大家好，又见面了，我是全栈君。
利用shared memory计算，并避免bank conflict；通过每个block内部规约，然后再把所有block的计算结果在CPU端累加
代码：
#include <cuda_runtime.h> #include <device_launch_parameters.h> #include <stdio.h> #include <stdlib.h> #include <memory> #include <iostream> #define DATA_SIZE 128 #define TILE_SIZE 64 __global__ void reductionKernel(float *in, float *out){ int tx = threadIdx.x; int bx = blockIdx.x; __shared__ float data_shm[TILE_SIZE]; data_shm[tx] = in[bx * blockDim.x + tx]; __syncthreads(); for (int i = blockDim.x / 2; i > 0; i >>= 1){ if (tx < i){ data_shm[tx] += data_shm[tx + i]; } __syncthreads(); } if (tx == 0) out[bx] = data_shm[0]; } void reduction(){ int out_size = (DATA_SIZE + TILE_SIZE - 1) / TILE_SIZE; float *in = (float*)malloc(DATA_SIZE * sizeof(float)); float *out = (float*)malloc(out_size*sizeof(float)); for (int i = 0; i < DATA_SIZE; ++i){ in[i] = i; } memset(out, 0, out_size*sizeof(float)); float *d_in, *d_out; cudaMalloc((void**)&d_in, DATA_SIZE * sizeof(float)); cudaMalloc((void**)&d_out, out_size*sizeof(float)); cudaMemcpy(d_in, in, DATA_SIZE * sizeof(float), cudaMemcpyHostToDevice); dim3 block(TILE_SIZE, 1); dim3 grid(out_size, 1); reductionKernel << <grid, block >> >(d_in, d_out); cudaMemcpy(in, d_in, DATA_SIZE * sizeof(float), cudaMemcpyDeviceToHost); cudaMemcpy(out, d_out, out_size * sizeof(float), cudaMemcpyDeviceToHost); float sum = 0; for (int i = 0; i < out_size; ++i){ sum += out[i]; } std::cout << sum << std::endl; // Check on CPU float sum_cpu = 0; for (int i = 0; i < DATA_SIZE; ++i){ sum_cpu += in[i]; } std::cout << sum_cpu << std::endl; }
转载于:https://www.cnblogs.com/haiyang21/p/7795678.html
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