What are the efficient alternatives to `std::vector` when using OpenMP parallel for loops, especially when resizing is needed?

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 < 100; i++) vec.push_back(i);

• 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 < 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());
      }
  }

• 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 < 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;

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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