Multiprocessing Stuck on One Core After Importing NumPy? Here's Why

The Issue:

Normally, the multiprocessing module allows your Python program to leverage multiple cores on your CPU. However, sometimes you might find that after importing NumPy, only one core is being used even when using multiprocessing.

The Cause:

This can happen because some scientific Python libraries, including NumPy, use optimized linear algebra libraries like OpenBLAS. These libraries might be compiled with multithreading enabled. When you import NumPy, these libraries can interfere with how Python assigns processes to cores (core affinity).

Solutions:

Here are a couple of ways to address this:

1. Control Threading in NumPy Libraries:

   • Environment variables like OMP_NUM_THREADS or MKL_NUM_THREADS can sometimes control how many threads these libraries use. You can set these before importing NumPy to limit the number of threads to 1 (e.g., os.environ["OMP_NUM_THREADS"] = "1").

2. Use multiprocessing.Pool with processes argument:

   • The multiprocessing.Pool class allows you to specify the number of processes to use. Set the processes argument in the Pool constructor to the desired number of cores.

Additional Considerations:

• Not all NumPy operations benefit from multiprocessing. If your tasks aren't well-suited for parallelization, using multiple cores might not give you a speedup.
• Consider if vectorizing your NumPy code with functions like .vectorize might be a better approach than multiprocessing for certain tasks.

By understanding this potential interaction and using the solutions above, you can ensure your Python code using multiprocessing and NumPy takes advantage of your multi-core CPU on Linux.

Controlling Threads in NumPy Libraries (Using Environment Variable):

import os
import multiprocessing

# Set environment variable to limit threads (example: OpenBLAS)
os.environ["OMP_NUM_THREADS"] = "1"

def square(x):
  return x * x

if __name__ == "__main__":
  # Number of cores (replace with your logic to get actual core count)
  num_cores = 4

  # Create pool with specified number of processes
  pool = multiprocessing.Pool(processes=num_cores)

  # Generate sample data
  data = range(10)

  # Use pool.map to distribute work across processes
  results = pool.map(square, data)

  # Print results (one process per core)
  print(results)

Using multiprocessing.Pool with processes argument:

import multiprocessing
import numpy as np

def square(x):
  return x * x

if __name__ == "__main__":
  # Number of cores (replace with your logic to get actual core count)
  num_cores = 4

  # Create pool with specified number of processes
  pool = multiprocessing.Pool(processes=num_cores)

  # Generate sample NumPy array
  data = np.array(range(10))

  # Use pool.map to distribute work across processes
  results = pool.map(square, data)

  # Print results (one process per core)
  print(results)

Explanation:

• Both examples define a function square(x) that squares a number.
• The first example sets the environment variable OMP_NUM_THREADS to 1 before importing NumPy to limit threads used by libraries like OpenBLAS.
• The second example creates a multiprocessing.Pool with the desired number of processes (obtained from num_cores) using the processes argument.

Important Notes:

• Replace the logic for obtaining num_cores with code that retrieves the actual number of cores on your system (e.g., multiprocessing.cpu_count()).
• Remember to adjust these examples based on your specific use case and the NumPy operations you're performing.

These examples showcase two ways to potentially address the single-core issue when using multiprocessing with NumPy. Choose the method that best suits your environment and coding style.

1. Vectorization with NumPy:

   • NumPy offers a rich set of vectorized functions that operate on entire arrays element-wise. These functions are often highly optimized for performance and can leverage multiple cores without the overhead of creating separate processes.
   • Look for vectorized alternatives to your current operations. For example, instead of a loop that squares each element, use data * data.

2. Threading with threading module:

   • If your tasks are well-suited for fine-grained parallelism within a single process, the threading module can be a good option. Threads share memory with the main process, reducing the overhead of copying data between processes.
   • However, using threads effectively can be tricky due to the Global Interpreter Lock (GIL) in Python. The GIL limits the number of Python threads that can truly execute in parallel.

3. Dask or Joblib for Distributed Computing:

   • For very large datasets or computationally intensive tasks, consider libraries like Dask or Joblib. These libraries can distribute workloads across multiple cores on a single machine or even a cluster of machines.

4. Specialized Libraries for Linear Algebra:

   • If your computations heavily rely on linear algebra operations, libraries like SciPy or libraries built on top of BLAS/LAPACK (like NumPy) might already be optimized for parallel execution on your hardware. Explore their functionalities to see if they meet your needs.

Choosing the Right Method:

The best method depends on several factors, including:

• Nature of your computations: Are they vectorizable or thread-friendly?
• Data size: Are you dealing with large datasets that benefit from distributed computing?
• Hardware: Are you using a single machine or a cluster?