Reference library

Concurrency & performance

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

3 matches

How to Use ProcessPoolExecutor for CPU Parallel Map in Python

Run a function over a sequence of inputs in parallel across multiple CPU cores with ProcessPoolExecutor.map.

concurrency processpoolexecutor parallelism

from concurrent.futures import ProcessPoolExecutor
import math

def compute_square(num):
    return num * num

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(math.sqrt(n)) + 1):
        if n % i == 0:
            return False
    return True

if __name__ == "__main__":
    numbers = rang…

11 0 Open

Concurrency & performance medium

How to Use a Bounded Buffer with threading.Condition in Python

Implement a thread-safe bounded buffer using threading.Condition and show a producer–consumer example with exact output.

threading condition producer-consumer

Python

import threading
import time
import random

class BoundedBuffer:
    def __init__(self, capacity):
        self.capacity = capacity
        self.buffer = []
        self.condition = threading.Condition()

    def put(self, item):
        with self.condition:
            while len(self.buffer) >= self.capacity:
       …

14 0 Open

Concurrency & performance medium

Profile Memory Usage with tracemalloc Snapshot Diff in Python

Use tracemalloc to take two memory snapshots, compute a diff, and print the top changes (size and count) by line number.

tracemalloc memory-profile performance

Python

import tracemalloc

def profile_memory():
    tracemalloc.start()
    
    # Allocate some objects to track
    data = [i * 2 for i in range(10000)]
    text = "x" * 5000
    nested = {"key": [1, 2, 3], "value": (4, 5)}
    
    # Take first snapshot
    snapshot1 = tracemalloc.take_snapshot()
    
    # Free some mem…

11 0 Open

Browse by section

Each section groups closely related Python snippets.