Adds analysis/pd_sep_paper_section/ as the home for the "PD separation is
net negative under agentic workloads" paper section: plot scripts for C1
(workload chars), C6 (roofline), C7 (routing-vs-PD-sep lever), the C6/C7
PDFs already rendered, and a README mapping candidate claims to required
figures plus open re-run items.
Removes --enforce-eager from bench.sh and all active launch scripts so
cuda graphs are captured -- the prior methodology suppressed one of
PD-sep's structural advantages (D-node fixed-shape decode). Legacy
scripts under scripts/legacy/ are intentionally untouched as historical
records.
Co-Authored-By: Claude Opus 4.7 <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>
