The B2 same-worker TPOT p90 idx is non-monotone: 7.89x at 32k drops
to 2.26x at 65k. The naive reading is "interference gets weaker for
huge prefills"; the actual mechanism is a regime shift, and reading
TPOT p90 alone is misleading.
Three superimposed effects:
1. Cost migration TPOT -> TTFT. A 32k prefill is short enough that
chunked-prefill keeps interleaving decode steps, so overlapping
decodes trickle tokens out at painful per-token rates. A 65k
prefill is long enough that overlapping decodes are *fully*
blocked for ~10s; once they break through, the injection is
winding down and subsequent iterations run unobstructed. The
cost lands on the TTFT clock (14s) instead of inflating TPOT.
2. Bimodal TPOT distribution. At 65k overlap, decodes split into
"blocked entire prefill then normal rate" and "trickled slowly
through prefill chunks". p99 sits on the second population and
grows 59 -> 169.5 ms; p90 sits on the first and shrinks.
3. "Clean" stops being clean. With 4x ~10s injections in 60s, the
110 "clean" decodes at 65k are squeezed into 2-3s recovery
pockets. TPOT p90 clean rises 6.9 -> 9.6 ms (40%), shrinking
the denominator of the ratio.
window_1_results.md adds a new B2 subsection laying out the
mechanism with the per-cell data table and the explicit reading
rule: headline interference metric is TTFT idx (monotone); TPOT
p99 is the right tail indicator; TPOT p90 alone is unsafe across
regime shifts. Direct implication: TTFT and TPOT need separate
SLO thresholds under PD-colo, because they measure costs from
different points in the request lifecycle and the cost migration
between them is workload-dependent.
current_results/characterization_claim_matrix.md adds a new
supported claim for the cost migration, listed against the existing
B2 evidence. current_results/reviewer_risk_register.md adds a
low-severity entry warning future readers off TPOT p90 alone.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Refresh the standing audit package now that B1' / B2 / B3 are complete.
current_results/characterization_claim_matrix.md
Flips seven entries from "not_yet_supported" / "partially_supported"
to "supported" with pointers into window_1_results/. New entries
cover per-session sequentiality, KV per request, real reuse
decomposition, theoretical APC ceiling, the LMetric locality gap,
Unified breaking the locality-vs-latency tradeoff, B2 causal
interference proof, sticky's interference inflation, and the
partial heavy-tail / hot-spot story. B4 SRR + B5 attribution stay
"not_yet_supported" (Window 2 work).
current_results/main_claim_allowed_runs.md
New "Allowed For Routing-Policy Comparison" section pins the five
B3 policy directories. New "Allowed For PD-colo Interference"
section pins the B2 sweep. Legacy section retained for the
pre-instrumentation 200/500/1000-req runs.
current_results/reviewer_risk_register.md
Marks the two old "high"-severity risks (sequentiality / reuse
decomposition) as resolved; adds new entries for the APC
contamination empirics, the b3_analyze.sh truncate-write bug that
cost unified's interference index, the GPU-0 EngineCore ghost
cleanup, the saturated-replay caveat for trace-timestamp dispatch,
and the synthetic B2 decode workload.
current_results/all_figures_index.md
Adds the 8 new Window 1 figures alongside the existing 6 from the
legacy summarize_runs run.
current_results/reproduction_commands.sh
Records the full B3 + B2 + figure pipeline.
analysis/characterization_todo_for_interns.md
Updates the Progress Snapshot table: B0, B1, B2, B3, B6 all DONE;
only B4 and B5 remain (Window 2).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
analysis/characterization/window_1_results.md is the headline write-up
for Window 1: workload characterization (KV per request, real reuse
decomposition, APC theoretical ceilings), B3 5-policy sweep with
per-policy interpretation, B2 same-vs-different-worker interference
microbench with causal reading, and an explicit list of what Window 1
does *not* answer (deferred to B4 SRR sweep + B5 attribution).
Under window_1_results/:
- 5 raw result JSONs from the B3 sweep, the B2 microbench, the APC
upper bound, and the KV footprint
- per-policy hotspot_index.json snapshots so render_window1_figures.py
can plot per-worker TTFT p90 distributions
- 8 PNG figures (figures/) covering the headline claims
Three takeaways the figures pin down:
1) intra-session reuse dominates (93.2%), so session-affinity routing
is the right primary lever
2) unified hybrid affinity hits 79.4% APC (97% of the 79.6% intra-
session ceiling) AND cuts TTFT p90 from lmetric's 15.6s to 7.24s
3) B2 different-worker control sits at idx ≈ 1.0 across 32× prefill-
size variation; same-worker TTFT idx scales 2.15× -> 218×, which
is the cleanest causal evidence for same-worker prefill-decode
interference
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Three CPU-only analysis pieces that turn raw Window 1 artifacts into
publishable numbers and figures.
scripts/compute_apc_upper_bound.py
Block-level trie walk over hash_ids to compute the theoretical APC
ceiling on a trace, decomposed into intra-session / any-session /
shared-prefix-only. Gives a fixed reference for what each routing
policy could *possibly* achieve. w600 result: 79.6% intra-session,
80.3% any-session, 0.1% shared-prefix.
analysis/characterization/b2_sweep_analysis.py (rewrite)
Previous version used joined_analysis.interference_index() which
labeled overlap = "any prefill in any other request during this
decode". With short-prompt decode load this is always true
(everyone's prefill overlaps everyone else's decode); n_overlap
was 239/240 even in the different-worker control.
New version labels overlap iff the decode's [t_first_token, t_finish]
intersects an actual large *injection* window, computed from the
cell's "prefill"-tagged metric rows. Different-worker control now
cleanly sits at idx ≈ 1.0, same-worker scales monotonically.
analysis/characterization/render_window1_figures.py
Renders 8 PNGs from the result JSONs: B3 latency / APC vs ceiling
/ APC vs hotspot scatter / per-worker TTFT / failure breakdown,
B2 TPOT and TTFT curves (overlap vs clean and idx), reuse
decomposition, KV footprint.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Documents each pick_instance_* function from cache_aware_proxy.py in
pseudocode so the policy semantics can be cited without re-reading
implementation details. Covers lmetric (main baseline), load_only
(no cache / no affinity control), sticky (hard affinity control),
unified (gated affinity + LMetric fallback), and capped (lmetric on
a per-session turn-capped trace).
Includes a decision matrix that maps each policy to whether it uses
session affinity, cache awareness, load awareness, and overload
break, plus a one-liner per control explaining what comparison
isolates which factor.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
scripts/b2_interference.py is the controlled microbench. It runs two
coroutines against the open proxy bypass (direct vLLM endpoints):
- decode_load: continuous short-prompt requests at fixed QPS into a
designated decode instance, to keep it decode-saturated.
- prefill_injections: N large one-token requests at fixed interval,
pointed at either the same instance (same-worker variant) or a
paired one (different-worker control).
Each cell (variant × prefill_size) gets its own metrics.jsonl plus a
run_window.json containing t_start_unix/t_end_unix. The shared
engine_*.jsonl from the scheduler patch is sliced by that window in
the aggregator.
analysis/characterization/b2_sweep_analysis.py walks the cell tree,
slices the per-worker step log by each cell's window, runs the A5
interference_index() against the slice, and emits a single
b2_sweep_summary.json with one row per cell. This is what feeds the
"interference vs uncached prefill size" figure.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Smoke validation on dash0 surfaced three real bugs that broke
interference and failure-attribution labels end-to-end:
1. endpoint_url in metrics is the proxy URL (e.g. http://h:9200);
the vLLM worker URL lives in breakdown's routed_to. The
interference index and label path were taking endpoint_url first,
so every request looked routed to a non-existent worker and the
overlap counter stayed at zero.
2. _normalize_worker hard-coded base port 8000, so a smoke run on
port 9100 resolved to engine_1100 instead of engine_0. Added a
--worker-map URL=engine_id CLI flag and _resolve_worker() that
prefers the explicit map and falls back to the heuristic.
3. vLLM rewrites the per-step rid as cmpl-<proxy_id>-<i>-<hash>, so
the str equality check between per_req rid and our proxy
request_id never matched -> every prefill step looked like
"other request prefill", which would have flipped overlap to
100%. Added _vllm_rid_matches() that strips the cmpl-/chatcmpl-
prefix.
After the fix, the same smoke run reports interference_index = 22.9
across 24 overlap / 6 clean requests on a single instance, which is
the expected shape for serial dispatch into a cold engine.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
New analysis/characterization/joined_analysis.py joins replayer
metrics.jsonl + proxy breakdown.json + worker_state.jsonl by
request_id, plus engine_*.jsonl by worker_id, and emits:
- joined.jsonl per-request merged record
- reuse_decomposition.json real intra/cross/shared classification
using session_id + hash_ids + cached_tokens
- interference_index.json TPOT_p90(same-worker prefill overlap)
/ TPOT_p90(clean), per Batch 2
- hotspot_index.json max/median worker TTFT-p90, per Batch 3
- failure_label.jsonl per-slow-request cause label, per Batch 5
- failure_breakdown.json label histogram
- window_summary.json SRR warmup/steady/drain aggregates
Closes the analyzer side of Phase A; replaces the
status: unavailable placeholders the existing scaffold emits when
join sources are missing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
