Final answer:
A butterfly-structured warp sum in CUDA involves using the __shfl_xor_sync intrinsic with a decreasing bitmask in a kernel. The kernel computes within-warp sums, and a main function sets up the problem and outputs the results.
Step-by-step explanation:
The question pertains to implementing a butterfly-structured warp sum using a 32-lane warp and the SHFL XOR SYNC instruction in CUDA, a parallel computing platform and API model created by Nvidia. The provided bitmask values (16, 8, 4, 2, 1) correspond to different steps in the butterfly sum algorithm where threads exchange data to progressively sum multiple elements. A kernel function will need to be written that utilizes these bitmask values in the SHFL instruction to efficiently compute sums within a warp. Additionally, a main function must be implemented to initialize data, call the kernel, and print the warp sum results.
To achieve a parallel reduction using a butterfly structure in CUDA, one would use the __shfl_xor_sync intrinsic to perform intra-warp communication without shared memory. The bitmask will dictate which lanes participate in each step of the reduction. With each step, the distance between the elements being summed doubles, which is consistent with the halving bitmask values.
The sample kernel may look like this:
__global__ void butterflyWarpSum(int *input, int *output){
int laneId = threadIdx.x % warpSize;
int value = input[threadIdx.x];
for(int mask = 16; mask > 0; mask /= 2) {
int n = __shfl_xor_sync(0xffffffff, value, mask);
value += n;
}
if(laneId == 0) output[blockIdx.x] = value;
}
This example assumes each thread block contains exactly one warp and that there are as many blocks as there are warps. The result of each warp's sum is then written to a distinct location in the output array by the first lane of each warp.