You've embarked on a journey through concurrency essentials, and now it's time to dive deeper into mutexes and deadlocks. In this lesson, we'll explore the critical components of safe concurrent programming. While we've touched on concurrency basics, here we delve into practical scenarios involving mutexes and deadlock prevention, ensuring you have the skills to manage multi-threaded applications effectively.

This unit touches more advanced use cases preparing you to tackle real-world challenges in the next units of this course. Let's dive in!

In this lesson, we will cover more use cases and scenarios involving mutexes and deadlocks. Here's a brief overview of what you'll learn:

• Mutexes and Locks with Collections: Safely synchronizing access to shared resources like vectors.
• Dynamic and Multiple Mutex Usage: Dynamically allocating mutexes and using a list of mutexes for better control.
• Nested Functions and Deadlocks: Understanding how nested function calls can lead to deadlocks and how to avoid them.

Let's go through each topic in detail.

• Mutexes and Locks with Collections: Understanding how to synchronize access to shared resources like vectors.

  Here's a quick reminder of using a mutex to safely push data into a shared vector:

  Notice, that we are using a single mutex to protect the shared vector. This is a common practice, but there are scenarios where you might need to use multiple mutexes, which brings us to the next topic.

• Dynamic and Multiple Mutex Usage: We'll discuss when to dynamically allocate mutexes and how to use a list of mutexes for better control.

  Consider this example where each vector in an array has its own mutex:

  In the above example, we have an array of vectors, and each vector has its own mutex. This approach can be useful when you need to protect multiple resources independently or when the number of resources is not known at compile time. Each vector and its corresponding mutex are independent. So, if one thread locks mutexes[0], other threads can still lock mutexes[1], mutexes[2], etc., and modify those vectors concurrently. This allows for efficient and safe concurrent access to multiple resources.

• Nested Functions and Deadlocks: We'll cover how nested function calls can lead to deadlocks, and how to avoid them.

  A simple scenario to consider:

  In the above example, funcA locks m1 and then calls funcB, which also tries to lock m1, but since m1 is already locked by funcA, a deadlock occurs and the program hangs.

  This is a simple representation, but in real life the situation can be more complex. To avoid such cases you can use try_lock by changing funcB as follows:

  With this change, funcB will only proceed if it can lock m1. If it can't, it will not block and will not cause a deadlock.

Understanding how to effectively use mutexes and avoid deadlocks is crucial in modern software development. As applications become more complex and multi-threaded, managing resources safely and efficiently becomes a paramount challenge. By mastering these concepts, you'll ensure that your applications are responsive, reliable, and maintainable — qualities highly sought after in the tech industry.

Are you ready to embark on this vital segment of your concurrency journey? Let's move to the practice section and put theory into practice!