Python multiprocessing.Value atomic read and write for double - Stack Overflow

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I am using the multiprocessing.Value class in Python and I have one writer process that sets value and a reader process that reads value. Suppose my writer process only sets value in the fashion of shared_value.value = new_value (not read-modify-write like shared_value+=1) and my reader reads by new_value=shared_value.value, is this process safe and can I not use a lock in this case?

I am using the multiprocessing.Value class in Python and I have one writer process that sets value and a reader process that reads value. Suppose my writer process only sets value in the fashion of shared_value.value = new_value (not read-modify-write like shared_value+=1) and my reader reads by new_value=shared_value.value, is this process safe and can I not use a lock in this case?

• python
• multiprocessing
• python-multiprocessing

Share Improve this question Follow edited Nov 19, 2024 at 12:20 Timus 11.4k55 gold badges1717 silver badges3131 bronze badges asked Nov 19, 2024 at 11:53 manner_teachermanner_teacher 3144 bronze badges 1

• 1 Please give a concrete example with actual code ; minimal reproducible example – MatBailie Commented Nov 19, 2024 at 12:01

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4 Answers 4

Sorted by: Reset to default 1

Yes, your usage of multiprocessing.Value for atomic read and write operations is safe without a Lock, as Value ensures atomicity for basic types like ctypes.c_double. Since you are only performing direct assignments (shared_value.value = new_value) and simple reads (new_value = shared_value.value) without any compound operations (e.g., +=), there is no risk of race conditions. However, if you introduce more complex operations or multiple writers in the future, consider using a Lock. Here's an example:

from multiprocessing import Process, Value
import ctypes
import time

def writer(shared_value):
    for i in range(5):
        shared_value.value = i * 1.1
        time.sleep(1)

def reader(shared_value):
    for _ in range(5):
        print(f"Read value: {shared_value.value}")
        time.sleep(1.5)

if __name__ == "__main__":
    shared_value = Value(ctypes.c_double, 0.0)
    Process(target=writer, args=(shared_value,)).start()
    Process(target=reader, args=(shared_value,)).start()

multiprocessing.Value has a built-in lock which you should use where there may be conflict because Value is not inherently atomic.

See this documentation

Here's an example:

from multiprocessing import Process, Value

STOP = 10_000

def add(v):
    for _ in range(STOP):
        with v.get_lock():
            v.value += 1

def subtract(v):
    for _ in range(STOP):
        with v.get_lock():
            v.value -= 1

def main():
    v = Value("i", 0)
    (p1 := Process(target=add, args=(v,))).start()
    (p2 := Process(target=subtract, args=(v,))).start()
    p1.join()
    p2.join()
    assert v.value == 0

if __name__ == "__main__":
    main()

There are subtleties to this question. The documentation for multiprocessing.RawArray states in part:

Note that setting and getting an element is potentially non-atomic – use Array() instead to make sure that access is automatically synchronized using a lock.

Typically if you have a Value or Array that is holding 32-bit values such as integers, reading and writing elements are atomic operations (at least on the hardware I am familiar with) and you do not need locking for code that looks like:

Writer Process: shared_value.value = 8

Reader Process: x = shared_value.value

But if the values in question are 64-bit floating points, then I don't think we can assume that storing such a value is atomic. It may very well be that the writing is done 4-bytes at a time and a process that is reading the floating point will get a partially written value. In this case, or if you are not sure, the prudent thing would be to use locking.

But let's work on the assumption that reads and writes are atomic. Then in the above scenario no locking is required. But what if the writer process is executing a non-atomic operation:

Writer Process: shared_value.value += 1

This is equivalent to:

x = shared_value.value
shared_value.value = x + 1

If you have multiple writer processes executing the above code, then you surely need to use locking since both writer processes might read the same value of x. But if there is only a single writer process I see no need for locking (again assuming that whatever value is being store is done so atomically).

Finally, a multiprocessing.Array created with lock=True (the default) will have a synchronized wrapper around it. If you do not need locking, then you will have better performance if you specify lock=False. But if you do need to use locking, you should either use a multiprocessing.RawArray instance with a separately created lock or use a multiprocessing.Array instance but do not access the array via the wrapper; use instead the underlying "raw" array obtained with a call to get_obj. The following code demonstrates the performance hit of accessing a synchronized array through its synchronization layer:

from multiprocessing import Array, RawArray, Lock
import time

N = 1_000_000

def writer1(arr: Array):
    """Increment an Array with locking through its synchronization layer."""
    
    lock = arr.get_lock()
    print(arr)

    t = time.time()

    for _ in range(N):
        with lock:
            arr[0] += 1

    elapsed = time.time() - t
    print(elapsed, arr[0])


def writer2(arr: Array):
    """Increment an Array with locking via underlying "raw" array."""

    lock = arr.get_lock()
    a = arr.get_obj()
    print(a)

    t = time.time()

    for _ in range(N):
        with lock:
            a[0] += 1

    elapsed = time.time() - t
    print(elapsed, arr[0])


def writer3(arr: RawArray, lock: Lock):
    """Increment a RawArray with locking."""
    
    print(arr)

    t = time.time()

    for _ in range(N):
        with lock:
            arr[0] += 1

    elapsed = time.time() - t
    print(elapsed, arr[0])


arr1 = Array('i', [0])
writer1(arr1)
print()

arr2 = Array('i', [0])
writer2(arr2)
print()

arr2 = RawArray('i', [0])
lock = Lock()
writer3(arr2, lock)

Prints:

<SynchronizedArray wrapper for <multiprocessing.sharedctypes.c_long_Array_1 object at 0x0000029063A405D0>>
3.0404653549194336 1000000

<multiprocessing.sharedctypes.c_long_Array_1 object at 0x0000029063A40E50>
1.5013427734375 1000000

<multiprocessing.sharedctypes.c_long_Array_1 object at 0x0000029063A40F50>
1.4996867179870605 1000000

In Python's multiprocessing module, when you use the multiprocessing.Value class to share data between processes, you need to be aware of how values are accessed and modified in a concurrent environment. In your case, you have a writer process that sets the value and a reader process that reads it.

Writer Process: Sets the value directly with shared_value.value = new_value.

Reader Process: Reads the value using new_value = shared_value.value.

Writing to shared_value.value in a separate process while another is reading it can lead to race conditions. A race condition might not occur every time, but it can happen if the read and write operations happen concurrently. This can result in the reader getting a stale or partially updated value, which can lead to unexpected behavior or bugs.

Here is a little exemple using Lock:

    from multiprocessing import Process, Value, Lock
import time

def writer(shared_value, lock):
    while True:
        with lock:
            new_value = ...
            shared_value.value = new_value
        time.sleep(1) 

def reader(shared_value, lock):
    while True:
        with lock:
            current_value = shared_value.value
        print(f'Read value: {current_value}')
        time.sleep(1)

if __name__ == '__main__':
    lock = Lock()
    shared_value = Value('i', 0) 
    writer_process = Process(target=writer, args=(shared_value, lock))
    reader_process = Process(target=reader, args=(shared_value, lock))

    writer_process.start()
    reader_process.start()

    writer_process.join()
    reader_process.join()

So in a lot of cases without a Lock, it is not guaranteed to be safe due to potential race conditions. It's better practice to use a Lock to ensure synchronized access to multiprocessing.Value in shared memory.

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