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docs(kvc): redesign gpu_utilization figure to lead with system-total compute

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Reviewer feedback: the original gpu_utilization figure was confusing.
"P does prefill" is a trivial restatement of the architecture; the
figure didn't make clear what insight it was supposed to convey.

The non-trivial insight WAS in the figure but buried in per-GPU
breakdown details: KVC v2's total system compute is 3.47M tokens
vs DP's 5.17M -- a 33% reduction for the same 4449-request workload.
That's the result of session affinity actually converting to less
work, not just to better locality.

Redesigned the figure to lead with that finding:

Left panel (NEW): system-wide compute as two stacked bars
  - KVC: P heavy prefill (1.07M) + D append-prefill (1.39M) + decode (1.01M)
  - DP:  full prefill (4.17M) + decode (1.00M)
  - Big "-33% total compute" badge bracketed by an arrow between the
    bar tops makes the headline number unmissable

Right panel (kept, simplified): per-GPU work distribution
  - Same color coding as the left panel, so the architecture story
    flows from "what work the system does" to "where it happens"
  - In-panel annotation boxes describe the two architectural shapes
    (specialized P + light D vs uniform fused workers)
  - Removed the second legend that was overlapping bars

Doc §4.5 rewritten to match:
  - Old title: "[辩驳 critic] Prefill GPU 90%+ 闲置 是设计意图，不是浪费"
    (inside-baseball framing that confused external readers)
  - New title: "KVC 的 compute 经济：session affinity 让系统总 compute 减少 33%"
    (leads with the non-trivial finding)
  - Body presents 3.47M vs 5.17M directly, decomposes into prefill /
    decode segments, shows why session affinity converts to compute
    reduction (mean uncached drops from 952 to 341 on the fast path)
  - Cross-references §3.5 (TPOT) to explain why "unequal GPU load"
    is a design feature, not a bug
  - Drops the audit-rebuttal framing; the rebuttal of "P is idle"
    is now implicit in the system-total comparison

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
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2026-05-13 10:39:15 +08:00
parent 722032a13b
commit 314c4cda0e
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43
docs/V2_DEEP_ANALYSIS_ZH.md
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@@ -367,33 +367,38 @@ Critic 的 framing：
→ 论文里这是 **contribution**，不是 caveat：KVC 的 mechanism 让 27% 更少的总池子产生了更高的 retention 效率。
### 4.5 [辩驳 critic] "Prefill GPU 90%+ 闲置" 是设计意图，不是浪费
### 4.5 KVC 的 compute 经济：session affinity 让系统总 compute 减少 33%
Critic 的 framing：
> KVC 1P3D 中 prefill GPU 只在 8.3% 请求时被激活；实际工作 GPU 只有 ~3.08 个，对比 4DP CA 的 4 个 fused GPU 不公平。
**头条事实**：在同样 4449 个请求的 workload 上，KVC v2 整个系统消耗的 compute tokens 比 4DP CA 少 33%。
**反驳**：按"请求计数"看 P 确实稀疏，但按"实际工作量"看 P 的负载和每个 D 相当——P 是**低频高 cost 的 safety net**，不是 idle 容量。
![System-wide compute economy + per-GPU work distribution](figures/gpu_utilization.png)
![Per-GPU utilization: 请求计数视图 vs 工作量视图](figures/gpu_utilization.png)
**左图 — 系统总 compute（堆叠条形图）**：
- KVC 1P3D v2 总 compute = **3.47M tokens**
- P-side 重 prefill（reseed/seed 路径，8.3% 请求）：1.07M
- D-side append-prefill（91.6% direct-to-D 路径，每个请求平均仅 341 token）：1.39M
- Decode：1.01M
- DP 4-way CA 总 compute = **5.17M tokens**
- Full prefill（每个请求都是 mean 952 uncached token）：4.17M
- Decode：1.00M
**左图 — 请求计数视图**：KVC P GPU 仅处理 328 个请求（7.4%），而 KVC D 各处理 ~1450 个（33%），DP 各处理 ~1100 个（25%）。**乍看像 critic 说的"P 闲着"**。
差异的根因**完全在 prefill 段**：DP 每个请求做 mean 952 token 的 uncached prefill，KVC 91.6% 请求只做 mean 341 token 的 append-prefill（剩 8.3% 走 P 做平均 5455 token 的重 prefill）。session affinity 让 91.6% 请求的 prefix KV **已经在目标 D 上 resident**，下次 turn 只需算 append delta——**这就是 cache 复用直接折算成 compute 减少的过程**。
**右图 — 工作量视图（compute tokens）**：
- KVC P GPU：**1.07M tokens 的 prefill 工作**（仅 prefill，无 decode）
- KVC D GPU 每个：~0.80M tokens（小量 append-prefill + 全部 decode）
- DP 每个 worker：~1.30M tokens（全套 prefill + decode）
**右图 — per-GPU 工作分布（同样 8 个 GPU）**：
- KVC 把 compute **不均匀分配**：P 专门承担 1.07M 的重 prefill（不做 decode），3 个 D 各自只承担 ~0.80M 的轻 append + decode 混合。
- DP 把 compute **均匀分配**：每个 fused worker ~1.25M（full prefill + decode 必须在同 GPU 上交替）。
→ **KVC P GPU 的 per-GPU 工作量与每个 KVC D GPU 相当**——只是分布在少数（328）个高强度请求上（每个 reseed 5K-90K tokens）。它不是空转，是 **low-frequency, high-cost safety net**。
这种"不均匀分配"是 KVC 的设计意图，不是 load imbalance bug：
1. **重 prefill 被隔离**——P 的 prefill kernel 不会插队进 D 的 decode batch，decode 端 batching 几乎无 jitter（详见 §3.5 TPOT 双方完全重合）
2. **D 端只做小 append**（mean 341 token vs DP 的 952 token），prefill kernel 占的 GPU 时间从 ~10ms 降到 ~1ms，对 decode batching 的干扰从主导变为可忽略
3. **总 compute 不依赖每个 GPU 满载** —— "P 闲着但当它工作时承担全部重活" 是合理的分工
**总工作量对比**：
- KVC 4 个 GPU 合计 ~3.47M tokens 工作
- DP 4 个 GPU 合计 ~5.17M tokens 工作（**KVC 减少 33% compute**——这是 session affinity 带来的 cache 复用收益）
**Paper 论述角度**：这张图证明 session affinity 不是只产生 locality 收益，而是直接把 locality **折算成系统层面的 compute 减少**。具体地：
- 91.6% 请求的 uncached_tokens 从 mean 952（DP）降到 mean 341（KVC direct-to-D）= 工作量减少 64%
- 8.3% 请求的 uncached_tokens 在 KVC 里上升（mean 5455 reseed vs DP 全部 mean 952）但请求数小
- 加权平均后 KVC 系统总 prefill compute 减少 67%（1.07M+1.39M vs 4.17M），加上不变的 decode 后总 compute 减少 33%
这两点综合：KVC 用 **同样 4 个 GPU、更少总 KV pool、更少总 compute**，做到了 latency / TTFT mean/p50/p90 全胜。
**论文应当把这条作为 architectural rationale 写出来：KVC 用 P 的低频专用化换 D 端的 TTFT 稳定性。**
历史尝试佐证：KVC 4D0P（取消 P 角色，所有 GPU 都做 P+D）已经实验过——整体性能下降，因为 prefill 与 decode 争 GPU 资源时 decode latency 抖动放大。
历史尝试佐证：KVC 4D0P（取消 P 角色，所有 GPU 都做 P+D，类似 DP）已经实验过——整体性能下降，因为 prefill 与 decode 争 GPU 资源时 decode latency 抖动放大。这反过来印证 "P 专门化" 的设计价值：它让 D 的 decode 路径**永不与重 prefill 在同 GPU 上争资源**。
### 4.6 v2 N=1 + 新代码路径未验证确定性 — **MINOR，方法学待办**

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scripts/analysis/plot_gpu_utilization.py
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@@ -1,24 +1,25 @@
#!/usr/bin/env python3
"""Per-GPU utilization breakdown: KVC 1P3D v2 vs 4-way DP CA.
"""System compute economy: KVC 1P3D v2 vs 4-way DP CA.
Generates docs/figures/gpu_utilization.png — two-panel:
left: per-GPU request count
right: per-GPU compute work (uncached prefill tokens + decode tokens, stacked)
Generates docs/figures/gpu_utilization.png -- two-panel:
left: total system compute (stacked by work type)
right: per-GPU compute distribution (specialized vs fused)
The point of the figure is to push back on the naïve reading
"KVC's prefill GPU is idle 90% of the time, so KVC is using fewer GPUs."
The punchline is the TOTAL system compute reduction:
KVC v2 system: 3.47 M tokens of compute (1.07 P-prefill + 1.39 D-append + 1.01 decode)
DP 4-way: 5.17 M tokens of compute (4.17 full-prefill + 1.00 decode)
→ KVC does 33% LESS compute for the SAME workload (same 4449 requests).
By request count, the prefill GPU is indeed touched by only ~8% of requests.
By compute work, the prefill GPU bears comparable per-GPU load to each
decode GPU — it is a low-frequency, high-cost safety net for cache misses,
not idle capacity.
This is the non-trivial finding: session affinity converts to reduced
system-wide work, not just locality. The per-GPU panel then explains
the architectural shape: KVC concentrates heavy prefill on a specialized
P worker, leaves D workers with light append + decode; DP forces every
worker to absorb the full prefill load mixed with decode.
Work attribution:
KVC direct-to-D path: prefill happens locally on the assigned D worker
(append-prefill of `uncached_tokens` tokens).
KVC seed/reseed/fallback path: prefill happens on prefill-0
(full uncached_tokens), decode on assigned D.
DP: all work on assigned direct-N worker.
The earlier version of this figure showed per-GPU request count + per-GPU
compute and was confusing to external reviewers ("P doing prefill is
trivial"). This version leads with the system-total comparison, which IS
the non-trivial result.
Aborted / errored requests are excluded.
"""
@@ -64,172 +65,211 @@ def main() -> None:
dp = [r for r in load(DP) if not is_failed(r)]
# ------------------------------------------------------------------
# KVC per-GPU attribution
# KVC per-GPU + per-work-type attribution
# ------------------------------------------------------------------
kvc_req_count = defaultdict(int)
kvc_prefill_tokens = defaultdict(int) # uncached prefill compute
kvc_prefill_tokens = defaultdict(int)
kvc_decode_tokens = defaultdict(int)
for r in kvc:
d = r["assigned_decode_node"] # decode-0/1/2
p = r["assigned_prefill_node"] # prefill-0
d = r["assigned_decode_node"]
p = r["assigned_prefill_node"]
mode = r.get("execution_mode", "")
if mode == "kvcache-direct-to-d-session":
# P is bypassed entirely; D does the append-prefill + decode
kvc_req_count[d] += 1
# P bypassed; D does small append-prefill + decode
kvc_prefill_tokens[d] += uncached(r)
kvc_decode_tokens[d] += out_tokens(r)
else:
# P does the full prefill; D handles decode
kvc_req_count[p] += 1
kvc_req_count[d] += 1 # decode side still counts
# P does heavy prefill; D handles decode
kvc_prefill_tokens[p] += uncached(r)
kvc_decode_tokens[d] += out_tokens(r)
# ------------------------------------------------------------------
# DP per-GPU attribution (fused P+D on every worker)
# ------------------------------------------------------------------
dp_req_count = defaultdict(int)
dp_prefill_tokens = defaultdict(int)
dp_decode_tokens = defaultdict(int)
for r in dp:
w = r["assigned_decode_node"] # direct-0..3
dp_req_count[w] += 1
w = r["assigned_decode_node"]
dp_prefill_tokens[w] += uncached(r)
dp_decode_tokens[w] += out_tokens(r)
# ------------------------------------------------------------------
# Build ordered GPU list, KVC then DP
# Aggregate work by category for the left panel
# ------------------------------------------------------------------
kvc_p_prefill = kvc_prefill_tokens.get("prefill-0", 0)
kvc_d_prefill = sum(v for k, v in kvc_prefill_tokens.items() if k.startswith("decode-"))
kvc_d_decode = sum(kvc_decode_tokens.values())
kvc_total = kvc_p_prefill + kvc_d_prefill + kvc_d_decode
dp_prefill_total = sum(dp_prefill_tokens.values())
dp_decode_total = sum(dp_decode_tokens.values())
dp_total = dp_prefill_total + dp_decode_total
M = 1e6
saving_pct = (1 - kvc_total / dp_total) * 100
# ------------------------------------------------------------------
# Colors
# ------------------------------------------------------------------
KVC_P_COLOR = "#E89D44" # orange — P GPU
KVC_D_PREF_COLOR = "#7AB6D9" # light blue — D-side small append-prefill
KVC_D_DEC_COLOR = "#1F77B4" # dark blue — D-side decode
DP_PREF_COLOR = "#E07474" # light red — DP full prefill
DP_DEC_COLOR = "#D62728" # dark red — DP decode
fig, axes = plt.subplots(1, 2, figsize=(15, 7.0))
# ==================================================================
# Left panel: System-wide compute, stacked by work type
# ==================================================================
ax = axes[0]
x = np.array([0, 1])
bar_w = 0.55
# KVC stack: P-prefill (bottom orange) + D-prefill (light blue) + D-decode (dark blue)
ax.bar(0, kvc_p_prefill / M, bar_w, color=KVC_P_COLOR,
edgecolor="black", linewidth=0.6,
label="KVC: P-side heavy prefill (reseed / seed)")
ax.bar(0, kvc_d_prefill / M, bar_w, bottom=kvc_p_prefill / M,
color=KVC_D_PREF_COLOR, edgecolor="black", linewidth=0.6,
label="KVC: D-side append-prefill (direct-to-D, small)")
ax.bar(0, kvc_d_decode / M, bar_w,
bottom=(kvc_p_prefill + kvc_d_prefill) / M,
color=KVC_D_DEC_COLOR, edgecolor="black", linewidth=0.6,
label="Decode (both)")
# DP stack: full prefill (light red) + decode (dark red)
ax.bar(1, dp_prefill_total / M, bar_w,
color=DP_PREF_COLOR, edgecolor="black", linewidth=0.6,
label="DP: fused worker prefill (full uncached)")
ax.bar(1, dp_decode_total / M, bar_w, bottom=dp_prefill_total / M,
color=DP_DEC_COLOR, edgecolor="black", linewidth=0.6,
label="_nolegend_")
# Inline labels for stack segments
def stack_label(xpos, ypos, text, color="white", fontsize=10):
ax.text(xpos, ypos, text, ha="center", va="center",
fontsize=fontsize, color=color, fontweight="bold")
stack_label(0, kvc_p_prefill / M / 2,
f"P heavy prefill\n{kvc_p_prefill/M:.2f}M")
stack_label(0, (kvc_p_prefill + kvc_d_prefill / 2) / M,
f"D append-prefill\n{kvc_d_prefill/M:.2f}M",
color="black")
stack_label(0, (kvc_p_prefill + kvc_d_prefill + kvc_d_decode / 2) / M,
f"D decode\n{kvc_d_decode/M:.2f}M")
stack_label(1, dp_prefill_total / M / 2,
f"Full prefill\n(every worker)\n{dp_prefill_total/M:.2f}M",
color="black")
stack_label(1, (dp_prefill_total + dp_decode_total / 2) / M,
f"Decode\n{dp_decode_total/M:.2f}M")
# Totals on top
ax.text(0, kvc_total / M + 0.15, f"{kvc_total/M:.2f}M tokens",
ha="center", va="bottom", fontsize=12, fontweight="bold",
color="#1F77B4")
ax.text(1, dp_total / M + 0.15, f"{dp_total/M:.2f}M tokens",
ha="center", va="bottom", fontsize=12, fontweight="bold",
color="#D62728")
# Big savings annotation — placed centrally inside the panel,
# bracketed by a horizontal arrow connecting the bar tops.
headroom_top = max(kvc_total, dp_total) / M * 1.42
arrow_y = max(kvc_total, dp_total) / M * 1.08
text_y = max(kvc_total, dp_total) / M * 1.22
ax.annotate("", xy=(0.78, arrow_y), xytext=(0.22, arrow_y),
arrowprops=dict(arrowstyle="<->", color="#2C8C2C", lw=1.8))
ax.text(
0.5, text_y, f"−{saving_pct:.0f}%\ntotal compute",
ha="center", va="center",
fontsize=13, fontweight="bold", color="#2C8C2C",
bbox=dict(facecolor="#E8F5E8", edgecolor="#2C8C2C", alpha=0.95, pad=5),
)
ax.set_xticks(x)
ax.set_xlim(-0.5, 1.5)
ax.set_xticklabels(["KVC 1P3D v2", "DP 4-way CA"], fontsize=12, fontweight="bold")
ax.set_ylabel("Total system compute (millions of token-equivalents)", fontsize=11)
ax.set_ylim(0, headroom_top)
ax.set_title("System-wide compute economy | same 4449-request workload",
fontsize=12, pad=10)
ax.grid(axis="y", linestyle=":", alpha=0.4)
ax.set_axisbelow(True)
ax.legend(loc="upper left", fontsize=8.5, framealpha=0.95)
# ==================================================================
# Right panel: per-GPU breakdown showing the architectural shape
# ==================================================================
ax = axes[1]
kvc_gpus = ["prefill-0", "decode-0", "decode-1", "decode-2"]
dp_gpus = ["direct-0", "direct-1", "direct-2", "direct-3"]
all_gpus = kvc_gpus + dp_gpus
def get(d, k):
return d.get(k, 0)
counts = [get(kvc_req_count, g) for g in kvc_gpus] + \
[get(dp_req_count, g) for g in dp_gpus]
prefill_tk = [get(kvc_prefill_tokens, g) for g in kvc_gpus] + \
[get(dp_prefill_tokens, g) for g in dp_gpus]
decode_tk = [get(kvc_decode_tokens, g) for g in kvc_gpus] + \
[get(dp_decode_tokens, g) for g in dp_gpus]
# Display labels: P/D role + worker id
labels = [
"KVC P\nprefill-0",
"KVC D\ndecode-0",
"KVC D\ndecode-1",
"KVC D\ndecode-2",
"DP P+D\ndirect-0",
"DP P+D\ndirect-1",
"DP P+D\ndirect-2",
"DP P+D\ndirect-3",
"KVC\nP-only", "KVC\nD-0", "KVC\nD-1", "KVC\nD-2",
"DP\nP+D-0", "DP\nP+D-1", "DP\nP+D-2", "DP\nP+D-3",
]
kvc_mask = [True, True, True, True, False, False, False, False]
KVC_P_COLOR = "#E89D44" # orange — P GPU stands out
KVC_D_COLOR = "#1F77B4" # blue
DP_COLOR = "#D62728" # red
bar_colors = [KVC_P_COLOR, KVC_D_COLOR, KVC_D_COLOR, KVC_D_COLOR,
DP_COLOR, DP_COLOR, DP_COLOR, DP_COLOR]
fig, axes = plt.subplots(1, 2, figsize=(15, 7.0))
x = np.arange(len(all_gpus))
# -- Left: per-GPU request count ----------------------------------
ax = axes[0]
bars = ax.bar(x, counts, color=bar_colors, edgecolor="black", linewidth=0.6)
for xi, c in zip(x, counts):
ax.text(xi, c + max(counts) * 0.015, f"{c:,}",
ha="center", va="bottom", fontsize=9.5)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9.5)
ax.set_ylabel("Number of requests touching this GPU", fontsize=11)
# Headroom for the annotation: extend ylim 35% above tallest bar
ax.set_ylim(0, max(counts) * 1.40)
ax.set_title("Per-GPU request count\n(naïve view: P seems idle)",
fontsize=12, pad=24)
ax.grid(axis="y", linestyle=":", alpha=0.4)
ax.set_axisbelow(True)
prefill_M = ([kvc_prefill_tokens.get(g, 0) / M for g in kvc_gpus]
+ [dp_prefill_tokens.get(g, 0) / M for g in dp_gpus])
decode_M = ([kvc_decode_tokens.get(g, 0) / M for g in kvc_gpus]
+ [dp_decode_tokens.get(g, 0) / M for g in dp_gpus])
# Annotate: KVC P GPU is "low frequency"
# Place in upper-right area (over DP group) so it doesn't sit on KVC D bars
p_idx = 0
ax.annotate(
f"P GPU only sees\n"
f"{counts[p_idx]:,} requests\n"
f"({counts[p_idx]/len(kvc)*100:.1f}% of all KVC requests)",
xy=(p_idx, counts[p_idx]),
xytext=(2.4, max(counts) * 1.20),
fontsize=10, color=KVC_P_COLOR, fontweight="bold", ha="center",
bbox=dict(facecolor="white", edgecolor=KVC_P_COLOR, alpha=0.92, pad=4),
arrowprops=dict(arrowstyle="->", color=KVC_P_COLOR, lw=1.0),
)
# Color by group: orange for KVC P, blue for KVC D, red for DP
bar_colors_prefill = [KVC_P_COLOR, KVC_D_PREF_COLOR, KVC_D_PREF_COLOR, KVC_D_PREF_COLOR,
DP_PREF_COLOR, DP_PREF_COLOR, DP_PREF_COLOR, DP_PREF_COLOR]
bar_colors_decode = [KVC_D_DEC_COLOR, KVC_D_DEC_COLOR, KVC_D_DEC_COLOR, KVC_D_DEC_COLOR,
DP_DEC_COLOR, DP_DEC_COLOR, DP_DEC_COLOR, DP_DEC_COLOR]
ax.bar(x, prefill_M, color=bar_colors_prefill,
edgecolor="black", linewidth=0.5, label="Prefill compute")
ax.bar(x, decode_M, bottom=prefill_M, color=bar_colors_decode,
edgecolor="black", linewidth=0.5, hatch="///",
alpha=0.75, label="Decode compute")
# -- Right: per-GPU compute work (stacked prefill + decode) -------
ax = axes[1]
prefill_M = [t / 1e6 for t in prefill_tk]
decode_M = [t / 1e6 for t in decode_tk]
total_M = [p + d for p, d in zip(prefill_M, decode_M)]
bars_p = ax.bar(x, prefill_M, color=[c for c in bar_colors],
edgecolor="black", linewidth=0.6, label="Uncached prefill tokens",
alpha=0.95)
bars_d = ax.bar(x, decode_M, bottom=prefill_M, color=[c for c in bar_colors],
edgecolor="black", linewidth=0.6, hatch="///",
label="Decode tokens", alpha=0.55)
for xi, t in zip(x, total_M):
ax.text(xi, t + max(total_M) * 0.015, f"{t:.2f}M",
ha="center", va="bottom", fontsize=9.5)
ax.set_xticks(x)
ax.set_xticklabels(labels, fontsize=9.5)
ax.set_ylabel("Compute tokens (millions)", fontsize=11)
# Headroom for the annotation
ax.set_ylim(0, max(total_M) * 1.45)
ax.set_title("Per-GPU compute work\n(work view: P is comparable to each D)",
fontsize=12, pad=24)
ax.set_ylabel("Compute (millions of token-equivalents)", fontsize=11)
ax.set_ylim(0, max(total_M) * 1.30)
ax.set_title("Where the work lives | specialized P + light D vs uniform fused workers",
fontsize=12, pad=10)
ax.grid(axis="y", linestyle=":", alpha=0.4)
ax.set_axisbelow(True)
# Legend placed at upper-left where bars are tallest is fine after raising ylim
ax.legend(loc="upper left", fontsize=10, framealpha=0.95)
# Annotate: KVC P GPU does similar work to each D.
# Place over DP region (right side) so it doesn't sit on KVC D bars.
ax.annotate(
f"P GPU does {total_M[p_idx]:.2f}M tokens of prefill\n"
f"— comparable per-GPU load to each KVC D worker\n"
f"(KVC D avg = {np.mean(total_M[1:4]):.2f}M)",
xy=(p_idx, total_M[p_idx]),
xytext=(5.5, max(total_M) * 1.30),
fontsize=10, color=KVC_P_COLOR, fontweight="bold", ha="center",
bbox=dict(facecolor="white", edgecolor=KVC_P_COLOR, alpha=0.92, pad=4),
arrowprops=dict(arrowstyle="->", color=KVC_P_COLOR, lw=1.0),
# Separator + headline takeaways under the GROUP labels (in axes
# fraction coords so they don't shift if ylim changes).
ax.axvline(3.5, color="gray", linestyle="--", linewidth=1.0, alpha=0.5)
ax.text(
0.22, 0.97,
f"KVC: P specialized for heavy prefill\nD workers ~{np.mean(total_M[1:4]):.2f}M each (light)",
transform=ax.transAxes, ha="center", va="top", fontsize=9.5,
bbox=dict(facecolor="#FFFAE6", edgecolor="#888", alpha=0.92, pad=4),
)
ax.text(
0.78, 0.97,
f"DP: every worker {np.mean(total_M[4:]):.2f}M (fused)\nfull prefill interleaved with decode",
transform=ax.transAxes, ha="center", va="top", fontsize=9.5,
bbox=dict(facecolor="#FFE8E8", edgecolor="#888", alpha=0.92, pad=4),
)
# Separator + group labels (placed in axes-fraction coords, below subplot
# title at pad=24 we now have safe room for these at y_axes_frac ≈ 1.02)
for ax in axes:
ax.axvline(3.5, color="gray", linestyle="--", linewidth=1.0, alpha=0.5)
ax.text(0.25, 1.02, "KVC 1P3D",
transform=ax.transAxes, ha="center", va="bottom",
fontsize=11.5, fontweight="bold", color="#444",
bbox=dict(facecolor="#F2F2F2", edgecolor="#888",
alpha=0.85, pad=3))
ax.text(0.75, 1.02, "DP 4-way CA",
transform=ax.transAxes, ha="center", va="bottom",
fontsize=11.5, fontweight="bold", color="#444",
bbox=dict(facecolor="#F2F2F2", edgecolor="#888",
alpha=0.85, pad=3))
# No second legend on the right panel — the colours are already
# introduced in the left panel and the in-panel annotation boxes
# explain what each group means. Decode being hatched is signalled
# in the right-panel bar style itself.
fig.suptitle(
"Per-GPU utilization: \"is KVC's prefill GPU wasted?\"\n"
"Left view says yes (only 8% of requests); right view says no (comparable work to each D).",
fontsize=13, y=1.02,
"KVC v2 reduces system-wide compute by 33% vs DP 4-way CA, same workload (4449 requests).\n"
"Mechanism: 91.6% of requests find their prefix cached on the affinity-pinned D worker\n"
"(append-prefill = 341 tokens on avg), so the total prefill work the system must do is much smaller.",
fontsize=12, y=1.05,
)
plt.tight_layout()
plt.savefig(OUT, dpi=150, bbox_inches="tight")
@@ -239,10 +279,19 @@ def main() -> None:
# ------------------------------------------------------------------
# Print numbers for doc reference
# ------------------------------------------------------------------
print("\n=== Per-GPU numbers ===")
print(f"{'GPU':<22} {'requests':>10} {'prefill(M)':>12} {'decode(M)':>12} {'total(M)':>10}")
for lbl, n, pM, dM in zip(labels, counts, prefill_M, decode_M):
print(f" {lbl.replace(chr(10), ' '):<20} {n:>10} {pM:>12.3f} {dM:>12.3f} {pM+dM:>10.3f}")
print("\n=== System totals ===")
print(f"KVC v2 total: {kvc_total/M:.3f}M tokens")
print(f" P heavy prefill: {kvc_p_prefill/M:.3f}M")
print(f" D append-prefill: {kvc_d_prefill/M:.3f}M")
print(f" D decode: {kvc_d_decode/M:.3f}M")
print(f"DP 4w total: {dp_total/M:.3f}M tokens")
print(f" Full prefill: {dp_prefill_total/M:.3f}M")
print(f" Decode: {dp_decode_total/M:.3f}M")
print(f"\nKVC vs DP: -{saving_pct:.1f}% total compute saved")
print("\n=== Per-GPU breakdown ===")
for lbl, p, d in zip(labels, prefill_M, decode_M):
print(f" {lbl.replace(chr(10), ' '):<14} prefill={p:.3f}M decode={d:.3f}M total={p+d:.3f}M")
if __name__ == "__main__":