Introduction¶

The mpi_array python package provides a numpy.ndarray API to a Partitioned Global Address Space array which utilizes MPI (via mpi4py) for parallelism.

Quick Start Example¶

The following quickstart.py script creates a zero-initialised array and performs some element assignments:

import mpi_array as mpia

# creates zero-initialized PGAS (distributed) array
dary = mpia.zeros((1000, 1000, 1000), dtype="uint16")

# Add one to all elements of array
dary += 1

# Assign to slice
dary[250:750, :, 250:750] = 8

# ufuncs
dary[...] = mpia.power(dary, 1.0/3.0)

The quickstart.py script can be executed serially (single process) as:

python quickstart.py

or in parallel (using 8 processes) as:

mpirun -n 8 python quickstart.py

Installation¶

Using pip from latest github source:

pip install --user git+git://github.com/mpi-array/mpi_array.git#egg=mpi_array

Requirements¶

Requires:

python-2 version >= 2.7 or python-3 version >= 3.3,

array_split version >= 0.4.0,

psutil >= 4.3.0

numpy version >= 1.13 (require __array_ufunc__ API),

an MPI implementation which supports at least MPI-3 (such as OpenMPI, MPICH or Intel MPI)

mpi4py version >= 2.0.

Testing¶

Run tests (unittest test-cases and doctest docstring test-cases) using:

python -m mpi_array.tests

or, from the source tree, run:

python setup.py test

Run tests with parallelism:

mpirun -n 8 python -m mpi_array.tests

Travis CI at:

https://travis-ci.org/mpi-array/mpi_array/

Documentation¶

Latest sphinx generated documentation at readthedocs.org:

http://mpi-array.readthedocs.io/en/latest

and at github gh-pages:

https://mpi-array.github.io/mpi_array/

Sphinx documentation can be built from the source:

python setup.py build_sphinx

with the HTML generated in docs/_build/html.

Latest source code¶

Source at github:

https://github.com/mpi-array/mpi_array

clone with:

git clone https://github.com/mpi-array/mpi_array.git

License information¶

See the file LICENSE.txt for terms & conditions, for usage and a DISCLAIMER OF ALL WARRANTIES.