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PD-disagg crossover: regular synthetic trace + goodput sweep + figure

Browse Source 
gen_synthetic_trace.py --mode regular: maximally-regular multi-turn trace
(fixed prefix/delta/turns, constant arrivals, zero session skew) to isolate
the structural PD cost (per-turn full-context transfer + P/D capacity split)
from the skew/hot-pin artifact.

analysis/crossover/: SLO-goodput PD_advantage sweeps bracketing the
prefill<->decode bottleneck axis (D1 grow input -> prefill-bound; D2 grow
output -> decode-bound). figs/crossover_pd_advantage.png shows the crossover
(y=1) with the agentic operating region annotated.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Gahow Wang
2026-05-29 18:19:23 +08:00
parent 48ae72467a
commit 847f52f03b
15 changed files with 1776 additions and 2 deletions
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142
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analysis/crossover/d2_o512_goodput.json Normal file
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@@ -0,0 +1,121 @@
{
"baseline": "8C-proxy",
"arms": {
"8C-proxy": {
"name": "8C-proxy",
"n_offered": 3552,
"n_success": 3552,
"completion_rate": 1.0,
"offered_window_s": 299.74141899999995,
"offered_qps": 11.850214134070008,
"wall_clock_s": 304.97662343096454,
"amplification": 1.0174657357946404,
"ttft": {
"count": 3552,
"mean": 0.16242270423816507,
"p50": 0.15836620499612764,
"p90": 0.17363918052287775,
"p99": 0.24847999344696287
},
"tpot": {
"count": 3552,
"mean": 0.013691483182659388,
"p50": 0.013865661168213087,
"p90": 0.015242900696529327,
"p99": 0.01626683903372128
},
"e2e": {
"count": 3552,
"mean": 7.160272567887971,
"p50": 7.247483663493767,
"p90": 7.962273431208451,
"p99": 8.480892732607899
}
},
"4P+4D": {
"name": "4P+4D",
"n_offered": 3552,
"n_success": 3551,
"completion_rate": 0.9997184684684685,
"offered_window_s": 299.74141899999995,
"offered_qps": 11.850214134070008,
"wall_clock_s": 744.9637431279989,
"amplification": 2.4853546954349977,
"ttft": {
"count": 3551,
"mean": 0.2446379999283825,
"p50": 0.24067969399038702,
"p90": 0.2630795220611617,
"p99": 0.3426029055262916
},
"tpot": {
"count": 3551,
"mean": 0.016916919575073883,
"p50": 0.016998299133030737,
"p90": 0.01775875886689886,
"p99": 0.018166751548973206
},
"e2e": {
"count": 3551,
"mean": 8.89104466149889,
"p50": 8.933106195996515,
"p90": 9.333998591057025,
"p99": 9.536390463996213
}
},
"6P+2D": {
"name": "6P+2D",
"n_offered": 3552,
"n_success": 3551,
"completion_rate": 0.9997184684684685,
"offered_window_s": 299.74141899999995,
"offered_qps": 11.850214134070008,
"wall_clock_s": 312.45468625507783,
"amplification": 1.0424141157985172,
"ttft": {
"count": 3551,
"mean": 0.36231219612768273,
"p50": 0.3462264990666881,
"p90": 0.4059687410481274,
"p99": 0.9837204645154998
},
"tpot": {
"count": 3551,
"mean": 0.03268022218101953,
"p50": 0.03333031399418835,
"p90": 0.03557429400772957,
"p99": 0.038558618127279086
},
"e2e": {
"count": 3551,
"mean": 17.068018403084057,
"p50": 17.392655145958997,
"p90": 18.56302695896011,
"p99": 20.159411454980727
}
}
},
"slo_grid": [
{
"ttft_slo_s": 2.0,
"tpot_slo_s": 0.05,
"arms": {
"8C-proxy": {
"attainment": 1.0,
"pd_advantage": 1.0,
"n_slo": 3552
},
"4P+4D": {
"attainment": 0.9997184684684685,
"pd_advantage": 0.9997184684684685,
"n_slo": 3551
},
"6P+2D": {
"attainment": 0.9997184684684685,
"pd_advantage": 0.9997184684684685,
"n_slo": 3551
}
}
}
]
}

121
analysis/crossover/d2_o64_goodput.json Normal file
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@@ -0,0 +1,121 @@
{
"baseline": "8C-proxy",
"arms": {
"8C-proxy": {
"name": "8C-proxy",
"n_offered": 3552,
"n_success": 3552,
"completion_rate": 1.0,
"offered_window_s": 299.74141899999995,
"offered_qps": 11.850214134070008,
"wall_clock_s": 300.21845804993063,
"amplification": 1.001591501940313,
"ttft": {
"count": 3552,
"mean": 0.1369056966029782,
"p50": 0.13516780693316832,
"p90": 0.13835511771030723,
"p99": 0.15317223175661632
},
"tpot": {
"count": 3552,
"mean": 0.005430781936309906,
"p50": 0.005039893150780468,
"p90": 0.007063257358303028,
"p99": 0.0077793481296283135
},
"e2e": {
"count": 3552,
"mean": 0.4793568905444592,
"p50": 0.45412574551301077,
"p90": 0.5810393166844734,
"p99": 0.6301419002050533
}
},
"4P+4D": {
"name": "4P+4D",
"n_offered": 3552,
"n_success": 3551,
"completion_rate": 0.9997184684684685,
"offered_window_s": 299.74141899999995,
"offered_qps": 11.850214134070008,
"wall_clock_s": 735.1059510430787,
"amplification": 2.452467041410379,
"ttft": {
"count": 3551,
"mean": 0.17374673821534295,
"p50": 0.17102365801110864,
"p90": 0.18273873499128968,
"p99": 0.24688138795318082
},
"tpot": {
"count": 3551,
"mean": 0.005485491774206834,
"p50": 0.0053864502226046865,
"p90": 0.006091995366168992,
"p99": 0.007109403222178419
},
"e2e": {
"count": 3551,
"mean": 0.5196563635758822,
"p50": 0.5100997349945828,
"p90": 0.5655082209268585,
"p99": 0.6639982180204242
}
},
"6P+2D": {
"name": "6P+2D",
"n_offered": 3552,
"n_success": 3552,
"completion_rate": 1.0,
"offered_window_s": 299.74141899999995,
"offered_qps": 11.850214134070008,
"wall_clock_s": 300.38101644301787,
"amplification": 1.0021338307036503,
"ttft": {
"count": 3552,
"mean": 0.18293367427152893,
"p50": 0.1822461549891159,
"p90": 0.1938482352765277,
"p99": 0.21272844232735222
},
"tpot": {
"count": 3552,
"mean": 0.007192309629699456,
"p50": 0.007143509595484902,
"p90": 0.008732455453672816,
"p99": 0.009842920153335268
},
"e2e": {
"count": 3552,
"mean": 0.636424056327808,
"p50": 0.6324848984950222,
"p90": 0.7393875011475757,
"p99": 0.8261980937235056
}
}
},
"slo_grid": [
{
"ttft_slo_s": 2.0,
"tpot_slo_s": 0.05,
"arms": {
"8C-proxy": {
"attainment": 1.0,
"pd_advantage": 1.0,
"n_slo": 3552
},
"4P+4D": {
"attainment": 0.9997184684684685,
"pd_advantage": 0.9997184684684685,
"n_slo": 3551
},
"6P+2D": {
"attainment": 1.0,
"pd_advantage": 1.0,
"n_slo": 3552
}
}
}
]
}

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79
microbench/fresh_setup/gen_synthetic_trace.py
View File

@@ -161,11 +161,65 @@ def gen_multiturn(
return rows
def gen_regular(
*,
session_qps: float,
duration_s: float,
turns: int,
prefix_len: int,
delta_len: int,
output_len: int,
inter_turn_gap_s: float,
seed: int,
) -> list[dict]:
"""Maximally-regular multi-turn trace: every session identical, no skew.
Each session has a FIXED reused prefix of `prefix_len` tokens (its own,
established on turn 1) and every turn appends a FIXED `delta_len` of fresh
tokens. So per turn: input = prefix_len + delta_len (fixed), reuse ratio =
prefix_len/(prefix_len+delta_len) (fixed), actual new-prefill = delta_len
(fixed). Constant-interval session arrivals, fixed inter-turn gap, fixed
turn count -> zero session-size skew, so session-affinity cannot hot-pin.
This isolates the *structural* PD cost (per-turn full-context KV transfer +
P/D capacity split) from the skew/hot-pin artifact.
"""
rows: list[dict] = []
next_hash = HASH_BASE
chat_id = 1
n_prefix = max(1, prefix_len // BLOCK_SIZE)
n_delta = max(1, delta_len // BLOCK_SIZE)
n_sessions = max(1, int(duration_s * session_qps))
for s in range(n_sessions):
start = s / session_qps
sid = f"r{s}"
prefix_blocks = list(range(next_hash, next_hash + n_prefix))
next_hash += n_prefix
prev = -1
for k in range(1, turns + 1):
delta_blocks = list(range(next_hash, next_hash + n_delta))
next_hash += n_delta
rows.append({
"chat_id": chat_id,
"parent_chat_id": prev,
"timestamp": round(start + (k - 1) * inter_turn_gap_s, 6),
"input_length": prefix_len + delta_len,
"output_length": output_len,
"type": "synthetic_regular",
"turn": k,
"hash_ids": prefix_blocks + delta_blocks,
"session_id": sid,
})
prev = chat_id
chat_id += 1
rows.sort(key=lambda r: r["timestamp"])
return rows
def main() -> None:
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--out", type=Path, required=True, help="output trace JSONL")
p.add_argument("--mode", choices=["vanilla", "multiturn"], default="vanilla")
p.add_argument("--mode", choices=["vanilla", "multiturn", "regular"], default="vanilla")
p.add_argument("--qps", type=float, help="vanilla: mean Poisson request rate; "
"multiturn: mean Poisson SESSION rate")
p.add_argument("--duration-s", type=float, default=600.0, help="trace span (s)")
@@ -182,7 +236,14 @@ def main() -> None:
p.add_argument("--new-user-tokens", type=int, default=256,
help="multiturn: fresh user tokens added each subsequent turn")
p.add_argument("--inter-turn-gap-s", type=float, default=1.6,
help="multiturn: mean think-time between turns")
help="multiturn: mean think-time; regular: FIXED think-time")
# regular knobs
p.add_argument("--prefix-len", type=int, default=16384,
help="regular: per-session fixed reused prefix length")
p.add_argument("--delta-len", type=int, default=512,
help="regular: fixed fresh new-prefill tokens per turn")
p.add_argument("--turns", type=int, default=8,
help="regular: fixed turns per session")
args = p.parse_args()
if args.mode == "vanilla":
@@ -194,6 +255,20 @@ def main() -> None:
cfg = {"mode": "vanilla", "qps": args.qps, "duration_s": args.duration_s,
"input_len": args.input_len, "output_len": args.output_len,
"seed": args.seed, "reuse": "none"}
elif args.mode == "regular":
assert args.qps, "regular needs --qps (session rate)"
rows = gen_regular(
session_qps=args.qps, duration_s=args.duration_s, turns=args.turns,
prefix_len=args.prefix_len, delta_len=args.delta_len,
output_len=args.output_len, inter_turn_gap_s=args.inter_turn_gap_s,
seed=args.seed,
)
cfg = {"mode": "regular", "session_qps": args.qps,
"duration_s": args.duration_s, "turns": args.turns,
"prefix_len": args.prefix_len, "delta_len": args.delta_len,
"output_len": args.output_len, "inter_turn_gap_s": args.inter_turn_gap_s,
"reuse_ratio": args.prefix_len / (args.prefix_len + args.delta_len),
"seed": args.seed, "reuse": "fixed-intra-session"}
else:
assert args.qps, "multiturn needs --qps (session rate)"
rows = gen_multiturn(