Java Concurrency: Fork/Join Framework and Divide-and-Conquer Parallelism

🔰 Introduction to Java Fork/Join Framework

The Fork/Join Framework is a powerful concurrency tool introduced in Java 7 that implements the divide-and-conquer paradigm for parallel processing. It's designed to efficiently handle recursive, computation-intensive tasks by breaking them down into smaller subtasks that can be processed in parallel across multiple CPU cores.

At its core, the Fork/Join Framework follows a simple principle: when faced with a large task, divide it into smaller, more manageable pieces (fork), process them concurrently, and then combine their results (join). This approach is particularly effective for problems that can be naturally decomposed into similar subproblems, such as sorting large arrays, processing large datasets, or traversing complex data structures.

The framework provides a special thread pool called ForkJoinPool that manages worker threads using a work-stealing algorithm. This means that idle threads can "steal" tasks from busy threads' queues, ensuring efficient CPU utilization and load balancing.

Unlike traditional thread pools, the Fork/Join Framework is optimized for tasks that spawn other tasks, making it ideal for recursive algorithms. It's built around the concept of lightweight tasks rather than threads, allowing you to create thousands or even millions of tasks without overwhelming system resources.

Note: While this tutorial doesn't focus on "read-heavy access," it's worth mentioning that the Fork/Join Framework is particularly useful for CPU-bound tasks rather than I/O-bound operations. For read-heavy workloads (where most operations involve reading data rather than writing), other concurrency patterns might be more appropriate.

🧠 Java Fork/Join Framework: Detailed Explanation

🧩 Core Components of the Java Fork/Join Framework

The Fork/Join Framework consists of several key components:

1. ForkJoinPool: A specialized thread pool that manages worker threads and task scheduling using the work-stealing algorithm.

2. ForkJoinTask: An abstract base class for tasks that run within the ForkJoinPool. It provides methods for task splitting, execution, and result joining.

3. RecursiveTask: A concrete implementation of ForkJoinTask that returns a result.

4. RecursiveAction: A concrete implementation of ForkJoinTask that doesn't return a result (void).

5. CountedCompleter: A more advanced implementation of ForkJoinTask that triggers an action when all tasks complete.

Let's explore each of these components in more detail:

🔄 ForkJoinPool in Java

The ForkJoinPool is the execution environment for fork/join tasks. It manages a pool of worker threads and distributes tasks among them using a work-stealing algorithm.

Key characteristics of ForkJoinPool:

• Work-Stealing Algorithm: Idle threads can take tasks from busy threads' queues, ensuring efficient load balancing.
• Parallelism Level: By default, it creates as many threads as there are processors available to the JVM.
• Common Pool: Java 8 introduced a global, static ForkJoinPool called the "common pool" that's used by parallel streams and other components.

Creating a ForkJoinPool:

// Create a pool with default parallelism (number of available processors)
ForkJoinPool defaultPool = new ForkJoinPool();

// Create a pool with custom parallelism
ForkJoinPool customPool = new ForkJoinPool(4); // 4 worker threads

// Access the common pool (Java 8+)
ForkJoinPool commonPool = ForkJoinPool.commonPool();

🔄 ForkJoinTask in Java

ForkJoinTask is the base class for tasks that run within a ForkJoinPool. It provides methods for forking (asynchronous execution) and joining (waiting for completion and getting results).

Key methods in ForkJoinTask:

• fork(): Submits the task for asynchronous execution.
• join(): Returns the result of the computation when it is done.
• invoke(): Executes the task and returns the result.
• invokeAll(): Executes multiple tasks and waits for all of them to complete.

📝 Analogy: Think of fork() as delegating a task to a colleague and continuing with your own work, while join() is like asking that colleague for their results when you need them.

🔄 RecursiveTask and RecursiveAction in Java

These are the two main concrete implementations of ForkJoinTask:

• RecursiveTask: For tasks that return a result of type V.
• RecursiveAction: For tasks that don't return a result (void).

Both classes require you to implement the compute() method, which contains the task's logic, including the decision of whether to split the task further or compute it directly.

📝 Analogy: A RecursiveTask is like a math problem that needs to be solved and returns an answer, while a RecursiveAction is like a chore that needs to be done but doesn't produce a specific result.

🧩 The Fork/Join Pattern in Java

The typical pattern for implementing a fork/join task follows these steps:

1. Check the base case: If the task is small enough, compute it directly.
2. Split the task: If the task is too large, divide it into subtasks.
3. Fork subtasks: Submit the subtasks for asynchronous execution.
4. Join results: Wait for the subtasks to complete and combine their results.

This pattern is often implemented with a threshold that determines when a task is small enough to be computed directly rather than split further.

📝 Analogy: This is similar to how you might organize a large house-cleaning project. If the job is small (clean one room), you do it yourself. If it's large (clean the whole house), you divide it among family members, let everyone work on their part, and then combine the results (a clean house).

🧩 Work-Stealing Algorithm in Java

One of the most powerful features of the Fork/Join Framework is its work-stealing algorithm:

• Each worker thread maintains a double-ended queue (deque) of tasks.
• When a thread creates subtasks, it pushes them onto the front of its own deque.
• When a thread finishes its current task, it takes the next task from the front of its own deque.
• When a thread's deque is empty, it "steals" a task from the back of another thread's deque.

This approach minimizes contention because most of the time, threads work with their own deques. Stealing only happens when a thread runs out of work.

📝 Analogy: Imagine a team of workers, each with their own to-do list. When workers finish their own tasks, instead of standing idle, they help their busiest colleagues by taking tasks from the bottom of their lists.

🧩 Divide-and-Conquer Strategy in Java

The divide-and-conquer strategy is a general algorithm design paradigm that works as follows:

1. Divide: Break the problem into smaller, independent subproblems.
2. Conquer: Solve the subproblems recursively.
3. Combine: Merge the solutions of the subproblems to create the solution to the original problem.

This strategy is particularly well-suited for parallel processing because the subproblems can be solved independently.

Common examples of divide-and-conquer algorithms include:

• Merge sort and quicksort
• Binary search
• Matrix multiplication
• Closest pair of points
• Fast Fourier Transform (FFT)

The Fork/Join Framework provides an efficient way to parallelize these algorithms by handling the distribution of subtasks across multiple threads.

💻 Java Fork/Join Framework: Complete Code Example

import java.util.concurrent.ForkJoinPool;
import java.util.concurrent.RecursiveTask;
import java.util.Arrays;

public class ArraySumExample {
    // Threshold for when to switch to sequential processing
    private static final int THRESHOLD = 1000;
    
    public static void main(String[] args) {
        // Create a large array of numbers
        int[] numbers = new int[1_000_000];
        for (int i = 0; i < numbers.length; i++) {
            numbers[i] = i;
        }
        
        // Create the ForkJoinPool (uses common pool in Java 8+)
        ForkJoinPool pool = ForkJoinPool.commonPool();
        
        // Create the task
        SumTask task = new SumTask(numbers, 0, numbers.length);
        
        // Execute the task and get the result
        long sum = pool.invoke(task);
        
        System.out.println("Sum: " + sum);
        
        // Verify with sequential calculation
        long expectedSum = 0;
        for (int n : numbers) {
            expectedSum += n;
        }
        System.out.println("Expected sum: " + expectedSum);
    }
    
    // RecursiveTask that computes the sum of an array segment
    static class SumTask extends RecursiveTask<Long> {
        private final int[] array;
        private final int start;
        private final int end;
        
        SumTask(int[] array, int start, int end) {
            this.array = array;
            this.start = start;
            this.end = end;
        }
        
        @Override
        protected Long compute() {
            // Base case: if the segment is small enough, compute directly
            if (end - start <= THRESHOLD) {
                long sum = 0;
                for (int i = start; i < end; i++) {
                    sum += array[i];
                }
                return sum;
            } else {
                // Divide the task into two subtasks
                int mid = start + (end - start) / 2;
                
                // Create and fork the first subtask
                SumTask leftTask = new SumTask(array, start, mid);
                leftTask.fork();
                
                // Create and compute the second subtask directly
                SumTask rightTask = new SumTask(array, mid, end);
                long rightResult = rightTask.compute();
                
                // Join the result of the first subtask
                long leftResult = leftTask.join();
                
                // Combine the results
                return leftResult + rightResult;
            }
        }
    }
}

📦 Java Fork/Join Code Snippets and Examples

Creating a Java RecursiveAction (No Return Value)

import java.util.concurrent.RecursiveAction;
import java.util.concurrent.ForkJoinPool;

public class ArrayTransformExample {
    public static void main(String[] args) {
        double[] array = new double[10000];
        for (int i = 0; i < array.length; i++) {
            array[i] = i;
        }
        
        TransformAction task = new TransformAction(array, 0, array.length);
        ForkJoinPool.commonPool().invoke(task);
        
        System.out.println("First few values after transformation:");
        for (int i = 0; i < 10; i++) {
            System.out.println(array[i]);
        }
    }
    
    static class TransformAction extends RecursiveAction {
        private final double[] array;
        private final int start;
        private final int end;
        private static final int THRESHOLD = 1000;
        
        TransformAction(double[] array, int start, int end) {
            this.array = array;
            this.start = start;
            this.end = end;
        }
        
        @Override
        protected void compute() {
            if (end - start <= THRESHOLD) {
                // Base case: transform the array segment directly
                for (int i = start; i < end; i++) {
                    array[i] = Math.sqrt(array[i]);
                }
            } else {
                // Divide the task
                int mid = start + (end - start) / 2;
                invokeAll(
                    new TransformAction(array, start, mid),
                    new TransformAction(array, mid, end)
                );
            }
        }
    }
}

Using Java CountedCompleter for Complex Task Coordination

import java.util.concurrent.CountedCompleter;
import java.util.concurrent.ForkJoinPool;
import java.util.concurrent.atomic.AtomicReference;

public class MaxFinderExample {
    public static void main(String[] args) {
        int[] array = new int[10000];
        for (int i = 0; i < array.length; i++) {
            array[i] = (int) (Math.random() * 1000000);
        }
        
        AtomicReference<Integer> result = new AtomicReference<>(Integer.MIN_VALUE);
        MaxFinder task = new MaxFinder(null, array, 0, array.length, result);
        ForkJoinPool.commonPool().invoke(task);
        
        System.out.println("Maximum value: " + result.get());
    }
    
    static class MaxFinder extends CountedCompleter<Void> {
        private final int[] array;
        private final int start;
        private final int end;
        private final AtomicReference<Integer> result;
        private static final int THRESHOLD = 1000;
        
        MaxFinder(MaxFinder parent, int[] array, int start, int end, 
                 AtomicReference<Integer> result) {
            super(parent);
            this.array = array;
            this.start = start;
            this.end = end;
            this.result = result;
        }
        
        @Override
        public void compute() {
            if (end - start <= THRESHOLD) {
                // Find max in this segment
                int max = Integer.MIN_VALUE;
                for (int i = start; i < end; i++) {
                    if (array[i] > max) {
                        max = array[i];
                    }
                }
                // Update the result atomically if this max is larger
                updateMax(max);
            } else {
                // Divide the task
                int mid = start + (end - start) / 2;
                // Pending count is 2 because we're creating 2 subtasks
                setPendingCount(2);
                new MaxFinder(this, array, start, mid, result).fork();
                new MaxFinder(this, array, mid, end, result).fork();
            }
            // This will execute after all subtasks complete
            tryComplete();
        }
        
        private void updateMax(int value) {
            // Atomically update the maximum value
            result.updateAndGet(current -> Math.max(current, value));
        }
    }
}

Using Java Common Pool with Parallel Streams

import java.util.Arrays;
import java.util.List;

public class ParallelStreamExample {
    public static void main(String[] args) {
        List<Integer> numbers = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10);
        
        // Sequential stream
        long startSeq = System.nanoTime();
        int sumSeq = numbers.stream()
                           .map(n -> computeExpensive(n))
                           .reduce(0, Integer::sum);
        long endSeq = System.nanoTime();
        
        // Parallel stream (uses ForkJoinPool.commonPool())
        long startPar = System.nanoTime();
        int sumPar = numbers.parallelStream()
                           .map(n -> computeExpensive(n))
                           .reduce(0, Integer::sum);
        long endPar = System.nanoTime();
        
        System.out.println("Sequential result: " + sumSeq + 
                          " in " + (endSeq - startSeq) / 1_000_000 + " ms");
        System.out.println("Parallel result: " + sumPar + 
                          " in " + (endPar - startPar) / 1_000_000 + " ms");
    }
    
    private static int computeExpensive(int n) {
        // Simulate an expensive computation
        try {
            Thread.sleep(100);
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
        }
        return n * n;
    }
}

🚀 Why Java Fork/Join Framework Matters: Real-World Use Cases

The Fork/Join Framework and divide-and-conquer parallelism are powerful tools for improving application performance in several key scenarios:

1. Big Data Processing

When dealing with large datasets, the ability to process data in parallel can significantly reduce computation time:

• Data Analysis: Calculating statistics, finding patterns, or applying transformations to large datasets.
• Machine Learning: Training models on large datasets by parallelizing the computation of gradients or feature extraction.
• ETL Processes: Extracting, transforming, and loading large volumes of data in parallel.

Real-world example: A financial institution analyzing millions of transactions to detect fraud patterns can use the Fork/Join Framework to divide the dataset into chunks and process them in parallel, reducing analysis time from hours to minutes.

2. Scientific Computing

Scientific applications often involve computationally intensive algorithms that can benefit from parallelization:

• Numerical Simulations: Physics simulations, weather modeling, or fluid dynamics.
• Image Processing: Medical imaging, satellite imagery analysis, or computer vision.
• Genetic Algorithms: Evolutionary computations with large populations.

Real-world example: A research lab processing high-resolution microscopy images can use divide-and-conquer to split images into tiles, process each tile in parallel, and then combine the results, enabling real-time analysis of living cells.

3. Graph and Tree Algorithms

Many algorithms that operate on hierarchical data structures can be naturally expressed using the divide-and-conquer approach:

• Graph Traversal: Breadth-first or depth-first search of large graphs.
• Tree Operations: Searching, counting, or transforming elements in large trees.
• Pathfinding: Finding shortest paths in complex networks.

Real-world example: A social network analyzing connection patterns can traverse its graph of billions of users and connections in parallel, identifying communities and influence patterns much faster than with sequential processing.

4. Performance-Critical Applications

Applications where response time is critical can leverage parallelism to meet performance requirements:

• Gaming Engines: Physics calculations, AI decision-making, or rendering.
• Financial Trading Systems: Real-time market analysis and automated trading decisions.
• Search Engines: Parallel query processing across distributed indexes.

Real-world example: A high-frequency trading platform can use the Fork/Join Framework to analyze market conditions across multiple instruments simultaneously, making trading decisions within microseconds.

5. System Scalability

As systems grow, the ability to scale computation across multiple cores becomes increasingly important:

• Web Servers: Handling multiple requests concurrently.
• Database Systems: Executing complex queries in parallel.
• Content Delivery Networks: Processing and optimizing content for delivery.

Real-world example: A content management system generating dynamic pages can parallelize the rendering process, allowing it to handle more concurrent users without increasing response time.

🧭 Java Fork/Join Framework: Best Practices

1. Task Granularity and Thresholds

✅ DO:

• Use appropriate thresholds to determine when to stop dividing tasks.
• Adjust thresholds based on the specific workload and hardware.
• Consider the overhead of task creation and management.

❌ DON'T:

• Create tasks that are too small, as the overhead will outweigh the benefits.
• Use a fixed threshold for all applications; it should be tuned for each use case.
• Ignore the cost of splitting and merging when determining thresholds.

// GOOD: Adaptive threshold based on array size
int threshold = array.length / (Runtime.getRuntime().availableProcessors() * 2);
threshold = Math.max(threshold, 1000); // Minimum threshold

// BAD: Fixed small threshold regardless of problem size
int threshold = 10; // Too small for most problems

2. Task Design and Implementation

✅ DO:

• Make tasks stateless and independent to avoid synchronization issues.
• Implement the compute() method to handle both the base case and the recursive case.
• Use local variables within tasks to avoid shared mutable state.

❌ DON'T:

• Share mutable state between tasks without proper synchronization.
• Create tasks with side effects that depend on execution order.
• Implement tasks that block on external resources.

// GOOD: Independent task with local state
class GoodTask extends RecursiveTask<Integer> {
    private final int[] array;
    private final int start, end;
    
    @Override
    protected Integer compute() {
        // Implementation using only local variables
    }
}

// BAD: Task with shared mutable state
class BadTask extends RecursiveTask<Integer> {
    private static int sharedCounter; // Shared mutable state
    
    @Override
    protected Integer compute() {
        // Implementation that modifies sharedCounter
    }
}

3. Fork and Join Operations

✅ DO:

• Fork subtasks before computing anything locally.
• Use invokeAll() when forking multiple subtasks.
• Consider computing one subtask directly instead of forking all subtasks.

❌ DON'T:

• Call join() immediately after fork(); this defeats the purpose of parallelism.
• Forget to join all forked subtasks.
• Use get() instead of join() for ForkJoinTasks.

// GOOD: Fork one task, compute the other directly
leftTask.fork();
long rightResult = rightTask.compute();
long leftResult = leftTask.join();

// BAD: Sequential execution disguised as parallel
leftTask.fork();
leftResult = leftTask.join(); // Immediately waiting defeats parallelism
rightTask.fork();
rightResult = rightTask.join();

4. Exception Handling

✅ DO:

• Handle exceptions within tasks when appropriate.
• Use invokeAll() to propagate exceptions automatically.
• Check for exceptions with ForkJoinTask.isCompletedAbnormally() if needed.

❌ DON'T:

• Swallow exceptions within tasks.
• Ignore the fact that join() doesn't throw the actual exception but wraps it.
• Forget that uncaught exceptions in tasks can terminate the worker thread.

// GOOD: Proper exception handling
try {
    result = task.invoke();
} catch (RuntimeException e) {
    // Handle the exception
    Throwable cause = e.getCause(); // Get the actual cause
    handleException(cause);
}

// BAD: Ignoring potential exceptions
result = task.join(); // If task threw an exception, this will wrap it

5. Resource Management

✅ DO:

• Reuse ForkJoinPools when appropriate.
• Consider using the common pool for most applications.
• Shut down custom pools when they're no longer needed.

❌ DON'T:

• Create a new ForkJoinPool for every operation.
• Shut down the common pool.
• Create pools with excessive parallelism levels.

// GOOD: Reuse the common pool
ForkJoinPool commonPool = ForkJoinPool.commonPool();
result1 = commonPool.invoke(task1);
result2 = commonPool.invoke(task2);

// BAD: Creating a new pool for each task
ForkJoinPool pool1 = new ForkJoinPool();
result1 = pool1.invoke(task1);
pool1.shutdown();

ForkJoinPool pool2 = new ForkJoinPool();
result2 = pool2.invoke(task2);
pool2.shutdown();

⚠️ Common Pitfalls with Java Fork/Join Framework

1. Task Granularity Issues

One of the most common mistakes is creating tasks that are too small or too large.

// PROBLEMATIC: Too fine-grained tasks
if (end - start > 1) { // Splitting until individual elements
    int mid = start + (end - start) / 2;
    invokeAll(new SumTask(array, start, mid),
              new SumTask(array, mid, end));
}

Why it fails: The overhead of creating and managing tasks outweighs the benefits of parallelism for very small tasks.

Solution: Use an appropriate threshold based on the workload and available processors.

2. Sequential Bottlenecks

Introducing sequential operations in parallel tasks can severely limit scalability.

// PROBLEMATIC: Sequential bottleneck
protected Long compute() {
    if (end - start <= THRESHOLD) {
        return computeDirectly();
    } else {
        int mid = start + (end - start) / 2;
        SumTask left = new SumTask(array, start, mid);
        SumTask right = new SumTask(array, mid, end);
        
        // Sequential bottleneck: waiting for left before starting right
        long leftResult = left.invoke();
        long rightResult = right.invoke();
        
        return leftResult + rightResult;
    }
}

Why it matters: This code executes the subtasks sequentially, defeating the purpose of the Fork/Join Framework.

Solution: Fork one task and compute the other directly, or use invokeAll() to fork both tasks.

3. Inappropriate Use of the Framework

Using the Fork/Join Framework for I/O-bound or blocking operations is a common mistake.

// PROBLEMATIC: I/O-bound tasks in Fork/Join
class FileProcessingTask extends RecursiveAction {
    @Override
    protected void compute() {
        // Reading from a file - I/O bound operation
        try (BufferedReader reader = new BufferedReader(new FileReader(file))) {
            String line;
            while ((line = reader.readLine()) != null) {
                processLine(line);
            }
        } catch (IOException e) {
            throw new RuntimeException(e);
        }
    }
}

Why it fails: The Fork/Join Framework is optimized for CPU-bound tasks. I/O-bound tasks waste threads by blocking them.

Solution: Use other concurrency utilities like CompletableFuture or dedicated thread pools for I/O-bound operations.

4. Shared Mutable State

Modifying shared state from multiple tasks without proper synchronization leads to race conditions.

// PROBLEMATIC: Shared mutable state
class CounterTask extends RecursiveAction {
    private static int counter = 0; // Shared mutable state
    
    @Override
    protected void compute() {
        if (small enough) {
            // Race condition: multiple tasks incrementing counter
            counter++;
        } else {
            // Split and fork
        }
    }
}

Why it matters: Multiple threads updating the counter simultaneously will lead to lost updates and incorrect results.

Solution: Avoid shared mutable state, or use proper synchronization or atomic variables.

5. Excessive Synchronization

Adding too much synchronization can negate the benefits of parallelism.

// PROBLEMATIC: Excessive synchronization
class SynchronizedTask extends RecursiveTask<Integer> {
    private static final Object lock = new Object();
    
    @Override
    protected Integer compute() {
        if (small enough) {
            synchronized(lock) {
                // All tasks will queue up here, becoming sequential
                return computeDirectly();
            }
        } else {
            // Split and fork
        }
    }
}

Why it fails: If all tasks need to acquire the same lock, they'll execute sequentially, not in parallel.

Solution: Redesign tasks to minimize or eliminate shared state and synchronization.

6. Forgetting to Join Forked Tasks

Forgetting to join tasks that were forked can lead to lost results or incomplete processing.

// PROBLEMATIC: Forgetting to join
protected Long compute() {
    if (end - start <= THRESHOLD) {
        return computeDirectly();
    } else {
        int mid = start + (end - start) / 2;
        SumTask left = new SumTask(array, start, mid);
        SumTask right = new SumTask(array, mid, end);
        
        left.fork();
        right.fork();
        
        // Oops! Forgot to join the tasks
        return 0L; // Wrong result
    }
}

Why it matters: The results of the forked tasks are never collected, leading to incorrect results.

Solution: Always join all forked tasks and combine their results.

📌 Java Fork/Join Framework: Key Takeaways

• Fork/Join Framework Basics:

  • Implements divide-and-conquer parallelism for CPU-intensive tasks
  • Uses a work-stealing algorithm for efficient load balancing
  • Consists of ForkJoinPool, ForkJoinTask, RecursiveTask, and RecursiveAction

• Core Concepts:

  • Tasks are divided until they reach a manageable size (threshold)
  • Worker threads steal tasks from busy threads' queues
  • Results are combined hierarchically as tasks complete
  • The framework handles task scheduling and thread management

• Implementation Pattern:

  • Extend RecursiveTask for tasks that return results
  • Extend RecursiveAction for tasks that don't return results
  • Implement the compute() method with the divide-and-conquer logic
  • Use fork() to submit subtasks and join() to collect results

• Performance Considerations:

  • Task granularity is critical for performance
  • Thresholds should be tuned based on workload and hardware
  • Avoid shared mutable state and excessive synchronization
  • The framework is optimized for CPU-bound, not I/O-bound, tasks

• Best Practices:

  • Fork subtasks before computing anything locally
  • Consider computing one subtask directly instead of forking all
  • Handle exceptions properly within tasks
  • Reuse ForkJoinPools when appropriate
  • Avoid sequential bottlenecks in parallel code

• Common Pitfalls:

  • Creating tasks that are too small or too large
  • Introducing sequential bottlenecks
  • Using the framework for inappropriate workloads
  • Sharing mutable state without proper synchronization
  • Adding excessive synchronization
  • Forgetting to join forked tasks

• Real-World Applications:

  • Big data processing
  • Scientific computing
  • Graph and tree algorithms
  • Performance-critical applications
  • System scalability

🧩 Exercises or Mini-Projects

Exercise 1: Parallel Merge Sort

Implement a parallel merge sort algorithm using the Fork/Join Framework.

Requirements:

• Create a ParallelMergeSort class that extends RecursiveAction
• Implement the divide-and-conquer strategy for merge sort
• Use an appropriate threshold to switch to sequential sorting for small arrays
• Compare the performance with sequential merge sort for arrays of different sizes
• Experiment with different threshold values and analyze their impact on performance

Hints:

• Remember that merge sort divides the array in half, sorts each half, and then merges the sorted halves
• The merging step needs to be done after both subtasks complete
• Consider using System.arraycopy() for efficient array manipulation

Exercise 2: Document Word Counter

Build a parallel document word counter that processes multiple text files concurrently.

Requirements:

• Create a WordCountTask class that extends RecursiveTask<Map<String, Integer>>
• The task should count word occurrences in a document or a section of a document
• Implement a divide-and-conquer approach:
  • For a directory, process each file in parallel
  • For a large file, split it into sections and process each section in parallel
• Combine the word counts from subtasks correctly
• Handle file I/O efficiently
• Compare the performance with a sequential implementation

Hints:

• Consider how to split text files efficiently (by lines, chunks, or paragraphs)
• Think about how to merge word count maps from subtasks
• Be careful with file I/O operations in parallel tasks

By mastering the Fork/Join Framework and divide-and-conquer parallelism, you'll have powerful tools for building efficient, scalable applications that can fully utilize modern multi-core processors. These concepts are not only valuable for performance optimization but also provide a structured approach to breaking down complex problems into manageable pieces.

Remember that effective parallelization requires careful consideration of task granularity, data dependencies, and resource utilization. With practice and experimentation, you'll develop the intuition needed to apply these techniques effectively in your own projects.