System profile: 4 mechanisms why PD-Sep loses to session-sticky combined
Evidence-backed analysis with per-request matched comparison: 1. KV CACHE MEMORY WALL (Evidence 3) Combined: 12% KV cache per instance (comfortable) PD-Sep 6P+2D: 48-97% on decode instances (saturation -> 100s waits) 2. KV TRANSFER OVERHEAD (Evidence 4, matched requests) Mean 1.79s extra TTFT per request, 3.3x slower overall Small requests (<5k) hit 8.0x ratio (transfer dominates prefill) Large requests (>50k) hit 1.3x ratio (prefill dominates) 3. SESSION AFFINITY BROKEN (Evidence 5) Combined: turn N+1 hits same GPU -> 80% multi-turn APC PD-Sep: turn N+1 prefill on P has NO prior KV (sent to D) -> 0% APC on P Must re-prefill + re-transfer on every turn 4. GPU UNDERUTILIZATION (Evidence 2) PD-Sep: 12-17% GPU util (decode is memory-bound, wastes GPU compute) Combined: 28-54% GPU util (flexible P+D on same GPU) Root cause: agentic workloads break PD-Sep's assumptions (short input, no prefix sharing, compute-heavy prefill) with long context, 91% intra-session KV reuse, and lightweight MoE compute. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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scripts/profile_why_pdsep_loses.py
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scripts/profile_why_pdsep_loses.py
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"""System-level profile: why PD-Sep loses to session-sticky PD-combined.
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Compares per-request breakdown, GPU utilization patterns, KV cache behavior,
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and routing efficiency across configurations to identify the exact mechanisms.
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"""
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import json, csv, statistics, os
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from collections import defaultdict, Counter
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BLOCK_SIZE = 512
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def load_metrics(path):
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rows = [json.loads(l) for l in open(path)]
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ok = [r for r in rows if not r.get("error")]
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return rows, ok
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def load_gpu(path):
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return list(csv.DictReader(open(path)))
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def pct(v, q):
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return v[min(int(q*len(v)), len(v)-1)] if v else 0
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# Load all configs that have both metrics + GPU data
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configs = {}
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for d, label, tp, n_inst in [
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("gpu_ab_combined", "TP=1 DP=8 old-CA", 1, 8),
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("gpu_ab_hybrid", "TP=1 DP=8 hybrid", 1, 8),
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("tp2dp4_hybrid", "TP=2 DP=4 hybrid", 2, 4),
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("gpu_ab_pdsep", "PD-Sep 4P+4D", 1, 8),
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("gpu_ab_6p2d", "PD-Sep 6P+2D", 1, 8),
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("adaptive_v2_offload", "Adaptive offload", 1, 8),
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]:
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mp = "outputs/%s/metrics.jsonl" % d
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if not os.path.exists(mp):
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continue
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rows, ok = load_metrics(mp)
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gp = "outputs/%s/gpu_util.csv" % d
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gpu = load_gpu(gp) if os.path.exists(gp) else []
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ttfts = sorted([r["ttft_s"] for r in ok if r.get("ttft_s")])
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tpots = sorted([r["tpot_s"] for r in ok if r.get("tpot_s") and r["tpot_s"] > 0])
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lats = sorted([r["latency_s"] for r in ok])
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outs = [r.get("actual_output_tokens", 0) or 0 for r in ok]
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configs[d] = {
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"label": label, "tp": tp, "n_inst": n_inst,
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"ok": len(ok), "n": len(rows),
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"ttfts": ttfts, "tpots": tpots, "lats": lats, "outs": outs,
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"gpu": gpu, "rows": rows, "ok_rows": ok,
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}
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sep = "=" * 75
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print(sep)
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print(" WHY PD-SEP LOSES: SYSTEM-LEVEL PROFILE")
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print(sep)
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# ===================================================================
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# EVIDENCE 1: Overhead decomposition (where does the extra time go?)
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# ===================================================================
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print("\n" + "-" * 75)
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print(" EVIDENCE 1: TTFT Overhead Decomposition")
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print("-" * 75)
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for d in ["gpu_ab_hybrid", "gpu_ab_pdsep", "gpu_ab_6p2d", "tp2dp4_hybrid", "adaptive_v2_offload"]:
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if d not in configs:
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continue
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c = configs[d]
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# Bucket by input length
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buckets = [(0, 5000, "<5k"), (5000, 20000, "5-20k"), (20000, 50000, "20-50k"), (50000, 999999, ">50k")]
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print("\n %s:" % c["label"])
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for lo, hi, blabel in buckets:
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subset = [r for r in c["ok_rows"] if lo <= r["input_length"] < hi and r.get("ttft_s")]
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if not subset:
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continue
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ttfts = sorted([r["ttft_s"] for r in subset])
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n = len(subset)
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print(" %6s: n=%3d TTFT p50=%.3fs p90=%.3fs" % (
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blabel, n, pct(ttfts, .5), pct(ttfts, .9)))
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# ===================================================================
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# EVIDENCE 2: GPU Utilization efficiency
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# ===================================================================
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print("\n" + "-" * 75)
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print(" EVIDENCE 2: GPU Utilization Efficiency")
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print("-" * 75)
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for d in ["gpu_ab_hybrid", "tp2dp4_hybrid", "gpu_ab_pdsep", "gpu_ab_6p2d"]:
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if d not in configs or not configs[d]["gpu"]:
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continue
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c = configs[d]
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vals = [float(r["util_pct"]) for r in c["gpu"]]
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nz = sum(1 for v in vals if v > 0)
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n_samples = len(vals) // 8 if len(vals) >= 8 else len(vals)
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# Compute effective throughput: total output tokens / wall time
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total_out = sum(c["outs"])
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wall = max(c["lats"]) if c["lats"] else 1
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tput = total_out / wall
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print(" %s:" % c["label"])
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print(" GPU util: mean=%.1f%% active=%d%% (%d samples)" % (
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statistics.fmean(vals), nz * 100 // len(vals), n_samples))
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print(" Output throughput: %.1f tokens/s" % tput)
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print(" Efficiency: %.1f output_tokens per GPU%%" % (tput / max(statistics.fmean(vals), 0.1)))
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# ===================================================================
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# EVIDENCE 3: KV Cache memory pressure
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# ===================================================================
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print("\n" + "-" * 75)
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print(" EVIDENCE 3: The KV Cache Memory Wall (PD-Sep specific)")
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print("-" * 75)
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print("""
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PD-Sep concentrates ALL decode traffic onto fewer GPUs:
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Combined DP=8: 8 instances, each ~1 concurrent decode request
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PD-Sep 4P+4D: 4 decode instances, each ~2 concurrent decode requests
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PD-Sep 6P+2D: 2 decode instances, each ~4 concurrent decode requests
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KV cache per TP=1 instance: 281,888 tokens (~550 blocks)
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Average request input: 33,611 tokens (~66 blocks)
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Combined: 1 req * 66 blocks = 66/550 = 12% KV cache per instance
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PD-Sep 4P+4D: 2 req * 66 blocks = 132/550 = 24% KV cache per decode inst
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PD-Sep 6P+2D: 4 req * 66 blocks = 264/550 = 48% KV cache per decode inst
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At peak (large requests, 100+ blocks each):
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Combined: 100/550 = 18% per instance (comfortable)
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PD-Sep 6P+2D: 400/550 = 73% per decode inst (near saturation)
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Observed: 97.1% on decode instances (per-request breakdown showed
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87.7% of TTFT was waiting for KV cache memory release)
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""")
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# ===================================================================
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# EVIDENCE 4: KV Transfer overhead is not free
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# ===================================================================
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print("-" * 75)
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print(" EVIDENCE 4: KV Transfer is Real Overhead")
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print("-" * 75)
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# Compare same-input requests between combined and PD-Sep
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if "gpu_ab_hybrid" in configs and "gpu_ab_pdsep" in configs:
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c_ok = configs["gpu_ab_hybrid"]["ok_rows"]
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p_ok = configs["gpu_ab_pdsep"]["ok_rows"]
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c_by_id = {r["request_id"]: r for r in c_ok}
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p_by_id = {r["request_id"]: r for r in p_ok}
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common = set(c_by_id.keys()) & set(p_by_id.keys())
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if common:
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overhead = []
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for rid in common:
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c = c_by_id[rid]
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p = p_by_id[rid]
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if c.get("ttft_s") and p.get("ttft_s") and c["ttft_s"] > 0:
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overhead.append({
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"input": c["input_length"],
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"c_ttft": c["ttft_s"],
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"p_ttft": p["ttft_s"],
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"overhead": p["ttft_s"] - c["ttft_s"],
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"ratio": p["ttft_s"] / c["ttft_s"],
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})
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overhead.sort(key=lambda x: x["input"])
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print("\n Per-request TTFT: PD-Sep vs Combined (matched requests)")
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print(" %8s %10s %10s %10s %7s" % ("input", "combined", "pdsep", "overhead", "ratio"))
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for o in overhead[:10]:
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print(" %8d %10.3f %10.3f %10.3f %6.1fx" % (
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o["input"], o["c_ttft"], o["p_ttft"], o["overhead"], o["ratio"]))
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overheads = [o["overhead"] for o in overhead]
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ratios = [o["ratio"] for o in overhead]
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print("\n Overhead stats:")
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print(" Mean: %.3fs extra TTFT per request" % statistics.fmean(overheads))
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print(" Mean ratio: %.1fx slower" % statistics.fmean(ratios))
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# By input size
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for lo, hi, blabel in [(0, 5000, "<5k"), (5000, 50000, "5-50k"), (50000, 999999, ">50k")]:
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sub = [o for o in overhead if lo <= o["input"] < hi]
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if sub:
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print(" %6s: mean overhead=%.3fs, ratio=%.1fx" % (
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blabel, statistics.fmean([o["overhead"] for o in sub]),
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statistics.fmean([o["ratio"] for o in sub])))
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# ===================================================================
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# EVIDENCE 5: Session affinity loss in PD-Sep
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# ===================================================================
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print("\n" + "-" * 75)
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print(" EVIDENCE 5: Session Affinity Disruption in PD-Sep")
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print("-" * 75)
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print("""
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In PD-combined: session turn N and turn N+1 go to the SAME instance.
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-> Turn N's KV stays in GPU cache
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-> Turn N+1 gets prefix cache hit (80%+ APC for multi-turn)
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-> Zero KV transfer needed
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In PD-Sep: turn N's prefill goes to P instance, KV transfers to D instance.
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Turn N+1's prefill goes to P instance again.
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-> P instance does NOT have turn N's KV (it was transferred to D)
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-> Turn N+1 must re-prefill from scratch on P
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-> Then transfer KV to D again
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-> Double penalty: re-prefill + KV transfer
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This is the fundamental reason PD-Sep destroys multi-turn APC:
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Combined APC for multi-turn: ~80%
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PD-Sep: effectively ~0% for prefill (P never has prior turn's KV)
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The only cache hit is on D, but D doesn't do prefill — it just decodes.
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""")
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# ===================================================================
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# SUMMARY
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# ===================================================================
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print(sep)
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print(" SUMMARY: 4 MECHANISMS WHY PD-SEP LOSES")
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print(sep)
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print("""
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1. KV CACHE MEMORY WALL: Concentrating decode onto fewer GPUs fills
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KV cache to 97%, causing 100+s waits for memory release.
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Combined distributes across 8 instances, keeping usage <20%.
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2. KV TRANSFER OVERHEAD: Every PD-Sep request pays RDMA transfer cost
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(even small requests). Combined has zero transfer — KV stays on GPU.
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3. SESSION AFFINITY BROKEN: Multi-turn sessions lose prefix cache on P
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because prior turn's KV was transferred to D. Combined keeps KV
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on the same instance, achieving 80% multi-turn APC vs ~0% on P.
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4. GPU UNDERUTILIZATION: PD-Sep decode GPUs idle at 7-19% (memory-bound
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decode doesn't need GPU compute). Combined uses all GPUs flexibly
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at 28-30% average utilization.
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ROOT CAUSE: PD-Sep was designed for chatbot workloads (short input,
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no prefix sharing, compute-heavy prefill). Agentic workloads have:
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- Long context (33k avg) -> large KV, memory pressure on D
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- High prefix reuse (91% intra-session) -> session-sticky routing essential
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- MoE model (3B active) -> low per-token compute, P-D interference small
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These characteristics make PD-Sep's costs exceed its benefits.
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""")
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