Multithreading vs Multiprocessing: Key Differences

Introduction

Understanding Concurrent Execution

Multithreading vs Multiprocessing: In today’s world of computing, efficiency and speed are critical. One important way to improve application performance is through concurrent execution — the ability to process multiple tasks seemingly at the same time. Whether you’re developing web servers, performing data analysis or building real-time systems, mastering concurrency is essential.

Concurrent execution can be achieved in two ways: Multithreading and multiprocessing. Although they sound similar, under the hood they work quite differently and are suitable for different types of tasks.

Meaning of multithreading and multiprocessing

Choosing the right concurrency model can have a significant impact on the performance, scalability and reliability of your applications.

• Choosing the wrong approach can lead to bottlenecks, memory issues or even application crashes.
• On the other hand, the right strategy can make your software faster, more efficient and more resilient to failures.

Knowing when and how to use multithreading and multiprocessing is a skill that separates inexperienced developers from experienced engineers.

Setting the stage for deeper understanding

In this blog post, we’ll go into more detail about what multithreading and multiprocessing mean, explain their differences and help you decide which method best suits your needs. By the end, you’ll have a clear framework to choose between the two approaches depending on the type of task you’re dealing with — whether it’s CPU-bound, I/O-bound or a mix of both.

What is Multithreading?

Definition and basic concepts

Multithreading is a concurrency technique in which multiple threads are created within a single process. These threads run in the same memory area and share resources such as variables and file handles.

Threads are often referred to as “lightweight processes” because creating and switching between threads is faster and uses fewer resources than processes. They are ideal for tasks that require time to wait, such as reading from a hard disk or network requests.

How do threads work?

Each thread works independently, but shares the memory of the process. This shared memory makes communication between threads fast and efficient, but also carries the risk of race conditions where two threads try to modify the same data at the same time.

Thread management is usually handled by a thread scheduler, which decides when and how threads are executed based on factors such as priority, workload and resource availability.

You can create a thread in Java by extending the Thread class or implementing the Runnable interface. Here’s an example using Runnable:

public class MyThread implements Runnable {
    public void run() {
        for (int i = 0; i < 5; i++) {
            System.out.println("Thread is running: " + i);
            try {
                Thread.sleep(500); // Pause for 500ms
            } catch (InterruptedException e) {
                System.out.println("Thread interrupted");
            }
        }
    }

    public static void main(String[] args) {
        MyThread runnable = new MyThread();
        Thread thread = new Thread(runnable);
        thread.start();

        for (int i = 0; i < 5; i++) {
            System.out.println("Main thread: " + i);
            try {
                Thread.sleep(500);
            } catch (InterruptedException e) {
                System.out.println("Main thread interrupted");
            }
        }
    }
}

Key Points in This Example:

Runnable allows you to define a task to run in a new thread.

The start() method begins execution of the new thread.

Both the main thread and the new thread run concurrently.

Applications in the real world

Multithreading is often used in:

• Web servers that handle multiple client requests.
• GUI applications that need to be responsive while performing background tasks.
• Games and simulations that run multiple actions simultaneously.

What is multiprocessing?

Definition and basic concepts

Multiprocessing is a concurrency technique in which several processes are executed simultaneously, each in its own independent memory area. Unlike threads, processes do not share memory by default, which makes them heavier but safer.

Each process has its own Python interpreter, memory and system resources. This separation ensures robustness, as a crash of one process does not necessarily affect the others.

How processes work

When you use multiprocessing, your program creates completely separate instances that communicate via mechanisms such as pipes, sockets or shared files. Since they don’t share memory, the risk of data corruption is lower, but communication between processes can be slower and more complex.

Operating systems manage processes by allocating resources and scheduling CPU time, which enables true parallel execution on multi-core processors.

Launching Another Java Process:
To demonstrate multiprocessing, you can run a completely separate Java program using ProcessBuilder:

import java.io.IOException;

public class LaunchProcess {
    public static void main(String[] args) {
        ProcessBuilder processBuilder = new ProcessBuilder("java", "OtherProgram");
        try {
            Process process = processBuilder.start();
            System.out.println("OtherProgram launched successfully.");

            int exitCode = process.waitFor();
            System.out.println("OtherProgram exited with code: " + exitCode);
        } catch (IOException | InterruptedException e) {
            e.printStackTrace();
        }
    }
}

Assuming you have another compiled Java class called OtherProgram:

public class OtherProgram {
    public static void main(String[] args) {
        System.out.println("Hello from OtherProgram!");
        for (int i = 0; i < 3; i++) {
            System.out.println("Processing: " + i);
            try {
                Thread.sleep(1000);
            } catch (InterruptedException e) {
                System.out.println("Interrupted");
            }
        }
    }
}

Key Points in This Example

ProcessBuilder is used to launch another Java application as a separate process.

Each program runs independently, with its own memory and resources.

You can manage the process’s input/output streams if needed.

Applications in the real world

Multiprocessing is ideal for:

• CPU-intensive tasks such as image processing or training machine learning models.
• Performing extensive mathematical calculations.
• Performing isolated tasks that must not interfere with each other.

The most important differences at a glance

A simple comparison table

Feature	Multithreading	Multiprocessing
Memory usage	Low (shared memory)	High (separate memory)
Communication	Simple and fast	More complex and slower
Crash isolation	Poor (one thread crash can affect all)	Strong (process crash is contained)
True parallelism	No (limited by GIL in Python)	Yes (multiple cores are fully utilized)
Best For	I/O-bound tasks	CPU-bound tasks

Quick reference for readers

Remember: Use multithreading if your tasks involve a lot of wait times and multiprocessing if your tasks require extensive calculations.

Advantages of Multithreading

Lightweight operations

Threads are lightweight and require minimal overhead when creating and switching between them. They share the same address space, making data sharing simple and efficient without the need for complex mechanisms.

Shared memory and fast context switching

Since threads share the same memory, switching between them is faster than between processes that have separate memory areas.

This makes multithreading particularly useful when tasks frequently interact with shared resources or require fast response times.

Best Use Cases

Multithreading is perfect for:

• Web servers processing thousands of incoming requests.
• Desktop applications that work in the background to maintain responsiveness.
• Programs that have many I/O wait times, such as reading databases or processing files.

Advantages of multiprocessing

True parallelism on multi-core CPUs

Multiprocessing enables true parallel execution, as each process runs independently on its own core. This completely bypasses restrictions such as the Global Interpreter Lock (GIL) in languages such as Python.

Error containment and crash safety

If a process crashes, this does not bring the entire application to a standstill. Each process works independently, providing better fault tolerance and reliability in complex systems.

Best use cases

Multiprocessing is best suited for:

• Data science and machine learning tasks that process large data sets.
• Video rendering or image processing tasks.
• Scientific simulations that require large amounts of CPU power.

Challenges and limitations

Race conditions and deadlocks in Multithreading

Since threads share memory, managing access to shared resources is a challenge. Errors such as race conditions and deadlocks can occur when two threads simultaneously attempt to access or modify shared data without proper synchronization.

The use of locks, semaphores and other synchronization mechanisms is necessary, but can complicate code and reduce performance.

High memory consumption and communication overhead with multiprocessing

Each process has its own memory, which leads to higher memory consumption. Communication between processes also becomes slower and more complex and requires special tools such as IPC (Inter-Process Communication) systems.

In addition, starting and managing multiple processes can lead to latency and overhead.

When should you use Multithreading?

Specific scenarios in which threading is used

Use multithreading when the tasks include the following:

• Network communication where significant latency occurs.
• File system operations where I/O delays dominate.
• Applications that need to be very responsive even under load.

Examples

• A chat application that manages multiple user connections.
• A file downloader that downloads several files at once.
• A browser that loads several website elements such as images and scripts simultaneously.

When is multiprocessing used?

Specific scenarios for multiprocessing

Use multiprocessing when the tasks:

• CPU-bound and require intensive calculations.
• Are heavy and can block the execution thread for a long time.

Examples

• A machine learning model that needs to process large data sets.
• A video encoding program that converts large video files.
• A scientific application that simulates complex systems on different computing cores.

Performance tips

Fine-tune Multithreading

• Minimize the use of locks: Excessive use of synchronization can negate the benefits of multithreading by creating bottlenecks. Use fine-grained locks or lock-free data structures when possible.
• Favor thread pools: Instead of creating a new thread each time, use a ThreadPoolExecutor (or ExecutorService in Java) to efficiently manage a pool of worker threads.
• Optimize access to shared resources: pool related data or use thread-local memory to reduce conflicts between threads.

Fine-tuning multiprocessing

• Minimize the creation of processes: Creating processes is expensive. If possible, reuse long-running processes instead of creating new ones over and over again.
• Use efficient communication: Prefer lightweight mechanisms such as sockets, memory mapped files or efficient serialization formats (e.g. protocol buffers instead of JSON) for communication between processes.
• Distribute the workload wisely: Ensure that work is distributed evenly across processes to maximize CPU utilization and avoid some processes terminating prematurely while others are overloaded.

Common mistakes to avoid

Multithreading errors

• Ignoring race conditions: Always assume that multiple threads can access shared data at the same time. If you don’t synchronize properly, this can lead to subtle, hard-to-find errors.
• Blocking locks: Holding locks for too long can lead to thread starvation or even deadlocks. Keep critical sections as short and fast as possible.
• Underestimating thread overhead: Too many threads can cause excessive context switching and reduce performance.

Multiprocessing errors

• Inefficient data sharing: Passing large objects between processes without proper serialization can be slow and memory intensive.
• Poor error handling: If a child process crashes, it can result in lost resources and incomplete work if the error is not handled properly.
• Overhead due to frequent process creation: If processes are constantly started and stopped without bundling work, performance gains can be wiped out.