| name | gpu-pipeline |
| description | 一键运行 GPU 加速非极化胶子 PDF 全流程验证管线 (CuPy/CUDA, 蒸馏2pt+OPE从头计算+huangcl分析) |
gpu-pipeline — GPU 加速胶子 PDF 验证管线
一键在当前环境运行完整的 disconnected 胶子 PDF 验证管线：
蒸馏两点函数 → OPE 从头计算 → huangcl 比率分析 → 图表 → 报告。
核心特征
- GPU 加速：VVV、Wick 收缩、F_{μν}、Wilson 线、OPE 缩并全部在 GPU 上执行
- 单精度 (complex64)：默认，内存减半，满足当前统计需求
- OPE 从头计算：gauge config (.lime) → Clover F_{μν} → Wilson 线 → OPE .npz
- 所有中间变量保存：VVV、F_{μν}、Wick 收缩、有效质量
- 4 张诊断图：ratio、diagnostics、effective mass、field strength
- 自动报告：Markdown 报告 + LaTeX 技术报告
固定参数
系综: beta6.20_mu-0.2770_ms-0.2400_L24x72 (L24x72)
格点: 72×24³, a=0.1053 fm, β=6.20
Nev: 100 (per-conf eigensystem)
组态: 6250, 6450, 6650 (Nconf=3)
动量: P=(0, 0, -2), mom_smear=-2
算符: _Cg5g4 (Cγ₅γ₄)
GPU: CUDA/CuPy, complex64, 自动检测设备
子命令
gpu-pipeline run — 运行完整 GPU 管线
cd /root/lattice-pdf/agent/docker-v20260727
python run_pipeline.py
python run_pipeline.py --conf-id 6250
python run_pipeline.py --conf-id 6250 --precision complex128
python run_pipeline.py --skip-2pt --skip-ope
python run_pipeline.py --conf-id 6250 --verbose
gpu-pipeline check — GPU 环境与数据检查
cd /root/lattice-pdf/agent/docker-v20260727 && python3 -c "
import sys, os
sys.path.insert(0,'.')
# ── GPU ──
import cupy as cp
d = cp.cuda.Device(); mem = d.mem_info
props = cp.cuda.runtime.getDeviceProperties(d.id)
name = props['name'].decode() if isinstance(props['name'], bytes) else props['name']
print(f'GPU: {name} | Mem: {mem[0]/1024**3:.1f}/{mem[1]/1024**3:.1f} GB free')
print(f'CuPy: {cp.__version__} | CUDA: {cp.cuda.runtime.runtimeGetVersion()}')
# ── Python modules ──
for mod in ['numpy','scipy','matplotlib','cupy']:
m = __import__(mod); print(f' {\"✓\"} {mod}: {getattr(m,\"__version__\",\"?\")}')
# ── Data ──
paths = {
'eig_6250': '/public/group/lqcd/eigensystem/beta6.20_mu-0.2770_ms-0.2400_L24x72/6250/',
'eig_6450': '/public/group/lqcd/eigensystem/beta6.20_mu-0.2770_ms-0.2400_L24x72/6450/',
'eig_6650': '/public/group/lqcd/eigensystem/beta6.20_mu-0.2770_ms-0.2400_L24x72/6650/',
'gauge_6250': '/public/group/lqcd/configurations/CLOVER/beta6.20_mu-0.2770_ms-0.2400_L24x72/beta6.20_mu-0.2770_ms-0.2400_L24x72_cfg_6250.lime',
'peram_6250': '/public/group/lqcd/perambulators/beta6.20_mu-0.2770_ms-0.2400_L24x72/light/6250/',
}
for name, p in paths.items():
ok = os.path.exists(p)
sz = ''
if ok and os.path.isfile(p): sz = f' ({os.path.getsize(p)/1024**3:.1f} GB)'
elif ok and os.path.isdir(p): sz = f' ({len(os.listdir(p))} files)'
print(f' {\"✓\" if ok else \"✗\"} {name}: {p}{sz}')
"
gpu-pipeline status — 查看最新运行状态
latest=$(ls -dt /root/lattice-pdf/agent/docker-v20260727/output_*/ 2>/dev/null | head -1)
[ -n "$latest" ] && {
echo "=== ${latest} ==="
echo "Timing:"
grep "elapsed=" "$latest/run.log" | grep -E "01_compute|02_compute|03_huang|Total time"
echo ""
echo "Summary:"
grep -E "Pipeline Complete|ALL OK|✗|ERROR|Ratio" "$latest/run.log" | tail -10
echo ""
echo "Plots:"
ls -lh "$latest/plots/"*.png 2>/dev/null
} || echo "No output found."
gpu-pipeline plots — 仅重新生成图表
cd /root/lattice-pdf/agent/docker-v20260727
latest=$(ls -dt output_*/ 2>/dev/null | head -1)
[ -z "$latest" ] && { echo "No output found. Run gpu-pipeline run first."; exit 1; }
python3 -c "
import sys; sys.path.insert(0,'.')
from analyze_ratio import run_analysis
from utils import setup_logging
import json
from pathlib import Path
config = json.load(open('run_config.json'))
data_dir = Path('$latest') / 'data'
plots_dir = Path('$latest') / 'plots'
logger = setup_logging(Path('$latest') / 'run.log', 'plot_fix')
results = run_analysis(config, data_dir, plots_dir, logger)
print(f'Status: {results[\"status\"]}')
"
gpu-pipeline report — 编译技术报告
cd /root/lattice-pdf/agent/docker-v20260727
xelatex -interaction=nonstopmode -halt-on-error \
-output-directory=report report/main.tex 2>/dev/null
xelatex -interaction=nonstopmode -halt-on-error \
-output-directory=report report/main.tex 2>/dev/null
echo "Report: $(ls -lh report/main.pdf | awk '{print $5}')"
gpu-pipeline clean — 清理所有输出
echo "Cleaning all pipeline outputs..."
rm -rf /root/lattice-pdf/agent/docker-v20260727/output_*/
rm -f /root/lattice-pdf/agent/docker-v20260727/report/main.{aux,log,out,toc,pdf}
echo "Done."
gpu-pipeline package — 打包输出
cd /root/lattice-pdf/agent/docker-v20260727
latest=$(ls -dt output_*/ 2>/dev/null | head -1 | sed 's:/$::')
[ -z "$latest" ] && { echo "No output to package."; exit 1; }
tar -czf "${latest}.tar.gz" "$latest"/{plots,run.log,timing.jsonl,final_report.md,run_config.json,gpu_info.json}
xelatex -interaction=nonstopmode -halt-on-error -output-directory=report report/main.tex >/dev/null 2>&1
xelatex -interaction=nonstopmode -halt-on-error -output-directory=report report/main.tex >/dev/null 2>&1
[ -f report/main.pdf ] && cp report/main.pdf "$latest/report.pdf"
echo "Packaged: ${latest}.tar.gz ($(du -h ${latest}.tar.gz | cut -f1))"
echo "Report: $latest/report.pdf"
管线步骤详解
| 步骤 | 描述 | GPU 加速 | 典型耗时 (Nconf=1/Nconf=3) |
|---|
| 0 | 环境检查 | — | <1s / <1s |
| 1 | 质子 2pt 蒸馏 | VVV, Wick 收缩 | 125s / 389s |
| 2 | OPE 从头计算 | F_{μν}, Wilson 线, 缩并 | 42s / 120s |
| 3 | huangcl 比率分析 | Jackknife 重采样 | 3s / 3s |
| 4 | 最终报告 | — | <1s / <1s |
输出结构
output_YYYYMMDD_HHMMSS/
├── run.log # 完整日志
├── timing.jsonl # 每步耗时与显存
├── final_report.md # Markdown 综合报告
├── gpu_info.json # GPU 设备信息
├── data/
│ ├── eigenvalues_Nev100.npy
│ ├── conf_6250/ # VVV, F_{μν}×3, OPE×3, 2pt, meff
│ ├── conf_6450/
│ └── conf_6650/
└── plots/
├── ratio.png # R(z) 比率图
├── ratio_diagnostics.png # 多 z 值 Re/Im 诊断
├── effective_mass.png # 有效质量 3-panel
└── field_strength_diagnostics.png # OPE 质量诊断
精度选择
| 选项 | 精度 | GPU 显存 | 耗时 | 适用场景 |
|---|
--precision complex64 (默认) | 单精度 float32 | 低 | 快 | 快速验证、统计研究 |
--precision complex128 | 双精度 float64 | 高 (2×) | 慢 (~20×) | 精密谱学 |
当前统计误差 ($N_{\rm conf}=3$) 远大于 complex64 的精度损失 ($\sim 10^{-3}$)，
默认单精度完全满足需求。
已知限制与改进方向
- $N_{\rm conf}=3$ 统计不足：disconnected 图需 $\gtrsim 100$ 组态
- ILDG 头部扫描：当前扫描 760 个偏移量，可缓存正确偏移
- Wick 收缩占 58%：可融合为 CUDA kernel 减少中间张量分配
