This last code performs the exact same function as our originally posted code. The first change, however, is on line three. Here, we import the process from the multiprocessing module. Our following, the calculatePrimeFactors method has not been touched.
You should then see that we pulled out the for loop that initially ran for 10,000 iterations. We now placed this in a function called executeProc, and we also reduced our for loops range to 1,000.
Within the main function, we then create an empty array called procs. We then create 10 different processes, and set the target to be the executeProc function, and pass in no args. We append this newly created process to our procs arrays, and then we start the process by calling proc.start().
After we've created 10 individual processes, we then cycle through these processes which are now in our procs array, and join them. This ensures that every process has finished its calculations before we proceed to calculate the total execution time.
If you execute this now, you should see the 10,000 outputs now print out in your console, and you should also see a far lower execution time when compared to your sequential execution. For reference, the sequential program executed in 3.9 seconds on my computer compared to 1.9 seconds when running the multiprocessing version.
This is just a very basic demonstration as to how we can implement multiprocessing into our applications. In future chapters, we'll explore how we can create pools and utilize executors. The key point to take away from this is that we can improve the performance of some CPU-bound tasks by utilizing multiple cores.
