OpenMP for Python in Numba for CPU/GPU parallel programming.
Currently, PyOMP is distributed as a full version of Numba which is based on a
Numba version a few versions behind mainline.
Since Numba is available for every combination of the past few Python versions
and the past few NumPy versions and various operating systems and architectures,
there is quite an extensive build infrastructure required to get all these
combinations and recently we have sorted out some of these combinations.
The architecture and operating system combinations that currently work are:
linux-64 (x86_64), osx-arm64 (mac), linux-arm64, and linux-ppc64le.
These distributions are available with the conda command in the next section.
Due to PyOMP using the LLVM OpenMP infrastructure, we also inherit its limitations which means that GPU support is only available on Linux.
In the future, we plan on converting PyOMP to a Numba extension which should eliminate the Python and NumPy versioning issues.
PyOMP is distributed as a package through Conda, currently supporting linux-64 (x86_64), osx-arm64 (mac), linux-arm64, and linux-ppc64le architectures.
conda install -c python-for-hpc -c conda-forge pyompYou can try it out for free on a multi-core CPU in JupyterLab at the following link:
https://mybinder.org/v2/gh/Python-for-HPC/binder/HEAD
We also provide pre-built containers for arm64 and amd64 architectures with PyOMP and Jupyter pre-installed. The following show how to access the container through the terminal or using jupyter.
First pull the container
docker pull ghcr.io/python-for-hpc/pyomp:latest
To use the terminal, run a shell on the container
docker run -it ghcr.io/python-for-hpc/pyomp:latest /bin/bash
To use Jupyter, run without arguments and forward port 8888.
docker run -it -p 8888:8888 ghcr.io/python-for-hpc/pyomp:latest
Jupyter will start as a service on localhost with token authentication by default. Grep the url with the token from the output and copy it to the browser.
...
[I 2024-09-15 17:24:47.912 ServerApp] http://127.0.0.1:8888/tree?token=<token>
...
Import Numba and add the @njit decorator to the function in which you want to use OpenMP.
Add with contexts for each OpenMP region you want to have, importing the
context openmp_context from the numba.openmp module.
For a list of supported OpenMP directives and more detailed information, check
out the Documentation.
PyOMP supports both CPU and GPU programming for NVIDIA GPUs through the target
directive for offloading.
For GPU programming, PyOMP supports the device clause and by convention the
default without using the clause or providing device(0) always refers to the
accelerator GPU device.
It is also possible to use the host as a multi-core CPU target device setting device(1).
This is an example of calculating parallel for loop.
from numba import njit
from numba.openmp import openmp_context as openmp
@njit
def calc_pi():
num_steps = 100000
step = 1.0 / num_steps
the_sum = 0.0
with openmp("parallel for reduction(+:the_sum) schedule(static)"):
for j in range(num_steps):
x = ((j-1) - 0.5) * step
the_sum += 4.0 / (1.0 + x * x)
pi = step * the_sum
return pi
print("pi =", calc_pi())