> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rightnowai.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Static Analysis

> See CUDA optimization tips without running code.

# Static Analysis

Real-time CUDA code analysis shows performance metrics directly above your kernels.

## What It Does

CodeLens automatically analyzes your CUDA code and shows:

* Execution time and GPU efficiency
* Register and shared memory usage
* Occupancy calculations
* Performance bottlenecks

## How to Use

<Steps>
  <Step title="Write Your CUDA Code">
    Open a .cu file and write your kernel.
  </Step>

  <Step title="Save the File">
    Press Ctrl+S. CodeLens updates automatically.
  </Step>

  <Step title="See Performance Metrics">
    Look above your kernel for two lines of performance data.
  </Step>

  <Step title="Fix Any Issues">
    If you see warnings, the CodeLens tells you what's limiting performance.
  </Step>
</Steps>

## What You See

<Frame>
  <img src="https://mintcdn.com/rightnowai/MyRQ6ILCdazu_XAM/images/codelens-metrics.webp?fit=max&auto=format&n=MyRQ6ILCdazu_XAM&q=85&s=dcf9bcb377910474d59b225669c80a90" alt="CodeLens Metrics" width="822" height="370" data-path="images/codelens-metrics.webp" />
</Frame>

### CodeLens Display

Two lines appear above each kernel:

**Line 1 - Runtime Performance:**

```
✅ addVectors: 18.5ms • SM:95.3% • Occ:87.5% • Mem:245.8GB/s
```

**Line 2 - Static Analysis:**

```
⚠️ Static: Registers: 64 • Shared: 8KB • Max occupancy: 50% (reg-limited)
```

### Performance Indicators

* ✅ **Green**: Excellent performance
* ⚠️ **Yellow**: Can be optimized
* ❌ **Red**: Performance issue needs attention

## Understanding the Metrics

### Runtime Metrics

* **Time**: How fast your kernel runs (ms)
* **SM**: GPU utilization percentage
* **Occ**: Thread occupancy percentage
* **Mem**: Memory bandwidth (GB/s)

### Static Metrics

* **Registers**: Per-thread register count
* **Shared**: Shared memory per block
* **Limiting factor**: What's preventing better occupancy

## Common Issues and Fixes

### "reg-limited"

Too many registers per thread:

* Simplify calculations
* Use shared memory for arrays
* Reduce local variables

### "smem-limited"

Too much shared memory:

* Reduce shared array sizes
* Use dynamic shared memory
* Process data in smaller tiles

### Register Spilling

CodeLens shows spill warnings:

* Reduce register pressure
* Split complex kernels
* Use compiler flags to limit registers

## Advanced Build Settings

Click "Advanced Build Settings" in editor toolbar to:

* ✅ Generate .ptx files
* ✅ Generate .cubin files
* ✅ Run after build

These options help with deeper optimization analysis.

## Tips

<Check>
  **Best Practices**

  * Keep occupancy above 50%
  * Fix red warnings first
  * Save file to update metrics
  * Use build settings for PTX analysis
</Check>

<Warning>
  **Remember**
  CodeLens shows estimates. Always benchmark to verify actual performance.
</Warning>

## Screenshots Needed

1. **CodeLens two-line display** - Both performance lines above kernel
2. **Static analysis warnings** - Yellow/red indicators with messages
3. **Advanced Build Settings** - Checkbox panel with PTX/CUBIN options

## Next Steps

* [Benchmarking](/benchmarking) - Measure actual performance
* [GPU Emulation](/gpu-emulation) - Test on different GPUs
* [Core Features](/core-features) - AI optimization help