Reference library

Concurrency & performance

asyncio, threading, multiprocessing, and profiling-friendly performance patterns.

8 matches
Concurrency & performance easy

How to Convert Data in Parallel with ThreadPoolExecutor in Python

This example demonstrates converting a list of items in parallel using ThreadPoolExecutor, showing performance gains over serial processing.

concurrency threadpoolexecutor parallelism
Python
import time
from concurrent.futures import ThreadPoolExecutor


def convert_data(item):
    """Simulate a CPU/IO-bound conversion task."""
    time.sleep(0.05)  # simulate work
    return item.upper()


if __name__ == "__main__":
    items = [f"item_{i}" for i in range(20)]

    start = time.perf_counter()
    serial_…
16 0 Open
Concurrency & performance easy

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.

multiprocessing pipe interprocess-communication
Python
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…
14 0 Open
Concurrency & performance easy

How to Use ThreadPoolExecutor and ProcessPoolExecutor in Python

Compares ThreadPoolExecutor and ProcessPoolExecutor by running CPU-bound and I/O-tolerant tasks over a large list, printing elapsed times and first results.

concurrency threadpool processpool
Python
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import math

numbers = list(range(1, 1000001))


def compute_square(n):
    return n * n


def compute_sqrt(n):
    return math.sqrt(n)


def run_executor(executor, func, data):
    start = time.perf_counter()
    results = list(executo…
15 0 Open
Concurrency & performance easy

How to Use ThreadPoolExecutor in Python for Parallel Processing

Use ThreadPoolExecutor with executor.map to run a function over many inputs concurrently and collect ordered results.

concurrency threadpoolexecutor parallel
Python
def worker(item):
    return item * item

if __name__ == "__main__":
    from concurrent.futures import ThreadPoolExecutor
    numbers = list(range(1, 11))
    with ThreadPoolExecutor(max_workers=4) as executor:
        results = list(executor.map(worker, numbers))
    print("Input:  ", numbers)
    print("Results:", …
13 0 Open
Concurrency & performance easy

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.

multiprocessing pool cpu-bound
Python
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…
11 0 Open
Concurrency & performance easy

How to Vectorize a Function with a Pure Python Fallback

Create a decorator that calls a scalar function directly for a single value and routes list inputs to a pure-Python fallback for vectorized processing without NumPy.

vectorization decorator fallback
Python
import math


def fallback_vectorize(func, fallback=None):
    """Vectorize a scalar function with a pure-Python fallback for lists."""
    if fallback is None:
        fallback = lambda x: [func(i) for i in x]

    def wrapped(*args):
        if len(args) == 1 and isinstance(args[0], (list, tuple)):
            retur…
14 0 Open
Concurrency & performance easy

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.

multiprocessing parallel concurrency
Python
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…
14 0 Open
Concurrency & performance easy

Synchronize Threads with a Barrier in Python

Demonstrates using threading.Barrier to synchronize multiple threads at phase boundaries, ensuring all workers wait for each other before proceeding.

threading synchronization barrier
Python
import threading
import time
from random import randint

def worker(barrier, worker_id):
    for phase in range(3):
        time.sleep(randint(1, 3))
        print(f"Worker {worker_id} finished phase {phase} at {time.time():.2f}")
        barrier.wait()
    print(f"Worker {worker_id}: all phases complete")

if __name_…
14 0 Open

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Concurrency & performance — Python code examples

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