Avoid accidental FP64 promotion in UMAP, t-SNE and HDBSCAN float kernels

[maxwbuckley]

Summary

A handful of float kernels in UMAP, t-SNE and HDBSCAN contain unsuffixed double literals (0.5, 1e-8, pow(x, 2.0 * b)) or double locals. C++ promotes the surrounding float expression to double, so nvcc emits FP64 instructions in kernels that are otherwise entirely single-precision.

This is close to free on datacenter parts, but consumer GPUs have heavily reduced FP64 throughput — 1/64 of FP32 on GeForce Blackwell — so the promoted arithmetic is disproportionately expensive there. This PR types those literals to value_t/T so the float instantiations stay in float.

Please read the benchmark section before treating this as a performance PR — the end-to-end gains are small, and zero for UMAP.

How the sites were found

Source grep alone is far too noisy (most double literals are compile-time conversions that nvcc folds away — e.g. smooth_knn_dist_kernel and optimize_batch_kernel look suspicious in source but emit no FP64 at all, and are deliberately left alone here).

Instead each candidate was compiled to sm_120 SASS, and FP64-class opcodes (DADD/DMUL/DFMA/DSETP/MUFU.RCP64H plus F2F.F64.F32/I2F.F64 conversions) were counted per kernel, keeping only kernels whose demangled signature contains no double. -lineinfo plus nvdisasm -g then attributed each instruction to a source line.

Across umap.cu, tsne.cu and the HDBSCAN TUs, FP64-class instructions in float-only kernels drop from 2173 to 12. The remaining 12 are in upstream raft::random Box-Muller code, outside this repo.

Kernels with double in their signature were checked separately: zero of them changed their FP64 count, so the double instantiations are unaffected.

Benchmarks (RTX 5090, CUDA 13.2, sm_120)

Per kernel the improvements are large:

kernel	before	after
t-SNE FunctionalSqrt transform (1M)	126.2 µs	2.7 µs	47×
UMAP Optimize::map_kernel (1M)	152.0 µs	4.1 µs	37×
HDBSCAN dist_membership_vector softmax map (200k×32)	203.9 µs	16.4 µs	12×
UMAP compute_membership_strength_kernel (500k×15)	334.4 µs	103.4 µs	3.2×
t-SNE FFT::IntegrationKernel (500k)	13.4 µs	9.9 µs	1.36×
control: cuVS pairwise-distance GEMM	492.5 µs	491.7 µs	1.00×
control: HDBSCAN min-dist map (no FP64)	344.0 µs	344.1 µs	1.00×

Those numbers are misleading as a headline, though. Whole-run profiling shows the kernels this PR touches account for 3.89 ms of 2193.6 ms of GPU kernel time (0.18%) across a combined UMAP + t-SNE + HDBSCAN session. End to end, on 200k×64 synthetic blobs:

workload	baseline	this PR
UMAP fit, k=15, 200 epochs	0.777 s	0.777 s	no change
t-SNE FFT, early-exits at iter 47	0.662 s	0.662 s	no change
t-SNE FFT, full 1000 iterations	2.324 s	2.224 s	−4.3%
HDBSCAN fit	0.931 s	0.933 s	no change
HDBSCAN all_points_membership_vectors, 20 clusters	5 ms	5 ms	no change
HDBSCAN all_points_membership_vectors, 500 clusters	69 ms	65 ms	−5.8%

So realistically:

• UMAP: no measurable gain. The fixed kernels run once per fit and total 0.14 ms against a 777 ms fit; the time is in kNN construction and optimize_batch_kernel, which had no FP64 to begin with.
• t-SNE: 0–4%, depending on whether the run hits min_grad_norm early. IntegrationKernel and the gradient-norm transform run every iteration, so the saving scales with n_iter.
• HDBSCAN: no gain on the fit; up to ~6% on soft clustering, and only with many clusters, since that kernel is O(n × n_clusters). It is capped there because the pairwise-distance GEMM and the min-reduction dominate that call.

I would suggest taking this on correctness/hygiene grounds — unintended type promotion in a single-precision kernel is a bug even where it is cheap — rather than as a performance improvement. It is a small diff and it makes the intent of the arithmetic explicit.

Numerics

Output buffers were fingerprinted (sum, sum-of-squares, max, NaN count) in both builds:

• t-SNE FunctionalSqrt: bit-identical
• HDBSCAN membership vectors and probabilities: bit-identical
• UMAP membership strength: 9e-9 relative on the sum over 7.5M values
• UMAP Optimize::f: identical to 10 significant digits
• no NaNs introduced anywhere

One caveat worth flagging: full-length t-SNE embeddings are no longer bit-reproducible against the previous build. Total spread agrees to 0.04% (sumsq 1.70359e8 vs 1.70430e8) and the embedding is equally good, but the changed rounding diverges over 1000 iterations. Tests that score trustworthiness are fine; anything pinning exact t-SNE coordinates would need regenerating.

One change is a deliberate reduction in working precision rather than a no-op: compute_membership_strength_kernel previously accumulated in double regardless of value_t, and now follows value_t like the rest of the fuzzy simplicial set construction. That is the change most worth a reviewer's eye.

Testing

I have not been able to run the C++ gtests (cpp/tests/sg/{umap_parametrizable,tsne,hdbscan}_test.cu) locally — my build is configured with a restricted CUML_ALGORITHMS set. Validation so far is the SASS analysis and the output fingerprinting above, so CI coverage on these three algorithms would be worth watching.

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📒 Files selected for processing (6)

• cpp/src/hdbscan/detail/condense.cuh
• cpp/src/hdbscan/detail/kernels/condense.cuh
• cpp/src/tsne/barnes_hut_kernels.cuh
• cpp/src/umap/fuzzy_simpl_set/naive.cuh
• cpp/src/umap/optimize.cuh
• cpp/src/umap/supervised.cuh

🚧 Files skipped from review as they are similar to previous changes (5)

• cpp/src/umap/supervised.cuh
• cpp/src/tsne/barnes_hut_kernels.cuh
• cpp/src/hdbscan/detail/kernels/condense.cuh
• cpp/src/umap/fuzzy_simpl_set/naive.cuh
• cpp/src/umap/optimize.cuh

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📝 Summary

Summary by CodeRabbit

• Bug Fixes
  • Improved numerical consistency across clustering, dimensionality reduction, and manifold learning calculations.
  • Reduced potential precision and type-conversion issues in CUDA-based HDBSCAN, t-SNE, and UMAP operations.
  • Preserved existing algorithm behavior while improving calculations across supported numeric types.
  • Improved reliability of distance, membership, optimization, and gradient calculations for different numeric precisions.
  • Maintained consistent results in specialized kernels without changing public interfaces or core algorithm behavior.

Walkthrough

The change aligns CUDA numeric literals and intermediate values with template types across HDBSCAN, t-SNE, and UMAP. It also replaces equivalent power-based square-root calculations with explicit square-root operations.

Changes

CUDA numeric type alignment

Layer / File(s)	Summary
HDBSCAN numeric updates cpp/src/hdbscan/detail/condense.cuh, cpp/src/hdbscan/detail/kernels/condense.cuh, cpp/src/hdbscan/detail/soft_clustering.cuh	HDBSCAN hierarchy, lambda, membership, outlier, probability, and final combination calculations now use value_t-typed constants and expressions.
t-SNE numeric updates cpp/src/tsne/barnes_hut_kernels.cuh, cpp/src/tsne/fft_kernels.cuh, cpp/src/tsne/fft_tsne.cuh	t-SNE thresholds and gain calculations now use typed constants. FunctionalSqrt now calls sqrtf on a converted float value.
UMAP numeric updates cpp/src/umap/fuzzy_simpl_set/naive.cuh, cpp/src/umap/optimize.cuh, cpp/src/umap/supervised.cuh	UMAP intermediate values and numeric constants now use value_t, T, or float types in membership, optimization, and supervised intersection calculations.

Estimated code review effort: 2 (Simple) | ~10 minutes

Merge Risk: 🔵 Low · up to 5a6d0

The numeric changes may alter t-SNE early-stop behavior for double-precision inputs. This is a bounded open correctness risk that should be addressed or explicitly accepted before merge.

Suggested reviewers: divyegala

🚥 Pre-merge checks | ✅ 5

✅ Passed checks (5 passed)

Check name	Status	Explanation
Description check	✅ Passed	The description clearly explains the FP64-promotion fixes, affected algorithms, performance results, numerical changes, and testing status.
Title check	✅ Passed	The title clearly and concisely summarizes the primary change: preventing accidental FP64 promotion in UMAP, t-SNE, and HDBSCAN float kernels.
Docstring Coverage	✅ Passed	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	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

Full details: Docstring Coverage

Explanation

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 files. (6 skipped: 6 unsupported.)

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Inline comments:
In `@cpp/src/tsne/fft_tsne.cuh`:
- Line 58: Update FunctionalSqrt to preserve value_t precision by returning
value_t and using sqrt(x) instead of converting x to float and calling sqrtf.

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📒 Files selected for processing (9)

• cpp/src/hdbscan/detail/condense.cuh
• cpp/src/hdbscan/detail/kernels/condense.cuh
• cpp/src/hdbscan/detail/soft_clustering.cuh
• cpp/src/tsne/barnes_hut_kernels.cuh
• cpp/src/tsne/fft_kernels.cuh
• cpp/src/tsne/fft_tsne.cuh
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