Random session sampling destroys cross-session hash block sharing
(52% -> 16%) because sessions sharing system prompts get scattered.
New approach: take a contiguous time window from the trace (preserving
temporal locality of shared-prefix sessions), then thin within the
window to hit target QPS. This preserves both intra-session reuse
(62% of reusable tokens) and cross-session sharing (38%).
Results (block sharing rate):
Old random r=0.002: 16.0% -> Window+thin: 29.7%
Old random r=0.016: 19.5% -> Window+thin: 42.7%
Full trace baseline: 52%
Also corrected the "91% intra-session" claim: actual split is
62% intra / 38% cross (token-level), making cross-session sharing
preservation critical for valid APC benchmarks.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The replayer was artificially limiting concurrency with --max-inflight-sessions
(semaphore) and --time-scale (time compression), producing unrealistically low
1 req/GPU load that masked prefill-decode interference.
Replayer changes:
- Remove session_sem and time_scale entirely
- Each request dispatched at its trace timestamp exactly
- Sessions still sequential (turn N+1 waits for turn N completion)
- If turn completes late, next turn fires immediately
Sampler changes:
- Add --sample-ratio for GPU-proportional session sampling
- Keep --target-requests for backwards compat
- No time compression (preserve original arrival pattern)
bench.sh: remove --time-scale and --max-inflight-sessions args
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Systematic study of prefill-decode disaggregation for agentic LLM workloads
using production GLM-5.1 coder trace (2.1M requests, 71B input tokens).
Key findings:
- Cache-aware routing improves TPOT p90 by 15% and APC from 20.8% to 44.7%
without PD separation, matching PD-Sep's decode isolation benefit
- PD separation adds +72% TTFT overhead (KV transfer) with no TPOT gain
when using the same cache-aware scheduler
- Prefill remains compute-bound even at 95% KV cache reuse (AI >1000x
vs decode AI <2), but absolute FLOPs drop 71% from cache hits
- For agentic MoE workloads, cache-aware routing > PD separation
Infrastructure:
- Trace sampler preserving session structure + hash_ids for prefix sharing
- Async trace replayer with streaming TTFT/TPOT/E2E measurement
- Unified cache-aware + token-level load-balanced global scheduler proxy
supporting both PD-colocated and PD-disaggregated (Mooncake/RDMA) modes
- vLLM 0.18.1 scheduler patch for KV transfer abort race condition
- Roofline analysis tool for prefill/decode compute characterization
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
