واجهة برمجة تطبيقات حاسبة Occupancy

Instead of hand-tuning, CUDA offers an occupancy API that computes a good block size for your kernel automatically.

One call does the heavy lifting: cudaOccupancyMaxPotentialBlockSize suggests a block size that maximizes occupancy.

It fills in a suggested block size and the minimum grid size needed to fully occupy the device for your kernel.

The API reads your kernels real register and shared memory use, so its suggestion fits that exact kernel, not a generic guess.

A sibling call reports how many active blocks per SM a config gives, so you can predict occupancy before launching.

Multiply blocks per SM by warps per block, divide by the SM warp max, and you get the theoretical occupancy percentage.

These calls take a dynamic shared memory argument, so the prediction stays accurate when your kernel sizes shared memory at launch.

Because it queries the running device, the same code picks good sizes on different GPUs without you hardcoding numbers.

Call the occupancy API once during initialization, cache the block size, then reuse it for every launch of that kernel.

The suggested size is an excellent starting point. You can still benchmark a few neighbors to find the true best for your data.

Hardcoded sizes like 256 break when resources change. The API adapts, keeping your kernel near peak occupancy automatically.

Recall what cudaOccupancyMaxPotentialBlockSize gives you.

You met the occupancy API: it suggests a portable, kernel-aware block size and predicts occupancy before you ever launch. ⚙️