In-depth Comparison and Application Scenarios of Multithreading vs. Multiprocessing in Python

Knowledge Point Description
Multithreading and multiprocessing are the two core methods for implementing concurrent programming in Python. Multithreading allows multiple tasks to be executed concurrently within the same process, sharing the same memory space; multiprocessing creates independent processes, each with its own separate memory space. Due to the limitation of Python's GIL (Global Interpreter Lock), multithreading performs poorly for CPU-intensive tasks but remains effective for I/O-intensive tasks. Multiprocessing can achieve true parallel computation but incurs greater overhead.

Detailed Explanation

1. Basic Concept Distinction

• Thread: The smallest unit scheduled by the operating system. Threads belonging to the same process share resources like memory and file handles.
• Process: The smallest unit of resource allocation. Each process has its own independent memory space and is isolated from others.

2. Implementation Methods in Python

# Multithreading Example
import threading
import time

def thread_task(n):
    print(f"Thread {n} starting")
    time.sleep(1)
    print(f"Thread {n} ending")

# Create and start threads
threads = []
for i in range(3):
    t = threading.Thread(target=thread_task, args=(i,))
    threads.append(t)
    t.start()

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

# Multiprocessing Example
import multiprocessing
import time

def process_task(n):
    print(f"Process {n} starting")
    time.sleep(1)
    print(f"Process {n} ending")

if __name__ == "__main__":
    processes = []
    for i in range(3):
        p = multiprocessing.Process(target=process_task, args=(i,))
        processes.append(p)
        p.start()

    for p in processes:
        p.join()

3. Impact Mechanism of GIL

• GIL is a characteristic of the CPython interpreter, ensuring only one thread executes Python bytecode at any given moment.
• For pure Python code: Multithreading cannot achieve true parallelism; multiprocessing is needed to bypass the GIL limitation.
• For I/O operations: Threads release the GIL while waiting for I/O, so I/O-intensive tasks can still benefit from multithreading.
• For C extensions: Some libraries (e.g., numpy) release the GIL at the C layer, enabling true parallelism.

4. Performance Comparison Experiment

import time
import threading
import multiprocessing

# CPU-intensive task
def cpu_bound(n):
    count = 0
    for i in range(10000000):
        count += i
    return count

# Test function
def benchmark(func, executor, workers=4):
    start = time.time()
    # Create workers and execute
    if executor == "thread":
        workers_list = [threading.Thread(target=cpu_bound, args=(i,)) for i in range(workers)]
    else:
        workers_list = [multiprocessing.Process(target=cpu_bound, args=(i,)) for i in range(workers)]
    
    for w in workers_list:
        w.start()
    for w in workers_list:
        w.join()
    
    return time.time() - start

# Comparison test
if __name__ == "__main__":
    print(f"Multithreading time: {benchmark(cpu_bound, 'thread')} seconds")
    print(f"Multiprocessing time: {benchmark(cpu_bound, 'process')} seconds")

5. Data Sharing and Communication Mechanisms

# Multithreading data sharing (direct sharing)
shared_data = 0
lock = threading.Lock()

def thread_increment():
    global shared_data
    with lock:
        shared_data += 1

# Multiprocessing data sharing (requires special mechanisms)
def process_increment(shared_value, lock):
    with lock:
        shared_value.value += 1

if __name__ == "__main__":
    # Multiprocessing shared value
    shared_value = multiprocessing.Value('i', 0)
    process_lock = multiprocessing.Lock()
    
    processes = []
    for _ in range(10):
        p = multiprocessing.Process(target=process_increment, args=(shared_value, process_lock))
        processes.append(p)
        p.start()
    
    for p in processes:
        p.join()
    
    print(f"Final value: {shared_value.value}")

6. Practical Application Scenario Selection Guide

Choose multithreading when:

• I/O-intensive tasks (network requests, file operations, database queries)
• Lightweight concurrency is needed and tasks require frequent communication
• GUI applications (to keep the interface responsive)

Choose multiprocessing when:

• CPU-intensive tasks (mathematical computation, image processing)
• Tasks require true parallel execution
• Tasks are relatively independent and do not require frequent communication
• Better fault isolation is needed (a process crash does not affect others)

7. Advanced Usage and Best Practices

# Using thread pools/process pools (recommended)
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

def task(n):
    return n * n

# Automatically manage resources
with ThreadPoolExecutor(max_workers=4) as executor:
    results = executor.map(task, range(10))
    print(list(results))

# Dynamically select based on task type
def smart_executor(tasks, task_type='io'):
    if task_type == 'cpu':
        executor_class = ProcessPoolExecutor
    else:
        executor_class = ThreadPoolExecutor
    
    with executor_class() as executor:
        return list(executor.map(task, tasks))

Summary
The key to understanding the distinction between multithreading and multiprocessing lies in: threads share memory but are limited by the GIL, while processes have independent memory but greater overhead. In practical projects, the appropriate concurrency model should be selected based on task type (I/O-intensive vs. CPU-intensive), data sharing requirements, and system resources. For hybrid tasks, asynchronous programming (asyncio) or other concurrency patterns can also be considered.