Old f2c plotted per-request KV footprint MiB against an "H20 ~95 GiB
usable" reference line. That ceiling was wrong — a 30B-A3B bf16
deployment burns roughly:
~50% HBM for model params (~48 GiB on 96 GiB H20)
~10% for runtime activation buffers
~40% left for the KV cache pool (~38.4 GiB)
so 95 GiB was overstating the available pool by 2.5×.
New f2c reframes the same data into the answer that actually motivates
the paper: how many concurrent decodes does a single instance hold,
and how does PD-disagg change that? Grouped bars per percentile show
system-wide concurrent decode capacity for three 8-GPU deployments:
Combined 8C, PD-disagg 4P+4D (N_D=4), PD-disagg 6P+2D (N_D=2)
Key reads off the figure:
p50 (1.8 GiB/req): 20 fit/inst → 160 / 80 / 40 system-wide
p90 (8.0 GiB/req): 4 fit/inst → 32 / 16 / 8
p95 (9.6 GiB/req): 4 fit/inst → 32 / 16 / 8
p99 (11.5 GiB/req): 3 fit/inst → 24 / 12 / 6
PD-disagg 4P+4D literally halves the decode population at the same
per-request KV pressure — this is the concrete §3.2 "KV memory wall"
penalty stated in terms users care about (concurrency).
- analysis/characterization/render_window1_figures.py:
fig_kv_footprint_cdf rewritten; reads same kv_footprint_summary.json
but computes floor(KV_pool / req_size) × N_D and annotates the
per-instance fit count below each percentile group.
- figs/f2c_kv_footprint_cdf.png: regenerated.
- MEETING.md / PAPER_OUTLINE.md §2.1, §2.4: prose updated with the
new ceiling and the "3 p99 decodes per instance / halved by PD-disagg"
framing.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The headline f6_e2e_latency_bars only shows p90, hiding three regimes:
- mean: unified dominates (3.3s TTFT, 7.0s E2E vs sticky 5.6s / 12.1s)
- p50: sticky and unified are tied on first-turn TTFT (0.5s each) —
sticky's first turn of each session is free, after which queues
accumulate. Unified beats sticky everywhere else.
- p99: tail amplification reveals unified's biggest gap —
TTFT 42.3s vs sticky 74.1s; E2E 68.8s vs sticky 139.7s.
The 12-panel figure is the honest full picture; the 3-panel headline
stays for slide-friendly summary.
- analysis/characterization/window_1_results/raw_stats/{policy}.json:
cached ttft/tpot/e2e {mean,p50,p90,p99} pulled from dash0
/home/admin/cpfs/wjh/agentic-kv/outputs/b3_sweep_20260525_095043/
(b3_policy_comparison.json doesn't record mean, only percentiles).
- analysis/characterization/render_window1_figures.py:
new fig_b3_latency_full_grid renders the 4×3 grid from the cache.
- figs/f6_e2e_latency_full_grid.png: 12-panel companion.
- PAPER_OUTLINE.md §5.2: both figures embedded; main table column
renamed from "Hotspot idx" to "Worker p90 (median / max)" to match
the new metric convention.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The max/median ratio inverts the actual user-facing p90 ranking:
sticky: hotspot=2.73 but system e2e p90 = 34.6s (worst)
unified: hotspot=3.67 but system e2e p90 = 18.0s (best)
because sticky's median is also high (everyone slow) while unified
concentrates the damage on one worker and keeps the other 7 fast.
Any "imbalance" metric structurally punishes the affinity-then-escape
schemes that we actually want to advocate for.
Changes:
- analysis/characterization/render_window1_figures.py:
fig_b3_per_worker_ttft now annotates each subplot with
"median X.Xs · max Y.Ys" instead of "hotspot=Y.YY"; docstring
documents why we drop the ratio.
- figs/f4c_per_worker_ttft.png: regenerated with new titles.
- figs/f4c_apc_vs_hotspot_tradeoff.png: deleted. The scatter's y-axis
was the deprecated ratio; superseded by f4c per-worker bars + f6
e2e bars which together carry the same information honestly.
- PAPER_OUTLINE.md: C3, §3.3, §4.1 wording, §5 metric list, §8
conclusion — replace "hotspot index" mentions with
"worst-worker p90" or "(median, max) worker p90"; promote the
§3.3 methodology note to a top-level sub-finding ("hot pin
failure must be measured with per-worker absolute latency,
not normalized ratio").
- MEETING.md: §3.3 narrative reworded to lead with the (median, max)
pair directly; explicit one-line note on why the ratio is dropped.
Conceptual uses of "hot session" / "hot instance" / "hot pin" remain
unchanged — only the *metric* called hotspot index is retired.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
'capped' is not a routing policy — it's lmetric run on a separately
truncated trace (sessions capped to 8 turns via build_capped_trace.py).
Putting it alongside lmetric/load_only/sticky/unified in per-policy
comparison figures is misleading because the workload differs, not
the routing decision. Comparing apples to a different-trace orange
inflates/deflates apparent policy gaps for the wrong reasons.
Regenerated 4 figures with --exclude-policies capped on
analysis/characterization/render_window1_figures.py:
- f4a_apc_loss.png (APC bars)
- f4c_apc_vs_hotspot_tradeoff.png (APC vs hotspot scatter)
- f4c_per_worker_ttft.png (per-worker TTFT panel)
- f6_e2e_latency_bars.png (TTFT/TPOT/E2E bars)
Added --exclude-policies CLI flag to the renderer so this is a
reversible choice, not a permanent script mutation. capped data remains
in b3_policy_comparison.json and can be brought back in workload-
sensitivity sections (where it actually belongs) by omitting the flag.
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>
