Multithreading & Multiprocessing Python

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In modern computing, we often hear terms like parallelism and concurrency. In Python, there are two main ways to do multiple things "at once": Multithreading and Multiprocessing.

The choice between them depends heavily on the type of task you are working on: whether it is I/O Bound or CPU Bound.

1. I/O Bound vs CPU Bound

• I/O Bound: Program spends most of its time waiting for input/output (example: network requests, reading disk files, database queries). CPU is often idle.
• CPU Bound: Program spends its time doing mathematical calculations or heavy data processing. CPU works at 100%.

2. Multithreading (For I/O Bound)

Threading uses threads inside the same single process. Threads share the same memory.

However, Python (CPython) has a GIL (Global Interpreter Lock), which prevents two Python threads from executing bytecode simultaneously on a single CPU core. So, Multithreading in Python does not make CPU-bound code faster (it can even be slower due to overhead).

But, Multithreading is very fast for I/O Bound because when one thread waits (e.g., waiting for web response), other threads can run.

import threading
import time

def download_page(url):
    print(f"Start downloading {url}...")
    time.sleep(2) # Simulate network delay
    print(f"Finished downloading {url}")

start = time.time()

threads = []
urls = ["web1", "web2", "web3"]

for url in urls:
    t = threading.Thread(target=download_page, args=(url,))
    threads.append(t)
    t.start()

# Wait for all threads to complete
for t in threads:
    t.join()

end = time.time()
print(f"Total time: {end - start:.2f} seconds")
# Output around 2 seconds, not 6 seconds!

3. Multiprocessing (For CPU Bound)

Multiprocessing creates separate new Python processes. Each process has its own Python interpreter and memory space. This bypasses GIL, so it can utilize multi-core CPU maximally.

Use this for computationally heavy tasks.

import multiprocessing
import time

def heavy_square_calculation(number):
    print(f"Process {number} starts...")
    result = sum(i * i for i in range(10**7)) # Heavy calculation
    print(f"Process {number} finished.")
    return result

if __name__ == "__main__":
    start = time.time()
    
    # Create 2 processes running parallel on different CPU cores
    p1 = multiprocessing.Process(target=heavy_square_calculation, args=(1,))
    p2 = multiprocessing.Process(target=heavy_square_calculation, args=(2,))
    
    p1.start()
    p2.start()
    
    p1.join()
    p2.join()
    
    end = time.time()
    print(f"Total time: {end - start:.2f} seconds")

Note: You must protect the main code with if __name__ == "__main__": when using multiprocessing in Windows.

4. Concurrent Futures (Modern Way)

Python provides concurrent.futures module which gives higher-level and easier interface for Threading and Multiprocessing.

from concurrent.futures import ThreadPoolExecutor
import time

def task(n):
    time.sleep(1)
    return f"Task {n} finished"

start = time.time()

with ThreadPoolExecutor(max_workers=3) as executor:
    results = executor.map(task, [1, 2, 3])
    
    for result in results:
        print(result)

print(f"Time: {time.time() - start:.2f} seconds")

Replace ThreadPoolExecutor with ProcessPoolExecutor if you want to switch to multiprocessing.

Conclusion

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