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Alternatives to std::vector in OpenMP Parallel For Loop
In OpenMP, working with a shared std::vector in a parallel for loop can pose performance challenges. This article explores potential alternatives that offer speed advantages, particularly when resizing is required during the loop's execution.
Candidate Alternatives
std::vector with OpenMP Reduction:
This approach involves using a user-defined reduction declared with #pragma omp declare reduction. The code below demonstrates how it can be applied to combine vectors in parallel:
#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 </int></int>
std::vector with Static Scheduling and Ordered Insertion:
If preserving the order of elements is critical, this technique can be employed. It utilizes a static schedule and an ordered section to insert vectors in the desired sequence:
std::vector<int> vec; #pragma omp parallel { std::vector<int> vec_private; #pragma omp for nowait schedule(static) for (int i = 0; i </int></int>
Prefix Sum Method:
This method avoids storing vectors for each thread, opting for a single vector merged in parallel. It leverages a prefix sum array to track insertion points:
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 </int></int>
These alternatives provide effective and efficient means of working with parallel for loops and resizing vectors in an OpenMP environment, surpassing the limitations posed by std::vector.
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