Sharing data between threads is a common requirement in multi-threaded programming. When it comes to shared ownership of resources, std::shared_ptr provides a convenient solution. However, it’s important to understand how to safely move std::shared_ptr between threads to avoid potential issues.

The problem with moving std::shared_ptr directly

Directly moving std::shared_ptr between threads using std::move or any other means can result in undefined behavior. This behavior stems from the fact that std::shared_ptr uses an internal reference count to track ownership. If two threads attempt to modify the reference count simultaneously, it can lead to race conditions and memory corruption.

Using std::shared_ptr with std::atomic

To safely move std::shared_ptr between threads, we can leverage std::atomic. std::atomic provides atomic operations for shared access to memory. By combining std::shared_ptr with std::atomic, we can ensure safe ownership transfer without risking race conditions.

#include <atomic>
#include <memory>
#include <thread>

std::atomic<std::shared_ptr<int>> globalPtr;

void threadA()
{
    std::shared_ptr<int> localPtr = std::make_shared<int>(42);
    globalPtr.store(localPtr);
}

void threadB()
{
    std::shared_ptr<int> localPtr = globalPtr.load();
    
    // Use localPtr as needed
}

int main()
{
    std::thread t1(threadA);
    std::thread t2(threadB);
    
    t1.join();
    t2.join();
    
    return 0;
}

In this example, threadA creates a std::shared_ptr<int> and stores it in the globalPtr atomic variable using std::atomic::store(). threadB then loads the std::shared_ptr<int> from globalPtr using std::atomic::load() and can safely use it since it guarantees the reference count remains valid during the move.

Conclusion

Moving std::shared_ptr between threads requires extra caution to prevent race conditions and memory corruption. By utilizing std::atomic to provide atomic operations, we can safely transfer ownership of std::shared_ptr without risking undefined behavior. Ensure to always follow safe practices when working with shared resources in multi-threaded environments.

#cpp #multithreading