Mlsys Experiments

MLSys Experiments

Use this before the evaluation is frozen. An MLSys evaluation exists to answer one compound question: does the named mechanism deliver the claimed system-level payoff on workloads that matter, at acceptable cost, for understood reasons? Each clause needs its own evidence, and this venue — which published the MLPerf Training methodology in its own proceedings — holds measurement to benchmark-committee standards.

Workload selection: the make-or-break choice

"Unrepresentative workload" is the modal fatal objection at this venue. Defend the choice explicitly:

• Anchor on recognizable workloads: MLPerf-family tasks, widely used open models, or published request traces. Bespoke workloads need a characterization section showing their statistics (request rates, sequence lengths, arrival burstiness, model mix).
• Cover the axis your mechanism exploits: a scheduler for bursty load must be tested on bursty and uniform load; the uniform case bounds your claim, it does not weaken it.
• Include at least one workload where you expect little or no gain, and show it. A non-win row is the cheapest credibility purchase available.
• State workload licensing/provenance — traces you cannot release constrain your future artifact badges (see mlsys-artifact-evaluation).

Baseline discipline

• Compare against the current strongest systems, version-pinned, in their recommended configurations — and tune them with the same budget you gave your own system. Document both budgets; asymmetric tuning is treated as invalidating.
• Include the "do nothing" configuration where meaningful (e.g., default framework behavior); reviewers use it to sanity-check absolute numbers.
• If a relevant baseline cannot run in your setting, demonstrate the incompatibility rather than asserting it.

The reporting quartet

Never report throughput alone. Each headline claim carries four coordinates:

Quality (accuracy/perplexity or task metric) rides along whenever the technique could plausibly change model output — quantization, sparsity, scheduling with dropping, approximation of any kind. "No quality change" is a claim requiring a measurement.

Ablation = attribution

Systems papers stack techniques; reviews ask which component pays.

• One ablation row per named mechanism, cumulative or leave-one-out — pick one scheme and say which.
• Ablate the insight, not only the code: if the claim is "lookahead scheduling wins," include the no-lookahead scheduler, not just the whole-system-off row.
• Sensitivity sweeps over the parameters a deployer must set (window sizes, thresholds); a mechanism needing per-workload hand-tuning must say so.

Scaling and sensitivity evidence

# experiment-matrix.yaml — one row per plotted point, generated, never hand-edited
sweep:
  models:    [llama-class-8b, llama-class-70b, moe-16e]
  hardware:  [1xA100, 8xA100-nvlink]        # state interconnect explicitly
  load:      [uniform-qps, bursty-trace-A]
  precision: [bf16, int4]
  trials: 5
  warmup_steps: 20
  metrics: [goodput@p99<250ms, p50, p99, peak_mem_gb, gpu_hours, quality]
  seeds: [1, 2, 3, 4, 5]

• Plot trends, not points: gain versus model size, versus load, versus GPU count. A single-configuration win invites the "will this survive scale?" objection you cannot answer in the four-day response window.
• Log everything; regenerate every figure from logs by script (measurement mechanics and variance rules live in mlsys-reproducibility).

Statistical floor for systems numbers

Systems papers rarely need hypothesis-testing machinery, but they always need variance honesty; the floor this venue's reviewers apply:

• Five or more trials for any number in a comparison table; report spread (stdev or IQR) and say which in the caption.
• Overlapping spreads mean the difference is not a finding — either run more trials or report the tie honestly; a "win" inside noise is worse than a reported tie because one reviewer rerun destroys it.
• For latency distributions, trials are not enough: within-run percentiles need enough requests that p99 is estimated from hundreds of samples, not three.
• Seed variation matters only where model quality enters the claim; systems noise (placement, thermal state, co-tenants) dominates latency variance and is controlled by measurement design, not seeds — the split is detailed in mlsys-reproducibility.

Evaluation-design review before running

Run this table exercise before burning GPU-hours: for each claim in the abstract, write the figure that will support it, the workload it needs, and the strongest objection a systems reviewer could raise — then check the matrix covers that objection. Claims without a planned figure get cut from the abstract, not padded later.

Common reject patterns to design against

• Microbenchmark gains that never appear end-to-end; always include the end-to-end run.
• Gains measured only at one scale/hardware and claimed generally.
• Quality silently degraded by the optimization, discovered by a reviewer from your own appendix table.
• Cost omitted where the honest accounting would erase the win.

Output format

[Evaluation readiness] strong / adequate / weak
[Workload defense] <named anchors + characterization + non-win case present?>
[Baseline symmetry] <versions pinned, tuning budgets equal + documented?>
[Quartet coverage] <throughput/latency-tails/memory/cost per headline claim>
[Attribution] <mechanism -> ablation row>
[Decision-critical missing run] <one experiment>

Source: brycewang-stanford/Awesome-Journal-Skills → MLSys-Skills/skills/mlsys-experiments/SKILL.md