C++ OpenMP Parallel For Loop - Alternatives to std::vector

Question

Based on this thread, OpenMP and STL vector, which data structures are good alternatives for a shared std::vector in a parallel for loop? The main aspect is speed, and the vector might require resizing during the loop.

Answer 1

I think you can use std::vector with OpenMP most of the time and still have good performance. The following code for example fills std::vectors in parallel and then combines them in the end. As long as your main loop/fill function is the bottleneck this should work well in general and be thread safe.

std::vector<int> vec;
#pragma omp parallel
{
    std::vector<int> vec_private;
    #pragma omp for nowait //fill vec_private in parallel
    for(int i=0; i<100; i++) {
        vec_private.push_back(i);
    }
    #pragma omp critical
    vec.insert(vec.end(), vec_private.begin(), vec_private.end());
}

Edit:

OpenMP 4.0 allows user-defined reductions using #pragma omp declare reduction. The code above can be simplified with to

#pragma omp declare reduction (merge : std::vector<int> : omp_out.insert(omp_out.end(), omp_in.begin(), omp_in.end()))

std::vector<int> vec;
#pragma omp parallel for reduction(merge: vec)
for(int i=0; i<100; i++) vec.push_back(i);

Edit: What I have shown so far does not fill the vector in order. If the order matters then this can be done like this

std::vector<int> vec;
#pragma omp parallel
{
    std::vector<int> vec_private;
    #pragma omp for nowait schedule(static)
    for(int i=0; i<N; i++) { 
        vec_private.push_back(i);
    }
    #pragma omp for schedule(static) ordered
    for(int i=0; i<omp_get_num_threads(); i++) {
        #pragma omp ordered
        vec.insert(vec.end(), vec_private.begin(), vec_private.end());
    }
}

This avoids saving a std::vector for each thread and then merging them in serial outside of the parallel region. I learned about this "trick" here. I'm not sure how to do this (or if it's even possible) for user-defined reductions.. It's not possible to do this with user-defined reductions.

I just realized that the critical section is not necessary which I figured out from this question parallel-cumulative-prefix-sums-in-openmp-communicating-values-between-thread. This method also gets the order correct as well

std::vector<int> vec;
size_t *prefix;
#pragma omp parallel
{
    int ithread  = omp_get_thread_num();
    int nthreads = omp_get_num_threads();
    #pragma omp single
    {
        prefix = new size_t[nthreads+1];
        prefix[0] = 0;
    }
    std::vector<int> vec_private;
    #pragma omp for schedule(static) nowait
    for(int i=0; i<100; i++) {
        vec_private.push_back(i);
    }
    prefix[ithread+1] = vec_private.size();
    #pragma omp barrier
    #pragma omp single 
    {
        for(int i=1; i<(nthreads+1); i++) prefix[i] += prefix[i-1];
        vec.resize(vec.size() + prefix[nthreads]);
    }
    std::copy(vec_private.begin(), vec_private.end(), vec.begin() + prefix[ithread]);
}
delete[] prefix;