Edit the launch
Start from a correct CUDA vector-add kernel and change Block X.
Learn CUDA by optimizing a real GPU kernel
Change one launch parameter, run the kernel on a real NVIDIA GPU, and watch achieved occupancy improve in the profiler.
Free to try Real NVIDIA GPU No CUDA install Compile + profile Guided CUDA exercise
See the lab in action
See how code changes affect real GPU performance
Block X 1 → 256 75%+ occupancy
Start the lab
Then go deeper
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- Shared-memory bank conflicts
- Matrix tiling
- Warp-shuffle reductions
- CUDA streams
- Tensor Cores (WMMA)
- PyTorch performance
Run and profile
Compile the kernel and collect performance data on an NVIDIA GPU.
Read the timeline
Inspect occupancy, kernel activity, transfers, and NVTX ranges in session details.