Occupancy

Occupancy Calculator

This CUDA kernel demonstrates how to calculate and optimize GPU occupancy using CUDA's occupancy API. Understanding occupancy is crucial for maximizing GPU performance and resource utilization.

Overview

The program calculates the occupancy of a simple kernel by determining how many active blocks can run per multiprocessor (SM). This information helps developers optimize their kernel configurations for better performance.

What is Occupancy?

Occupancy is the ratio of active warps to the maximum number of warps supported on a multiprocessor. Higher occupancy doesn't always guarantee better performance, but it provides more opportunities to hide latency through context switching.

Code Explanation

The kernel performs simple element-wise multiplication:

__global__ void MyKernel(int* d, int* a, int* b)
{
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    d[idx] = a[idx] * b[idx];
}

The host code uses cudaOccupancyMaxActiveBlocksPerMultiprocessor() to determine:

• Maximum number of active blocks per SM for a given block size
• Resulting occupancy as a percentage of maximum possible warps

Key Concepts

• Block Size: Number of threads per block (64 in this example)
• Active Blocks: Maximum blocks that can execute simultaneously on one SM
• Active Warps: Number of warps that can be active (numBlocks × blockSize / warpSize)
• Occupancy: Percentage of maximum warps that are active

Sample Output

Occupancy: 100%

This indicates the kernel configuration achieves maximum theoretical occupancy on the device.

Usage

Compile and run to see the occupancy percentage for your GPU:

nvcc kernel.cu -o occupancy
./occupancy

The program will display the occupancy percentage based on your GPU's capabilities.