possibly related issues

• NVIDIA/cuvs#2691 — The PR adds template-based tiered index state and helper functions in cagra.hpp, matching the issue’s audit scope.

Suggested labels: feature request, breaking, Build

Suggested reviewers: cjnolet, achirkin

Merge Risk: Minimal · up to 47443

No actionable issue is established for this revision. The opt-in FlowANN changes are mergeable subject to normal build and test checks.

Pre-merge checks: 4 · 1

Failed checks: 1 warning

Check name	Status	Explanation	Resolution
Docstring Coverage		Docstring coverage is 15.26% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documenta…	Write docstrings for the functions missing them to satisfy the coverage threshold.

Passed checks: 4 passed

Check name	Status	Explanation
Title check		The title clearly identifies the main changes: native FlowANN benchmarks and the cuVS-bench backend.
Description check		The description directly explains the benchmark executables, cuVS-bench backend, measurement validation, build options, tests, and scope of the changes.
Linked Issues check		Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check		Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

Docstring coverage is 15.26% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 190 functions across 24 files. (7 skipped: 7 unsupported.)

Resolution

Write docstrings for the functions missing them to satisfy the coverage threshold.

Finishing Touches

Generate unit tests (beta)

• Create a new PR

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Inline comments:
Review comments at @cpp/include/cuvs/neighbors/cagra.hpp:
- Around line 1012-1019: Add an API/ABI change note covering the layout changes
to cagra::index, index_params, and search_params and their binary
incompatibility with consumers built against prior headers. Update the graph()
API documentation and fern page to state that graph() throws for tiered indexes;
use require_device_graph_ and tiered_ to locate the relevant behavior.

Review comments at @cpp/src/neighbors/detail/cagra/cagra_build.cuh:
- Around line 1083-1099: In build_graph, iterative_build_graph reads degrees
from params instead of the locally adjusted values, causing its output width to
disagree with host_graph. Copy params, set the copy’s intermediate_graph_degree
and graph_degree to the adjusted local values, and pass it to
iterative_build_graph in both dataset branches; validate that device_graph’s
second extent matches graph_degree before copying.

Review comments at
@cpp/src/neighbors/detail/cagra/jit_lto_kernels/search_single_cta_jit.cuh:
- Around line 366-374: After the `graph_provider.expand` call, invalidate
unwritten candidate slots from `internal_topk +
computed_children_this_iteration` through `result_buffer_size` and synchronize
before radix selection. In the `topk_cta_11_core` final-filtering loop, use
`result_buffer_size` as the bound so seed-expanded buffers are covered.

Review comments at @examples/cpp/CMakeLists.txt:
- Around line 64-79: Add the fetched cuvs source src directory as a private
include directory for both FLOWANN_BENCHMARK and FLOWANN_BUILD_BENCHMARK, which
use private FlowANN headers not exposed by cuvs::cuvs.

Review comments at @examples/cpp/src/flowann_benchmark.cu:
- Around line 578-584: Update the cuvs::neighbors::refine call to use the loaded
index’s metric instead of hardcoding L2Expanded, so CPU reranking uses the
metric restored by the legacy graph loader.

Review comments at @fern/pages/cpp_api/cpp-api-neighbors-cagra.md:
- Around line 46-51: Update the graph_storage_kind table so its device and
tiered entries have meaningful values: show their implicit enumerator values, 0
and 1, or remove the Value column for this enum.

After applying the fix, consider running `coderabbit review --agent` for local
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• Repository: NVIDIA/cuvs
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Commits

Reviewing files that changed from the base of the PR and between 44df703 and bbf098a.

Files selected for processing: 49

• cpp/CMakeLists.txt
• cpp/include/cuvs/detail/jit_lto/flowann/flowann_fragments.hpp
• cpp/include/cuvs/neighbors/cagra.hpp
• cpp/src/neighbors/cagra.cuh
• cpp/src/neighbors/cagra_serialize.cuh
• cpp/src/neighbors/cagra_tiered.cu
• cpp/src/neighbors/detail/cagra/add_nodes.cuh
• cpp/src/neighbors/detail/cagra/cagra_build.cuh
• cpp/src/neighbors/detail/cagra/cagra_merge.cuh
• cpp/src/neighbors/detail/cagra/cagra_serialize.cuh
• cpp/src/neighbors/detail/cagra/jit_lto_kernels/search_multi_cta_jit.cuh
• cpp/src/neighbors/detail/cagra/jit_lto_kernels/search_single_cta_jit.cuh
• cpp/src/neighbors/detail/flowann/README.md
• cpp/src/neighbors/detail/flowann/build.hpp
• cpp/src/neighbors/detail/flowann/cagra_adapter.hpp
• cpp/src/neighbors/detail/flowann/flowann.hpp
• cpp/src/neighbors/detail/flowann/flowann_build.hpp
• cpp/src/neighbors/detail/flowann/flowann_serialize.hpp
• cpp/src/neighbors/detail/flowann/grouping.hpp
• cpp/src/neighbors/detail/flowann/kernel.cuh
• cpp/src/neighbors/detail/flowann/launcher.hpp
• cpp/src/neighbors/detail/flowann/planner.hpp
• cpp/src/neighbors/detail/flowann/queue.cuh
• cpp/src/neighbors/detail/flowann/search_multi_cta.cuh
• cpp/src/neighbors/detail/flowann/search_multi_cta_jit.cuh
• cpp/src/neighbors/detail/flowann/search_multi_cta_kernel.cu.in
• cpp/src/neighbors/detail/flowann/search_multi_cta_matrix.json
• cpp/src/neighbors/detail/flowann/search_single_cta.cuh
• cpp/src/neighbors/detail/flowann/search_single_cta_jit.cuh
• cpp/src/neighbors/detail/flowann/search_single_cta_kernel.cu.in
• cpp/src/neighbors/detail/flowann/search_single_cta_matrix.json
• cpp/src/neighbors/flowann.cu
• cpp/src/neighbors/flowann_build.cu
• cpp/src/neighbors/flowann_queue.cpp
• cpp/tests/CMakeLists.txt
• cpp/tests/neighbors/ann_cagra/cagra_tiered.cu
• cpp/tests/neighbors/ann_cagra/flowann_build.cu
• cpp/tests/neighbors/ann_cagra/flowann_queue.cu
• cpp/tests/neighbors/ann_cagra/flowann_search.cu
• cpp/tests/neighbors/ann_cagra/flowann_serialize.cu
• cpp/tests/neighbors/ann_cagra/flowann_storage.cu
• examples/cpp/CMakeLists.txt
• examples/cpp/src/flowann_benchmark.cu
• examples/cpp/src/flowann_build_benchmark.cu
• fern/pages/cpp_api/cpp-api-neighbors-cagra.md
• fern/pages/neighbors/cagra.md
• python/cuvs_bench/cuvs_bench/backends/__init__.py
• python/cuvs_bench/cuvs_bench/backends/flowann.py
• python/cuvs_bench/cuvs_bench/tests/test_flowann_backend.py

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