diff --git a/MEETING.md b/MEETING.md index bddb260..e0fed0a 100644 --- a/MEETING.md +++ b/MEETING.md @@ -26,7 +26,7 @@ L = Λ · N · W_turn(L) # agentic, T_human≈0 | | 数据 | 图 | |---|---|---| | KV reuse 几乎只在 session 内 | intra 93.2% / cross 5.7% / shared 1.1% | ![](figs/f2a_reuse_topology.png) | -| Session 极度偏斜 | replay 上 top 1% / 5% / 10% = 24% / 62% / 76% input mass（production 全 trace 更陡，top 1% = 46.5%） | ![](figs/f2b_session_skew.png) | +| Session 极度偏斜 | production trace 上 top 1% / 5% / 10% / 25% / 50% = **46.5% / 66.5% / 74.6% / 87.5% / 96.0%** input mass | ![](figs/f2b_session_skew.png) | | 单请求 KV footprint 已经很大 | p99 = 11.8 GiB ≈ H20 12% | ![](figs/f2c_kv_footprint_cdf.png) | 理论 APC 上界 = intra-session 79.6% / any-session 80.3%，差 <1pp。**任何不 affinity 的调度都丢绝大部分 reuse。** @@ -58,7 +58,7 @@ agentic 平均请求 33.6k token 需 3.3GB KV；4P+4D / 6P+2D 在 agentic regime | sticky | **20.3s** | 55.4s | **34.6s** | | unified | **10.3s** | 37.7s | **18.0s** | -机制：top 5% 的 session 占 ~62% input 量、且 hot session 数量远多于 instance 数（8 个），sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢。Unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 几乎 2x 快。 +机制：production trace 上 top 1% 的 session 占 46.5% input 量、且 hot session 数量远多于 instance 数（8 个），sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢。Unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 几乎 2x 快。 --- diff --git a/PAPER_OUTLINE.md b/PAPER_OUTLINE.md index 9fc46b8..3e50547 100644 --- a/PAPER_OUTLINE.md +++ b/PAPER_OUTLINE.md @@ -48,7 +48,7 @@ Agentic workload 与 chatbot 的三个本质差异： - **Multi-turn, programmatic continuation**：每个 turn 由上一个 turn 的 tool-call 结果触发，没有人类 think-time - **Prefill-dominated**：input/output token ratio **75x**，98% 计算在 prefill 阶段（chatbot 为 1-10x） -- **Skewed sessions**：在 replay trace 上 top 1% session 贡献 **24.3%** input token，top 5% **61.9%**，top 10% **75.8%**（vs uniform 1/5/10%）；production 全 trace（1.3M session）skew 更极端，top 1% 达 46.5% +- **Skewed sessions**（来自 Qwen3 production trace，n=1.3M session / 2.1M req / 7200s）：top 1% 贡献 **46.5%** input token，top 5% **66.5%**，top 10% **74.6%**，top 25% **87.5%**，top 50% **96.0%** —— 半数 session 几乎占满全部 input mass 平均 session 长度 TBD turn、TBD 输入 token；p99 单请求 KV 占用 **11.49 GiB**（H20 96GB HBM 的 12%）。 @@ -68,7 +68,7 @@ Trace 上 KV reuse 的分解： ![F2a Reuse topology — intra 93.2% / cross 5.7% / shared 1.1%](figs/f2a_reuse_topology.png) -![F2b Session skew CDF — top 1% = 24.3%, top 5% = 61.9%, top 10% = 75.8% input mass (replay trace)](figs/f2b_session_skew.png) +![F2b Session input-token mass CDF — production trace top 1%/5%/10%/25%/50% = 46.5%/66.5%/74.6%/87.5%/96.0% (replay window overlaid for sanity)](figs/f2b_session_skew.png) ![F2c KV footprint CDF — p99 = 11.8 GiB ≈ 12% of H20](figs/f2c_kv_footprint_cdf.png) @@ -137,7 +137,7 @@ Round-robin 和 load-aware routing（如 LMetric, OSDI'26）最大化 instance | `unified` (affinity + LMetric fallback) | **10.3 s** | 37.7 s | **18.0 s** | | `lmetric` | 14.0 s | 31.3 s | 24.8 s | -机制：top 5% session 占 ~62% input mass，hot session 数量远大于 instance 数（8）；sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢到 20s 量级。unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 在 e2e p90 上 ~2x 快于 sticky。 +机制：production trace 上 top 1% session 占 46.5% input mass、top 5% 占 66.5%，hot session 数量远大于 instance 数（8）；sticky 的 hash 绑定让 **每个 worker 都自己承接一份 hot session**，median worker 也被拖慢到 20s 量级。unified 用 LMetric fallback 把 cold/new session 重路由到非 hot worker，保留 7/8 worker 的速度。系统 p90 由大多数请求决定，所以 unified 在 e2e p90 上 ~2x 快于 sticky。 **注意**：hotspot ratio (max/median) 单独看是误导性的 —— sticky 的 2.73 比 unified 的 3.67 *低*，但因为 sticky 的 median 也高（20.3s vs unified 的 10.3s），系统整体更慢。一个有用的 §3.3 sub-finding：**hot pin failure 必须用 per-worker absolute latency 衡量，不能用 normalized ratio**。 diff --git 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differ diff --git a/scripts/plot_session_skew_cdf.py b/scripts/plot_session_skew_cdf.py index 204e9a1..4fe1477 100644 --- a/scripts/plot_session_skew_cdf.py +++ b/scripts/plot_session_skew_cdf.py @@ -1,12 +1,17 @@ #!/usr/bin/env python3 """Plot a CDF of cumulative input-token mass by session rank. -Reads a JSONL trace (chat_id, session_id, input_length, ...), aggregates -per-session input_length, sorts sessions descending by total, and plots -cumulative fraction of input-token mass vs session-rank percentile. +Primary curve is the *production* trace +(``/home/admin/cpfs/wjh/ali-trace/trace-glm5.1-formatted/051315-051317.jsonl`` +on dash0), which has 1.3 M sessions across 2.1 M records over a 7200 s +window. Because the full raw trace is not co-located with this repo, we +sample 456 (rank_pct, cum_pct) points on dash0 and cache the result in +``analysis/characterization/data/production_session_skew_cdf.json``. Any +top-K%% mass figure can be read off the resulting curve. -The figure replaces the previous discrete top-1%/5%/10% bars with a -continuous curve so any percentile can be read off directly. +The replay-trace CDF (``traces/w600_r0.0015_st30.jsonl``, n=274) is +overlaid for sanity — the replay window samples a thin slice of the head +so its top-1%% is lower, but the shape is preserved. """ from __future__ import annotations @@ -19,66 +24,85 @@ import matplotlib.pyplot as plt import numpy as np -def load_session_input_tokens(trace_path: Path) -> dict[str, int]: +def load_replay_cdf(trace_path: Path) -> tuple[np.ndarray, np.ndarray, int]: totals: dict[str, int] = defaultdict(int) with trace_path.open() as f: for line in f: row = json.loads(line) totals[row["session_id"]] += int(row["input_length"]) - return dict(totals) + n = len(totals) + sorted_vals = np.sort(np.array(list(totals.values())))[::-1] + cum = np.cumsum(sorted_vals) / sorted_vals.sum() + rank_pct = np.arange(1, n + 1) / n * 100 + return rank_pct, cum * 100, n + + +def load_production_cdf( + cache_path: Path, +) -> tuple[np.ndarray, np.ndarray, int, dict[str, float]]: + d = json.loads(cache_path.read_text()) + samples = d["samples"] + xs = np.array([s["rank_pct"] for s in samples]) + ys = np.array([s["cum_pct"] for s in samples]) + return xs, ys, d["n_sessions"], d["anchors_check"] def main() -> None: parser = argparse.ArgumentParser() parser.add_argument( - "--trace", + "--replay-trace", default="traces/w600_r0.0015_st30.jsonl", - help="JSONL trace path", ) parser.add_argument( - "--out", - default="figs/f2b_session_skew.png", - help="Output figure path", + "--prod-cache", + default="analysis/characterization/data/production_session_skew_cdf.json", ) + parser.add_argument("--out", default="figs/f2b_session_skew.png") args = parser.parse_args() - session_totals = load_session_input_tokens(Path(args.trace)) - n_sessions = len(session_totals) - sorted_vals = np.sort(np.array(list(session_totals.values())))[::-1] - cum = np.cumsum(sorted_vals) / sorted_vals.sum() - rank_pct = np.arange(1, n_sessions + 1) / n_sessions * 100 + prod_x, prod_y, prod_n, prod_anchors = load_production_cdf(Path(args.prod_cache)) + replay_rank_pct, replay_cum_pct, replay_n = load_replay_cdf(Path(args.replay_trace)) - marks = [1, 5, 10, 25, 50] - mark_idx = [int(np.ceil(n_sessions * p / 100)) - 1 for p in marks] + fig, ax = plt.subplots(figsize=(9, 5.5)) - fig, ax = plt.subplots(figsize=(8, 5)) - ax.plot(rank_pct, cum * 100, color="#2f6fab", lw=2.2, - label="cumulative input-token mass") - ax.plot([0, 100], [0, 100], color="#999", ls="--", lw=1, - label="uniform reference (y = x)") + ax.plot( + prod_x, prod_y, + color="#c44e52", lw=2.4, + label=f"production trace (n={prod_n:,} sessions, 456-pt sampled)", + ) - for p, i in zip(marks, mark_idx): - y = cum[i] * 100 - ax.scatter([p], [y], color="#c44e52", zorder=5, s=40) + annotate_pts = [1.0, 5.0, 10.0, 25.0, 50.0] + for p in annotate_pts: + y = float(np.interp(p, prod_x, prod_y)) + ax.scatter([p], [y], color="#c44e52", s=55, zorder=5) ax.annotate( - f"top {p}% → {y:.1f}%", + f"top {p:g}% → {y:.1f}%", xy=(p, y), - xytext=(p + 2, y - 5), - fontsize=9, - color="#333", + xytext=(p + 2.5, y - 6), + fontsize=10, + color="#7a1d1d", ) + ax.plot( + replay_rank_pct, replay_cum_pct, + color="#2f6fab", lw=1.6, + alpha=0.85, + label=f"replay window (n={replay_n} sessions, raw CDF)", + ) + + ax.plot( + [0, 100], [0, 100], + color="#888", ls="--", lw=1, + label="uniform reference (y = x)", + ) + ax.set_xlim(0, 100) ax.set_ylim(0, 102) ax.set_xlabel("Session rank percentile (top → bottom by input-token mass)") ax.set_ylabel("Cumulative % of input-token mass") - ax.set_title( - f"Session input-token mass CDF " - f"(n={n_sessions} sessions, " - f"total={sorted_vals.sum() / 1e6:.1f} M tokens)" - ) + ax.set_title("Session input-token mass CDF — Qwen3 production trace") ax.grid(True, alpha=0.3) - ax.legend(loc="lower right", framealpha=0.9) + ax.legend(loc="lower right", framealpha=0.92, fontsize=9) out_path = Path(args.out) out_path.parent.mkdir(parents=True, exist_ok=True)