aituner/.research/experiments/frontier-workload-regime-taxonomy-20260720.md

Frontier workload-regime taxonomy

• Date: 2026-07-20
• Status: proposed; awaiting review before workload generation or GPU runs
• Scope: explain when Frontier preserves the real-system config ranking, rather than merely comparing Fixed with Trace

Claim under test

Frontier reliability is controlled by three quantities:

1. the latency-model residual between simulator and real execution;
2. the closed-loop gain from timing to scheduler state (batch, MoE routing, CUDA-graph bucket, MNS occupancy, admission/KV pressure);
3. the real decision margin between configurations.

For a config pair a,b, define

D_real(a,b) = log L_real(a) - log L_real(b)
delta(a,b)  = [log L_sim(a)-log L_real(a)]
              - [log L_sim(b)-log L_real(b)]
slack(a,b)  = sign(D_real) * [D_real + delta]

slack < 0 means the simulator reverses the real pairwise ordering. The primary hypothesis is that reversals occur when simulator and real execution land on different sides of a scheduler-state knee, or when the real decision margin is too small to absorb the differential residual. Fixed and Trace are not themselves the causal classes.

Existing evidence motivating the experiment

• Q30 Trace-PD preserves all six objective winners, but many pairwise residuals oppose the real winner. Its success is therefore often margin protection, not zero residual.
• Q235 Trace-PD preserves TTFT/TPOT winners but misses E2E p90 by 6.2%; Trace is not universally safe.
• Q30/Q235 Fixed-PD decode objectives show negative minimum signed slack and 13--37% regret.
• In Q30 low-load Fixed-PD, Frontier's batch-1 TP ordering is correct, while the closed-loop simulator increases TP4's effective batch and changes the MoE cost enough to reverse the ordering. This identifies a concrete state knee, but does not yet establish a general rule.

Workload families

All comparisons use the same request multiset where applicable, the same total observation window, and the same normalized offered decode load

rho = request_rate * E[output_tokens] / measured_reference_decode_capacity.

This avoids equating equal request rates with equal load.

ID	Shape / request lengths	Arrival process	Prefix/session state	Isolated effect
W0	short fixed 2048 -> 128	uniform	off	known low-residence failure anchor
W1	trace-mean fixed ISL/OSL	uniform	off	homogeneous baseline
W2	trace-mean fixed ISL/OSL	trace timestamps	off	arrival burst only
W3	exact trace ISL/OSL multiset	uniform	off	length heterogeneity only
W4	exact trace ISL/OSL multiset	trace timestamps	off	length + burst
W5	exact trace prompts/ISL/OSL	uniform	exact prefix/session identity	prefix state without burst
W6	exact trace prompts/ISL/OSL	trace timestamps	exact prefix/session identity	full production trace

Prefix is intentionally a nested factor: enabling a synthetic prefix graph on fixed identical requests would introduce a different workload rather than isolate production prefix reuse. Therefore this is not presented as a full 2^3 factorial.

Load sweep and expected patterns

Simulator discovery sweep: rho in {0.05, 0.25, 0.50, 0.90, 1.20}. The points mean deep low load, light batching, moderate batching, capacity knee, and overload; their request rates are derived independently for every workload family.

Pattern	Observable state	Prediction for Frontier
P1 singleton-linear	real and sim stay below the first batch/graph knee	works if the batch-1 operator ordering is correct
P2 knee-straddling	real and sim occupy opposite sides of a batch/MoE/graph/MNS knee	fails systematically; Fixed-PD is the current example
P3 same-side batched	both systems cross the same knee and remain below admission pressure	works if batch-conditioned operator ordering is correct
P4 capacity/admission aligned	both systems are governed by the same capacity bottleneck	TTFT/config winner may work despite large absolute error; E2E/MNS can remain fragile
P5 heterogeneity-smoothed	broad lengths reduce coherent threshold occupancy at matched rho	may work; this is a hypothesis, not an established explanation
P6 burst-sensitive	same request multiset, but transient queue/MNS occupancy differs	mean ranking may work while TTFT/E2E tail ranking fails
P7 prefix-state-sensitive	hit/eviction and reused-token distributions differ	TTFT ranking fails unless prefix-state transitions are modeled; decode TPOT may remain stable
P8 decision-boundary	real config margin is comparable to run variance/residual	fragile; an exact winner match is not reliable evidence

Hypotheses and distinguishing tests

H1: state-regime hypothesis (primary)

I believe config-ranking failures occur when the latency residual moves a workload across a scheduler-state knee, because the residual is then amplified into a different batch/resource trajectory. I will verify this by checking whether signed-slack zero crossings co-locate with measured real/simulator state-knee crossings.

H2: heterogeneity-smoothing hypothesis

I believe length heterogeneity can reduce coherent threshold amplification, because requests reach scheduler boundaries at dispersed times. I will verify it with W1 vs W3 and W2 vs W4 at matched rho, requiring a smaller real/sim state-distribution gap rather than merely a correct winner.

H3: bottleneck/margin-protection alternative

Trace success may instead be explained entirely by a large real decision margin or a shared capacity bottleneck. This hypothesis wins over H2 if W3/W4 do not reduce state-distribution error after matching load and margin, while ranking correctness remains predicted by margin alone.

H4: burst and prefix are independent failure channels

I believe arrival bursts primarily affect waiting/admission and tail TTFT/E2E, whereas prefix mismatch primarily affects prefill/TTFT state. I will verify this with W1/W2, W3/W4, and W3/W5 paired comparisons.

Configuration and model scope

Discovery uses Qwen30B because its 12-cell TP x MNS surface already has simulator and real anchors:

• TP: {1, 2, 4}
• MNS: {8, 16, 32, 64}
• objectives: mean/p90 TTFT, TPOT, E2E

Qwen235B is a held-out confirmation, not pooled into discovery:

• existing four feasible TP/MNS configurations;
• only the workload/load patterns that discriminate H1--H4 after Q30 converges.

Measurements

End-to-end:

• completed/failed requests and achieved request/token rate;
• TTFT, TPOT, E2E mean/p50/p90/p95;
• config regret, pairwise agreement, signed decision slack;
• run-to-run winner stability.

Closed-loop state:

• prefill/decode batch-size histograms and time-weighted batch;
• Running/Waiting distributions and admission delay;
• MNS active-token occupancy and KV/context pressure;
• CUDA-graph bucket residency and fallback frequency;
• prefix hit/reused-token/eviction distributions for W5/W6.

Decision rules

A workload/load region is:

• reliable if regret is at most 5%, pairwise agreement is at least 0.8 at two adjacent load points, and the winner is stable across confirmation trials;
• fragile if regret is at most 5% but the real margin overlaps run uncertainty, or a small rate/timing perturbation changes the winner;
• failed if regret exceeds 5% or a decision-critical pair has negative signed slack;
• mechanistically explained by H1 only if the ranking transition co-locates with an observed state-regime transition. Correlation with the Fixed/Trace label is insufficient.

H2 is supported only if the heterogeneous member of a matched pair reduces state-distribution error and shifts the failure boundary in repeated trials. A correct winner alone does not support smoothing.

Execution plan after review

1. Materialize W0--W6 with one manifest recording request multiset, arrival timestamps, prefix identity, rate contract, and hashes.
2. Run the simulator sweep across rho and the Q30 surface; emit a per-stage state ledger.
3. Select real-machine pilot points only around the predicted knees plus one safe-side control. Use guard configs TP1/MNS64, TP4/MNS8, and TP4/MNS64; add TP2/MNS32 only if the transition is not bracketed.
4. Use only dash1, dash2, dash3, and dash4, each verified as an 8×H20 host. dash0 is excluded from probing, synchronization, and execution. Pin one independent experiment group to each host so at most four groups run in parallel; do not split one trial across hosts.
5. Run one pilot trial per selected point. Confirm only hypothesis-discriminating points with three fresh-server trials and rotated order.
6. Apply the resulting classifier unchanged to the Q235 held-out cases.

Provisional four-way allocation after the simulator identifies the discriminating points:

Host	Experiment group	Primary contrast
dash1	homogeneous controls	W0/W1 across safe side and first knee
dash2	arrival effect	W1 vs W2 and W3 vs W4
dash3	length heterogeneity	W1 vs W3 and W2 vs W4
dash4	prefix/full trace	W3 vs W5 and W4 vs W6

The groups are logical queues, not permanent ownership: if a host probe fails, that host is excluded and its group waits or moves to another permitted idle host. Cross-host latency values are not pooled until a common canary config verifies that host effects are within run uncertainty.

No GPU run is authorized by this card yet. The review decision is whether the workload decomposition and decision rules are sufficient to implement the materializer and launch Phase 1.

Expected figure

The accompanying mock figure is schematic, not data. Panel A shows the state knee that real and simulator trajectories may cross at different loads. Panel B shows the corresponding minimum signed decision slack; a negative value denotes a ranking reversal. The claim is supported only if measured zero crossings and state knees align across workload families.

Risks and controls

• Equal rho does not guarantee equal prefill pressure; report both prefill and decode offered work and stratify if necessary.
• Full-trace overload can collapse all configs to similarly poor latency. Such points identify a capacity-limited region but cannot validate fine-grained ranking.
• MNS ties and censored/failed requests can create false winners; exclude invalid cells before calculating regret and report the exclusion.
• One trace cannot establish generality. The initial result is a mechanism boundary for this trace/model/hardware, followed by held-out Q235 validation.