CODEWITHSUNDEEP
randomcudanvidiathrust
Anoracx
Anoracx

Reputation: 438

Cuda Random Number Generation

I was wondering what was the best way to generate one pseudo random number between 0 and 49k that would be the same for each thread, by using curand or something else.

I prefer to generate the random numbers inside the kernel because I will have to generate one at the time but about 10k times.

And I could use floats between 0.0 and 1.0, but I've no idea how to make my PRN available for all threads, because most post and example show how to have different PRN for each threads.

Thanks

Upvotes: 1

Views: 5513

Answers (1)

Robert Crovella
Robert Crovella

Reputation: 151829

Probably you just need to study the curand documentation, especially for the device API. The key to getting the same sequence for each thread is to create state for each thread (most examples do this) and then pass the same sequence number to the init function for each thread. In curand_init, the sequence of parameters is as follows:

curand_init(seed, subsequence number, offset, state)

by setting the seed for each init call the same, we generate the same sequence for each thread. by setting the subsequence and offset numbers the same, we select the same starting value within that sequence, for each thread.

Here is code to demonstrate:

// 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");

}

Upvotes: 10

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