在Cuda内核中生成变化范围内的随机数

Question

我试图在cuda内核中生成随机数随机数.我希望从均匀分布和整数形式生成随机数,从1到8开始.随机数对于每个线程都是不同的.可以生成随机数的范围也可以从一个线程到另一个线程而变化.一个线程中的最大范围可能低至2,或者在另一个线程中,它可以高达8,但不高于该高.所以,我在下面提供了一个如何生成数字的示例:

In thread#1 --> maximum of the range is 2 and so the random number should be between 1 and 2
In thread#2 --> maximum of the range is 6  and so the random number should be between 1 and 6
In thread#3 --> maximum of the range is 5 and so the random number should be between 1 and 5

等等...

Answer 1

编辑:我编辑了我的答案,以解决其他答案(@tudorturcu)和评论中指出的一些缺陷.

1. 使用CURAND生成 介于0.0和1.0之间的均匀分布
2. 然后将其乘以所需范围(最大值 - 最小值+ 0.999999).
3. 然后添加偏移量(+最小值).
4. 然后截断为整数.

您的设备代码中有类似的内容:

int idx = threadIdx.x+blockDim.x*blockIdx.x;
// assume have already set up curand and generated state for each thread...
// assume ranges vary by thread index
float myrandf = curand_uniform(&(my_curandstate[idx]));
myrandf *= (max_rand_int[idx] - min_rand_int[idx] + 0.999999);
myrandf += min_rand_int[idx];
int myrand = (int)truncf(myrandf);

你应该:

#include <math.h>

对于 truncf

这是一个完整的例子:

$ cat t527.cu
#include <stdio.h>
#include <curand.h>
#include <curand_kernel.h>
#include <math.h>
#include <assert.h>
#define MIN 2
#define MAX 7
#define ITER 10000000

__global__ void setup_kernel(curandState *state){

  int idx = threadIdx.x+blockDim.x*blockIdx.x;
  curand_init(1234, idx, 0, &state[idx]);
}

__global__ void generate_kernel(curandState *my_curandstate, const unsigned int n, const unsigned *max_rand_int, const unsigned *min_rand_int,  unsigned int *result){

  int idx = threadIdx.x + blockDim.x*blockIdx.x;

  int count = 0;
  while (count < n){
    float myrandf = curand_uniform(my_curandstate+idx);
    myrandf *= (max_rand_int[idx] - min_rand_int[idx]+0.999999);
    myrandf += min_rand_int[idx];
    int myrand = (int)truncf(myrandf);

    assert(myrand <= max_rand_int[idx]);
    assert(myrand >= min_rand_int[idx]);
    result[myrand-min_rand_int[idx]]++;
    count++;}
}

int main(){

  curandState *d_state;
  cudaMalloc(&d_state, sizeof(curandState));
  unsigned *d_result, *h_result;
  unsigned *d_max_rand_int, *h_max_rand_int, *d_min_rand_int, *h_min_rand_int;
  cudaMalloc(&d_result, (MAX-MIN+1) * sizeof(unsigned));
  h_result = (unsigned *)malloc((MAX-MIN+1)*sizeof(unsigned));
  cudaMalloc(&d_max_rand_int, sizeof(unsigned));
  h_max_rand_int = (unsigned *)malloc(sizeof(unsigned));
  cudaMalloc(&d_min_rand_int, sizeof(unsigned));
  h_min_rand_int = (unsigned *)malloc(sizeof(unsigned));
  cudaMemset(d_result, 0, (MAX-MIN+1)*sizeof(unsigned));
  setup_kernel<<<1,1>>>(d_state);

  *h_max_rand_int = MAX;
  *h_min_rand_int = MIN;
  cudaMemcpy(d_max_rand_int, h_max_rand_int, sizeof(unsigned), cudaMemcpyHostToDevice);
  cudaMemcpy(d_min_rand_int, h_min_rand_int, sizeof(unsigned), cudaMemcpyHostToDevice);
  generate_kernel<<<1,1>>>(d_state, ITER, d_max_rand_int, d_min_rand_int, d_result);
  cudaMemcpy(h_result, d_result, (MAX-MIN+1) * sizeof(unsigned), cudaMemcpyDeviceToHost);
  printf("Bin:    Count: \n");
  for (int i = MIN; i <= MAX; i++)
    printf("%d    %d\n", i, h_result[i-MIN]);

  return 0;
}


$ nvcc -arch=sm_20 -o t527 t527.cu -lcurand
$ cuda-memcheck ./t527
========= CUDA-MEMCHECK
Bin:    Count:
2    1665496
3    1668130
4    1667644
5    1667435
6    1665026
7    1666269
========= ERROR SUMMARY: 0 errors
$