omp4py 0.1

An OpenMP Implementation for Python

OMP4Py: a pure Python implementation of OpenMP

OMP4Py is a Python library that provides an implementation of OpenMP, which is widely recognized as the standard programming model for exploiting multithreading parallelism in HPC. OMP4Py brings OpenMP’s familiar directive-based parallelization paradigm to Python, allowing developers to write parallel code with the same level of control and flexibility as in C, C++, or Fortran.

OMP4Py, as a pure Python implementation, provides greater flexibility compared to the Numba-based PyOMP solution. Unlike PyOMP, it does not impose restrictions on using functions from non-Numba-optimized libraries or certain Python objects and data structures. Additionally, OMP4Py code can be combined with mpi4py to develop parallel applications that exploit both intra-node and inter-node parallelism.

Features

• Native Python library
• Simplified parallelization of loops and code sections
• Efficient thread management and synchronization
• API compliant with OpenMP 3.0 standard

Installation

You can install omp4py via pip:

pip install omp4py

Note: OMP4Py is compatible with Python versions 3.10 and later, which include the Global Interpreter Lock (GIL). However, to fully exploit multithreading for scaling applications, it is necessary to use Python 3.13 (free threading) or later, which offers a no-GIL option.

Usage

OMP4Py defines a function omp that operates similarly to OpenMP directives in C/C++, maintaining the same syntax and functionality. The function itself has no effect when executed; it serves solely as a container for the OpenMP directives. Note that when a OpenMP directive must be used within structured blocks, the omp function is used together as part of a with block; otherwise, it is used as a standalone function call. Note that functions or classes containing the OpenMP directives must be decorated with the @omp decorator.

Here's a basic example of how to use OMP4Py to calculate $\pi$:

from omp4py import *
import random 
    
@omp
def pi(num_points):
    count = 0
    with omp("parallel for reduction(+:count)"):
        for i in range(num_points):
            x = random.random()
            y = random.random()
            if x * x + y * y <= 1.0:
                count += 1
    pi = count / num_points
    return pi

print(pi(10000000))  

Tests

To run the unit tests and check the coverage, you can use the following commands with Poetry*:

1. Run the unit tests:

    poetry run coverage run
   

2. Generate a coverage report:

    poetry run coverage html
   

* Test dependencies are required, and pip only installs project dependencies. Use poetry install to install them.