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>
