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reduce_coarsening.cu
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reduce_coarsening.cu
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#include <iostream>
#include <cuda.h>
#define BLOCK_DIM 1024
#define COARSE_FACTOR 2
__global__ void CoarsenedReduction(float* input, float* output, int size) {
__shared__ float input_s[BLOCK_DIM];
unsigned int i = blockIdx.x * blockDim.x * COARSE_FACTOR + threadIdx.x;
unsigned int t = threadIdx.x;
float sum = 0.0f;
// Reduce within a thread
for (unsigned int tile = 0; tile < COARSE_FACTOR; ++tile) {
unsigned int index = i + tile * blockDim.x;
if (index < size) {
sum += input[index];
}
}
input_s[t] = sum;
__syncthreads();
//Reduce within a block
for (unsigned int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
if (t < stride) {
input_s[t] += input_s[t + stride];
}
__syncthreads();
}
//Reduce over blocks
if (t == 0) {
atomicAdd(output, input_s[0]);
}
}
int main() {
const int size = 10000;
const int bytes = size * sizeof(float);
// Allocate memory for input and output on host
float* h_input = new float[size];
float* h_output = new float;
// Initialize input data on host
for (int i = 0; i < size; i++) {
h_input[i] = 1.0f; // Example: Initialize all elements to 1
}
// Allocate memory for input and output on device
float* d_input;
float* d_output;
cudaMalloc(&d_input, bytes);
cudaMalloc(&d_output, sizeof(float));
// Copy data from host to device
cudaMemcpy(d_input, h_input, bytes, cudaMemcpyHostToDevice);
cudaMemset(d_output, 0, sizeof(float)); // Initialize output to 0
// Launch the kernel with coarsening
// Adjust the launch configuration if necessary
int numBlocks = (size + BLOCK_DIM * COARSE_FACTOR - 1) / (BLOCK_DIM * COARSE_FACTOR);
CoarsenedReduction<<<numBlocks, BLOCK_DIM>>>(d_input, d_output, size);
// Copy result back to host
cudaMemcpy(h_output, d_output, sizeof(float), cudaMemcpyDeviceToHost);
// Print the result
std::cout << "Sum is " << *h_output << std::endl;
// Cleanup
delete[] h_input;
delete h_output;
cudaFree(d_input);
cudaFree(d_output);
return 0;
}