Don't duplicate a device-resident dataset in CAGRA sort_knn_graph

[maxwbuckley]

Part of #2837.

Problem

cagra::detail::graph::sort_knn_graph always allocates a full-size device copy of the dataset and copies into it before launching the sort kernel:

auto d_dataset = raft::make_device_mdarray<DataT>(res, large_tmp_mr, raft::make_extents<int64_t>(dataset_size, dataset_dim));
raft::copy(res, d_dataset.view(), dataset);

It does this even when dataset is already a device mdspan. Every CAGRA build that uses NN-Descent on device data calls it (build_knn_graph<NN-DESCENT>), so the peak VRAM of the whole build is "dataset + another full fp32 copy of the dataset".

Change

Read device-accessible datasets in place (if constexpr (!d_accessor::is_device_accessible)), and only stage host datasets as before.

Results

The copy itself is cheap on this GPU (~20 ms for 16 GB device-to-device), so build time is within noise. This is a memory fix:

CAGRA build, device fp32 input	Peak extra VRAM before	after
Elastic config, 4M × 1024	15,962 MiB	1,082 MiB
Elastic config, 1M × 1024	3,992 MiB	272 MiB
Elastic config, wiki_all 1M × 768	3,014 MiB	272 MiB
cuVS defaults, 1M × 1024	4,398 MiB	620 MiB

This directly increases the largest dataset that can be built with NN-Descent on a given GPU. The host input path is unchanged. CAGRA (1456) and NN-Descent (870) tests pass; recall is unchanged.

A possible follow-up, which is a maintainer decision: for host input, NN-Descent could hand its fp16 device buffer to the sort, or the exact re-sort could be skipped, since sort_lists already orders by fp16 distance. Then the host path would not need the staging copy either.

How this was measured

RTX PRO 6000 Blackwell Server Edition (sm_120a), CUDA 13.0, driver 580, main @ 24b0b17 + #2531. CAGRA builds use NN-Descent. "Elastic config" is what Elasticsearch's GPU codec derives from the Lucene HNSW defaults: graph_degree 12, intermediate_graph_degree 22, 11 NN-Descent iterations, cosine. "cuVS defaults" is 64 / 128 / 20, L2. Device input is Elastic's flush path (CuVSMatrix.deviceBuilder); host input is their merge path.

Each timed number is the mean of 5–6 builds after a warm-up build, with a 95% CI on the speedup. Rows marked interleaved rotate the variant order across rounds to remove run-order bias. Peak VRAM is NVML-sampled above the resident dataset. Kernel times come from nsys.

NN-Descent is not run-to-run deterministic even on main: the slot claim in add_rev_edges_kernel decides which reverse edges survive the 32-entry cap. Graph equality was therefore checked at the kernel level instead. A standalone harness launches the dense local-join kernels on fixed inputs (dims 64/128/130/768/1024 × L2/IP/cosine × WMMA-half/WMMA-float/SIMT, 20k rows) and compares the canonicalised output lists byte for byte. It also checks every stored (id, dist) pair against an exact recomputation.

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• cpp/src/neighbors/detail/cagra/graph_core.cuh

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📝 Summary

Summary by CodeRabbit

• Performance
  • K-nearest-neighbor graph sorting can use datasets already accessible on the active GPU, including managed memory, without an extra copy. Host datasets and data on another GPU are copied to the active GPU before sorting.

Walkthrough

sort_knn_graph uses the dataset pointer directly when it is device-accessible and resides on the resource’s GPU or in managed memory. Otherwise, it copies the dataset to a temporary device array before sorting.

Changes

CAGRA graph sorting

Layer / File(s)	Summary
Dataset access and sorting cpp/src/neighbors/detail/cagra/graph_core.cuh	sort_knn_graph uses eligible device-accessible datasets directly. For other datasets, including host-accessible datasets and device memory owned by another GPU, it copies the dataset to a temporary device array before sorting.

Priority: ➖ Normal

Estimated code review effort: 2 (Simple) | ~8 minutes

Change: Feature

Suggested reviewers: achirkin

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Merge Risk: ⚪ Minimal · up to 41212

The change uses eligible device and managed datasets in place and stages other inputs. The reviewed evidence does not establish a concrete merge-blocking failure.

Pre-merge checks | 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Title check		The title clearly describes the main change: avoiding a duplicate device-resident dataset in CAGRA's sort_knn_graph.
Description check		The description explains the memory problem, the change, its scope, and reported test and measurement results. It is directly related to the changeset.
Docstring Coverage		No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
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.

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Inline comments:
Review comments at @cpp/src/neighbors/detail/cagra/graph_core.cuh:
- Around line 899-917: In the dataset staging logic before
launch_sort_knn_graph, reject device-accessible datasets whose allocation
belongs to a different GPU than the resource by checking dataset_ptr against the
resource device and failing on a mismatch. Preserve direct use for datasets
owned by the resource GPU and the existing staging path for
non-device-accessible datasets.

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• cpp/src/neighbors/detail/cagra/graph_core.cuh

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