Difference Between Fibers And Threads: A Comprehensive Guide

Introduction

In the world of software development, understanding how a program manages tasks can be crucial for optimizing performance. Two fundamental concepts developers often encounter are threads and fibers.  Both concepts are means of handling multitasking in an application, but they do so in very diverse ways. How do they work? What are the differences, and how can you decide which to use?

This blog aims to explain threads and fibers, breaking down their basic differences concerning performance impact, memory consumption, and hence the efficiency of your applications. Whether you are an experienced programmer or a fresher, this blog will make all these concepts simple for you to understand.

What are Threads?

Threads are the smallest quantum of execution in a process. It allows an application to do multiple things at a time. A process may contain multiple threads, and all those threads can execute parts of the program’s tasks in parallel. All the threads belonging to a particular process run within the same memory space. Hence, communication between such threads is much faster.

Examples include web browsers, which may have one thread taking input from the user, another thread loading the web pages, and a third thread rendering or showing the content to the screen all at once.

What are Fibers?

Unlike threads, fibers are also units of execution but lightweight and cooperative. Threads are a system resource and the operating system schedules threads whereas, fibers are a language resource and are scheduled by the program itself.  Fibers do not run concurrently on multiple CPU cores; instead, they switch control only when the running task decides to hand over execution.

This manual control gives developers finer control over execution flow but also adds complexity since the fibers must yield at appropriate times for other fibers to run.

Major Differences Between Threads and Fibers

Although threads and fibers both allow for concurrent execution within an application, their underlying mechanisms are very different. These differences can have a significant impact on how an application performs and how tasks are managed. Some critical differences between threads and fibers are discussed next.

Scheduling Mechanism

The most significant difference between threads and fibers is how they are scheduled.

Threads are scheduled to run by the operating system on a preemptive basis. In other words, based on time slice or task priority among others, the OS will determine when to switch from one thread to another. Because threads can be interrupted at any moment, a developer does not have to manage any thread stopping from executing.

Whereas fibers are scheduled pre-emptively, fibers are cooperatively scheduled, which means that a fiber will execute until it explicitly yields. The application is responsible for any fiber switching. The additional manual flow control increases the complexity of handling fibers since they also need to make sure tasks yield at appropriate times.

Performance and Memory

The other important difference between threads and fibers involves exactly how each handles memory and performance.

Since threads have a separate stack, threads are inherently heavier in their memory use. This context switch-overhead of the CPU, where it has to save the state of one thread and load another’s state, can be considerable when there are many threads involved in this game. Still, because threads can run in parallel on different cores, they typically make up for this with a global performance gain.

Fibers use significantly less memory. Because the fibers share a stack and run in the same thread, that makes them much lighter than threads concerning memory usage. Also, fiber switching can be faster because the OS doesn’t get involved, and it does not require a full context switch. However, because of how fibers are implemented-essentially, preemptively within a single thread, they cannot utilize multiple cores like threads can; this handicaps them in exploiting true parallelism in boosting performance during CPU-bound tasks.

Context Switching

A context switch means saving the state of one task and loading the state of another whether it is a thread or a fiber.

Threads require a full context switch, which includes saving and restoring the CPU state, stack, and memory. Since the operating system (OS) handles this process, thread context switching can be relatively slow. The OS must manage each thread’s state, which can become time-consuming, especially when dealing with hundreds of threads in a program.

Fibers, on the other hand, are scheduled at the application level. This means that context switching for fibers is much more efficient. Only the fiber’s current position needs to be saved, resulting in much lower overhead compared to thread switching.

Parallelism

Parallelism means performing more than one task at the same time.

Threads can run in parallel across multiple CPU cores. This is because the operating system can schedule different threads to run on different cores at the same time. This is one of the key advantages of threads, as it allows for true parallelism.

Unlike threads, fibers are managed within a single thread at the application level, meaning only one fiber can execute at a time, even if there are multiple CPU cores available. While fibers can switch between tasks quickly and efficiently, they do not allow simultaneous execution across multiple cores. As a result, fibers provide faster task switching but lack the parallel execution capabilities that threads can leverage.

Preemption

As you know, Preemption is the operating system’s ability to interrupt a task that is currently running and switch to another task.

Threads are preemptive, meaning the OS can interrupt a thread at any time to switch to another thread. This allows the OS to manage multiple tasks efficiently without the need for the developer to explicitly manage task switching.

Fibers, however, are non-preemptive. A fiber will continue running until it decides to stop and give control back (this is called yielding). The developer must explicitly tell the fiber when to pause and allow another fiber to run. While this gives developers more control over how tasks are managed, it also adds the responsibility to ensure that no fiber runs for too long and monopolizes system resources.

Suitability for Asynchronous Operation

Asynchronous operations let tasks run in the background so the main application stays fast and responsive. Threads handle this well by running tasks in parallel, while fibers can switch between tasks but only one at a time.

One of the main use cases for threads is asynchronous operations, where tasks are performed in the background without blocking the main application. Threads are perfect for that because they can handle several tasks running in parallel with task switching performed by an operating system.

Fibers can also be used for managing asynchronous input/output operations. For example, when reading or writing files, fibers can yield control during I/O operations, allowing other tasks to run concurrently. This keeps the application responsive, especially in scenarios where waiting for data can create delays.

Operating System Dependency

Threads are like eager performers who depend on the operating system (OS) to set the stage for them. The OS handles scheduling and resource management, but this reliance can lead to some interesting challenges. For instance, their performance can vary significantly across different platforms, which means tasks may not execute as quickly as you’d like.

Now, let’s talk about fibers. They’re pretty cool because they are OS-independent and managed entirely by the application itself! This gives developers like you greater control over how tasks are executed, allowing for customized scheduling to fit specific needs. But with great power comes great responsibility—you need to manage those fibers effectively.

Use Cases and Applications

Let’s dive into some interesting use cases that will help you better understand the concepts of fibers and threads.

Threads in Practice

Threads can be envisioned in a wide range of applications where multitasking and parallelism are required. Common examples include:

Web Servers

Threads share the same memory space and resources. Multiple client requests can be served by one web server using threads. For example, when many users access a website at the same time, different threads may be assigned to serve different users’ requests so that the server is always responsive.

Game Engines

Threads are applied in game development to render graphics, handle user input, and carry out other game logic. This enables the smooth running of games with responsiveness, even when multiple things are happening at the same time.

Streaming Services

Threads handle multiple streams of audio or video, ensuring smooth playback even with multiple concurrent users.

Real-time Communication

Messaging apps utilize threads to manage multiple conversations and notifications without delays.

Practice of Fibers

Typical use cases for fibers are the following:

Networking Applications

In network applications, fibers can help manage multiple connections efficiently. By using fibers, developers can handle multiple tasks such as processing data packets and responding to client requests without the complexity and resource demands of threads.

Game Development

Game engines can also utilize fibers for rendering tasks, physics calculations, AI, and event handling, amongst others. The usage of context switches between tasks would avail efficiency to the developers in knowing that time-critical operations, like rendering frames of an image, occur at just the right times without interference from less important tasks.

Cooperative Multitasking Systems

Systems utilizing cooperative multitasking – meaning, the tasks must voluntarily yield control – include some operating systems that have become old and others that are embedded systems. On such systems, Fiber can be viewed as a perfect fit since the developer decides on task switching without the application of the operating system.

Simulations

In the simulation environments, where many tasks run concurrently, like the simulation of agents or environments are in common usage. Since fibers are lightweight, a developer can create a large number of fibers to represent different entities in the simulation with less overhead compared to threads.

Performance Considerations

When deciding between threads and fibers, performance is a key factor to consider. Both constructs offer advantages, but they also have trade-offs depending on the specific requirements of your application.

Threads

Threads are generally more overhead and usually more expensive than fibers, since each thread has its stack, and the context switch is more expensive. As the number of threads increases, the overhead associated with managing them (such as context switching, memory allocation, and synchronization) can become significant.

When it is parallel execution bound, such as in data processing, video encoding, and even real-time systems, the threads are the way to go. They allow several tasks to execute on many cores simultaneously, hence fit for CPU-bound tasks.

Fibers

While fibers offer advantages in terms of lightweight concurrency and efficient context switching, they also come with certain limitations compared to threads. Fibers are typically managed within a user-space scheduler, meaning they rely on the application to yield control to other fibers. This can introduce challenges in ensuring fair scheduling and preventing potential deadlocks.

Additionally, fibers may not be as well-integrated with system-level operations like I/O, potentially leading to performance bottlenecks if not carefully managed. Furthermore, debugging fiber-based applications can be more complex due to the lack of direct OS-level support and the need to analyze user-space scheduling decisions.

When to Use Threads Instead of Fibers?

Which to use would be entirely based on the nature of your application and, of course, the specifics of the requirements of your tasks. Here are some scenarios where threads would be preferable to use:

Parallel Processing

Applications that have to perform many tasks simultaneously, such as processing large datasets, or any CPU-bound activity, are a natural fit for threads. Threads can now run true parallel on multi-core and will, therefore, find excellent applications in all those areas where true parallel execution is a significant advantage.

Threaded Environments

Normally, threaded environments can be applied in the managing of multiple tasks running at the same time in a high concurrency environment, such as Web servers, game engines, and real-time systems.

When to Use Fibers Instead of Threads

Fibers are better suited for applications that require fine-grained control over the execution of tasks, whereby lightweight concurrency levels will be beneficial to them. Here are scenarios where fibers would be the better option:

Lightweight Tasks

For applications that have to work with a lot of lightweight tasks that do not require true parallel execution, fibers are much lighter compared to threads. They can perform task switches faster with much lower memory use compared to threads.

Game Development

The game engines, which have to perform parallel tasks such as rendering, AI, and physics simulations, are done better with fibers, as they lack thread management overhead.

Cooperative multitasking is a method where, at the base, the tasks need to explicitly yield control, providing the flexibility and control that is needed with fibers.

Relating fibers and threads to Coroutines

Up to a point, both fibers and threads are similar to yet another concurrency paradigm:

Coroutines

Coroutines are a somewhat general notion in programming whereby a function may suspend its execution at a particular point and later resume from that point. Loosely, one may think of coroutines as a lighter-weight version of fibers since multiple coroutines share the very same thread.

Modern languages such as Python, Kotlin, and JavaScript are already implementing coroutines. One of the nice features of using them is the enablement of non-blocking asynchronous operations instances, waiting for data from a server or reading data from a file without holding the main thread.

Comparison of Coroutine and Fibers

Stack Management

Coroutines are stackless, meaning they don’t maintain their call stack but share the stack of the calling function.

Fibers, on the other hand, are sackful, meaning they have their call stack, allowing them to manage more complex, independent operations.

Control

Coroutines are often integrated into programming languages and are language-managed, making them easier to use for asynchronous tasks without needing explicit thread management.

Fibers are application-managed, giving developers finer control over their execution, but requiring them to explicitly manage when the fiber yields or resumes, adding more responsibility.

Common Misconceptions

To understand threads, fibers, and coroutines, first, there are a few common misconceptions. Let’s take a few of them:

Threads versus Processes

So many developers need to understand threads for processes. Both are used for multitasking, yet both differ entirely. A process is an independent program running in its memory space, whereas threads exist within a process, sharing the same memory. Threads are lighter in weight than processes, and switching between threads is relatively much faster because there is no need for a switch in memory contexts.

Fibers Are Not Processes

Another myth is that fiber is another name for a process. Fibers are not processes but rather lightweight units of execution running within a thread. Unlike processes or threads, fibers do not own memory space, and their multitasking is cooperative, while both processes and threads rely on the operating system for preemption.

Conclusion

In conclusion, threads and fibers are useful tools for managing tasks in software development, but they serve different purposes. Understanding these differences can help you choose the right approach based on your application’s needs, whether it’s performance, memory efficiency, or control over execution flow.