perf(cuda): vectorize the deterministic all-gather copy and right-size its single-block path

[fusheng-ji]

What

DeterministicCollective.all_gather was very slow for small and medium messages. Below the 256 KiB single-block threshold, its cost grew linearly with the gathered output, at about 4 µs per KiB on 2 × B200 and 6.5 µs per KiB on 8 × B200. A 128 KiB-per-rank gather on 2 GPUs took 1.08 ms, against 46 µs just above the threshold.

The cause was the copy loop shared by all three all-gather kernels (single-block, fused stage+gather, multi-block). It moved one byte per thread and recomputed the source peer with a 64-bit division for every byte.

• copy_rank_ordered: when the output, the size and every peer payload are 16-byte aligned, one flat loop over all peers' 16-byte vectors keeps every thread busy across peers; otherwise each peer is copied in turn (vectors where aligned, byte-wise tail). It is still a pure rank-ordered copy, so results are byte-identical. (A first version copied peer by peer even when aligned; on 8 GPUs that was 8–14 µs slower than main at 64–192 KiB/rank, which the flat loop fixes.)
• kAllGatherSingleBlockMaxBytes = 64 KiB: a one-block gather copies the whole output by itself, so it only beats the multi-block path below about a 64 KiB output. The crossover was measured at 2 and 8 ranks. all_reduce and reduce_scatter keep kSingleBlockFastPathMaxBytes (256 KiB). Their kernels and arithmetic are untouched.

Results (B200, BF16, slowest rank, median of 100 calls)

per-rank input	2 GPUs before	2 GPUs after	8 GPUs before	8 GPUs after	NCCL, 8 GPUs
1 KiB	28.7 µs	22.6 µs	93.4 µs	46.8 µs	29.0 µs
8 KiB	86.2 µs	24.5 µs	454.1 µs	70.7 µs	31.0 µs
32 KiB	285.2 µs	39.0 µs	1688.0 µs	58.0 µs	32.5 µs
128 KiB	1079.5 µs	36.1 µs	71.6 µs	62.1 µs	34.4 µs
1 MiB	52.9 µs	43.1 µs	133.6 µs	76.8 µs	58.6 µs
4 MiB	86.1 µs	49.7 µs	345.5 µs	143.1 µs	130.2 µs

"Before" is main at 43f150f and "after" is 8ccb03c. For each world size, both builds ran back to back on the same node. The raw reports, figure and notes are in benchmarks/results/deterministic_all_gather_b200/. In these runs, all_reduce, reduce_scatter and all_gather_many agree before and after within 4% at every size.

Tests

python -m pytest tests/distributed/test_deterministic_all_gather_sizes.py tests/distributed/test_deterministic_all_gather.py -v
torchrun --standalone --nproc-per-node=8 benchmarks/benchmark_deterministic_collectives.py --output report.json

Check	Result
new test_deterministic_all_gather_sizes.py: byte sizes across the threshold, odd tails, misaligned input and output, compared with NCCL (2 and 8 GPUs)	passed
test_deterministic_all_gather.py, test_transport_deterministic_collective.py (8 GPUs)	passed (39 passed with the new test)
test_deterministic_all_reduce.py, test_deterministic_reduce_scatter.py (8 GPUs)	fail identically on unmodified main (43f150f): a CUDA "misaligned address" in the all-reduce run, and reduce_scatter_many() does not accept the outs= argument the test passes. Both are unrelated to this change.

Not in this PR

The single-block paths of all_reduce and reduce_scatter grow with size in the same way. For example, an 8-GPU all-reduce takes 523 µs at 256 KiB per rank and 70 µs at 512 KiB. Fixing them means touching the reduction kernels and the CUDA-graph-safe fused protocol, so that is left for a separate change.

Summary by CodeRabbit

• Performance
  • Improved deterministic all-gather performance for tested message sizes and GPU configurations. Other tested collective operations showed similar timings to previous results.
• New Features
  • Added tools to benchmark deterministic collectives against NCCL and compare results across runs.
• Documentation
  • Added benchmark results and a report covering two- and eight-GPU configurations, including measurement details and a noted outlier.

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📥 Commits

Reviewing files that changed from the base of the PR and between d32b9c2 and b2d28f9.

📒 Files selected for processing (6)

• benchmarks/benchmark_deterministic_collectives.py
• benchmarks/plot_deterministic_collectives.py
• benchmarks/results/deterministic_all_gather_b200/report.md
• tests/distributed/test_deterministic_all_gather_sizes.py
• tests/test_benchmark_deterministic_collectives.py
• tests/test_plot_deterministic_collectives.py

📝 Walkthrough

Walkthrough

The all-gather kernels now use rank-ordered copying and a 64 KiB single-block threshold. The change adds distributed size and offset tests, benchmark collection and plotting scripts, and B200 benchmark reports comparing results before and after the change.

Changes

All-gather path and measurement

Layer / File(s)	Summary
All-gather copy path csrc/cuda/distributed/deterministic_collective.cu	All-gather kernels use a rank-ordered copy helper. It uses flat 16-byte vector copies when alignment permits, with peer-by-peer copies and byte handling otherwise. The single-block output threshold changes from 256 KiB to 64 KiB.
Distributed size and offset tests tests/distributed/test_deterministic_all_gather_sizes.py	The new distributed test compares deterministic all-gather with NCCL across selected sizes and four input/output offsets. It checks output identity, gathered bytes, and untouched output-prefix bytes.
Benchmark collection benchmarks/benchmark_deterministic_collectives.py	The new script times deterministic and NCCL operations, reports the slowest rank’s timing, and can write benchmark metadata and rows to JSON.
Benchmark plots and recorded results benchmarks/plot_deterministic_collectives.py, benchmarks/results/deterministic_all_gather_b200/*	The plotting script compares paired before and after reports. Added B200 results cover two- and eight-rank runs, and the report describes the measurement setup and recorded results.

Priority: ⬇️ Low

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Refactor

Sequence Diagram(s)

sequenceDiagram
  participant Benchmark
  participant DeterministicCollective
  participant NCCL
  participant DistributedReduction
  participant Rank0
  participant JSONFile
  Benchmark->>DeterministicCollective: Run selected deterministic collectives
  Benchmark->>NCCL: Run matching NCCL collectives
  Benchmark->>DistributedReduction: Reduce timings to the slowest rank
  DistributedReduction->>Rank0: Provide reduced timings
  Rank0->>JSONFile: Write metadata and result rows when an output path is set

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Merge Risk: 🔵 Low · up to d32b9

The all-gather change has no established blocking defect, but mismatched reports can produce misleading plots and some NCCL reference timings include extra work. Correct the measurement tools or merge with those limitations understood.

Security Architecture Review

Security architecture risk: 🔵 Low · up to d32b9

The copy optimization preserves rank ordering and existing device and size checks. The main uncertainty is recovery after an interrupted staged gather: more message sizes now use a path that can leave the collective pending after a launch failure. No security exploit was established.

Retained concerns

• Low · reliability · inferred: For gathered outputs above 64 KiB through 256 KiB, staging now precedes separate gather and completion launches. If staging succeeds but a later checked launch fails before cleanup, pending state remains and subsequent fused gathers are rejected. Missing completion can also prevent peers from reusing staging. This expands an existing failure-containment dependency; coordinated abort or recovery for this interval is not established.

Security review details

Security Blast Radius

• inferred — The supported failure-propagation scope is the collective's same-host group, up to eight ranks, because scratch reuse depends on peer completion. No expansion to another tenant, service or environment was demonstrated; broader production exposure is not established by the available context.

Trust Boundaries and Controls

• observed — Caller-controlled tensors pass through existing device, size, shape and dtype checks before the Python CUDA dispatch. The wrapper serializes host dispatch with a lock, while IPC creation checks rank and handle structure. These are existing controls, not newly added authentication or isolation guarantees between ranks.
• observed — The routed plotting entrypoint reads operator-selected JSON files and writes an operator-selected image. The new distributed test owns its worker processes and uses a loopback rendezvous. These inspected entrypoints do not introduce a remote request handler or additional service credentials.

Resilience and Maintainability Implications

• observed — The existing close method synchronizes the device before destroying IPC state; it is not an explicit group-abort protocol. Successful completion clears pending host state, but an exception before that cleanup has no corresponding recovery handling in the inspected gather wrapper.

Hardening Proposals

• proposed — Define coordinated terminal behavior after a staged gather is interrupted: either a proven recovery protocol or group-wide teardown and recreation. Clearing only the host pending flag should not be treated as recovery, because peer completion governs safe payload reuse.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 5.88% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 17 functions across 4 files. (5 skipped: 5…	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
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.
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly and concisely describes the main changes: vectorizing the deterministic all-gather copy and adjusting its single-block threshold.

Full details: Docstring Coverage

Explanation

Docstring coverage is 5.88% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 17 functions across 4 files. (5 skipped: 5 unsupported.)

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Actionable comments posted: 2

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• 🪄 Fix CodeRabbit comments on this PR

🤖 Prompt to fix review comments

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Inline comments:
Review comments at @benchmarks/benchmark_deterministic_collectives.py:
- Line 111: Update the NCCL timing callbacks for dist.reduce_scatter_tensor and
all_reduce to use tensors prepared before the CUDA-event interval, so cloning or
copying is excluded from nccl_us. Reset the all_reduce input between samples
outside the timed interval.

Review comments at @benchmarks/plot_deterministic_collectives.py:
- Line 55: Before plotting each paired before/after series, validate that both
reports have the same world size and input-size sequence; reject mismatched
pairs instead of deriving sizes from the after report and plotting misaligned
timings. Update the plotting logic around the `sizes` calculation and the
`before`/`after` reports.

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📒 Files selected for processing (9)

• benchmarks/benchmark_deterministic_collectives.py
• benchmarks/plot_deterministic_collectives.py
• benchmarks/results/deterministic_all_gather_b200/after_w2.json
• benchmarks/results/deterministic_all_gather_b200/after_w8.json
• benchmarks/results/deterministic_all_gather_b200/before_w2.json
• benchmarks/results/deterministic_all_gather_b200/before_w8.json
• benchmarks/results/deterministic_all_gather_b200/report.md
• csrc/cuda/distributed/deterministic_collective.cu
• tests/distributed/test_deterministic_all_gather_sizes.py

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[fusheng-ji]

The two actionable comments in review 5447550669 are fixed in b2d28f9, individually replied to, and resolved.

The docstring coverage warning is addressed too: all 21 Python functions in the changed scripts/tests now have docstrings (verified by AST inspection). Existing CUDA helper documentation is unchanged.

Validation: 8 CPU regressions passed; a fresh CUDA 13/sm100 build of the current collective source passed 38 distributed/transport tests on 2 B200 GPUs, with 1 existing eight-GPU test skipped. A two-GPU benchmark smoke run produced 12 valid timing rows across all four operations at 1/32/128 KiB per rank. The stored before/after reports still plot successfully. Black, isort, Ruff, and git diff --check passed.

The historical report now explicitly identifies the original NCCL reduction timings as including per-call tensor cloning; those stored measurements are preserved as historical results.