Gahow Wang
547611e022
D2: run_benchmark.sh and run_experiments.sh still pass --time-scale and --max-inflight-sessions to the replayer, but those flags were removed when the project moved to trace-driven dispatch. The scripts cannot run as-is. D3: ~25 ad-hoc analyze_* / compare_* / profile_* / final_* scripts and a handful of single-experiment run_*.sh point at /home/admin/cpfs paths, deleted output directories, or a sampled trace file that no longer exists. Keep them in scripts/legacy/ for historical reference; the scripts that remain in scripts/ (analyze_trace, analyze_breakdown, analyze_cache_hit, analyze_eviction, compare_results, compute_roofline, sample_trace, analyze_agentic_patterns, simulate_cache_policies, plus launch_*.sh, gpu_monitor.sh, bench.sh) cover the current workflow. Adds scripts/legacy/README.md to document the archival policy.
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
"""Analyze GPU utilization A/B test results."""
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import csv, json, statistics, os
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def gpu_analysis(path, label, groups):
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rows = list(csv.DictReader(open(path)))
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by_gpu = {}
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for r in rows:
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g = int(r["gpu"])
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by_gpu.setdefault(g, []).append(float(r["util_pct"]))
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n = len(rows) // 8
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print(f"\n{'='*70}")
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print(f" {label} ({n} time points)")
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print(f"{'='*70}")
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for gname, indices in groups.items():
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vals = []
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for i in indices:
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vals.extend(by_gpu.get(i, []))
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if vals:
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s = sorted(vals)
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p = lambda q: s[min(int(q*len(s)), len(s)-1)]
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nz = sum(1 for v in vals if v > 0)
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print(f" {gname}:")
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print(f" mean={statistics.fmean(vals):.1f}% p50={p(.5):.0f}% p90={p(.9):.0f}% max={max(vals):.0f}%")
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print(f" active_samples={nz}/{len(vals)} ({nz*100//len(vals)}%)")
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for g in sorted(by_gpu.keys()):
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vals = by_gpu[g]
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nz = sum(1 for v in vals if v > 0)
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print(f" GPU {g}: mean={statistics.fmean(vals):.1f}% max={max(vals):.0f}% active={nz*100//len(vals)}%")
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def metrics_analysis(path, label):
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rows = [json.loads(l) for l in open(path)]
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ok = [r for r in rows if not r.get("error")]
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err = [r for r in rows if r.get("error")]
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ttfts = sorted([r["ttft_s"] for r in ok if r.get("ttft_s")])
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tpots = sorted([r["tpot_s"] for r in ok if r.get("tpot_s") and r["tpot_s"]>0])
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lats = sorted([r["latency_s"] for r in ok])
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p = lambda v,q: v[min(int(q*len(v)),len(v)-1)] if v else 0
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print(f"\n {label}: {len(ok)}/{len(rows)} OK, {len(err)} err")
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if ttfts: print(f" TTFT p50={p(ttfts,.5):.3f} p90={p(ttfts,.9):.3f}")
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if tpots: print(f" TPOT p50={p(tpots,.5):.3f} p90={p(tpots,.9):.3f}")
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if lats: print(f" E2E p50={p(lats,.5):.3f} p90={p(lats,.9):.3f}")
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gpu_analysis("outputs/gpu_ab_combined/gpu_util.csv", "COMBINED TP=1 DP=8 (cache-aware)",
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{"All GPUs": list(range(8))})
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gpu_analysis("outputs/gpu_ab_pdsep/gpu_util.csv", "PD-SEP TP=1 4P+4D (cache-aware Mooncake)",
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{"Prefill (GPU 0-3)": [0,1,2,3], "Decode (GPU 4-7)": [4,5,6,7], "All GPUs": list(range(8))})
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print(f"\n{'='*70}")
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print(f" LATENCY COMPARISON")
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print(f"{'='*70}")
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metrics_analysis("outputs/gpu_ab_combined/metrics.jsonl", "COMBINED")
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metrics_analysis("outputs/gpu_ab_pdsep/metrics.jsonl", "PD-SEP")
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print(f"\n{'='*70}")
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print(f" BOTTLENECK SUMMARY")
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print(f"{'='*70}")
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print("""
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1. DECODE GPU UNDERUTILIZATION
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PD-Sep decode GPUs: mean ~20%, max ~47%
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Combined GPUs: mean ~30%, max 100%
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-> Decode is memory-bound, GPU compute wasted on dedicated decode GPUs
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-> 4 GPUs reserved for decode never exceed 50% utilization
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2. PREFILL GPU BURSTINESS
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PD-Sep prefill: high util when active (~86% p50), but idle ~48% of time
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Combined: more evenly distributed, active 64% of time
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-> await-prefill serializes P then D, creating idle gaps between requests
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3. KV TRANSFER OVERHEAD
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TTFT(PD-Sep) - TTFT(Combined) = pure KV transfer + proxy routing cost
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This penalty grows with input length (more KV to transfer)
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4. RESOURCE PARTITIONING INEFFICIENCY
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PD-Sep: fixed 4P+4D split cannot adapt to workload phase
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Combined: 8 GPUs flexibly serve both P and D based on demand
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""")
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