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我正在尝试使用 CURAND 库生成从 0 到 100 完全相互独立的随机数。因此，我将时间作为每个线程的种子并指定“id = threadIdx.x + blockDim.x * blockIdx.x "作为序列和偏移量。然后将随机数设为浮点数后，将其乘以 100 并取其整数值。

现在，我面临的问题是它为线程 [0,0] 和 [0,1] 获得相同的随机数，无论我运行多少次 11 的代码。我无法理解是什么我做错了。请帮忙。

我在下面粘贴我的代码：

#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include<curand_kernel.h>
#include "util/cuPrintf.cu"
#include<time.h>

#define NE WA*HA //Total number of random numbers 
#define WA 2   // Matrix A width
#define HA 2   // Matrix A height
#define SAMPLE 100 //Sample number
#define BLOCK_SIZE 2 //Block size

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

__global__ void generate( curandState* globalState, float* randomMatrix )
{
int ind = threadIdx.x + blockIdx.x * blockDim.x;
if(ind < NE){
    curandState localState = globalState[ind];
    float stopId = curand_uniform(&localState) * SAMPLE;
    cuPrintf("Float random value is : %f",stopId);
    int stop = stopId ;
    cuPrintf("Random number %d\n",stop);
    for(int i = 0; i < SAMPLE; i++){
            if(i == stop){
                    float random = curand_normal( &localState );
                    cuPrintf("Random Value %f\t",random);
                    randomMatrix[ind] = random;
                    break;
            }
    }
    globalState[ind] = localState;
}
}

/////////////////////////////////////////////////////////
// Program main
/////////////////////////////////////////////////////////

int main(int argc, char** argv)
{

// 1. allocate host memory for matrix A
unsigned int size_A = WA * HA;
unsigned int mem_size_A = sizeof(float) * size_A;
float* h_A = (float* ) malloc(mem_size_A);
time_t t;

// 2. allocate device memory
float* d_A;
cudaMalloc((void**) &d_A, mem_size_A);

// 3. create random states    
curandState* devStates;
cudaMalloc ( &devStates, size_A*sizeof( curandState ) );

// 4. setup seeds
int n_blocks = size_A/BLOCK_SIZE;
time(&t);
printf("\nTime is : %u\n",(unsigned long) t);
setup_kernel <<< n_blocks, BLOCK_SIZE >>> ( devStates, (unsigned long) t );
// 4. generate random numbers
cudaPrintfInit();
generate <<< n_blocks, BLOCK_SIZE >>> ( devStates,d_A );
cudaPrintfDisplay(stdout, true);
cudaPrintfEnd();
// 5. copy result from device to host
cudaMemcpy(h_A, d_A, mem_size_A, cudaMemcpyDeviceToHost);


// 6. print out the results
printf("\n\nMatrix A (Results)\n");
for(int i = 0; i < size_A; i++)
{
   printf("%f ", h_A[i]);
   if(((i + 1) % WA) == 0)
      printf("\n");
}
printf("\n");

// 7. clean up memory
free(h_A);
cudaFree(d_A);

}

我得到的输出是：

时间为：1347857063 [0, 0]：浮点随机值为：11.675105[0, 0]：随机数 11 [0, 0]：随机值 0.358356 [0, 1]：浮点随机值为：11.675105[0, 1 ]：随机数 11 [0, 1]：随机值 0.358356 [1, 0]：浮点随机值是：63.840496[1, 0]：随机数 63 [1, 0]：随机值 0.696459 [1, 1]：浮点随机值为：44.712799[1, 1]：随机数 44 [1, 1]：随机值 0.735049

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1 回答 1

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这里有一些问题，我在这里解决第一个问题以帮助您入门：

一般要点

  • 请检查所有 CUDA API 调用的返回值，请参阅此处了解更多信息。
  • 请运行 cuda-memcheck 来检查明显的事情，比如越界访问。

具体点

  • When allocating space for the RNG state, you should have space for one state per thread (not one per matrix element as you have now).
  • Your thread ID calculation in setup_kernel() is wrong, should be threadIdx.x + blockIdx.x * blockDim.x (* instead of +).
  • You use the thread ID as the sequence number as well as the offset, you should just set the offset to zero as described in the cuRAND manual:

For the highest quality parallel pseudorandom number generation, each experiment should be assigned a unique seed. Within an experiment, each thread of computation should be assigned a unique sequence number.

最后你每个块运行两个线程，这是非常低效的。查看 CUDA C 编程指南，在“最大化利用率”部分以获取更多信息，但您应该寻找每个块启动 32 个线程的倍数（例如 128、256）和大量块（例如数万）。如果您的问题很小，那么考虑一次运行多个问题（在单个内核启动中批处理或作为不同流中的内核以获得并发执行）。

于 2012-09-17T09:01:28.310 回答