Concurrency & performance
asyncio, threading, multiprocessing, and profiling-friendly performance patterns.
How to Demonstrate the GIL with Python Threads vs Processes
Measure and compare wall-clock time for CPU-bound work using Python threads (limited by the GIL) versus multiprocessing (which bypasses the GIL).
import threading
import multiprocessing
import time
import os
def cpu_heavy(n):
return sum(i * i for i in range(n))
def run_threads(n):
threads = [threading.Thread(target=cpu_heavy, args=(n,)) for _ in range(2)]
start = time.perf_counter()
for t in threads:
t.start()
for t in threads:
…How to Send and Receive Messages Between Processes with multiprocessing.Pipe in Python
Use multiprocessing.Pipe to create a two-way connection between two processes, send a message from parent to child, and receive a reply back.
import multiprocessing
def child_process(conn):
"""Receive from parent and send back a response."""
message = conn.recv()
print(f"Child received: {message}")
conn.send("Hello from child!")
if __name__ == "__main__":
parent_conn, child_conn = multiprocessing.Pipe()
process = multiprocessing…How to Share Memory Between Processes in Python with multiprocessing.Value and Array
Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.
import multiprocessing
def worker(shared_value, shared_array, index):
shared_value.value += 10
shared_array[index] = shared_array[index] * 2
if __name__ == "__main__":
shared_value = multiprocessing.Value("i", 5)
shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])
processes = []
for i…How to Share a Dict and List Between Processes with multiprocessing Manager in Python
This code demonstrates how to share a dictionary and a list between multiple processes using multiprocessing.Manager, enabling safe concurrent updates.
import multiprocessing as mp
def worker(shared_dict, shared_list, name):
shared_dict[name] = name.upper()
shared_list.append(name)
print(f"{name} added to shared structures")
def main():
with mp.Manager() as manager:
shared_dict = manager.dict()
shared_list = manager.list()
…How to Share a Queue Between Processes in Python
Use multiprocessing.Queue to pass work from a producer process to multiple consumer processes, coordinating with a sentinel stop message.
import multiprocessing
import time
def producer(queue, items):
for item in items:
queue.put(item)
time.sleep(0.1)
queue.put("STOP")
def consumer(queue, name):
while True:
item = queue.get()
if item == "STOP":
break
print(f"{name} processed: {item}")
…How to Use pool.map for CPU-Bound Tasks in Python
Distribute CPU-intensive functions across processes with multiprocessing.Pool.map and measure the performance gain.
from multiprocessing import Pool
import time
def cpu_bound_task(n):
"""Mock CPU-bound work: compute sum of squares."""
total = 0
for i in range(n):
total += i * i
return total
if __name__ == "__main__":
numbers = [10_000_000, 12_000_000, 8_000_000, 15_000_000]
start = time.perf_count…How to spawn multiple worker processes in Python with multiprocessing.Process
Spawns three separate worker processes using multiprocessing.Process, runs them concurrently, and waits for all to finish before printing a completion message.
import multiprocessing
import time
def worker(name):
print(f"Worker {name} started")
time.sleep(1)
print(f"Worker {name} finished")
return name
if __name__ == "__main__":
processes = []
for i in range(3):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p…Browse by section
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Concurrency & performance — Python code examples
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