Gahow Wang
5e6e98aee7
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was the deprecated ratio; superseded by f4c per-worker bars + f6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>
324 lines
13 KiB
Python
324 lines
13 KiB
Python
"""Render PNG figures for Window 1 results (B1', B2, B3).
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Inputs (all expected under <results-dir>):
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- b3_policy_comparison.json (per-policy table)
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- b2_sweep_summary.json (per-cell B2 sweep)
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- apc_upper_w600.json (theoretical bounds)
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- lmetric_reuse.json (intra/cross/shared decomp)
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- kv_footprint_summary.json (full trace KV stats)
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Outputs (under <out-dir>):
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- fig_b3_apc_vs_hotspot.png
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- fig_b3_latency_bars.png
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- fig_b3_apc_vs_upper.png
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- fig_b3_failure_breakdown.png
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- fig_b3_per_worker_ttft_p90.png
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- fig_b2_tpot_vs_prefill.png
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- fig_b2_ttft_vs_prefill.png
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- fig_reuse_decomposition.png
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- fig_kv_footprint_cdf.png
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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POLICY_ORDER = ["lmetric", "load_only", "sticky", "unified", "capped"]
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POLICY_COLOR = {
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"lmetric": "#1f77b4",
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"load_only": "#ff7f0e",
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"sticky": "#d62728",
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"unified": "#2ca02c",
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"capped": "#9467bd",
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}
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def _load(results_dir: Path, name: str) -> dict:
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return json.loads((results_dir / name).read_text())
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def fig_b3_apc_vs_hotspot(comp: dict, upper: dict, out: Path) -> None:
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upper_intra = upper["apc_upper_intra_session"]
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fig, ax = plt.subplots(figsize=(6, 4.5))
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for r in comp["rows"]:
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pol = r["policy"]
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if pol not in POLICY_ORDER:
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continue
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ax.scatter(r["apc_ratio"] * 100, r["hotspot_index_ttft_p90"],
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s=180, color=POLICY_COLOR.get(pol, "gray"), label=pol,
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edgecolors="black", linewidths=0.5)
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ax.annotate(pol, (r["apc_ratio"] * 100, r["hotspot_index_ttft_p90"]),
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xytext=(7, 7), textcoords="offset points",
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fontsize=9)
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ax.axvline(upper_intra * 100, linestyle="--", color="gray", alpha=0.6,
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label=f"intra-session APC upper {upper_intra * 100:.1f}%")
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ax.set_xlabel("APC achieved (%)")
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ax.set_ylabel("hotspot_index = max(worker TTFT p90) / median")
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ax.set_title("B3: APC vs hot-spot tradeoff across policies")
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ax.grid(alpha=0.3)
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_b3_latency_bars(comp: dict, out: Path) -> None:
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by = {r["policy"]: r for r in comp["rows"]}
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pols = [p for p in POLICY_ORDER if p in by]
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metrics = [("TTFT p90 (s)", "ttft_p90_s"),
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("TPOT p90 (ms)", "tpot_p90_s"),
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("E2E p90 (s)", "e2e_p90_s")]
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fig, axes = plt.subplots(1, 3, figsize=(12, 4))
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for ax, (label, key) in zip(axes, metrics):
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vals = [by[p][key] * (1000 if "TPOT" in label else 1) for p in pols]
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ax.bar(pols, vals, color=[POLICY_COLOR.get(p, "gray") for p in pols],
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edgecolor="black", linewidth=0.5)
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ax.set_title(label)
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ax.tick_params(axis="x", rotation=20)
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for i, v in enumerate(vals):
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ax.text(i, v, f"{v:.1f}", ha="center", va="bottom", fontsize=9)
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ax.grid(alpha=0.3, axis="y")
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fig.suptitle("B3 headline latencies per policy")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_b3_apc_vs_upper(comp: dict, upper: dict, out: Path) -> None:
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by = {r["policy"]: r for r in comp["rows"]}
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pols = [p for p in POLICY_ORDER if p in by]
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achieved = [by[p]["apc_ratio"] * 100 for p in pols]
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fig, ax = plt.subplots(figsize=(6.5, 4))
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bars = ax.bar(pols, achieved,
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color=[POLICY_COLOR.get(p, "gray") for p in pols],
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edgecolor="black", linewidth=0.5)
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ax.axhline(upper["apc_upper_intra_session"] * 100, linestyle="--",
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color="black", alpha=0.7,
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label=f"intra-session ceiling {upper['apc_upper_intra_session'] * 100:.1f}%")
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ax.axhline(upper["apc_upper_any_session"] * 100, linestyle=":",
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color="darkgray", alpha=0.7,
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label=f"any-session ceiling {upper['apc_upper_any_session'] * 100:.1f}%")
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for b, v in zip(bars, achieved):
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ax.text(b.get_x() + b.get_width() / 2, v + 1, f"{v:.1f}%",
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ha="center", fontsize=9)
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ax.set_ylim(0, 100)
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ax.set_ylabel("APC ratio (%)")
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ax.set_title("B3: APC achieved vs theoretical ceiling")
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ax.legend(loc="upper right", fontsize=9)
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ax.grid(alpha=0.3, axis="y")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_b3_failure_breakdown(comp: dict, out: Path) -> None:
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by = {r["policy"]: r for r in comp["rows"]}
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pols = [p for p in POLICY_ORDER if p in by]
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causes = ["same_worker_prefill_overlap", "hot_worker_queue",
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"cache_miss_large_append", "high_kv_occupancy", "unknown"]
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cause_color = {
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"same_worker_prefill_overlap": "#d62728",
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"hot_worker_queue": "#ff7f0e",
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"cache_miss_large_append": "#1f77b4",
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"high_kv_occupancy": "#8c564b",
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"unknown": "#7f7f7f",
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}
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fig, ax = plt.subplots(figsize=(7, 4.5))
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bottom = [0.0] * len(pols)
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for c in causes:
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vals = [(by[p].get("failure_counts") or {}).get(c, 0) for p in pols]
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ax.bar(pols, vals, bottom=bottom, label=c.replace("_", " "),
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color=cause_color[c], edgecolor="black", linewidth=0.3)
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bottom = [a + b for a, b in zip(bottom, vals)]
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for i, total in enumerate(bottom):
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ax.text(i, total + 3, f"n={int(total)}", ha="center", fontsize=9)
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ax.set_ylabel("slow request count (TTFT > 2× p90 threshold)")
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ax.set_title("B3: slow-request cause breakdown per policy")
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ax.legend(fontsize=8, loc="upper right")
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ax.grid(alpha=0.3, axis="y")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_b3_per_worker_ttft(results_dir: Path, comp: dict, out: Path) -> None:
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"""Per-worker TTFT p90 grouped bars; title shows median + max worker p90.
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We deliberately do NOT report a max/median 'hotspot index' here: it is a
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ratio and treats unified (most workers fast, one hot) as worse than
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sticky (all workers slow), which inverts the actual user-facing p90.
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"""
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import statistics
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by = {r["policy"]: r for r in comp["rows"]}
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pols = [p for p in POLICY_ORDER if p in by]
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fig, axes = plt.subplots(1, len(pols), figsize=(3 * len(pols), 4),
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sharey=True)
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if len(pols) == 1:
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axes = [axes]
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for ax, pol in zip(axes, pols):
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path = results_dir / f"per_worker_{pol}.json"
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if not path.exists():
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ax.text(0.5, 0.5, f"{pol}: no data", ha="center", va="center",
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transform=ax.transAxes)
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continue
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per = json.loads(path.read_text()).get("per_worker_ttft_p90_s") or {}
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items = sorted(per.items(), key=lambda kv: int(kv[0].rsplit(":", 1)[1]))
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labels = [f"e{int(k.rsplit(':', 1)[1]) - 8000}" for k, _ in items]
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vals = [v for _, v in items]
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ax.bar(labels, vals, color=POLICY_COLOR.get(pol, "gray"),
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edgecolor="black", linewidth=0.5)
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for i, v in enumerate(vals):
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ax.text(i, v, f"{v:.1f}", ha="center", va="bottom", fontsize=8)
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median_v = statistics.median(vals)
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max_v = max(vals)
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ax.set_title(
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f"{pol}\nmedian {median_v:.1f}s · max {max_v:.1f}s",
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fontsize=10,
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)
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ax.tick_params(axis="x", labelsize=8)
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ax.grid(alpha=0.3, axis="y")
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axes[0].set_ylabel("worker TTFT p90 (s)")
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fig.suptitle("B3 per-worker TTFT p90 distribution")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_b2_curves(b2: dict, out_tpot: Path, out_ttft: Path) -> None:
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sizes = sorted({r["prefill_size"] for r in b2["rows"]})
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by_var = {"same": {}, "different": {}}
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for r in b2["rows"]:
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by_var[r["variant"]][r["prefill_size"]] = r
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for name, key, ylabel, ymax_log, out in [
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("TPOT", "tpot_p90", "TPOT p90 (ms)", True, out_tpot),
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("TTFT", "ttft_p90", "TTFT p90 (s)", True, out_ttft),
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]:
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fig, axes = plt.subplots(1, 2, figsize=(11, 4))
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ax_abs, ax_idx = axes
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for variant in ("different", "same"):
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xs, ys_o, ys_c, idxs = [], [], [], []
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for sz in sizes:
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r = by_var[variant].get(sz)
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if not r: continue
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ov = r.get(f"{key}_overlap_s")
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cl = r.get(f"{key}_clean_s")
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if ov is None or cl is None: continue
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xs.append(sz)
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scale = 1000 if name == "TPOT" else 1.0
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ys_o.append(ov * scale)
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ys_c.append(cl * scale)
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idxs.append(ov / cl)
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color = "#d62728" if variant == "same" else "#1f77b4"
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ax_abs.plot(xs, ys_o, "o-", color=color,
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label=f"{variant} (overlap)")
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ax_abs.plot(xs, ys_c, "s--", color=color, alpha=0.5,
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label=f"{variant} (clean)")
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ax_idx.plot(xs, idxs, "o-", color=color, label=variant,
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linewidth=2)
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ax_abs.set_xscale("log", base=2)
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ax_abs.set_yscale("log")
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ax_abs.set_xlabel("prefill injection size (tokens)")
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ax_abs.set_ylabel(ylabel + " (log)")
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ax_abs.set_title(f"B2 {name} absolute (overlap vs clean)")
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ax_abs.legend(fontsize=8)
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ax_abs.grid(alpha=0.3, which="both")
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ax_idx.set_xscale("log", base=2)
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if ymax_log:
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ax_idx.set_yscale("log")
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ax_idx.axhline(1.0, color="black", linestyle=":", alpha=0.5)
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ax_idx.set_xlabel("prefill injection size (tokens)")
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ax_idx.set_ylabel(f"{name} idx = overlap / clean")
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ax_idx.set_title(f"B2 {name} interference index (same vs different worker)")
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ax_idx.legend()
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ax_idx.grid(alpha=0.3, which="both")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_reuse_decomposition(reuse: dict, out: Path) -> None:
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fr = reuse.get("fractions") or {}
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labels = ["intra-session", "cross-session", "shared-prefix", "unclassified"]
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vals = [fr.get("intra", 0), fr.get("cross", 0),
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fr.get("shared", 0), fr.get("unclassified", 0)]
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colors = ["#2ca02c", "#ff7f0e", "#9467bd", "#7f7f7f"]
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fig, ax = plt.subplots(figsize=(6, 3))
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bottom = 0.0
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for label, v, c in zip(labels, vals, colors):
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ax.barh(["lmetric run"], [v], left=[bottom], color=c, edgecolor="black",
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linewidth=0.5, label=f"{label} ({v * 100:.1f}%)")
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bottom += v
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ax.set_xlabel("fraction of cached_tokens")
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ax.set_xlim(0, 1)
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ax.set_title("Real reuse decomposition (w600 lmetric run)")
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ax.legend(fontsize=9, loc="lower right")
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ax.grid(alpha=0.3, axis="x")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def fig_kv_footprint_cdf(kv: dict, out: Path) -> None:
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s = kv.get("kv_mib_per_request") or {}
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vals = [s.get(k) for k in ("p50", "p90", "p95", "p99")]
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labels = ["p50", "p90", "p95", "p99"]
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fig, ax = plt.subplots(figsize=(6, 3.5))
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ax.bar(labels, vals, color="#1f77b4", edgecolor="black", linewidth=0.5)
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for i, v in enumerate(vals):
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ax.text(i, v, f"{v:.0f} MiB", ha="center", va="bottom", fontsize=9)
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ax.axhline(95 * 1024, color="red", linestyle="--", alpha=0.5,
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label="H20 ~95 GiB usable")
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ax.set_ylabel("KV bytes per request (MiB)")
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ax.set_title("B1' Per-request KV footprint (Qwen3-Coder-30B-A3B, 98304 B/token)")
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ax.legend()
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ax.grid(alpha=0.3, axis="y")
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fig.tight_layout()
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fig.savefig(out, dpi=120)
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plt.close(fig)
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def main() -> None:
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p = argparse.ArgumentParser()
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p.add_argument("--results-dir", type=Path, required=True)
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p.add_argument("--out-dir", type=Path, required=True)
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p.add_argument("--exclude-policies", default="",
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help="Comma-separated policies to drop from per-policy figures")
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args = p.parse_args()
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args.out_dir.mkdir(parents=True, exist_ok=True)
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excluded = {s.strip() for s in args.exclude_policies.split(",") if s.strip()}
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if excluded:
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global POLICY_ORDER
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POLICY_ORDER = [p for p in POLICY_ORDER if p not in excluded]
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print(f"excluding policies: {sorted(excluded)} -> kept {POLICY_ORDER}")
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comp = _load(args.results_dir, "b3_policy_comparison.json")
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upper = _load(args.results_dir, "apc_upper_w600.json")
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b2 = _load(args.results_dir, "b2_sweep_summary.json")
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reuse = _load(args.results_dir, "lmetric_reuse.json")
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kv = _load(args.results_dir, "kv_footprint_summary.json")
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fig_b3_apc_vs_hotspot(comp, upper, args.out_dir / "fig_b3_apc_vs_hotspot.png")
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fig_b3_latency_bars(comp, args.out_dir / "fig_b3_latency_bars.png")
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fig_b3_apc_vs_upper(comp, upper, args.out_dir / "fig_b3_apc_vs_upper.png")
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fig_b3_failure_breakdown(comp, args.out_dir / "fig_b3_failure_breakdown.png")
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fig_b3_per_worker_ttft(args.results_dir, comp,
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args.out_dir / "fig_b3_per_worker_ttft_p90.png")
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fig_b2_curves(b2,
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args.out_dir / "fig_b2_tpot_vs_prefill.png",
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args.out_dir / "fig_b2_ttft_vs_prefill.png")
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fig_reuse_decomposition(reuse, args.out_dir / "fig_reuse_decomposition.png")
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fig_kv_footprint_cdf(kv, args.out_dir / "fig_kv_footprint_cdf.png")
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print(f"wrote 8 figures to {args.out_dir}")
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if __name__ == "__main__":
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main()
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