私は C と CUDA の両方が初めてで、内積関数を書いていましたが、正しい結果が得られません。親切な魂が私を見てくれますか？
また、2つ質問がありますが、
- dot() が正しく機能しない理由と、
57 行目で、product[index] ではなく product[threadIdx.x] になっているのはなぜですか? 書けないかな
製品[インデックス] = a[インデックス] * b[インデックス]; ... if(index==0) {...} そして、このようにゼロ番目のスレッドで各要素を合計しますか?
どうもありがとう。
デバイスクエリ:
Device 0: "GeForce GTX 570"
CUDA Driver Version / Runtime Version 6.0 / 5.5
CUDA Capability Major/Minor version number: 2.0
Makefile: nvcc -arch=sm_20 cuda_test.cu -o cuda_test
cuda_test.cu で:
#include <stdio.h> // printf, scanf, NULL etc.
#include <stdlib.h> // malloc, free, rand etc.
#define N (3) //Number of threads we are using (also, length of array declared in main)
#define THREADS_PER_BLOCK (1) //Threads per block we are using
#define N_BLOCKS (N/THREADS_PER_BLOCK)
/* Function to generate a random integer between 1-10 */
void random_ints (int *a, int n)
{
int i;
srand(time(NULL)); //Seed rand() with current time
for(i=0; i<n; i++)
{
a[i] = rand()%10 + 1;
}
return;
}
/* Kernel that adds two integers a & b, stores result in c */
__global__ void add(int *a, int *b, int *c) {
//global indicates function that runs on
//device (GPU) and is called from host (CPU) code
int index = threadIdx.x + blockIdx.x * blockDim.x;
//threadIdx.x : thread index
//blockIdx.x : block index
//blockDim.x : threads per block
//hence index is a thread counter across all blocks
c[index] = a[index] + b[index];
//note that pointers are used for variables
//add() runs on device, so they must point to device memory
//need to allocate memory on GPU
}
/* Kernel for dot product */
__global__ void dot(int *a, int *b, int *c)
{
__shared__ int product[THREADS_PER_BLOCK]; //All threads in a block must be able
//to access this array
int index = threadIdx.x + blockIdx.x * blockDim.x; //index
product[threadIdx.x] = a[index] * b[index]; //result of elementwise
//multiplication goes into product
//Make sure every thread has finished
__syncthreads();
//Sum the elements serially to obtain dot product
if( 0 == threadIdx.x ) //Pick one thread to sum, otherwise all will execute
{
int sum = 0;
for(int j=0; j < THREADS_PER_BLOCK; j++) sum += product[j];
//Done!
atomicAdd(c,sum);
}
}
int main(void)
{
int *a, *b, *c, *dotProduct; //host copies of a,b,c etc
int *d_a, *d_b, *d_c, *d_dotProduct; //device copies of a,b,c etc
int size = N * sizeof(int); //size of memory that needs to be allocated
int i=0; //iterator
//Allocate space for device copies of a,b,c
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
//Setup input values
a = (int *)malloc(size); random_ints(a,N);
b = (int *)malloc(size); random_ints(b,N);
c = (int *)malloc(size);
//Copy inputs to device
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
//Launch add() kernel on GPU
add<<<N_BLOCKS,THREADS_PER_BLOCK>>>(d_a, d_b, d_c);
// triple angle brackets mark call from host to device
// this is also known as a kernel launch
// N/THREADS_PER_BLOCK = NO. OF BLOCKS
//Copy result back to host
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
//Output results
printf("a = {");
for (i=0; i<N; i++) printf(" %d",a[i]);
printf(" }\n");
printf("b = {");
for (i=0; i<N; i++) printf(" %d",b[i]);
printf(" }\n");
printf("c = {");
for (i=0; i<N; i++) printf(" %d",c[i]);
printf(" }\n");
//Calculate dot product of a & b
dotProduct = (int *)malloc(sizeof(int)); //Allocate host memory to dotProduct
*dotProduct = 0; //initialise to zero
cudaMalloc((void **)&d_dotProduct, sizeof(int)); //Allocate device memory to d_dotProduct
dot<<<N_BLOCKS,THREADS_PER_BLOCK>>>(d_a, d_b, d_dotProduct); //Perform calculation
cudaMemcpy(dotProduct, d_dotProduct, sizeof(int), cudaMemcpyDeviceToHost); //Copy result into dotProduct
printf("\ndot(a,b) = %d\n", *dotProduct); //Output result
//Cleanup
free(a); free(b); free(c); free(dotProduct);
cudaFree(d_a); cudaFree(d_b); cudaFree(d_c); cudaFree(d_dotProduct);
return 0;
} //End of main