c++ - 在并行区域内有选择地启用 OpenMP for 循环

Question

是否可以使用模板参数或运行时变量有选择地启用 openmp 指令？

this (all threads work on the same for loop).
#pragma omp parallel
{
    #pragma omp for
    for (int i = 0; i < 10; ++i) { /*...*/ }
}
versus this (each thread works on its own for loop)
#pragma omp parallel
{
    for (int i = 0; i < 10; ++i) { /*...*/ }
}

---

更新(测试if子句)

测试.cpp:

#include <iostream>
#include <omp.h>

int main() {
    bool var = true;
    #pragma omp parallel 
    {
        #pragma omp for if (var)
        for (int i = 0; i < 4; ++i) {
            std::cout << omp_get_thread_num() << "\n";
        }
    }
}

错误消息(g++ 6，使用 g++ test.cpp -fopenmp 编译)

test.cpp: In function ‘int main()’:
test.cpp:8:25: error: ‘if’ is not valid for ‘#pragma omp for’
         #pragma omp for if (var)
                         ^~

Answer 1

有点变通。不知道是否可以摆脱获取线程 ID 的条件。

#include <iostream>
#include <omp.h>
#include <sstream>
#include <vector>
int main() {
    constexpr bool var = true;
    int n_threads = omp_get_num_procs();
    std::cout << "n_threads: " << n_threads << "\n";
    std::vector<std::stringstream> s(omp_get_num_procs());

    #pragma omp parallel if (var) 
    {

        const int thread_id0 = omp_get_thread_num();
        #pragma omp parallel
        {
            int thread_id1;
            if (var) {
                thread_id1 = thread_id0;
            } else {
                thread_id1 = omp_get_thread_num();
            }

            #pragma omp for
            for (int i = 0; i < 8; ++i) {
                s[thread_id1] << i << ", ";
            }
        }
    }

    for (int i = 0; i < s.size(); ++i) {
        std::cout << "thread " << i << ": " 
                  << s[i].str() << "\n";
    }
}

输出(当 var == true 时):

n_threads: 8
thread 0: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 1: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 2: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 3: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 4: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 5: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 6: 0, 1, 2, 3, 4, 5, 6, 7, 
thread 7: 0, 1, 2, 3, 4, 5, 6, 7,

输出(当 var == false 时):

n_threads: 8
thread 0: 0, 
thread 1: 1, 
thread 2: 2, 
thread 3: 3, 
thread 4: 4, 
thread 5: 5, 
thread 6: 6, 
thread 7: 7,