Understanding Threads in C++: From "std::thread" to Thread Pools

Have you ever wondered how an application can download a file, process data, and remain responsive at the same time?

Or how a program can divide a large calculation into smaller pieces and work on them concurrently?

One important concept behind this is multithreading.

Threads are a fundamental part of modern C++ development. They allow a program to execute multiple sequences of instructions concurrently and, when hardware and workload permit, in parallel.

But how do threads actually work? When should you use them? And what's the difference between creating individual threads and using a thread pool?

Let's explore.

1. What exactly is a thread?

A thread is an independent sequence of execution within a process.

When a C++ application starts, it begins with its main thread, which executes the code inside main().

However, a program can create additional threads to perform other work.

For example, an application might have:

• A main thread handling application logic.
• A worker thread processing data.
• Another thread handling background operations.
• Several workers performing independent calculations.

All these threads belong to the same process and share its address space.

That shared memory is useful, because threads can access the same application data. However, it also means we must think carefully about how shared data is accessed and modified.

2. Why use multiple threads?

Imagine processing four independent chunks of data.

With a single thread, the work might happen sequentially:

Main Thread
    |
    v
Process Chunk 1
    |
    v
Process Chunk 2
    |
    v
Process Chunk 3
    |
    v
Process Chunk 4
    |
    v
Finished

With multiple threads, independent chunks may be processed concurrently:

             Main Thread
                  |
             Distribute Work
                  |
        +---------+---------+
        |         |         |
        v         v         v
     Thread 1  Thread 2  Thread 3
        |         |         |
        v         v         v
     Chunk 1   Chunk 2   Chunk 3

                  |
                  v
               Thread 4
                  |
                  v
               Chunk 4

The operating system schedules threads, and the actual execution depends on available CPU resources.

If the machine has multiple available CPU cores, some threads may execute at the same time. Even on a system with fewer cores, the operating system can switch between threads so their work overlaps.

Common uses for multithreading include:

• Background processing.
• Parallel calculations.
• Processing large datasets.
• Handling independent requests.
• Keeping user interfaces responsive.
• Performing work while other parts of an application continue.

However, more threads do not automatically mean better performance.

Thread creation, scheduling, synchronization, and communication all introduce overhead. For small tasks, sequential execution can sometimes be faster.

3. Creating a thread with standard C++

C++ provides the <thread> header for creating and managing threads.

Here's a small example:

#include <iostream>
#include <thread>

void BackgroundWork()
{
    std::cout << "Hello from another thread!\n";
}

int main()
{
    std::thread worker(BackgroundWork);

    std::cout << "Hello from the main thread!\n";

    worker.join();

    return 0;
}

Let's look at the important parts.

Creating the thread:

std::thread worker(BackgroundWork);

This creates a new thread and starts executing BackgroundWork().

Waiting for it to finish:

worker.join();

join() blocks the calling thread until the worker finishes.

This is important because a std::thread object must be joined or detached before its destructor runs. Destroying a still-joinable std::thread causes the program to terminate.

For this simple example, join() is the right choice because we want to wait for the worker to complete.

4. What is a thread pool?

Creating a new thread every time an application needs to perform a small operation can become inefficient.

Imagine an application that needs to execute hundreds of short tasks.

Creating and destroying a separate thread for every task introduces repeated thread-management overhead.

A thread pool approaches this differently.

Instead of creating a new thread for every task, a thread pool maintains a collection of worker threads that can execute submitted work.

A simplified design looks like this:

             Application
                  |
                  v
              Submit Task
                  |
                  v
               Task Queue
                  |
          +-------+-------+
          |       |       |
          v       v       v
       Worker 1 Worker 2 Worker 3
          |       |       |
          v       v       v
        Task A  Task B  Task C

When a worker finishes its current task, it can retrieve another task from the queue.

This allows worker threads to be reused instead of repeatedly creating new ones.

When are thread pools useful?

Thread pools are particularly useful when:

• An application has many independent tasks.
• Tasks are relatively short-lived.
• You want to reuse worker threads.
• You want to control how many workers execute concurrently.
• You need a central place to manage background work.

A thread pool doesn't automatically solve synchronization problems, though. If multiple tasks access shared mutable data, you still need appropriate synchronization.

5. Using a thread pool with Arctix

Now let's look at a practical example.

I've been developing Arctix, a C/C++ systems toolkit that brings several low-level utilities together.

Its functionality includes:

• Memory allocation.
• Memory pools.
• Owning byte buffers.
• Virtual memory utilities.
• Thread management.
• Thread pools.

One of its components is:

arctix::Thread::ThreadPool

The thread pool provides FIFO task execution through a reusable collection of worker threads.

Let's use it to submit several independent tasks.

Setting up the SDK

For this example, we'll use the precompiled C++ SDK.

You'll need:

• arctixc.hpp
• arctixc.lib
• arctixc.dll

Configure the SDK's include directory in your project, link the import library, and make sure the DLL is placed next to your executable so Windows can find it at runtime.

The example below uses the C++-only single-header SDK.

Example: Submitting multiple tasks

#include "arctixc.hpp"

#include <cstdio>

struct Job
{
    int id;
};

void Work(void* userData)
{
    const auto* job = static_cast<const Job*>(userData);

    std::printf("Executing task %d\n", job->id);
}

int main()
{
    // Create a thread pool with four workers.
    arctix::Thread::ThreadPool pool(4);

    // Prepare some jobs.
    Job jobs[] = {
        { 1 },
        { 2 },
        { 3 },
        { 4 }
    };

    // Submit the jobs.
    for (Job& job : jobs)
    {
        pool.Submit(Work, &job);
    }

    // Wait until the submitted work has finished.
    pool.WaitIdle();

    // Shut down the pool.
    pool.Shutdown();

    return 0;
}

Let's break it down.

1. Create the pool

arctix::Thread::ThreadPool pool(4);

This creates a thread pool with four worker threads.

2. Define the task function

void Work(void* userData)

Arctix uses a task function that receives a void* user-data pointer.

This lets us pass a pointer to the data associated with each task.

3. Submit work

pool.Submit(Work, &job);

Each call submits a task to the pool's FIFO queue.

The worker threads retrieve and execute the submitted tasks.

4. Wait for completion

pool.WaitIdle();

This waits until the pool has finished its submitted work and is idle.

Notice that the jobs array remains alive until after WaitIdle() returns. That's important because each task receives a pointer into that array.

5. Shut down

pool.Shutdown();

This explicitly shuts down the thread pool when we're finished.

That's the basic workflow: create the pool, submit work, wait for completion, and shut it down.

You can explore the project and its SDK documentation here:

Arctix on GitHub

6. Important concepts to understand

Multithreading introduces some challenges that become especially important in larger applications.

Race conditions

A race condition can occur when multiple threads access shared data concurrently and at least one access modifies that data without proper synchronization.

For example, if multiple threads increment the same ordinary integer at the same time, the result may be incorrect.

C++ provides tools such as mutexes and atomic variables to help manage shared data safely.

Synchronization

Sometimes one thread needs to wait for another thread or coordinate access to a shared resource.

Some useful C++ synchronization tools include:

• std::mutex
• std::lock_guard
• std::unique_lock
• std::condition_variable
• std::atomic

Understanding when and how to use these tools is an important part of writing concurrent programs.

Thread safety

A function isn't automatically thread-safe just because it runs on a separate thread.

If multiple threads call a function that modifies shared state, you need to consider whether those operations can safely happen concurrently.

Lifetime of task data

When submitting work to a thread pool, make sure any pointers passed to tasks remain valid until those tasks have finished using them.

This is especially important when working with local variables, arrays, and dynamically allocated memory.

7. Individual threads vs. thread pools

Which approach should you use?

It depends on the problem.

Situation	Possible approach
One independent background operation	std::thread
Many independent tasks	Thread pool
Repeated short-lived tasks	Thread pool
Shared mutable data	Synchronization
Simple sequential operations	Single thread

There isn't one solution that fits every application.

The goal is to choose the simplest approach that meets your application's requirements.

Conclusion

Threads are one of the fundamental building blocks of concurrent programming.

Understanding how they work, how to coordinate them, and when to use a thread pool can help you build more capable C++ applications.

A good way to start is with std::thread, then explore synchronization and thread pools as your programs become more complex.

I've also been exploring these concepts while developing Arctix, which includes a reusable FIFO thread pool alongside other systems utilities.

If you're interested in checking it out:

• GitHub: https://github.com/Yummyzzzz/Arctix
• Whitepoint Software Development: https://whitepoint.pages.dev/

Feedback from C/C++ developers is welcome.

---

Question for the community:

When working on a multithreaded C++ application, do you usually create individual threads with std::thread, or do you prefer using a thread pool?

I'd be interested to hear how others approach this!