OpenMP 并行收集减少

此示例说明了使用 std::vector 和 OpenMP 执行缩减或收集的概念。

假设我们有一个场景，我们希望多个线程帮助我们生成一堆东西，int 在这里用于简单，可以用其他数据类型替换。

当你需要合并来自从属的结果以避免出现分段错误或内存访问冲突并且不希望使用库或自定义同步容器库时，这尤其有用。

//    The Master vector
//    We want a vector of results gathered from slave threads
std::vector<int> Master;    

//    Hint the compiler to parallelize this { } of code
//    with all available threads (usually the same as logical processor qty)
#pragma omp parallel
{
    //    In this area, you can write any code you want for each
    //    slave thread, in this case a vector to hold each of their results
    //    We don't have to worry about how many threads were spawn or if we need
    //    to repeat this declaration or not.
    std::vector<int> Slave;

    //    Tell the compiler to use all threads allocated for this parallel region
    //    to perform this loop in parts. Actual load appx = 1000000 / Thread Qty
    //    The nowait keyword tells the compiler that the slave threads don't
    //    have to wait for all other slaves to finish this for loop job
    #pragma omp for nowait
    for (size_t i = 0; i < 1000000; ++i
    {
        /* Do something */
        ....
        Slave.push_back(...);
    }

    //    Slaves that finished their part of the job
    //    will perform this thread by thread one at a time
    //    critical section ensures that only 0 or 1 thread performs
    //    the { } at any time
    #pragma omp critical
    {
        //    Merge slave into master
        //    use move iterators instead, avoid copy unless
        //    you want to use it for something else after this section
        Master.insert(Master.end(), 
                      std::make_move_iterator(Slave.begin()), 
                      std::make_move_iterator(Slave.end()));
    }
}

//    Have fun with Master vector
...