Atomic Operation

  • An atomic operation ensures that when many threads simultaneously access the same variable, the accesses are serialized so that race conditions do not occur.
  • For example, suppose there is a variable called result that stores the sum of all elements in an array.
  • If multiple threads attempt to update this variable without atomic operations, the order of execution may be interleaved incorrectly, resulting in an inaccurate final value.
  • Atomic operations are also used in other cases such as counters, histograms, and any scenario where multiple threads must update the same variable.

Code Example

#include <cuda_runtime.h>

// Kernel to sum an array using atomicAdd
__global__ void atomicSumKernel(const float *input, float *result, int N) {
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    
    // Each thread adds its element to the result using atomicAdd
    if (idx < N) {
        atomicAdd(result, input[idx]);
    }
}
  • By using the atomicAdd() function, threads update the result variable safely, one at a time.

Drawbacks

  • Since atomic operations serialize parallel execution, excessive use can lead to bottlenecks.
  • To mitigate this, optimization strategies can be applied:
    • For example, when summing an array, each block can first compute a partial sum using shared memory and then combine results at the end, reducing reliance on atomics.

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