In concurrent programming, memory ordering refers to the consistency guarantees imposed on read and write operations performed by multiple threads accessing shared memory. One common approach to memory ordering is through the use of memory barriers or synchronization primitives. In this blog post, we will explore relaxed memory ordering and provide examples of how memory barriers can be used to enforce ordering constraints.

Understanding Relaxed Memory Ordering

Relaxed memory ordering allows for optimizations to be applied to memory operations, which might lead to reordering or overlapping of these operations. This can result in improved performance but may also introduce subtle issues if not handled carefully.

For instance, consider the following code snippet:

#include <atomic>
#include <thread>

std::atomic<int> sharedValue = 0;

void thread1()
{
    sharedValue.store(42, std::memory_order_relaxed);
}

void thread2()
{
    int value = sharedValue.load(std::memory_order_relaxed);
    if (value == 42)
    {
        // Perform some operation
    }
}

In this example, both thread1 and thread2 access the sharedValue variable. The store and load operations are performed with relaxed memory ordering. This allows the operations to be reordered or executed concurrently for potential optimization purposes.

Memory Barriers to Enforce Ordering

To ensure that certain operations execute in the desired order, memory barriers can be used. A memory barrier puts constraints on the ordering of memory operations, preventing certain reorderings from occurring.

In the above example, if we want to ensure that the modification made by thread1 is visible to thread2 before it performs its operation, we can modify the code as follows:

#include <atomic>
#include <thread>
#include <cstdint>

std::atomic<int> sharedValue = 0;
std::atomic<bool> readyFlag = false;

void thread1()
{
    sharedValue.store(42, std::memory_order_relaxed);
    std::atomic_thread_fence(std::memory_order_release);
    readyFlag.store(true, std::memory_order_relaxed);
}

void thread2()
{
    while (!readyFlag.load(std::memory_order_relaxed))
    {
        std::this_thread::yield();
    }
    std::atomic_thread_fence(std::memory_order_acquire);
    int value = sharedValue.load(std::memory_order_relaxed);
    if (value == 42)
    {
        // Perform some operation
    }
}

In this updated code, we introduced two memory barriers using the std::atomic_thread_fence() function. The memory_order_release barrier in thread1 ensures that the modification made to sharedValue is visible to other threads before the readyFlag is set to true. On the other hand, the memory_order_acquire barrier in thread2 ensures that the load operation on sharedValue occurs only after the readyFlag is observed as true.

These memory barriers provide the necessary synchronization to enforce the desired ordering and ensure the correctness of the concurrent execution.

Conclusion

Understanding the concept of relaxed memory ordering and how memory barriers can be used to enforce ordering constraints is crucial in concurrent programming. By carefully applying memory barriers where needed, we can strike a balance between performance optimization and maintaining correct program behavior.