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A similar interface to the executor service was added in Java 7 that included functionality to split a task into smaller subtasks and re-enqueue them into the thread pool. This is particularly useful for more intensive tasks and actually implements parallelism to do it (we’ll touch on this more in the next exercise).

This new interface is part of a Fork-Join framework and is called ForkJoinPool, which distributes a task across several worker threads and waits for a result.

Another big difference is the type of task object we’ll be passing to this pool. Instead of implementing the Runnable class, we’ll actually be extending either RecursiveAction or RecursiveTask depending on the implementation we want.

Here is the difference between the two:

  • RecursiveAction: does not return any result
  • RecursiveTask: returns a result

As the namesake suggests, the Fork-Join framework utilizes recursion to perform its subtask splitting, which we’ll limit by defining a set number of threads. This controls how many times the task is allowed to split itself since each subtask will be assigned to an individual thread.

Additionally, instead of calling execute() as we did before to add new tasks to the thread pool queue, we’ll be calling invoke(). Since we’re splitting a singular task across the worker threads, we only have to call invoke() once, which means we can invoke the task in the same place we create the pool itself:

private static final int N = 4; ForkJoinExecutor pool = new ForkJoinPool(N); pool.invoke(task);

Instructions

1.

For this exercise, we’re going to do something generally very computationally heavy. We’re going to compute the factorial of a very, very large number! Let’s go out on a limb and say… 5000!!

That probably sounds scary, but don’t worry, a simple for loop can actually compute this serially in less than 100 ms. The result however is so big that we need to utilize Java’s BigInteger class to compute and display it, otherwise the normal primitives overflow.

MakeBigIntArray.java has been completed for you, and given the complexity of this exercise, most of RecursiveFactorial.java has also been provided. We’re going to focus on the overridden compute() method inside RecursiveFactorial.java.

  • In compute(), create an if / else statement. Since we’re utilizing RecursiveAction to compute our results recursively, we’ll want a stopping point. To do this, make your if condition list.length == 1. If this is true, set result = list[0].

In the else case, we have a lot more work to do. For instance, we need to perform the actual forking and joining as we reduce our problem set. But first, the reduction process.

  • Inside your else statement, create a local int called midpoint and calculate the midpoint index of list (you can let int naturally truncate this). Using this midpoint, create two new BigInterger arrays and set their values as follows:
BigInteger[] l1 = Arrays.copyOfRange(list, 0, midpoint); BigInteger[] l2 = Arrays.copyOfRange(list, midpoint, list.length);
  • Now, the recursive part. Still within the else, create two new RecursiveFactorial objects called rf1 and rf2 and pass l1 inside the first one’s parameter and l2 inside the second one. Once created, fork() both rf1 and rf2 then join() them in the same order after forking.

The act of doing this will cascade the recursive aspects of our code via creating new threads and waiting for them to finish.

  • Lastly, add this as the final line of the else block:
result = rf1.result.multiply(rf2.result);

This performs the factorial multiplication each step of the way utilizing BigIntegers mulitply() method.

2.

So, with all that uncontrollable forking, how do we actually limit how many threads exist at any given time? We’ll be using a ForkJoinPool to do this. Think of this like the Executor framework’s thread pool.

A quick way to avoid eating up too much RAM is to just make a call to the running JVM and ask it how many available processors it has. This way, the program will never try to use more resources than it has.

We can do this by calling this line and storing it in an int called nThreads:

int nThreads = Runtime.getRuntime().availableProcessors();
  • In Main.java, print nThreads to get a grasp of how many processors the current JVM has during runtime (note: you don’t have to write any code for this step, you can just run Main.java).

Run your code and see what that number is.

3.
  • Now that we’ve established a safe limit for our number of threads, create a RecursiveFactorial object called rSum in Main.java and pass it test.getList(). For context, test currently contains an array of BigInteger objects that range from 1 to 5000.

  • After, create a ForkJoinPool called pool and pass it nThreads. This will establish that safe upper bound we found earlier.

  • To invoke the pool, call:

pool.invoke(rSum);

This acts like execute from the Executor framework and enqueues the recursive action we coded into the pool’s queue. The threads now process that task and continuously break it down into subtasks until it’s done.

Because we’ve embedded some joins into the recursive class being processed, calling invoke() will naturally wait until all processing is done.

  • After invoking the pool, create a BigInteger called result and assign it the value rSum.result. Then print result as follows:
System.out.println("Pooled Result: " + result);

Run your code and compare the result to the serially printed result computed in the for loop. Take note of the computed time in ms as well.

4.
  • Let’s see how the ForkJoinPool compares to serial processing. On the lines directly before and after pool.invoke(rSum), add these:
start = clock.millis(); pool.invoke(rSum); stop = clock.millis();
  • Print this time in ms on the line preceding the pooled result:
System.out.println("Time in ms: " + (stop - start));

Run your code and compare the speed difference between the methods.

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