Working-set figure: linear node axes + benefit/cost split
Drop log node axis (decade ticks were unreadable). Left = APC vs #nodes (linear), right = #nodes vs retention window T. Mark the 1-node budget crossing (~7s reuse, ~8% APC) and the 14-node oracle ceiling. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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@@ -156,48 +156,72 @@ def plot(ws, hw, block_bytes, label, out_path):
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bgb = block_bytes / GB
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pool = hw["kv_pool_gb"] # KV pool per node (= per replica)
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gpr = hw["gpus_per_replica"]
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node_lbl = f"1 node = {gpr}x {hw['gpu']} = {pool:.0f} GB KV"
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# everything in node units: nodes = footprint_GB / pool
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peak_nodes = np.array([r["peak_blocks"] * bgb / pool for r in ws["taus"]])
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apc = np.array([r["apc"] * 100 for r in ws["taus"]])
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oracle_nodes = ws["oracle_peak_blocks"] * bgb / pool
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ceil = ws["apc_ceiling"] * 100
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oracle_nodes = ws["oracle_peak_blocks"] * bgb / pool
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# operating points up to the ceiling: beyond oracle, TTL is strictly worse, so drop.
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rows = [r for r in ws["taus"] if r["tau"] <= 300]
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nodes = np.array([r["peak_blocks"] * bgb / pool for r in rows])
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apc = np.array([r["apc"] * 100 for r in rows])
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tau = np.array([r["tau"] for r in rows])
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XMAX = 16
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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# --- panel 1: APC vs nodes of HBM needed ---
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ax1.plot(peak_nodes, apc, "o-", color="#1f77b4", lw=2, ms=7, label="TTL-LRU W(T)")
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for r, x, y in zip(ws["taus"], peak_nodes, apc):
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ax1.annotate(f"{r['tau']:g}s", (x, y), fontsize=8,
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textcoords="offset points", xytext=(4, 5))
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ax1.scatter([oracle_nodes], [ceil], marker="*", s=320, color="#d62728", zorder=5,
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label=f"oracle / ceiling ({ceil:.1f}% @ {oracle_nodes:.0f} nodes)")
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# ===== panel 1: benefit vs cost -- APC you get per cluster size =====
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ax1.plot(nodes, apc, "o-", color="#1f77b4", lw=2, ms=7, zorder=4, label="TTL-LRU cache")
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# interpolated APC exactly at the 1-node budget
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apc_at_1 = float(np.interp(1.0, nodes, apc))
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ax1.scatter([1], [apc_at_1], s=90, facecolors="none", edgecolors="#ff7f0e",
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lw=2, zorder=6)
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ax1.annotate(f"1 node -> ~{apc_at_1:.0f}% APC\n(TTL model; real LRU higher)",
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(1, apc_at_1), textcoords="offset points", xytext=(12, -2),
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fontsize=9, color="#ff7f0e", va="top")
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# label the well-separated decision-zone points
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for r, x, y in zip(rows, nodes, apc):
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if x >= 1.5:
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ax1.annotate(f"keep {r['tau']:g}s reuse", (x, y),
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textcoords="offset points", xytext=(6, 6), fontsize=8.5)
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ax1.annotate("T<=10s reuse:\nall < 1.4 nodes", (0.5, 22), fontsize=8.5,
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color="#1f77b4", ha="left")
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# budget + ceiling
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ax1.axvspan(0, 1, color="#2ca02c", alpha=.08)
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ax1.axvline(1, ls="--", color="#2ca02c", lw=1.8)
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ax1.text(1.05, 96, "1 B300 node (your budget)", color="#2ca02c", fontsize=9, va="top")
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ax1.scatter([oracle_nodes], [ceil], marker="*", s=340, color="#d62728", zorder=7)
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ax1.annotate(f"ceiling {ceil:.1f}%\noracle: {oracle_nodes:.0f} nodes",
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(oracle_nodes, ceil), textcoords="offset points", xytext=(-10, -8),
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fontsize=9, color="#d62728", ha="right", va="top")
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ax1.axhline(ceil, ls=":", color="#d62728", alpha=.5)
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for k in (1, 2, 4, 8, 16, 32):
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ax1.axvline(k, ls="--", color="#2ca02c", alpha=.45)
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ax1.text(k, 2, f"{k} node / {k*gpr} GPU",
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rotation=90, va="bottom", ha="right", fontsize=8, color="#2ca02c")
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ax1.axvspan(0.1, 1, color="#2ca02c", alpha=.06) # "fits in 1 node" region
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ax1.set_xscale("log")
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ax1.set_xlabel(f"# nodes of GPU HBM that must hold the KV ({node_lbl})")
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ax1.set_ylabel("Achievable prefix-cache hit rate (APC %)")
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ax1.set_title("APC vs cluster size (nodes)")
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ax1.grid(alpha=.3, which="both"); ax1.legend(loc="lower right"); ax1.set_ylim(0, 100)
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ax1.set_xlim(0, XMAX); ax1.set_ylim(0, 100)
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ax1.set_xticks(range(0, XMAX + 1, 2)); ax1.set_xticks(range(0, XMAX + 1), minor=True)
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ax1.set_xlabel(f"# nodes of GPU HBM needed (1 node = {gpr}x {hw['gpu']} = {pool:.0f} GB KV)")
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ax1.set_ylabel("Prefix-cache hit rate (APC %)")
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ax1.set_title("Benefit vs cost: APC per cluster size", fontweight="bold")
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ax1.grid(alpha=.3); ax1.grid(alpha=.15, which="minor"); ax1.legend(loc="center right")
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# --- panel 2: nodes needed by retention window ---
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sel = [r for r in ws["taus"] if r["tau"] in (2, 30, 300, 600)]
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xs = np.arange(len(sel)); w = 0.38
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ax2.bar(xs - w/2, [r["peak_blocks"]*bgb/pool for r in sel], w, label="peak", color="#1f77b4")
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ax2.bar(xs + w/2, [r["p50_blocks"]*bgb/pool for r in sel], w, label="median", color="#aec7e8")
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ax2.axhline(1, ls="--", color="#2ca02c", lw=2, label="your budget: 1 node")
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ax2.axhline(oracle_nodes, ls=":", color="#d62728", lw=2,
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label=f"oracle full-ceiling ({oracle_nodes:.0f} nodes)")
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ax2.set_xticks(xs); ax2.set_xticklabels([f"T={r['tau']:g}s\nAPC={r['apc']*100:.0f}%" for r in sel])
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ax2.set_ylabel("# nodes of HBM needed")
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ax2.set_yscale("log")
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ax2.set_title("Cluster size by retention window")
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ax2.grid(alpha=.3, axis="y", which="both"); ax2.legend(loc="upper left", fontsize=9)
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# ===== panel 2: cost -- nodes needed to retain T seconds of reuse =====
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ax2.plot(tau, nodes, "s-", color="#1f77b4", lw=2, ms=7, zorder=4)
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ax2.axhline(1, ls="--", color="#2ca02c", lw=1.8, label="1 node (your budget)")
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ax2.axhline(oracle_nodes, ls=":", color="#d62728", lw=1.8,
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label=f"full ceiling = {oracle_nodes:.0f} nodes")
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# where the curve crosses the 1-node budget
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tau_at_1 = float(np.interp(1.0, nodes, tau))
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ax2.scatter([tau_at_1], [1], s=90, facecolors="none", edgecolors="#ff7f0e", lw=2, zorder=6)
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ax2.annotate(f"1 node only retains\n~{tau_at_1:.0f}s of reuse", (tau_at_1, 1),
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textcoords="offset points", xytext=(8, 14), fontsize=9, color="#ff7f0e")
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for r, x, y in zip(rows, tau, nodes):
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if y >= 1.5:
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ax2.annotate(f"{r['tau']:g}s", (x, y), textcoords="offset points",
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xytext=(4, 5), fontsize=8.5)
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ax2.set_xscale("log")
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ax2.set_xticks([1, 2, 5, 10, 30, 60, 300])
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ax2.set_xticklabels(["1s", "2s", "5s", "10s", "30s", "60s", "300s"])
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ax2.set_ylim(0, XMAX); ax2.set_yticks(range(0, XMAX + 1, 2))
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ax2.set_xlabel("retention window T (how long-idle a session's KV we keep)")
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ax2.set_ylabel("# nodes of GPU HBM needed")
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ax2.set_title("Cost: nodes needed to retain T-seconds of reuse", fontweight="bold")
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ax2.grid(alpha=.3, which="both"); ax2.legend(loc="upper left", fontsize=9)
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fig.suptitle(label, fontsize=13, fontweight="bold")
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fig.tight_layout(rect=[0, 0, 1, 0.97])
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