Python shared memory

Python Shared Memory

In Python, shared memory is a method for sharing data between multiple processes. Through shared memory, different processes can access and modify the same memory area, thereby enabling inter-process communication and data sharing.

Why is shared memory necessary?

In multi-process programming, it is sometimes necessary for multiple processes to communicate, transfer data, or share resources. Common scenarios include:

• Data transfer between parent and child processes
• Resource sharing between different child processes
• Multiple processes simultaneously reading and writing the same resource

In these scenarios, using shared memory can reduce inter-process communication overhead and improve program efficiency and performance.

Shared Memory in Python

The Python multiprocessing module provides a shared memory implementation. Shared memory objects can be created using Value and Array, enabling data sharing between processes.

Creating Shared Memory Using Value

Value creates a shared memory object to store a single value. Its usage is as follows:

from multiprocessing import Process, Value

def f(n):
n.value = 3.1415926

if __name__ == ‘__main__’:
num = Value(‘d’, 0.0)
p = Process(target=f, args=(num,))
p.start()
p.join()

print(num.value)

Running the above code prints 3.1415926. In the child process, the value of the shared memory object num is modified to 3.1415926, and the value is read and printed in the main process.

Creating Shared Memory Using Array

Array can be used to create a shared memory object to store an array. Its usage is as follows:

from multiprocessing import Process, Array

def f(arr):

for i in range(len(arr)):

arr[i] = i

if __name__ == '__main__':

arr = Array('i', 5)

p = Process(target=f, args=(arr,))

p.start()

p.join()

print(arr[:])

Running the above code prints [0, 1, 2, 3, 4]. In the child process, the value of the shared memory object arr is modified to [0, 1, 2, 3, 4], and the array is read and printed in the main process.

Shared Memory Precautions

When using shared memory, be aware of the following:

• Modifications to shared memory objects are performed in-place and do not trigger a copy. Therefore, concurrent access to shared memory must be handled with caution to avoid race conditions.
• The type and length of a shared memory object are determined at creation time and cannot be changed dynamically. Therefore, ensure that the correct type and length are specified when creating it.
• The lifecycle of a shared memory object is managed by the creating process. When the process exits, the shared memory object is also destroyed. Therefore, ensure that the corresponding shared memory object is created when it is needed and release resources at the appropriate time.

The shared memory functionality provided by the multiprocessing module allows us to easily implement data sharing and communication between processes in Python, making multi-process programming more flexible and efficient.