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MPI4Py (MPI for Python)
=======================
OpenMPI interface to Python
Introduction
------------
MPI for Python provides bindings of the Message Passing Interface (MPI)
standard for the Python programming language, allowing any Python
program to exploit multiple processors.
This package is constructed on top of the MPI-1/2 specifications and
provides an object oriented interface which closely follows MPI-2 C++
bindings. It supports point-to-point (sends, receives) and collective
(broadcasts, scatters, gathers) communications of any picklable Python
object, as well as optimized communications of Python object exposing
the single-segment buffer interface (NumPy arrays, builtin
bytes/string/array objects).
On Anselm MPI4Py is available in standard Python modules.
Modules
-------
MPI4Py is build for OpenMPI. Before you start with MPI4Py you need to
load Python and OpenMPI modules. You can use toolchain, that loads
Python and OpenMPI at once.
$ module load Python/2.7.9-foss-2015g
Execution
---------
You need to import MPI to your python program. Include the following
line to the python script:
from mpi4py import MPI
The MPI4Py enabled python programs [execute as any other
OpenMPI](Running_OpenMPI.html) code.The simpliest way is
to run
$ mpiexec python <script>.py
For example
$ mpiexec python hello_world.py
Examples
--------
### Hello world!
from mpi4py import MPI
comm = MPI.COMM_WORLD
print "Hello! I'm rank %d from %d running in total..." % (comm.rank, comm.size)
comm.Barrier() # wait for everybody to synchronize
###Collective Communication with NumPy arrays
from __future__ import division
from mpi4py import MPI
import numpy as np
comm = MPI.COMM_WORLD
print("-"*78)
print(" Running on %d cores" % comm.size)
print("-"*78)
comm.Barrier()
# Prepare a vector of N=5 elements to be broadcasted...
N = 5
if comm.rank == 0:
A = np.arange(N, dtype=np.float64) # rank 0 has proper data
else:
A = np.empty(N, dtype=np.float64) # all other just an empty array
# Broadcast A from rank 0 to everybody
comm.Bcast( [A, MPI.DOUBLE] )
# Everybody should now have the same...
print "[%02d] %s" % (comm.rank, A)
Execute the above code as:
$ qsub -q qexp -l select=4:ncpus=24:mpiprocs=24:ompthreads=1 -I
$ module load Python/2.7.9-foss-2015g
$ mpiexec --map-by core --bind-to core python hello_world.py
In this example, we run MPI4Py enabled code on 4 nodes, 24 cores per
node (total of 96 processes), each python process is bound to a
different core.
More examples and documentation can be found on [MPI for Python
webpage](https://pythonhosted.org/mpi4py/usrman/index.html).