写一段用cuda的共享内存执行的直方图计算代码
时间: 2023-03-02 15:40:41 浏览: 79
以下是一个使用CUDA共享内存计算直方图的示例代码:
```
__global__ void histogram(int *input, int *output, int num_bins, int data_size)
{
// Define shared memory for each block
__shared__ int shared_hist[BLOCK_SIZE];
// Initialize shared memory to zero
for (int i = threadIdx.x; i < num_bins; i += blockDim.x) {
shared_hist[i] = 0;
}
__syncthreads();
// Compute histogram in shared memory
int tid = threadIdx.x + blockIdx.x * blockDim.x;
while (tid < data_size) {
atomicAdd(&shared_hist[input[tid]], 1);
tid += blockDim.x * gridDim.x;
}
__syncthreads();
// Merge shared memory histogram into global memory
for (int i = threadIdx.x; i < num_bins; i += blockDim.x) {
atomicAdd(&output[i], shared_hist[i]);
}
}
```
在主机代码中,您需要将数据复制到GPU内存中,然后调用这个CUDA内核来计算直方图:
```
int main()
{
// Allocate memory on host and device
int *h_data, *d_data, *h_hist, *d_hist;
int data_size = 1000000;
int num_bins = 256;
size_t data_bytes = data_size * sizeof(int);
size_t hist_bytes = num_bins * sizeof(int);
h_data = (int*)malloc(data_bytes);
h_hist = (int*)malloc(hist_bytes);
cudaMalloc(&d_data, data_bytes);
cudaMalloc(&d_hist, hist_bytes);
// Initialize data on host
for (int i = 0; i < data_size; i++) {
h_data[i] = rand() % num_bins;
}
// Copy data from host to device
cudaMemcpy(d_data, h_data, data_bytes, cudaMemcpyHostToDevice);
cudaMemset(d_hist, 0, hist_bytes);
// Launch histogram kernel
int block_size = 256;
int grid_size = (data_size + block_size - 1) / block_size;
histogram<<<grid_size, block_size>>>(d_data, d_hist, num_bins, data_size);
// Copy histogram from device to host
cudaMemcpy(h_hist, d_hist, hist_bytes, cudaMemcpyDeviceToHost);
// Free memory on host and device
free(h_data);
free(h_hist);
cudaFree(d_data);
cudaFree(d_hist);
return 0;
}
```
在上面的示例中,我们使用共享内存来存储每个块的直方图。在每个块中,我们首先初始化共享内存为零,然后计算每个线程负责的元素的直方图。计算完成后,我们使用原子操作将每个线程的结果添加到共享内存中。最后,我们将共享内存中的结果合并到全局直方图中。请注意,在合并过程中,我们使用原子操作来避免竞争条件。
此外,我们在调用内核时使用了一个网格和块的结构。我们根据数据大小计算网格大小和块大小,并将其传递给内核。在内核中,我们使用线程ID和块ID来计算每个线程负责的
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