☀️ Parallel processing in Python
Parallel Processing in Python
Parallel processing is a programming method that allows developers to design their programs in a way that different parts of the program run simultaneously, thereby reducing execution time. Python supports parallel processing as well. There are several methods for parallel processing in Python, including using libraries like multiprocessing, threading, and concurrent.futures. Below is a simple example of each of these methods:
1. Using the multiprocessing library:
pythonCopy codeimport multiprocessing
def worker(num):
"""To calculate the square of a number"""
print(num * num)
if __name__ == '__main__':
# Specify the number of processes
processes = []
for i in range(10):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p)
p.start()
for process in processes:
process.join()
2. Using the threading library:
pythonCopy codeimport threading
def worker(num):
"""To calculate the square of a number"""
print(num * num)
if __name__ == '__main__':
# Specify the number of threads
threads = []
for i in range(10):
t = threading.Thread(target=worker, args=(i,))
threads.append(t)
t.start()
for thread in threads:
thread.join()
3. Using the concurrent.futures library:
pythonCopy codeimport concurrent.futures
def worker(num):
"""To calculate the square of a number"""
print(num * num)
if __name__ == '__main__':
# Specify the number of processes
with concurrent.futures.ProcessPoolExecutor() as executor:
executor.map(worker, range(10))
In these examples, the squares of numbers from 0 to 9 are calculated simultaneously. It’s worth mentioning that these methods can also be used for more complex, real-world applications, and the best approach can be chosen based on specific needs and circumstances.
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