Welcome back! Now that you have a good grasp of thread lifecycles and basic operations, let’s move forward to a critical and exciting part of concurrent programming: data sharing between threads. In this lesson, we will explore how threads can share data using primitive approaches and understand the importance of synchronizing this access.

Data sharing between threads, while powerful, can lead to unpredictable behavior if not managed correctly. We will cover:

1. Shared Variables and Risks of Unsynchronized Access:
   • Learn how threads can share data through shared variables.
   • Understand the risks of unsynchronized access, such as race conditions.
2. Introduction to Synchronization Primitives:
   • Explore the basic synchronization primitives like std::mutex and std::lock_guard.
   • Understand how these tools prevent race conditions by ensuring that only one thread can access the shared resource at a time.
3. Code Example: Observing Race Conditions and Fixing Them:
   • We’ll demonstrate a race condition and then fix it using std::mutex and std::lock_guard.

Let's start by an example that demonstrates the risks of unsynchronized access to shared variables:

This code might produce different results each time it's run due to race conditions. This is because both threads are accessing the shared variable counter without any synchronization. Here is a quick scenario that explains the issue:

1. Thread 1 reads the value of counter (let's say it's 15).
2. Thread 2 reads the value of counter (also 15).

Understanding how to manage data sharing between threads is paramount for writing reliable and efficient concurrent programs. Here’s why:

• Avoiding Race Conditions: Race conditions can lead to unpredictable and erroneous behavior in your application. Synchronization helps to prevent such issues.
• Data Integrity: By ensuring that shared data is accessed in a controlled manner, you can maintain the integrity of your program’s state.
• Enhancing Robustness: Synchronization primitives make your concurrent code more robust and easier to debug, as they eliminate many common concurrency-related bugs.

Excited to dive deeper into this crucial aspect of concurrency? Let’s move on to the practice section and solidify your understanding through hands-on coding.