Demystifying Multiprocessing Woes: Why It Might Stick to One Core After Importing NumPy (and How to Fix It)


Understanding the Issue:

  • Background: While multiprocessing allows you to spawn multiple processes to leverage multiple cores, importing numpy can sometimes hinder this by affecting core affinity. This is due to numpy's linkage to multithreaded libraries like OpenBLAS, which can bind these threads to a specific core, preventing multiprocessing from effectively distributing tasks across other cores.

Example Code (Illustrative):

import os
import numpy as np
from multiprocessing import Pool

def square(x):
    return x ** 2

def test_multiprocessing():
    num_cores = len(os.sched_getaffinity(0))  # Check available cores
    print("Available cores:", num_cores)

    start_core = os.sched_getaffinity(0)[0]  # Get currently pinned core
    print("Core affinity before numpy:", start_core)

    # Simulate task distribution:
    with Pool(4) as pool:  # Attempt to use 4 processes
        results =, range(10))

    end_core = os.sched_getaffinity(0)[0]  # Get core affinity after numpy
    print("Core affinity after numpy:", end_core)

if __name__ == "__main__":

Possible Fixes and Explanations:

  1. Force Core Affinity:

    • Use os.sched_setaffinity(0, [cores_to_use]) before creating the Pool to manually set core affinity for the parent process.
    • Example: os.sched_setaffinity(0, [0, 2, 3]) to use cores 0, 2, and 3.


    • Not portable across all systems.
    • Might not provide significant speedup if the task doesn't involve heavy NumPy operations.
  2. Leverage Threads Within Processes:

    • Use ThreadPoolExecutor from concurrent.futures instead of Pool to create threads within each process.
    • This allows utilizing multiple cores within each process, even if NumPy affects the main process's core affinity.


    from concurrent.futures import ThreadPoolExecutor
    with ThreadPoolExecutor(max_workers=4) as executor:
        results =, range(10))


    • Not suitable for global interpreter lock (GIL)-bound operations (I/O, network).
    • May require careful memory management.
  3. Alternative Numerical Libraries:

    • If NumPy isn't essential, consider libraries like dask or PyTorch that are designed for parallelism and distributed computing.


    • Different syntax and API, requiring code changes.
    • Might not be suitable for all use cases.

Choosing the Right Fix:

  • Consider the nature of your tasks, performance requirements, and system dependencies.
  • Experiment with different approaches to find the most suitable solution for your specific scenario.

I hope this comprehensive explanation and potential fixes empower you to effectively address the multiprocessing and numpy interaction in your Python projects!

python linux numpy

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