How to Work with Containers of Mutexes in Multi-Threaded Programming?

Working with Containers of Mutexes

In modern multi-threaded programming, safeguarding access to shared resources is crucial. Mutexes provide an effective mechanism for this purpose. However, leveraging containers like std::vector can pose challenges due to the absence of copy or move constructors in std::mutex.

Alternatives to std::vector

To overcome this limitation, consider alternative containers such as std::deque or std::forward_list. These containers do not impose strict requirements on their elements' movability, allowing you to store mutexes within them. By utilizing functions like emplace() or resize() for element construction, you can effectively manage the size of your container without causing errors.

Additional Considerations

Certain operations, such as insert() and push_back(), will not be supported with the aforementioned containers. Therefore, direct insertion into the collection should be done during object construction or via functions like emplace().

Alternative Approaches

Aside from container-based solutions, consider the following approaches:

• Fixed-Size Mutex Pool with Hashing: Allocate a fixed number of mutexes and employ a hash function to map objects to their respective mutexes. This method can handle potential collisions through a sufficiently high number of mutexes.
• Wrapper Class for Mutex: Introduce a custom wrapper class that inherits from std::mutex and provides copy and move constructors. By using a vector of these wrapper objects, you can achieve the desired functionality.
• Dynamic Memory Management with std::unique_ptr: Employ std::unique_ptr to manage individual mutexes on the heap. Adjust the number of mutexes dynamically as needed to accommodate changing requirements.

Selection Criteria

The best approach depends on specific requirements. Fixed-size mutex pools may be viable if the number of accessed objects is predictable. Wrapper classes offer a convenient but indirection-introducing solution. Dynamic memory management provides flexibility but carries a performance overhead due to heap allocation.

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