并行化函数将对所有向量进行计数，其总和等于向量元素和不大于k的元素

时间：2013-10-18 17:51:29

标签： c cuda

我想在CUDA C中并行化一个函数，该函数将计算所有向量的和向量元素和不大于k的元素。例如，如果向量元素的数量n是5，sum = 10且k = 3，那么满足这个条件的向量的数量是101.我已经在CUDA C中做了这个函数，但问题是当数量为块和线程大于1.我知道问题出现在for个周期中，我应该更改它，但我不知道从哪里开始。当我使用块和线程等于1来调用函数时，函数以经典方式工作并且一切都很好但在这种情况下函数不是并行化的。

该程序的源代码是：

//function that count number of vectors
__device__ void count(int *vector, int *total, int n, int s)
{
 int i,sum=0;
 for(i=blockIdx.x*blockDim.x+threadIdx.x;i<n;i+=blockDim.x*gridDim.x)
 {    
   sum+=vector[i];
   __syncthreads();
 }
 if(sum==s)
 {  
   total[0]=total[0]+1;
 }
}

//main function
__global__ void computeVectors(int *vector, int n, int kk, int s, int *total)
{
 int k=0;
 int j,i,next;

 while(1)
 {
  //this is the problem, in for cycle
  for(j=blockIdx.x*blockDim.x+threadIdx.x; j<=kk; j+=blockDim.x*gridDim.x)
  {
   vector[k]=j;
   count(vector, total, n, s);
   __syncthreads();
  }
  for(i=blockIdx.x*blockDim.x+threadIdx.x; i<n; i+=blockDim.x*gridDim.x)
  {

   if(vector[i]<kk)
      break;
  }  
  next=i;
  vector[next]++;
  for(i=blockIdx.x*blockDim.x+threadIdx.x; i<sledno; i+=blockDim.x*gridDim.x)
  {
   vector[i]=0;
   __syncthreads();
  }
  k=0;
  if(next>=n)
    break;
 }
}

int main()
{
  cudaError_t err = cudaSuccess;

  int n,k,sum;
  int counter=0;

  printf("Enter the length of vector n=");
  scanf("%d",&n);
  printf("Enter the max value of vector elements k=");
  scanf("%d",&k);
  printf("Enter the sum of vector elements sum=");
  scanf("%d",&sum);

  //initial vector with length n
  int *vec_h, *vec_d;
  size_t sizevec=n*sizeof(int);
  vec_h=(int *)malloc(sizevec);
  cudaMalloc((void **) &vec_d, sizevec);

  for(counter=0; counter<n; counter++)
  {
   vec_h[counter]=0;
  }
  cudaMemcpy(vec_d, vec_h, sizevec, cudaMemcpyHostToDevice);

  int *total_h, *total_d;
  size_t size=1*sizeof(int);
  total_h=(int *)malloc(size);
  cudaMalloc((void **) &total_d, size);
  total_h[0]=0;
  cudaMemcpy(total_d, total_h, size, cudaMemcpyHostToDevice);

  //calling the main function
  computeVectors<<<1, 1>>>(vec_d, n, k, sum, total_d);

  cudaThreadSynchronize(); 

  err = cudaGetLastError();
  if (err != cudaSuccess)
  {
    fprintf(stderr, "Error: %s!\n", cudaGetErrorString(err));
    exit(EXIT_FAILURE);
  }
  cudaMemcpy(total_h, total_d, size, cudaMemcpyDeviceToHost);
  printf("Number of vectors that satisfy condition is %d\n", total_h[0]);

  free(vec_h); 
  cudaFree(vec_d);

  free(total_h); 
  cudaFree(total_d);

  return 0;
}

3 个答案:

答案 0 :(得分：1)

这是一个示例蛮力程序，用于枚举所有可能的向量，然后测试每个向量的总和，看它是否与所需的总和相匹配。

  • 假设n =“数字”中的向量长度
  • 假设每个向量“数字”由unsigned数量
    • 表示
  • 假设k =最大“数字”值+ 1
  • 向量空间的大小由k ^ n
    • 给出
  • 将此空间划分为每个线程要处理的连续向量组：（k ^ n）/ grid_size
  • 为每个线程生成起始向量（即每组中的起始向量）
  • 然后每个线程循环测试向量和并在必要时递增计数，然后“递增”向量，直到每个线程处理了它指定的连续向量组

该计划：

#include <stdio.h>
#include <thrust/host_vector.h>
#include <sys/time.h>
#include <time.h>

#define MAX_N 12
#define nTPB 256
#define GRIDSIZE (32*nTPB)


#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)


// thrust code is to quickly prototype a CPU based
// method for verification
int increment(thrust::host_vector<unsigned> &data, unsigned max){
  int pos = 0;
  int done = 0;
  int finished = 0;

  while(!done){
    data[pos]++;
    if (data[pos] >= max) {
      data[pos] = 0;
      pos++;
      if (pos >= data.size()){
        done = 1;
        finished = 1;
        }
      }
    else done = 1;
  }
  return finished;
}

__constant__ unsigned long powers[MAX_N];

__device__ unsigned vec_sum(unsigned *vector, int size){
  unsigned sum = 0;
  for (int i=0; i<size; i++) sum += vector[(i*nTPB)];
  return sum;
}

__device__ void create_vector(unsigned long index, unsigned *vector, int size){
  unsigned long residual = index;
  unsigned pos = size;
  while ((residual > 0) && (pos > 0)){
    unsigned long temp = residual/powers[pos-1];
    vector[(pos-1)*nTPB] = temp;
    residual -= temp*powers[pos-1];
    pos--;
    }
  while (pos>0) {
   vector[(pos-1)*nTPB] = 0;
   pos--;
   }
}
__device__ void increment_vector(unsigned *vector, int size, int k){
  int pos = 0;
  int done = 0;

  while(!done){
    vector[(pos*nTPB)]++;
    if (vector[pos*nTPB] >= k) {
      vector[pos*nTPB] = 0;
      pos++;
      if (pos >= size){
        done = 1;
        }
      }
    else done = 1;
  }
}

__global__ void find_vector_match(unsigned long long int *count, int k, int n, unsigned sum){
  __shared__ unsigned vecs[MAX_N *nTPB];
  unsigned *vec = &(vecs[threadIdx.x]);
  unsigned long idx = threadIdx.x+blockDim.x*blockIdx.x;
  if (idx < (k*powers[n-1])){
    unsigned long vec_count = 0;
    unsigned long vecs_per_thread = (k*powers[n-1])/(gridDim.x*blockDim.x);
    vecs_per_thread++;
    unsigned long vec_num = idx*vecs_per_thread;
    create_vector((vec_num), vec, n);
    while ((vec_count < vecs_per_thread) && (vec_num < (k*powers[n-1]))){
      if (vec_sum(vec, n) == sum) atomicAdd(count, 1UL);
      increment_vector(vec, n, k);
      vec_count++;
      vec_num++;
      }
   }
}

int main(){

// calculate on CPU first for verification
  struct timeval t1, t2, t3;
  int n, k, sum;
  printf("Enter the length of vector (maximum: %d) n=", MAX_N);
  scanf("%d",&n);
  printf("Enter the max value of vector elements k=");
  scanf("%d",&k);
  printf("Enter the sum of vector elements sum=");
  scanf("%d",&sum);
  int count = 0;
  gettimeofday(&t1, NULL);
  k++;

  thrust::host_vector<unsigned> test(n);
  thrust::fill(test.begin(), test.end(), 0);
  int finished = 0;
  do{
    if (thrust::reduce(test.begin(), test.end()) == sum) count++;
    finished = increment(test, k);
    }
    while (!finished);
  gettimeofday(&t2, NULL);
  printf("CPU count = %d, in %d seconds\n", count, t2.tv_sec - t1.tv_sec);
  unsigned long h_powers[MAX_N];
  h_powers[0] = 1;
  if (n < MAX_N)
    for (int i = 1; i<n; i++) h_powers[i] = h_powers[i-1]*k;
  cudaMemcpyToSymbol(powers, h_powers, MAX_N*sizeof(unsigned long));
  cudaCheckErrors("cudaMemcpyToSymbolfail");
  unsigned long long int *h_count, *d_count;
  h_count = (unsigned long long int *)malloc(sizeof(unsigned long long int));
  cudaMalloc((void **)&d_count, sizeof(unsigned long long int));
  cudaCheckErrors("cudaMalloc fail");
  *h_count = 0;
  cudaMemcpy(d_count, h_count, sizeof(unsigned long long int), cudaMemcpyHostToDevice);
  cudaCheckErrors("cudaMemcpy H2D fail");
  find_vector_match<<<(GRIDSIZE + nTPB -1)/nTPB, nTPB>>>(d_count, k, n, sum);
  cudaMemcpy(h_count, d_count, sizeof(unsigned long long int), cudaMemcpyDeviceToHost);
  cudaCheckErrors("cudaMemcpy D2H fail");
  gettimeofday(&t3, NULL);
  printf("GPU count = %d, in %d seconds\n", *h_count, t3.tv_sec - t2.tv_sec);

  return 0;
}

编译：

$ nvcc -O3 -arch=sm_20 -o t260 t260.cu

示例输出：

$ ./t260
Enter the length of vector (maximum: 12) n=2
Enter the max value of vector elements k=3
Enter the sum of vector elements sum=4
CPU count = 3, in 0 seconds
GPU count = 3, in 0 seconds
$ ./t260
Enter the length of vector (maximum: 12) n=5
Enter the max value of vector elements k=3
Enter the sum of vector elements sum=10
CPU count = 101, in 0 seconds
GPU count = 101, in 0 seconds
$ ./t260
Enter the length of vector (maximum: 12) n=9
Enter the max value of vector elements k=9
Enter the sum of vector elements sum=20
CPU count = 2714319, in 12 seconds
GPU count = 2714319, in 1 seconds
$ ./t260
Enter the length of vector (maximum: 12) n=10
Enter the max value of vector elements k=9
Enter the sum of vector elements sum=20
CPU count = 9091270, in 123 seconds
GPU count = 9091270, in 4 seconds

因此，对于大问题规模，天真的暴力GPU代码似乎比天真的暴力单线程CPU代码快约30倍。 （...在我的特定机器设置上：CPU = Xeon X5560，GPU = Quadro5000，CentOS 5.5，CUDA 5.0）

答案 1 :(得分：0)

问题是__ syncthreads（）。要使__ syncthreads（）正常工作，块内的所有线程都应该能够到达它，否则一些线程会永远等待，并且程序无法运行。 在您的程序中，某些部分中__ syncthreads（）的执行是有条件的。这就是为什么你的程序不能在一个块中使用多个线程的原因。

答案 2 :(得分：0)

罗伯特在评论中说，如果你想在GPU上生成所有（k + 1）^ n个排列并测试它们，你可以想到这样的GPU内核：

__device__ int count;  //global variable must be initialized to zero before kernel call
__global__ void perm_generator(int k, int n, int sum) {
   int tid = blockIdx.x*blockDim.x+threadIdx.x;
   int id = tid;
   int mysum = 0;
   for ( int i = n; i > 1; i-- ) { //all n-1 vector elements
     mysum += (id % (k+1));
     id /= (k+1);
   }
   mysum += id; //last element
   if ( mysum == sum ) atomicAdd( &count, 1 );
}

应该使用（k + 1）^ n个线程调用内核。如果您碰巧使用更多线程调用内核（仅仅因为经验法则块大小应该是32的倍数），您需要事先在内核中检查 tid 的值。 此外，不推荐使用 cudaThreadSynchronize（）。请改用 cudaDeviceSynchronize（）。

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