How to use the multiprocessing module for inter-process communication in Python 2.x

Python is a high-level programming language that is fast and efficient when handling large tasks and data sets. One way is to use multi-threading to improve program performance. However, in some cases, we may need more processing power, in which case multiple processes can be used to fully utilize the system's resources. Python's multiprocessing module provides tools to implement multi-process programming, including inter-process communication mechanisms.

Inter-process communication is a way to exchange data and resources between different processes. In Python, we can use the Queue object in the multiprocessing module for inter-process communication.

In the following example, we will use the multiprocessing module to create two processes, which will communicate through the Queue object. One process will generate a series of numbers and put them into the Queue, and another process will get the numbers from the Queue and process them.

import multiprocessing

# 创建一个Queue对象用于进程间通信
queue = multiprocessing.Queue()

# 生成数字的进程函数
def generate_numbers():
    for i in range(10):
        # 将数字放入Queue中
        queue.put(i)

# 处理数字的进程函数
def process_numbers():
    while not queue.empty():
        # 从Queue中获取数字并进行处理
        number = queue.get()
        result = number * 2
        print("Processed number: {}".format(result))

if __name__ == "__main__":
    # 创建生成数字的进程
    generate_process = multiprocessing.Process(target=generate_numbers)
    # 创建处理数字的进程
    process_process = multiprocessing.Process(target=process_numbers)

    # 启动进程
    generate_process.start()
    process_process.start()

    # 等待进程结束
    generate_process.join()
    process_process.join()

Running the above code, we can see that the processes communicate smoothly. The process that generates numbers puts the numbers into the Queue, and the process that processes the numbers gets the numbers from the Queue and processes them. We can verify this by printing the results to the console.

In addition to Queue objects, the multiprocessing module also provides several other methods of inter-process communication, such as Pipe and Manager. Using these tools, we can achieve parallel computing and task distribution more easily.

On multi-processor systems, using multiple processes can significantly improve performance for certain computationally intensive tasks. The inter-process communication mechanism allows different processes to share data and resources safely and efficiently. Python's multiprocessing module provides a wealth of tools that can help us implement multi-process programming and inter-process communication.

To summarize, in Python 2.x, using the multiprocessing module for inter-process communication is very simple. By appropriately choosing the appropriate inter-process communication mechanism, we can make full use of system resources and improve program performance. This is very helpful for working with large tasks and data sets.

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