[CUDA]异构并行vector查询，CPU端vector转GPU端数组

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include 
#include "iostream"
#include "cstdlib"
#include "vector"
#include "thread"
using namespace std;
#define USE_CPU 1
#define USE_GPU 0
struct stu_info
{
    char stu_num[14];
    int try_seat_num;
    int exam_seat_num;
};
struct select_info
{
    char stu_num[14];
    int try_seat_num;
    int exam_seat_num;
};
 
vector stu;
vector select;
__device__ void gpu_strcpy(char* a, char *b)
{
    for (int i = 0; i < 14; i++)
    {
        a[i] = b[i];
    }
}
 
void cpu_strcpy(char* a, char *b)
{
    for (int i = 0; i < 14; i++)
    {
        a[i] = b[i];
    }
}
__global__ void gpu_select_kernel(stu_info *dev_stu,select_info *dev_select,int *n)
{
    int index = threadIdx.x;
    for (int i = 0; i < *n; i++)
    {
        if (dev_select[index].try_seat_num == dev_stu[i].try_seat_num)
        {
            gpu_strcpy(dev_select[index].stu_num, dev_stu[i].stu_num);
            dev_select[index].exam_seat_num = dev_stu[i].exam_seat_num;
            break;
        }
    }
}
 
void fun_select_cpu(int index, int n)
{
    for (int i = 0; i < n; i++)
    {
        if (select[index].try_seat_num == stu[i].try_seat_num)
        {
            //cout << stu[index].stu_num << " " << stu[index].exam_seat_num<> > (dev_stu, dev_select, dev_n);
 
    cudaMemcpy(host_stu, dev_stu, sizeof(stu_info)*n, cudaMemcpyDeviceToHost);
    cudaMemcpy(host_select, dev_select, sizeof(select_info)*n, cudaMemcpyDeviceToHost);
 
    for (int i = 0; i < n; i++)
    {
        cpu_strcpy(select[i].stu_num, host_select[i].stu_num);
        select[i].exam_seat_num = host_select[i].exam_seat_num;
    }
    cudaFree(dev_stu);
    cudaFree(dev_select);
    cudaFree(dev_n);
}
int main()
{
    stu_info info_temp;
    select_info select_temp;
    int n, m,sign;
    cudaError_t cudaStatus;
    cudaStatus = cudaSetDevice(0);
    if (cudaStatus != cudaSuccess) {
        cout << "检测到你的计算机没有支持CUDA的NVIDIA的GPU设备，程序将使用CPU并行查询" << endl;
        sign = USE_CPU;
    }
    else
    {
        cout << "检测到你的计算机有支持CUDA的NVIDIA的GPU设备，程序将使用GPU并行查询" << endl;
        sign = USE_GPU;
    }
    cin >> n;
    for (int i = 0; i < n; i++)
    {
        cin >> info_temp.stu_num >> info_temp.try_seat_num >> info_temp.exam_seat_num;
        stu.push_back(info_temp);
    }
    cin >> m;
    for (int i = 0; i < m; i++)
    {
        cin >> select_temp.try_seat_num;
        select.push_back(select_temp);
    }
 
    if (sign == USE_CPU)
    {
        thread **thread_p = new thread*[m];
        int thread_id = 0;
        for (thread_id; thread_id < m; thread_id++)
        {
            thread_p[thread_id] = new thread(fun_select_cpu, thread_id, n);
            thread_p[thread_id]->detach();
        }
        delete[] thread_p;
    }
    else if (sign == USE_GPU)
    {
        fun_select_gpu(n, m, m);
    }
    for (int i = 0; i < m; i++)
    {
        cout << select[i].stu_num << " " << select[i].exam_seat_num << endl;
    }
    system("pause");
    return 0;
}

　　

 

转载于:https://www.cnblogs.com/lee-li/p/8560609.html