How to let different threads fill an array together?

Question

Suppose I have some tasks (Monte Carlo simulations) that I want to run in parallel. I want to complete a given number of tasks, but tasks take different amount of time so not easy to divide the work evenly over the threads. Also: I need the results of all simulations in a single vector (or array) in the end.

So I come up with below approach:

 int Max{1000000};
 //SimResult is some struct with well-defined default value.
 std::vector<SimResult> vec(/*length*/Max);//Initialize with default values of SimResult
 int LastAdded{0};
 void fill(int RandSeed)
 { 
      Simulator sim{RandSeed};
      while(LastAdded < Max)
      {
           // Do some work to bring foo to the desired state
           //The duration of this work is subject to randomness
           vec[LastAdded++] 
                 = sim.GetResult();//Produces SimResult. 
      }
 }
 main()
 { 
       //launch a bunch of std::async that start
       auto fut1 = std::async(fill,1);
       auto fut2 = std::async(fill,2);
       //maybe some more tasks.


      fut1.get();
      fut2.get();
      //do something with the results in vec. 
 }

The above code will give race conditions I guess. I am looking for a performant approach to avoid that. Requirements: avoid race conditions (fill the entire array, no skips) ; final result is immediately in array ; performant.

Reading on various approaches, it seems atomic is a good candidate, but I am not sure what settings will be most performant in my case? And not even sure whether atomic will cut it; maybe a mutex guarding LastAdded is needed?

Answer 1

Since you already know how many elements your are going to work with and never change the size of the vector, the easiest solution is to let each thread work on it's own part of the vector. For example

Update

to accomodate for vastly varying calculation times, you should keep your current code, but avoid race conditions via a std::lock_guard. You will need a std::mutex that is the same for all threads, for example a global variable, or pass a reference of the mutex to each thread.

void fill(int RandSeed, std::mutex &nextItemMutex)
{ 
      Simulator sim{RandSeed};
      size_t workingIndex;
      while(true)
      {
          {
               // enter critical area
               std::lock_guard<std::mutex> nextItemLock(nextItemMutex);

               // Acquire next item
               if(LastAdded < Max)
               {
                   workingIndex = LastAdded;
                   LastAdded++;
               } 
               else 
               {
                   break;
               }
               // lock is released when nextItemLock goes out of scope
          }

           // Do some work to bring foo to the desired state
           // The duration of this work is subject to randomness
           vec[workingIndex] = sim.GetResult();//Produces SimResult. 
      }
 }

Problem with this is, that snychronisation is quite expensive. But it's probably not that expensive in comparison to the simulation you run, so it shouldn't be too bad.

Version 2:

To reduce the amount of synchronisation that is required, you could acquire blocks to work on, instead of single items:

void fill(int RandSeed, std::mutex &nextItemMutex, size_t blockSize)
{ 
      Simulator sim{RandSeed};
      size_t workingIndex;
      while(true)
      {
          {
               std::lock_guard<std::mutex> nextItemLock(nextItemMutex);

               if(LastAdded < Max)
               {
                   workingIndex = LastAdded;
                   LastAdded += blockSize;
               } 
               else 
               {
                   break;
               }
          }
          
          for(size_t i = workingIndex; i < workingIndex + blockSize && i < MAX; i++)
              vec[i] = sim.GetResult();//Produces SimResult. 
      }
 }

---

Simple Version

void fill(int RandSeed, size_t partitionStart, size_t partitionEnd)
{ 
      Simulator sim{RandSeed};
      for(size_t i = partitionStart; i < partitionEnd; i++)
      {
           // Do some work to bring foo to the desired state
           // The duration of this work is subject to randomness
           vec[i] = sim.GetResult();//Produces SimResult. 
      }
 }

main()
{ 
    //launch a bunch of std::async that start
    auto fut1 = std::async(fill,1, 0, Max / 2);
    auto fut2 = std::async(fill,2, Max / 2, Max);

    // ...
}

Answer 2

One thing I would say is that you need to be very careful with the standard library random number functions. If your 'Simulator' class creates an instance of a generator, you should not run Monte Carlo simulations in parallel using the same object, because you'll get likely get repeated patterns of random numbers between the runs, which will give you inaccurate results.

The best practice in this area would be to create N Simulator objects with the same properties, and give each one a different random seed. Then you could pool these objects out over multiple threads using OpenMP, which is a common parallel programming model for scientific software development.

std::vector<SimResult> generateResults(size_t N_runs, double seed) 
{
    std::vector<SimResult> results(N_runs);
    #pragma omp parallel for
    for(auto i = 0; i < N_runs; i++)
    {
        auto sim = Simulator(seed + i);
        results[i] = sim.GetResult();
    }
}

Edit: With OpenMP, you can choose different scheduling models, which allow you to for e.g. dynamically split work between threads. You can do this with:

#pragma omp parallel for schedule(dynamic, 16)

which would give each thread chunks of 16 items to work on at a time.