Cuda随机数生成

Question

我想知道在0到49k之间生成一个伪随机数的最佳方法是什么,对于每个线程来说,通过使用curand或其他东西是相同的.

我更喜欢在内核中生成随机数,因为我必须在那时生成一个但大约10k次.

我可以使用介于0.0和1.0之间的浮点数,但我不知道如何使我的PRN可用于所有线程,因为大多数帖子和示例显示如何为每个线程使用不同的PRN.

谢谢

Answer 1

可能你只需要研究一下文档文档,特别是对于设备API.为每个线程获取相同序列的关键是为每个线程创建状态(大多数示例执行此操作),然后将相同的序列号传递给每个线程的init函数.在curand_init中,参数序列如下:

curand_init(seed, subsequence number, offset, state)

通过为每个init调用设置种子相同,我们为每个线程生成相同的序列.通过设置子序列和偏移数相同,我们为每个线程在该序列中选择相同的起始值.

这是代码来演示:

// compile with: nvcc -arch=sm_20 -lcurand -o t89 t89.cu
#include <stdio.h>
#include <curand.h>
#include <curand_kernel.h>

#define SCALE 49000
#define DSIZE 5000
#define nTPB 256

#define cudaCheckErrors(msg) \
    do { \
        cudaError_t __err = cudaGetLastError(); \
        if (__err != cudaSuccess) { \
            fprintf(stderr, "Fatal error: %s (%s at %s:%d)\n", \
                msg, cudaGetErrorString(__err), \
                __FILE__, __LINE__); \
            fprintf(stderr, "*** FAILED - ABORTING\n"); \
            exit(1); \
        } \
    } while (0)

__device__ float getnextrand(curandState *state){

  return (float)(curand_uniform(state));
}

__device__ int getnextrandscaled(curandState *state, int scale){

  return (int) scale * getnextrand(state);
}


__global__ void initCurand(curandState *state, unsigned long seed){
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    curand_init(seed, 0, 0, &state[idx]);
}

__global__ void testrand(curandState *state, int *a1, int *a2){
    int idx = threadIdx.x + blockIdx.x * blockDim.x;

    a1[idx] = getnextrandscaled(&state[idx], SCALE);
    a2[idx] = getnextrandscaled(&state[idx], SCALE);
}

int main() {

    int *h_a1, *h_a2, *d_a1, *d_a2;
    curandState *devState;

    h_a1 = (int *)malloc(DSIZE*sizeof(int));
    if (h_a1 == 0) {printf("malloc fail\n"); return 1;}
    h_a2 = (int *)malloc(DSIZE*sizeof(int));
    if (h_a2 == 0) {printf("malloc fail\n"); return 1;}
    cudaMalloc((void**)&d_a1, DSIZE * sizeof(int));
    cudaMalloc((void**)&d_a2, DSIZE * sizeof(int));
    cudaMalloc((void**)&devState, DSIZE * sizeof(curandState));
    cudaCheckErrors("cudamalloc");



     initCurand<<<(DSIZE+nTPB-1)/nTPB,nTPB>>>(devState, 1);
     cudaDeviceSynchronize();
     cudaCheckErrors("kernels1");
     testrand<<<(DSIZE+nTPB-1)/nTPB,nTPB>>>(devState, d_a1, d_a2);
     cudaDeviceSynchronize();
     cudaCheckErrors("kernels2");
     cudaMemcpy(h_a1, d_a1, DSIZE*sizeof(int), cudaMemcpyDeviceToHost);
     cudaMemcpy(h_a2, d_a2, DSIZE*sizeof(int), cudaMemcpyDeviceToHost);
     cudaCheckErrors("cudamemcpy");
     printf("1st returned random value is %d\n", h_a1[0]);
     printf("2nd returned random value is %d\n", h_a2[0]);

     for (int i=1; i< DSIZE; i++){
       if (h_a1[i] != h_a1[0]) {
         printf("mismatch on 1st value at %d, val = %d\n", i, h_a1[i]);
         return 1;
         }
       if (h_a2[i] != h_a2[0]) {
         printf("mismatch on 2nd value at %d, val = %d\n", i, h_a2[i]);
         return 1;
         }
       }
     printf("thread values match!\n");

}