Concurrency and parallelism are often confused terms, especially in multi-threaded programming and distributed systems. However, it’s crucial to understand that concurrency is NOT parallelism—they are related but distinct concepts. In this article, we will explore their differences, implications for Java professionals, and best practices for writing concurrent and parallel applications.

Defining Concurrency and Parallelism

Concurrency

Concurrency is the ability of a system to deal with multiple tasks at once by interleaving their execution. This does not necessarily mean executing them simultaneously but rather managing them efficiently to improve responsiveness.

Example: A single-core CPU running multiple threads by rapidly switching between them (context switching) achieves concurrency but not parallelism.

Parallelism

Parallelism is the simultaneous execution of multiple tasks or computations. It requires multiple processing units, such as multiple CPU cores, to actually run tasks at the same time.

Example: A multi-core processor running multiple threads, each on a separate core, achieves true parallelism.

Key Differences Between Concurrency and Parallelism

Aspect	Concurrency	Parallelism
Execution	Tasks are interleaved (time-slicing)	Tasks run simultaneously
Hardware Requirement	Can work on a single core	Requires multiple cores/processors
Goal	Improves responsiveness	Improves performance (throughput)
Complexity	Involves synchronization and coordination	Requires efficient task distribution
Example	Context switching between tasks	Executing multiple tasks in parallel

Concurrency in Java

Java provides various tools and APIs to implement concurrency, including:

1. Threads: Using Thread class or Runnable interface to create and manage concurrent tasks.
2. Executor Framework: ExecutorService provides thread pooling and task scheduling.
3. Fork/Join Framework: Allows parallel execution of divide-and-conquer tasks using ForkJoinPool.
4. CompletableFuture: Asynchronous programming using the CompletableFuture API.

Example of Concurrency Using Executors

import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class ConcurrencyExample {
    public static void main(String[] args) {
        ExecutorService executor = Executors.newFixedThreadPool(3);
        for (int i = 1; i <= 5; i++) {
            int taskId = i;
            executor.submit(() -> {
                System.out.println("Executing task " + taskId + " in thread " + Thread.currentThread().getName());
            });
        }
        executor.shutdown();
    }
}

Key Takeaways:

• The Executors.newFixedThreadPool(3) allows concurrent execution but does not guarantee parallelism.
• If executed on a single-core system, tasks are merely interleaved, not run in parallel.

Parallelism in Java

Parallelism is often achieved through:

1. Parallel Streams: Java 8 introduced parallel streams using parallelStream().
2. Fork/Join Framework: Best suited for recursive divide-and-conquer tasks.
3. Thread Pools with CPU-intensive tasks: Optimizing thread usage with Executors.newWorkStealingPool().

Example of Parallelism Using Fork/Join Framework

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

class SumTask extends RecursiveTask<Integer> {
    private final int[] array;
    private final int start, end;

    SumTask(int[] array, int start, int end) {
        this.array = array;
        this.start = start;
        this.end = end;
    }

    @Override
    protected Integer compute() {
        if (end - start <= 5) { // Base case: sum small chunks directly
            int sum = 0;
            for (int i = start; i < end; i++) sum += array[i];
            return sum;
        }
        int mid = (start + end) / 2;
        SumTask leftTask = new SumTask(array, start, mid);
        SumTask rightTask = new SumTask(array, mid, end);
        leftTask.fork();
        return rightTask.compute() + leftTask.join();
    }
}

public class ParallelismExample {
    public static void main(String[] args) {
        int[] array = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
        ForkJoinPool pool = new ForkJoinPool();
        int sum = pool.invoke(new SumTask(array, 0, array.length));
        System.out.println("Sum: " + sum);
    }
}

Key Takeaways:

• The ForkJoinPool enables parallel execution by splitting tasks across multiple threads.
• Tasks run in parallel if multiple cores are available.

Choosing Between Concurrency and Parallelism

1. Use Concurrency when:
   • You need to handle multiple tasks efficiently (e.g., handling multiple user requests in a web server).
   • Tasks involve I/O operations (e.g., reading files, network calls).
2. Use Parallelism when:
   • You perform CPU-intensive computations (e.g., matrix multiplication, data processing).
   • The problem can be broken down into independent subtasks.

Best Practices for Java Developers

• Avoid excessive thread creation: Use thread pools instead of creating new threads manually.
• Use synchronized mechanisms carefully: Use synchronized, Lock, and ConcurrentHashMap where necessary to prevent race conditions.
• Profile before optimizing: Use tools like VisualVM, JProfiler, and Java Flight Recorder to analyze performance bottlenecks.
• Prefer higher-level abstractions: Use CompletableFuture, ExecutorService, and Parallel Streams instead of managing threads manually.
• Beware of false sharing: Optimize data structures for cache efficiency to avoid performance degradation.

Conclusion

Understanding the distinction between concurrency and parallelism is essential for Java professionals. While concurrency improves responsiveness by interleaving tasks, parallelism boosts performance by executing tasks simultaneously. Choosing the right approach depends on the nature of the workload—whether it’s I/O-bound or CPU-bound. By leveraging Java’s concurrency utilities and best practices, developers can build efficient, scalable applications that maximize performance on modern hardware.

Credits: Babar Shahzad