Multithreading vs. Multiprocessing (With Benefits and Tips)
By Indeed Editorial Team
Published 2 June 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Companies may update their algorithms as they gain more data to improve their computing system speed and performance. Multiprocessing and multithreading are both used to boost a system's computing capabilities. If you work in data science, programming or information technology (IT), understanding the underlying differences between multithreading and multiprocessing may help you make better decisions. In this article, we describe multithreading and multiprocessing, discuss the fundamental distinctions between the two approaches, explore their benefits and drawbacks and offer advice on when to use each.
Multithreading vs. multiprocessing definitions
Before comparing the differences between multithreading vs. multiprocessing, it's helpful to review the definition of each:
What is multithreading?
Multithreading is a programming approach in which you may assign a single process to many code segments. Threads are code chunks that are executed both parallel and concurrently. Within a parent process, these threads have the same memory area, which reduces system memory usage, boosts computation speed and boosts application performance.
For example, you might have many browser tabs open when searching the Internet while working on your computer. You can listen to music on your computer via a desktop program. Even though they're running simultaneously, the Internet browser and the music application are two independent processes. Multiple tabs open when you're browsing the Internet while representing threads of your Internet browser, which is the parent process.
What is multiprocessing?
This refers to a system with over two central processing units (CPUs). Each additional CPU boosts a system's speed, power and memory. It enables users to run many processes at the same time. Each CPU can also work independently, and if there isn't anything to process, some CPUs can sit idle. If technical issues develop, the one not in use can operate as a backup, which can improve a system's reliability. You may divide multiprocessing systems into two categories:
Symmetric multiprocessing: This system uses computer hardware and software that connects two or more identical processors over a single memory space. These processors have access to all input and output devices and they all receive the same treatment.
Asymmetric multiprocessing: Different CPUs have distinct input and output (I/O) devices in this multiprocessing system. One CPU, for example, might focus on I/O tasks, while another handles operating system maintenance.
Key differences between multithreading and multiprocessing
The following are some of the key differences between multithreading and multiprocessing:
Typically, these methods boost computer performance in different ways. Multiprocessing boosts computing power by using two or more CPUs, whereas multithreading boosts computing power using a single process with several code segments. These capabilities allow a multiprocessor to operate many processes, whereas multithreading runs multiple threads simultaneously.
There are several CPUs in a multiprocessing system. This means you can use it as a backup plan if a technical problem occurs and it's idle. As opposed to multithreading, multiprocessing is a more trustworthy system because it only creates concurrent threads.
Multiprocessing takes a long time to set up when developing systems, but multithreading is relatively simple. For higher computer power, multiprocessing may cause the use of extra CPUs. Multiprocessing is, therefore, more expensive than multithreading because it consumes fewer resources.
Except for the stack, threads share everything. All resources are separate in processes. The OS structure determines which resources they may share, unshare and clone. For instance, multithreading uses a single address space for all threads, whereas multiprocessing establishes a separate address space for each process.
Benefits of multithreading
Here are a few of multithreading's significant advantages:
Easy memory access: The server has a substantially higher overall throughput. Large or sophisticated requests and delayed customers don't interfere with other service requests, making memory access easy.
Easy switching between threads: Threads share the same parent process, meaning applications don't freeze or show the hourglass if you launch each request on its thread. An application may not block or wait for another request to finish, making a switch between threads quick and straightforward.
Reduced cost of communication: You can improve the process of communication by using thread synchronization functions. It's relatively affordable to communicate across threads because they exchange a significant amount of data via independent execution threads in the same address space.
Program structure simplification: Threads can help simplify the construction of complex applications like server class and multimedia apps. Complex programs are easier to design and develop when you write simple routines for each operation, making them more adaptable to many requests.
Minimised system resource usage: Threads have a negligible influence on the system's resources. The overhead for creating, maintaining and managing threads is lower than that of a traditional process.
Drawbacks of multithreading
Here are a few of the potential disadvantages of multithreading:
Coding challenges: It's quite challenging to create multithreaded and multi-context apps. Expert programmers are supposed to code these apps because understanding the code may be more difficult.
Additional overhead cost and concurrency issues: For simple jobs, the overheads for handling several threads may be prohibitive. Managing thread concurrency is a complex undertaking that can create additional costs in a program.
Debugging and troubleshooting issues: In a multithreaded or multi-contexted application, reproducing an error is significantly more difficult than in a single-threaded, single-contexted application. As a result, identifying and verifying underlying causes when mistakes arise is more challenging in the former instance.
Memory issue: You may constrain the process memory space if each thread uses more memory. In multi-process execution, when each process has its own memory space, this problem doesn't arise.
Clogging the host computer: While there may be an excessive number of threads running, the CPU may spend too much time switching contexts, releasing memory segments to the file system and causing I/O bottlenecks. It may slow down the entire application and block the host machine.
Benefits of multiprocessing
Here are a few advantages of multiprocessing:
High speed: A multiprocessor employs many CPUs to enhance efficiency. It speeds up the completion of activities and analyses enormous amounts of data, resulting in increased throughput.
Less electricity usage: There's an additional burden on a single processor system because you may conduct many processes simultaneously. You can execute multiple tasks in a multiprocessor system only a few times, resulting in lower energy consumption than a single processor.
High reliability: Many processors share their tasks and perform their work collaboratively in a multiprocessor system. It shows that these systems are dependable.
High performance: It takes advantage of many CPUs to boost a system's overall performance.
Synchronization primitives: It gets rid of the primitives for synchronization.
Relatively low cost: A multiprocessor uses over one CPU. The CPUs execute more tasks and these systems become less expensive than systems with only one processor.
Drawbacks of multiprocessing
Here are a few of the potential disadvantages of multiprocessing:
Less memory space: Each CPU demands memory space because many processors work together. It requires more memory and overhead than threads to transmit data between processes.
Underperformance: If one processor fails, they split the work among the remaining processors. The negative effect is that you may perform the task in a short amount of time, lowering system performance.
Inter-process communication issues: An inter-process communication (IPC) model is necessary to share things between processes. The operating system implementation is challenging to manage because multiple processors communicate with one another.
High cost: These systems are expensive to buy. The overhead expenses may increase if you give each subprocess the entire memory.
Tips for deciding whether to use multithreading or multiprocessing
Here are some pointers to consider while choosing between multithreading and multiprocessing:
Use multiprocessing to develop programs that are CPU intensive. Multiprocessing can aid in the speeding up of processes. It can also provide trustworthy solutions for applications involving many CPU activities.
Use a multithreading system when you've got a lot of data syncing. Data synchronisation simplifies by multithreading since all threads use the same memory area. If your parallel parts (threads or processes) require a lot of synchronization, multithreading can help development.
Creating I/O bound or network bound applications
Use multithreading to create I/O bound or network bound applications. Threads can provide you with all the processing power you require when connecting web servers and downloading data from the Internet. Many data scientists, for example, employ multithreading to build web scraping programs.
Making user interface (UI) programs responsive
Make user interface (UI) programs more responsive by using multithreading. Threads supply enough computational power because these applications often wait for user engagement. For example, if you're creating an online writing application, you might employ multithreading in a way that one thread logs the user's keystrokes, another thread displays the text for the user to read and a third thread proofreads the text for spelling and grammar issues.
Computing scalable programs
Use multiprocessing when computing scalable programs. Multiprocessing code is more scalable because it easily scales up using clusters, grid systems or even the public cloud. You can switch your code from functioning on a single machine to working on many using a multi-process. Some solutions can allow you to scale and use idle computers and cores across your network or public cloud writing no code. As a result, you may achieve scalability by effectively turning the machine hosting your application into a supercomputer with thousands of cores and gigabytes of memory.
Explore more articles
- Work from Home vs. the Office (With Pros and Cons)
- How to Combine PDF Files (With Steps, Benefits and Tips)
- 12 Talent Examples in the Workplace and How to Detect Them
- Information Overload: Overview, Effects and How to Avoid It
- How To Write an Address (With Multiple Examples)
- What Is Networking? (With Types and How To Network)
- 11 Network Engineer Skills (With Tips to Improve Them)
- Relationship-Building Skills: Definition and Examples
- Great Extracurricular Activities to Include on Your Resume
- How to Use Effective Communication in the Workplace
- Conceptual Skills: Definition, Types and Steps To Develop Them
- Quality Engineering vs. Quality Assurance: A Comparison