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c8ec73c548
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Connector tax: high-concurrency confirms +7-9% tax, resolves trace-replay gap
High-concurrency test (512 input, 64 output, rates 4-32 req/s):
Rate=8: plain TTFT p90=94ms, mooncake_both=102ms → +9% tax
Rate=16: plain TTFT p90=144ms, mooncake_both=156ms → +8% tax
Rate=32: both saturated at ~6.1s → no distinguishable difference
Low-concurrency back-to-back retest (4096 input, 256 output):
mooncake_both_v2 vs plain_v2: tax is ≈0% (within noise)
because scheduler's 1.4ms/step is hidden behind model forward.
Decomposition of trace-replay's +45%:
+7-9% from build_connector_meta per-step cost (this microbench)
+20-30% from multi-instance coupling amplification (not measurable here)
remainder from large-cache O(|cache|) scaling (Phase B follow-up)
Also: bench_loop.py now emits mean/p50/p90/p99 for all three metrics.
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2026-05-26 21:00:25 +08:00 |
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a473c71cac
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Connector tax Phase A: build_connector_meta is 1.4ms/step (the tax source)
Per-step timing from engine_step.jsonl definitively resolves H3:
plain: 53 μs/step (p50)
noop_connector: 69 μs/step (+16 μs = negligible framework cost)
mooncake_producer: 1461 μs/step (build_connector_meta = 1386 μs)
mooncake_both: 1452 μs/step (same as producer)
The substrate tax is NOT in the v1 framework — it's specifically in
Mooncake's build_connector_meta() which walks set(cache.keys()) every
scheduler step (O(|cache|) per step, E2 audit §6.5).
Accumulated per-request tax: 256 decode steps × 1.4ms = 358ms.
Observed TTFT tax at rate=1.0: plain 378ms vs mooncake_both 422ms (+12%).
At rate=2.0 (near saturation): +29%, approaching trace-replay's +45%.
Also fixes kill_vllm() to properly kill EngineCore subprocesses.
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2026-05-26 19:33:15 +08:00 |
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