agentic-kvc/analysis/pd_sep_paper_section/README.md

Paper section: PD separation under agentic workloads

This directory collects everything produced for the "PD-sep is net negative on agentic workloads" paper section. It is one section of a larger paper, not the whole paper.

Layout

analysis/pd_sep_paper_section/
├── README.md                       # this file
├── system_analysis.md              # why PD-sep loses despite compute-bound prefill (6 layers)
├── scripts/
│   ├── plot_workload.py            # C1: input/output CDF + KV reuse decomposition
│   ├── plot_roofline.py            # C6: prefill roofline at varying cache reuse
│   ├── plot_routing_lever.py       # C7: routing vs PD-sep as design levers
│   └── plot_kv_memory_wall.py      # KV mem-wall: the system-level explanation
└── figures/
    ├── fig_c1a_io_cdf.pdf          # input/output token CDF (from traces/w600_r0.0015_st30.jsonl)
    ├── fig_c1b_reuse.pdf           # KV reuse decomposition: 79% intra-session
    ├── fig_c6_roofline.pdf         # analytical roofline
    ├── fig_c7_routing_lever.pdf    # routing vs PD-sep (legacy data, footer caveat)
    └── fig_kv_memory_wall.pdf      # the explanatory figure for system_analysis.md

Candidate claims -> figures (status)

Claim	Figure	Status
C1a: agentic input distribution (p50=33.5k, p90=101k, p99=132k); I/O = 142x	figures/fig_c1a_io_cdf.pdf	rendered
C1b: 79% intra-session reuse + 0.8% cross-session	figures/fig_c1b_reuse.pdf	rendered
C2: PD-sep vs Combined headline numbers	(not yet)	needs re-run without --enforce-eager on traces/w600_r0.0015_st30.jsonl
C3: decode KV cache memory wall (time-series)	(not yet)	needs step-level vLLM telemetry during PD-sep run
C4: TTFT stacked breakdown (prefill / KV pull / decode wait)	(not yet)	needs per-request breakdown.json from PD-sep run
C5: cuda-graph ablation (eager vs cudagraph × Combined vs PD-sep)	(not yet)	needs the 2×2 matrix
C6: prefill stays compute-bound at 95% reuse	figures/fig_c6_roofline.pdf	rendered
C7: cache-aware routing is a larger lever than PD-sep	figures/fig_c7_routing_lever.pdf	rendered (legacy data, footer caveat)
KV-WALL: per-D-instance KV demand vs PD layout (system mechanism)	figures/fig_kv_memory_wall.pdf	rendered (analytical, audit constants in script)

System-level argument (system_analysis.md)

The doc answers: if prefill stays compute-bound even at 95% reuse, why does PD separation not help? Six layers, each pointing to a figure in this directory:

1. compute-bound is a kernel property, not a system claim
2. absolute prefill work after cache hit is small (~hundreds of ms savings ceiling)
3. PD separation relocates compute; it doesn't accelerate it
4. PD separation's costs (KV transfer, decode-side concentration) scale with workload size
5. decode-side KV memory wall — quantified in fig_kv_memory_wall.pdf
6. the DistServe / Splitwise assumption that silently breaks: concurrent × KV/req / (N_D × HBM) is ≪ 1 for chatbot but ≥ 1 for agentic at p90+ context

In-place edits made for this task

These edits are in the repo, not in this directory, because they modify existing launch scripts. --enforce-eager was removed so cuda graphs can be captured — PD-sep's D-node is a particularly clean case for cuda-graph benefit and the prior methodology suppressed it.

File	Lines	Change
scripts/bench.sh	150, 161	drop --enforce-eager (elastic + baseline modes)
scripts/launch_pd_mooncake.sh	47, 64	drop --enforce-eager (P and D instances)
scripts/launch_pd_separated.sh	52, 68	drop --enforce-eager (P and D instances)
scripts/launch_phase1_ps.sh	32, 43	drop --enforce-eager (C and PS instances)
scripts/launch_elastic_p2p.sh	57	drop --enforce-eager (kv_both instances)

scripts/legacy/*.sh are intentionally left as-is — they record the configuration of past experiments.

REPORT.md and analysis/pd_separation_analysis.md still describe the old --enforce-eager setup. Update them once the new runs land.

Reproducing the figures

From repo root:

# C1 (needs traces/w600_r0.0015_st30.jsonl; ~1.2 MB, pull from dash0 if missing)
.venv/bin/python analysis/pd_sep_paper_section/scripts/plot_workload.py \
    --trace traces/w600_r0.0015_st30.jsonl

# C6 (analytical, runs anywhere with matplotlib)
.venv/bin/python analysis/pd_sep_paper_section/scripts/plot_roofline.py

# C7 (hardcoded REPORT.md §3.1 numbers; no inputs)
.venv/bin/python analysis/pd_sep_paper_section/scripts/plot_routing_lever.py

# KV mem-wall (analytical; audit constants at top of the script)
.venv/bin/python analysis/pd_sep_paper_section/scripts/plot_kv_memory_wall.py

All four default --outdir to analysis/pd_sep_paper_section/figures.

Caveats / open items

• C7 uses legacy data. The footer of fig_c7_routing_lever.pdf says so: PD-sep numbers come from the random-sampled trace + --enforce-eager. Re-run on traces/w600_r0.0015_st30.jsonl with cuda-graphs on before paper-grade citation. The plotting code keeps the source numbers in a single ROWS table (top of plot_routing_lever.py) for a one-line swap.
• C2/C3/C4/C5 figures are not produced because the experiments have not been re-run. The 4h matrix proposed in the prior conversation turn (Combined + RR, Combined + cache-aware, PD-sep 4P+4D, PD-sep 6P+2D, plus eager-vs-cudagraph ablation, ×3 seeds) is the prerequisite.
• C6 is analytical, so it is independent of any re-run. The numbers match scripts/compute_roofline.py (constants are duplicated; if one changes, the other must change too).