Agentic workload PD separation analysis with trace-driven benchmarks

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>

scripts/launch_pd_separated.sh

@@ -0,0 +1,89 @@
+#!/bin/bash
+# PD-Disaggregated serving: 1 prefill (TP=4, GPUs 0-3) + 1 decode (TP=4, GPUs 4-7)
+# Uses vLLM 0.18.1's P2pNcclConnector + XpYd proxy.
+#
+# Architecture:
+#   Client → Proxy (port 10001)
+#     → Prefill (port 20003, kv_port 21001) [max_tokens=1, does prefill + KV push]
+#     → Decode  (port 20005, kv_port 22001) [full generation, KV pulled from prefill]
+#
+# Usage: bash scripts/launch_pd_separated.sh
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_DIR="$(dirname "$SCRIPT_DIR")"
+VENV="$PROJECT_DIR/.venv/bin"
+VLLM="$VENV/vllm"
+
+MODEL_PATH="${MODEL_PATH:-$HOME/models/Qwen/Qwen3-Coder-30B-A3B-Instruct}"
+PROXY_PORT=30001      # ZMQ service discovery
+CLIENT_PORT=10001     # HTTP proxy for clients
+PREFILL_PORT=20003
+DECODE_PORT=20005
+KV_PORT_P=21001
+KV_PORT_D=22001
+
+trap 'echo "Cleaning up..."; kill $(jobs -p) 2>/dev/null; wait 2>/dev/null' EXIT INT TERM
+
+echo "=== PD-Disaggregated vLLM 0.18.1 ==="
+echo "  Model:   $MODEL_PATH"
+echo "  Prefill: GPUs 0-3 (TP=4), port $PREFILL_PORT, kv_port $KV_PORT_P"
+echo "  Decode:  GPUs 4-7 (TP=4), port $DECODE_PORT, kv_port $KV_PORT_D"
+echo "  Proxy:   ZMQ=$PROXY_PORT, HTTP=$CLIENT_PORT"
+echo ""
+
+# Step 1: Start proxy FIRST (P/D instances register via ZMQ)
+echo "[1/3] Starting proxy..."
+PROXY_SCRIPT="$PROJECT_DIR/third_party/vllm/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py"
+$VENV/python "$PROXY_SCRIPT" &
+PROXY_PID=$!
+sleep 2
+echo "  Proxy PID=$PROXY_PID"
+
+# Step 2: Start prefill instance (KV producer)
+echo "[2/3] Starting prefill instance..."
+CUDA_VISIBLE_DEVICES=0,1,2,3 $VLLM serve "$MODEL_PATH" \
+    --host 0.0.0.0 \
+    --port $PREFILL_PORT \
+    --tensor-parallel-size 4 \
+    --trust-remote-code \
+    --enable-prefix-caching \
+    --enforce-eager \
+    --dtype auto \
+    --gpu-memory-utilization 0.9 \
+    --kv-transfer-config \
+    "{\"kv_connector\":\"P2pNcclConnector\",\"kv_role\":\"kv_producer\",\"kv_buffer_size\":\"1e1\",\"kv_port\":\"$KV_PORT_P\",\"kv_connector_extra_config\":{\"proxy_ip\":\"127.0.0.1\",\"proxy_port\":\"$PROXY_PORT\",\"http_port\":\"$PREFILL_PORT\",\"send_type\":\"PUT_ASYNC\",\"nccl_num_channels\":\"16\"}}" &
+PREFILL_PID=$!
+echo "  Prefill PID=$PREFILL_PID"
+
+# Step 3: Start decode instance (KV consumer)
+echo "[3/3] Starting decode instance..."
+CUDA_VISIBLE_DEVICES=4,5,6,7 $VLLM serve "$MODEL_PATH" \
+    --host 0.0.0.0 \
+    --port $DECODE_PORT \
+    --tensor-parallel-size 4 \
+    --trust-remote-code \
+    --enable-prefix-caching \
+    --enforce-eager \
+    --dtype auto \
+    --gpu-memory-utilization 0.8 \
+    --kv-transfer-config \
+    "{\"kv_connector\":\"P2pNcclConnector\",\"kv_role\":\"kv_consumer\",\"kv_buffer_size\":\"8e9\",\"kv_port\":\"$KV_PORT_D\",\"kv_connector_extra_config\":{\"proxy_ip\":\"127.0.0.1\",\"proxy_port\":\"$PROXY_PORT\",\"http_port\":\"$DECODE_PORT\",\"send_type\":\"PUT_ASYNC\",\"nccl_num_channels\":\"16\"}}" &
+DECODE_PID=$!
+echo "  Decode PID=$DECODE_PID"
+
+# Wait for readiness
+echo ""
+echo "Waiting for instances..."
+timeout 1200 bash -c "until curl -s localhost:$PREFILL_PORT/v1/completions > /dev/null 2>&1; do sleep 5; done"
+echo "  Prefill ready!"
+timeout 1200 bash -c "until curl -s localhost:$DECODE_PORT/v1/completions > /dev/null 2>&1; do sleep 5; done"
+echo "  Decode ready!"
+
+echo ""
+echo "=== All ready ==="
+echo "  Send requests to: http://localhost:$CLIENT_PORT"
+echo ""
+
+wait