How to do parallel and distributed computing in Python

How to perform parallel computing and distributed computing in Python

With the continuous development of computer technology and the improvement of hardware performance, multi-core processors are used for parallel computing and Distributed computing has become one of the important means to improve program performance. As a simple, easy-to-use and powerful programming language, Python also provides a wealth of libraries and tools to support parallel computing and distributed computing.

This article will introduce how to perform parallel computing and distributed computing in Python, and give specific code examples.

1. Parallel Computing
A common method for parallel computing in Python is to use multi-threads or multi-processes. The following is a sample code for parallel computing using Python's built-in threading and multiprocessing libraries.

1. Use threading for parallel calculations

import threading

def calculate_square(numbers):
    for num in numbers:
        print(f"Square of {num} is {num*num}")

if __name__ == '__main__':
    numbers = [1, 2, 3, 4, 5]
    threads = []
    
    for i in range(5):
        t = threading.Thread(target=calculate_square, args=(numbers,))
        threads.append(t)
        t.start()

    for t in threads:
        t.join()

In the above code, we define a calculate_square function to calculate the number Square, and use threading.Thread to create multiple threads to perform calculation tasks in parallel. Finally, use the join function to wait for all threads to complete calculations.

2. Use multiprocessing for parallel computing

import multiprocessing

def calculate_square(numbers):
    for num in numbers:
        print(f"Square of {num} is {num*num}")

if __name__ == '__main__':
    numbers = [1, 2, 3, 4, 5]
    processes = []
    
    for i in range(5):
        p = multiprocessing.Process(target=calculate_square, args=(numbers,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

In the above code, we use multiprocessing.Process to create multiple processes to perform computing tasks in parallel. Finally, use the join function to wait for all processes to complete calculations.

2. Distributed Computing
In addition to using multi-threads or multi-processes for parallel computing, Python also provides some distributed computing frameworks, such as pySpark and dask, can perform large-scale parallel computing in a distributed environment.

1. Use pySpark for distributed computing

from pyspark import SparkContext

def calculate_square(num):
    return num * num

if __name__ == '__main__':
    sc = SparkContext()
    numbers = [1, 2, 3, 4, 5]
    rdd = sc.parallelize(numbers)
    
    squares = rdd.map(calculate_square).collect()
    for num, square in zip(numbers, squares):
        print(f"Square of {num} is {square}")

    sc.stop()

In the above code, we use the pyspark library to create a SparkContext object, and use the parallelize function to parallelize the data into an RDD (elastic distributed data set), and then use the map function to each element in the RDD calculate. Finally, use the collect function to collect the calculation results.

2. Use dask for distributed computing

import dask

@dask.delayed
def calculate_square(num):
    return num * num

if __name__ == '__main__':
    numbers = [1, 2, 3, 4, 5]
    results = []

    for num in numbers:
        result = calculate_square(num)
        results.append(result)

    squared_results = dask.compute(*results)
    for num, square in zip(numbers, squared_results):
        print(f"Square of {num} is {square}")

In the above code, we use the dask.delayed function to The calculation task is encapsulated as a delayed calculation object, and the dask.compute function is used to execute the calculation task. Finally, use the zip function to combine the input data and calculation results and output them.

Summary:
This article introduces how to perform parallel computing and distributed computing in Python, and gives specific code examples. Through parallel computing and distributed computing, the performance and efficiency of programs can be improved, which is especially important when processing large-scale data and complex computing tasks. Readers can choose appropriate methods and tools to parallelize and distribute computing tasks according to actual needs.

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