What are the Best Alternatives to `std::vector` in OpenMP Parallel For Loops?

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

OpenMP's parallel for loops provide a convenient way to parallelize code. However, using shared data structures within these loops can introduce performance bottlenecks. One commonly used data structure, std::vector, may not sempre be the best choice for shared use in parallel loops.

Alternatives to std::vector

For optimal performance and thread safety in parallel for loops, consider these alternatives to std::vector:

std::vector with User-Defined Reductions

OpenMP 4.0 introduces user-defined reductions, allowing you to define custom reduction operations for custom data structures. This approach can improve performance by avoiding the overhead of locking shared data.

Example:

#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 <p><strong>Ordered Vector</strong></p>
<p>If the order of elements in the shared vector is crucial, consider the following approach:</p>
<pre class="brush:php;toolbar:false">std::vector<int> vec;
#pragma omp parallel
{
    std::vector<int> vec_private;
    #pragma omp for schedule(static) nowait
    for (int i = 0; i <p><strong>Custom Parallel Vector Class</strong></p>
<p>For complex shared data structures, you may need to implement custom parallel vector classes to handle resizing during the loop while ensuring thread safety and efficient performance.</p>
<p><strong>Example:</strong></p>
<pre class="brush:php;toolbar:false">class ParallelVector {
private:
    std::vector<int> data;
    std::atomic<size_t> size;

public:
    void push_back(int value) {
        size++;
        data.push_back(value);
    }

    size_t getSize() {
        return size.load();
    }
};

ParallelVector vec;

#pragma omp parallel
{
    #pragma omp for
    for (int i = 0; i <p>The choice of alternative to std::vector depends on the specific requirements of your parallel loop. Consider factors such as thread safety, performance, and ease of implementation to select the most appropriate solution for your application.</p></size_t></int>

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