OpenMP 並行收集減少

此示例說明了使用 std::vector 和 OpenMP 執行縮減或收集的概念。

假設我們有一個場景，我們希望多個執行緒幫助我們生成一堆東西，int 在這裡用於簡單，可以用其他資料型別替換。

當你需要合併來自從屬的結果以避免出現分段錯誤或記憶體訪問衝突並且不希望使用庫或自定義同步容器庫時，這尤其有用。

//    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
...