Multi-process programming in Python

Multi-process programming in Python

Python is a very popular programming language. Its powerful functions, ease of learning and use, and interoperability with other programming languages ​​make Python widely used, from Python is used in everything from web development to artificial intelligence. At the same time, multi-process programming in Python is also recognized for its simple and efficient programming model.

What is multi-process programming?

Multi-process programming refers to using multiple processes to handle different tasks at the same time through Python programs. Compared with single-process programming, multi-process programming can ensure that the program runs faster and can handle multiple tasks at the same time, improving computer utilization efficiency.

Why use multi-process programming?

In some specific scenarios, using multi-process programming has more advantages than single-process programming. Some of the advantages are as follows:

1. Improve program execution speed: Multi-process programming can process multiple processes at the same time. Tasks, calculations between different processes are parallel, which greatly improves the speed of program running.

2. Improve computer utilization efficiency: Multi-process programming can use computer CPU, memory and other resources at the same time, making full use of computer performance, thus improving computer utilization efficiency.

3. Enhance program stability: If your program has some unstable factors, using multi-process technology can reduce the possibility of the entire program crashing due to the crash of a sub-process.

How to implement multi-process programming?

In Python, multi-process programming can be achieved through the following two methods:

1. Use Python’s built-in multiprocessing module. This module is implemented in an object-based manner and can be easily Used to create and manage multiple processes.

2. Use Python’s os module to call the process creation and management functions of the underlying operating system.

Below, we take the multiprocessing module as an example to introduce how to implement multi-process programming.

Basic usage of the multiprocessing module:

The main classes of the multiprocessing module are as follows:

1. Process: used to create new sub-processes.

2.Pool: Used to create a group of processes to execute multiple tasks in parallel.

3.Queue: Provides a method for communication between multiple processes.

4.Pipe: Provides an implementation method for bidirectional pipeline communication.

Below, we take the implementation of a simple calculator program as an example to introduce the specific usage of the multiprocessing module.

from multiprocessing import Process

def calculate(num):

result = num * num
print("The square of {} is {}".format(num, result))

if name == '__main__':

p1 = Process(target=calculate, args=(5,))
p2 = Process(target=calculate, args=(8,))

p1.start()
p2.start()

p1.join()
p2.join()

In this program, we define a calculate function, which is used to calculate the square of a number. We use the Process class to create two child processes that calculate the square of 5 and the square of 8 respectively. In the main program, we use the start method to start two child processes, and then use the join method to wait for the two processes to finish running. Running this program, you can get the following results:

The square of 5 is 25
The square of 8 is 64

The two processes in this program are executed in parallel. There is no blocking in between, so it runs very fast.

Conclusion:

Multi-process programming is a very important module in Python. Programming using many processes can greatly improve the calculation speed of the program and make full use of the computer's performance. , improve computer utilization. At the same time, in multi-process programming, we also need to pay attention to the issue of process communication, so as to make multi-process programming more efficient.

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