Gahow Wang
1e8628581b
Same-condition comparison (both fresh restart, same trace, same params): Baseline (combined): TTFT=2.383/27.622 TPOT90=0.117 E2E=10.232 Elastic P2P (cap=4): TTFT=1.315/13.179 TPOT90=0.075 E2E=5.708 Delta: -45% / -52% -36% -44% Key finding: TPOT p90 dropped 36% — confirming heavy prefill DOES disrupt decode in combined mode, and elastic offload effectively isolates it. Previous comparisons missed this because baselines were run under different conditions (stale instances, different time_scale). GPU util: elastic uses less GPU (15.8% vs 28.7%) but achieves better latency — higher efficiency through better cache distribution. APC: elastic has more balanced per-instance APC (36-38% prefix + 30-35% external) vs baseline's skewed distribution (3.8% - 68.3%). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
"""Plot per-GPU utilization timeline for elastic vs baseline."""
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import csv, json, sys, os
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def load_gpu(path):
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"""Load GPU util CSV, return {gpu_id: [(timestamp, util%)]]}."""
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by_gpu = {}
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with open(path) as f:
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for r in csv.DictReader(f):
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g = int(r["gpu"])
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t = float(r["timestamp"])
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u = float(r["util_pct"])
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by_gpu.setdefault(g, []).append((t, u))
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# Normalize timestamps to start at 0
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if by_gpu:
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t0 = min(pts[0][0] for pts in by_gpu.values())
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for g in by_gpu:
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by_gpu[g] = [(t - t0, u) for t, u in by_gpu[g]]
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return by_gpu
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def print_timeline(by_gpu, label, max_time=None):
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"""Print ASCII timeline of GPU utilization."""
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print(f"\n{'='*70}")
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print(f" {label}")
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print(f"{'='*70}")
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if not by_gpu:
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print(" No data")
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return
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# Bucket into 10s windows
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window = 10.0
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if max_time is None:
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max_time = max(t for pts in by_gpu.values() for t, _ in pts)
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n_windows = min(int(max_time / window) + 1, 40) # cap at 40 columns
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for gpu in sorted(by_gpu.keys()):
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pts = by_gpu[gpu]
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buckets = [[] for _ in range(n_windows)]
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for t, u in pts:
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b = min(int(t / window), n_windows - 1)
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buckets[b].append(u)
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avgs = [sum(b)/len(b) if b else 0 for b in buckets]
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# ASCII bar: . = 0-10%, o = 10-30%, O = 30-60%, # = 60-100%
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bar = ""
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for a in avgs:
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if a < 1: bar += " "
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elif a < 10: bar += "."
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elif a < 30: bar += "o"
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elif a < 60: bar += "O"
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else: bar += "#"
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mean = sum(a for a in avgs) / len(avgs) if avgs else 0
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print(f" GPU{gpu}: |{bar}| mean={mean:.0f}%")
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print(f" Time: {'0':>1}{'':>{n_windows-6}}{int(max_time)}s")
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print(f" Legend: ' '=0% .=1-10% o=10-30% O=30-60% #=60-100%")
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# Per-GPU stats
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print(f"\n Per-GPU mean utilization:")
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for gpu in sorted(by_gpu.keys()):
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pts = by_gpu[gpu]
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vals = [u for _, u in pts]
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mean = sum(vals) / len(vals)
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nz = sum(1 for v in vals if v > 0)
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print(f" GPU{gpu}: mean={mean:.1f}% active={nz*100//len(vals)}% samples={len(vals)}")
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# Load and compare
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configs = [
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("outputs/baseline_dash1/gpu_util.csv", "Baseline (8 combined, dash1)"),
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("outputs/elastic_v4/gpu_util.csv", "Elastic P2P v4 (dash0)"),
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]
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for path, label in configs:
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if os.path.exists(path):
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by_gpu = load_gpu(path)
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print_timeline(by_gpu, label)
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else:
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print(f"\n {label}: {path} NOT FOUND")
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# Imbalance metric
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print(f"\n{'='*70}")
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print(f" LOAD IMBALANCE ANALYSIS")
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print(f"{'='*70}")
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for path, label in configs:
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if not os.path.exists(path):
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continue
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by_gpu = load_gpu(path)
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means = []
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for gpu in sorted(by_gpu.keys()):
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vals = [u for _, u in by_gpu[gpu]]
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means.append(sum(vals) / len(vals))
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if means:
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avg = sum(means) / len(means)
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max_m = max(means)
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min_m = min(means)
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imbalance = max_m / max(min_m, 0.1)
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print(f" {label}:")
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print(f" Per-GPU means: {['%.1f' % m for m in means]}")
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print(f" Avg={avg:.1f}% Min={min_m:.1f}% Max={max_m:.1f}% Imbalance={imbalance:.1f}x")
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