Ncu Cuda Profiling

# 使用 --set full 采集所有指标，并持久化保存
ncu --set full \
    -o <report_name> \
    --target-processes all \
    ./your_kernel

# 示例
ncu --set full -o matmul_analysis --target-processes all ./matmul0_perf

# 自动生成:
# - matmul_analysis.ncu-rep    (NCU 报告文件)
# - matmul_analysis.csv        (CSV 格式指标)

# 从已保存的报告提取关键指标 (无需重新运行 kernel)
ncu --import matmul_analysis.ncu-rep --print-summary per-kernel

# 导出为 CSV
ncu --import matmul_analysis.ncu-rep --page raw --csv > metrics.csv

# 直接导入已有报告
ncu --import <file.ncu-rep> --print-summary per-kernel

# 完整采集并持久化
ncu --set full -o <report_name> --target-processes all ./kernel

project_root/
├── ncu_reports/                    # NCU 报告目录
│   ├── matmul_analysis.ncu-rep    # 完整报告
│   ├── matmul_analysis.csv        # CSV 指标
│   └── matmul_analysis.md         # AI 分析报告
└── ...

def auto_diagnose(metrics):
    roofline = metrics.get('roofline_ratio', 0)
    dram = metrics.get('dram_throughput', 0)
    l1tex = metrics.get('l1tex_throughput', 0)
    sm_busy = metrics.get('sm_busy', 0)
    occupancy = metrics.get('occupancy', 0)
    
    if roofline < 30:
        if dram > 70:
            return "DRAM_MEMORY_BOUND"
        elif l1tex > 80 and dram < 30:
            return "L1_PRESSURE_BOUND"
        else:
            return "LATENCY_BOUND"
    elif roofline > 60:
        if sm_busy > 80:
            return "COMPUTE_BOUND"
        else:
            return "OCCUPANCY_BOUND"
    else:
        return "MIXED_BOUND"

# NCU 性能分析报告

## 📁 报告信息
- **Kernel**: {kernel_name}
- **采集时间**: {timestamp}
- **报告文件**: {report_file}
- **原始数据**: {csv_file}

## 📈 执行摘要

| 项目 | 数值 |
|------|------|
| **主要瓶颈** | {bottleneck_type} |
| **置信度** | {confidence} |
| **性能** | {performance} GFLOPS |
| **优化潜力** | {potential}x |

## 📊 关键指标

### 性能指标
| 指标 | 数值 | 健康阈值 | 状态 |
|------|------|----------|------|
| Roofline 性能比 | {roofline}% | > 60% | {status} |
| SM Busy | {sm_busy}% | > 70% | {status} |
| Occupancy | {occupancy}% | > 50% | {status} |

### 内存指标
| 指标 | 数值 | 健康阈值 | 状态 |
|------|------|----------|------|
| DRAM Throughput | {dram}% | < 50% | {status} |
| L1/TEX Throughput | {l1tex}% | < 80% | {status} |
| L2 Throughput | {l2}% | < 80% | {status} |

## 🔍 诊断详情

**瓶颈类型**: {bottleneck_type}

**判断依据**:
- {reason_1}
- {reason_2}

## 💡 优化建议

### 高优先级
{high_priority_suggestions}

## 🛠️ 下一步操作

### 建议的 NCU 命令
```bash
# 优化后重新采集
ncu --set full -o {report_name}_optimized --target-processes all ./kernel_optimized

---

## 🔧 工具使用说明

### 完整采集 (推荐)

```bash
# 采集所有指标并保存
ncu --set full -o my_analysis --target-processes all ./kernel

# 参数说明:
# --set full          # 采集完整指标集
# -o my_analysis      # 输出文件名 (生成 my_analysis.ncu-rep)
# --target-processes all  # 监控所有进程

# 从已有报告提取特定指标
ncu --import my_analysis.ncu-rep --print-summary per-kernel

# 导出为 CSV 便于分析
ncu --import my_analysis.ncu-rep --page raw --csv > metrics.csv

cd examples/

# 全自动分析
./auto_profile.sh ./kernel report_name

# Python 分析器
python ncu_analyzer.py --import report_name.ncu-rep

IF dram_throughput > 70% AND roofline < 30%:
    诊断: DRAM_MEMORY_BOUND (置信度: HIGH)
    
    优化策略:
    1. Block Tiling (共享内存缓存)
    2. Vectorized Load (float4)
    3. Prefetching (数据预取)

IF l1tex_throughput > 80% AND dram_throughput < 30%:
    诊断: L1_PRESSURE_BOUND (置信度: HIGH)
    
    优化策略:
    1. Shared Memory Padding
    2. Data Transpose
    3. Fragment Caching

IF sm_busy < 50% AND occupancy > 60%:
    诊断: LATENCY_BOUND (置信度: HIGH)
    
    优化策略:
    1. Double Buffering
    2. Instruction-level Parallelism
    3. Loop Unrolling

IF roofline > 60% AND sm_busy > 80%:
    诊断: COMPUTE_BOUND (置信度: HIGH)
    
    优化策略:
    1. Use FMA instructions
    2. Reduce precision (FP32 -> FP16/TF32)
    3. Tensor Cores

IF occupancy < 30% AND sm_busy > 70%:
    诊断: OCCUPANCY_BOUND (置信度: HIGH)
    
    优化策略:
    1. Reduce register usage
    2. Adjust block size
    3. Use __launch_bounds__

# 完整采集 (推荐)
ncu --set full -o report_name --target-processes all ./kernel

# 指定 sections
ncu --section SpeedOfLight,Occupancy,LaunchStats -o report_name ./kernel

# 特定指标
ncu --metrics sm__throughput.avg.pct,dram__throughput.avg.pct -o report_name ./kernel

# 查看摘要
ncu --import report.ncu-rep --print-summary per-kernel

# 查看详情
ncu --import report.ncu-rep --page details

# 导出 CSV
ncu --import report.ncu-rep --page raw --csv > metrics.csv

# 对比两个报告
ncu --diff report1.ncu-rep report2.ncu-rep