GPU Occupancy Calculator

What Occupancy Measures

Occupancy is the ratio of active warps on an SM to the maximum warps the SM can hold. Higher occupancy means more warps are available to hide memory latency, but it is not the only performance metric. A kernel with 100% occupancy can still be slow if every thread does redundant work.

The formula is straightforward:

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For example, if an SM supports 48 warps and your kernel places 24 warps on it, occupancy is 50%.

Three Limits That Cap Block Count

Three resources compete to determine how many thread blocks fit on one SM:

1. Registers per thread: fixed register file divided by registers_per_thread * threads_per_block
2. Shared memory per block: fixed shared pool divided by bytes requested per block
3. Warps per block: threads_per_block / 32, capped by max warps per SM

If a kernel uses 128 registers per thread with 512 threads per block, register demand alone may allow only one block on a 64K-register SM. Shared memory can be the bottleneck too: four blocks at 24KB each fill a 96KB pool even when registers are plentiful.

• Memory-bound kernels usually need high occupancy to hide VRAM latency
• Compute-bound kernels may tolerate lower occupancy if each thread does heavy math
• Block size must be a multiple of 32 (warp size)

For This Exercise

You will read registers_per_thread, shared_memory_per_block, and threads_per_block from stdin and compute occupancy, identifying whether registers, shared memory, or warps is the limiting factor. This task asks you to test three configurations and explain whether higher occupancy would help. The calculator you build mirrors what nvcc --ptxas-options=-v reports when CUDA authors debug register pressure.