# Profiling (2D FDTD)

**Profiling** investigates where simulation time and hardware resources are spent. Select a workload and tools in the repository's `config` file, then run `make profile`:

```bash
# config: example GPU profile settings
PROFILE_TOOLS="nsys ncu"
PROFILE_BACKEND="cuda"
PROFILE_PERF_SIZE=1024
PROFILE_PERF_STEPS=200
```

These settings use Nsight Systems to trace API calls and GPU work, and Nsight Compute to inspect kernel metrics. Results are saved under `results/profiles/`.

For CPU work, select tools such as `perf-stat`, `perf-record`, `callgrind`, or `cachegrind`. The profile build includes symbols, frame pointers, and CUDA line information to relate observations to code.

Profiling instrumentation changes execution cost, particularly under Valgrind. Use [[fdtd-cuda-benchmarking|benchmark timings]] for performance comparisons and profiles to explain them.

Source: [config.example](https://github.com/yanevskiv/fdtd-cuda/blob/main/config.example).