Nsight Systems Analysis for Megatron Bridge

Turn an Nsight Systems trace into a critical-path diagnosis and a ranked, evidence-backed optimization plan. Treat the profile as timing evidence, not as a list of long kernels to optimize.

Use these principles throughout:

• Use the performance model as the ceiling and the profiler as the agenda.

• Compute wall time with interval unions, never summed stream or kernel time.

• Inventory the active communication backend; distributed work is not necessarily NCCL-only.

• Treat visible communication and copies as coincident work until a dependency proves that they blocked compute.

• Separate a measured phenomenon from its source-level root cause.

• Report a gain ceiling from recoverable critical-path time; do not invent a likely gain without comparable measured evidence.

Establish the analysis contract

Record before calculating:

1. Trace path, rank id, hostname, GPU, Nsight Systems version, Bridge and MCore revisions, container, and whether CUDA graphs were enabled.

2. Model, precision, sequence length, micro/global batch sizes, task, and the measured step definition.

3. TP, PP, VPP, CP, EP, ETP, and DP sizes plus the rank-to-stage mapping.

4. The steady-state iteration range, number of analyzed iterations, and same-run unprofiled reference steps when available.

5. Capture diagnostics, hardware- versus software-instrumented mode, and any warning that events may be incomplete.

6. Whether source for the exact profiled revision is available.

Never infer a rank id or pipeline stage from a filename. Never compare ranks that hold different model parts as if they performed identical work.

Preserve the input report. Export a separate SQLite file when necessary:

nsys export --type sqlite -o profile.sqlite profile.nsys-rep

Check the available tables before using a query because schemas vary by Nsight version. Read references/sql-recipes.md for portable inspection queries and references/pitfalls.md before making a bottleneck claim.

Choose the workflow

• One implementation, one rank: produce an absolute time budget and ranked optimization opportunities.

• One implementation, several ranks: survey every supplied rank before selecting representatives; diagnose stage imbalance, jitter, and the slowest-rank step.

• Two implementations: verify identical workload and topology, then reconcile their median per-iteration delta.

For distributed runs, declare representatives rather than selecting them silently. Cover at least the slowest trainer-step rank and one representative for every distinct PP stage or rank fingerprint relevant to the question. Rank 0 is not automatically representative. If the mapping is unavailable, report the rank survey first and label any provisional selection.

Run the analysis

1. Establish iteration windows

Prefer an NVTX range that exactly matches the user’s step definition. Otherwise select a recurring anchor whose count is stable per iteration:

1. a repeated optimizer or train-step range;

2. a repeated NCCL collective sequence;

3. optimizer kernels;

4. a stable recurring kernel sequence.

Drop capture warmup, graph capture, and cooldown. Do not use the outer trace span as a step. When CUDA profiler APIs define the capture boundary, clip the device ROI after profiler-start and before profiler-stop or buffer flush, and state that exclusion. Do not use a trainer step containing profiler stop/flush as a steady reference; it can be extended by trace flushing. Report median, minimum, maximum, and n. Cross-check with a second anchor when possible. Stop and state the ambiguity when candidate anchors imply materially different step times.

Measure capture perturbation from trainer logs, not from the PROFILER_OVERHEAD table. Compare the profiled step or median against same-run unprofiled steady steps with identical workload and topology. Report both values, the reference range and n, and the percentage slowdown. The overhead table accounts for profiler activity recorded in the trace; it is not total end-to-end profiler slowdown. If slowdown is material relative to the natural step spread, label absolute trace budgets and gain ceilings perturbed.

For a comparison, require matching iteration semantics, model inputs, batch shape, precision, topology, and capture mode. Describe mismatches before showing a delta.

2. Survey ranks before drilling down

For every supplied rank, report:

• median/min/max iteration time and n;

• non-communication busy time;

• compute, collective, memcpy, and idle fingerprints;

• collective types and counts;

• pipeline stage or model part when known.

Group ranks only when their fingerprints and model parts match. Within each declared part, report the fastest and slowest rank, the spread, and whether the same rank is repeatedly slow or the straggler rotates.

Relate timestamps from different exports only after rebasing with TARGET_INFO_SESSION_START_TIME. Same-host clocks can be compared after this rebase. Across hosts, refine a constant offset with many matched collective end timestamps and report a robust center plus residual error or percentiles. Duration-based spread remains usable when cross-host arrival time cannot be established, but it does not prove late completion. After alignment, compare both iteration starts and ends: a longer window that starts earlier and ends with its peer is start skew, not a straggler. If only a subset of ranks was captured, limit the verdict to those ranks and do not rule out an unsampled straggler.

3. Build the per-iteration device budget

Clip all intervals to each iteration window and compute interval unions across all streams:

Metric	Definition
Device busy	Union of kernels, memcpy, and memset
Device idle	Iteration minus device busy
Non-communication/dispatcher busy	Union after removing copies and the complete declared communication/dispatcher taxonomy
Communication/dispatcher busy	Union of NCCL plus backend-specific transport, synchronization, dispatch, and combine kernels; overlaps other semantic categories unless explicitly partitioned
Compute busy	Union of compute kernels only
Compute-absent	Iteration minus compute busy
Occupied-not-computing	Device busy minus compute busy

Require compute busy <= non-communication/dispatcher busy <= device busy <= iteration. Reconcile device busy + device idle to iteration time. Show kernel-duration sums only as work volume and label them explicitly; never present them as wall time.

4. Attribute compute-absent time

Split each interval with no compute on any stream into:

• Launch-starved: the next compute kernel had not been issued by the host.

• Blocking: the kernel was issued and a resolved producer finished after the gap began; require a device dependency and negative slack.

• Dependency-stalled: the kernel was issued, but no resolved producer explains the remaining delay.

Follow relayed event dependencies to the operation that produced the awaited event. Classify that operation, not its stream. Join CUDA event records using eventSyncId, not the reused eventId handle. Report unresolved wait share and whether the blocking measurement is an upper or lower bound.

CUDA graph replay may give many kernels one launch and omit per-kernel launch rows. In that case, describe the missing dependency resolution and avoid a false host-launch or call-site conclusion.

Verify:

launch-starved + blocking + dependency-stalled ~= compute-absent

Investigate a residual above 0.5 ms per iteration instead of absorbing it into the largest bucket.

5. Analyze communication and overlap

Inventory the active dispatcher and transport before calculating. Do not assume that communication is NCCL-only. Flex/HybridEP, DeepEP, NVSHMEM, and similar backends may expose dispatch, combine, RDMA, or device-synchronization kernels. Inspect exact material names and source when available. Keep packing, routing, and control work labeled as dispatcher work unless evidence proves it is pure transport. Report NCCL and backend-specific components separately plus their interval union. Never rename the complete dispatcher union as network time.

Report three different quantities over the complete communication/dispatcher taxonomy:

1. Communication/dispatcher volume: operation count and total device work, split into NCCL, transport/synchronization, and packing/routing/control when distinguishable.

2. Exposed communication/dispatcher: its interval union not overlapped by any device operation outside that taxonomy. Treat it as an upper bound.

3. Blocking communication: compute-absent time whose resolved dependency producer is a collective or verified dispatcher transport/sync operation. Use this as the recoverable critical-path bound.

Never call a collective or dispatcher a bottleneck from duration or exposed time alone. Report exposed and blocking values together. Include NCCL-only figures for comparability when useful, but never present them as total distributed cost when material backend kernels exist.

When all collective participants are available, match instances and split a rank’s collective residence into:

• Transfer proxy: the shortest participant duration for the instance.

• Jitter wait: this rank’s duration minus that proxy.

Report both together with matched/unmatched instance counts. If jitter wait dominates, pursue the late participant or load imbalance rather than network bandwidth. If only one participant is present, do not make a bandwidth claim.

Calculate overlap as an observed timeline property, not as proof of causality:

overlapped_comm_dispatcher = total_comm_dispatcher_union - exposed_comm_dispatcher
overlap_pct = overlapped_comm_dispatcher / total_comm_dispatcher_union

Use blocking communication, available independent compute, and matched A/B measurements to judge whether an overlap knob can help.

6. Classify compute and transfers

Break compute work into at least GEMM, attention, normalization, elementwise or fused, optimizer, and other. Keep NCCL, memcpy, and memset separate. Inspect every regex-matched kernel above 1% of iteration time and list material unclassified kernels. Treat observed exact names as stronger evidence than regexes.

For copies, report direction, bytes, stream, occupancy, achieved bandwidth, and pinned versus pageable memory when present. Require a saturated engine or a blocking dependency before claiming a copy-bandwidth bottleneck.

Use Nsight Compute for kernel-level SOL, instruction, occupancy, or memory roofline conclusions. Nsight Systems shows placement and duration, not why an individual kernel underuses the GPU.

7. Use metrics without overclaiming

Prefer a separate, short metric-sampling capture over representative ranks at 100 kHz. Keep timing and metrics passes separate because sampling greatly increases trace size. Join them by operator name, not individual launch.

For every utilization number, report sample count and exclusivity—the share of samples in which only that operator was resident. Mark low-exclusivity rows as contaminated instead of correcting them.

• Use SM Issue and Tensor Active as compute-throughput evidence.

• Use DRAM read/write throughput for memory pressure.

• Use NVLink response user-data throughput for interconnect saturation.

• Use SMs Active only as residency context, not compute throughput.

• Treat an unsampled operator as unknown, never zero.

If GPU metrics are absent or permission is denied, continue with timing analysis and state the limitation.

8. Link kernels to code carefully

Prefer source evidence from the exact revision. Nsight Runtime API correlation can connect a kernel to a CUDA API launch but often not to a Python or framework call site.

Report kernel-to-CUDA-runtime correlation coverage separately from source call-stack link coverage. A correlation-id join can be 100% while source call-stack coverage is zero; never describe the former as call-stack coverage.

If a separate CUDA call-stack capture is available, correlate launches by (rank, thread, launch ordinal) against the same workload, configuration, ROI, and declared ranks. Do not capture call stacks under Nsight Systems. Report the link rate beside every call-site-derived figure. A missing capture means regex-only taxonomy and no source-scope claim; zero links does not mean zero launches, especially with CUDA graphs.

For each root cause, provide:

1. the measured trace phenomenon;

2. the source/config mechanism that creates it;

3. the exact MBridge knob or code anchor to change;

4. status: trace-verified, source-verified, inferred, or unverified.

Without source, stop at trace phenomena and proposed verification steps.

9. Estimate gain without double counting

Rank opportunities by recoverable critical-path milliseconds per iteration. Use one of these evidence bases:

• matched before/after median delta on the same workload;

• launch-starved time affected by a verified launch-reduction mechanism;

• blocking time attributed through a dependency edge;

• a non-overlapping, source-verified critical-path interval.

For current step time T and recoverable bound R:

new_step_ceiling = T - R
throughput_gain_ceiling_pct = (T / (T - R) - 1) * 100
new_mfu_ceiling = old_mfu * T / (T - R)

Use the MFU formula only when the algorithmic numerator and precision are unchanged. Call these ceilings, not forecasts. Give a likely gain only when a comparable measured implementation supports it, and cite that measurement. Do not add opportunity bounds unless their intervals are disjoint and their fixes are independent. When profiling materially perturbs step time, calculate trace-derived ceilings against the profiled T and label them perturbed. Do not transplant a trace-derived R onto the unprofiled reference step or use the PROFILER_OVERHEAD table to correct it.

Map findings to MBridge actions

Proven dominant cost	Next skill or action
Launch-starved	@skills/nemo-mbridge-perf-cuda-graphs/SKILL.md; inspect CPU affinity, Python work, GC, and microbatch size
Blocking TP/DP/PP communication	@skills/nemo-mbridge-perf-tp-dp-comm-overlap/SKILL.md
Blocking MoE dispatch/combine	@skills/nemo-mbridge-perf-moe-comm-overlap/SKILL.md and dispatcher selection
Long-context communication/layout	@skills/nemo-mbridge-perf-hierarchical-context-parallel/SKILL.md and parallelism strategies
Blocking HtoD/DtoH	@skills/nemo-mbridge-perf-cpu-offloading/SKILL.md; inspect prefetch distance and pinned memory
Memory-constrained layout	@skills/nemo-mbridge-perf-memory-tuning/SKILL.md
Low kernel SOL	Capture Nsight Compute and inspect precision, fusion, shape, and kernel choice
Rank jitter	Inspect slow rank, topology, data imbalance, CPU affinity, and straggler telemetry

Enable knobs only after the corresponding critical-path cost is established.

Output contract

Return sections in this order:

1. Verdict: one paragraph naming the dominant cost and trace limitations.

2. Capture quality: inputs, ranks/stages, iteration anchor, n, CUDA graph state, source availability, diagnostics/event completeness, instrumentation mode, same-run profiler slowdown, metric availability, runtime-correlation rate, and source call-stack link rate.

3. Per-step budget: iteration, device busy/idle, compute busy/absent, and launch-starved/blocking/dependency-stalled reconciliation.

4. Rank and communication findings: backend taxonomy, transfer proxy, jitter wait, exposed communication/dispatcher, blocking communication, overlap, sampled-rank coverage, alignment residual, and straggler verdict where supported.

5. Ranked opportunities: evidence, recoverable bound, throughput/MFU ceiling, confidence, action, and verification step.

6. Claim status: distinguish source-verified facts from trace inference.

State all times per iteration and include n. Include an uncertainty range or the observed min/max when estimating a gain.

Design note

Keep this workflow standalone. It does not require an external orchestrator or proprietary trace-processing service; use standard Nsight Systems exports and the evidence available for the profiled Megatron Bridge revision.