如何在Python中进行并行计算和分布式计算

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()

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()

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()

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}")