Boost.MPI provides an alternative MPI interface from the Python
programming language via the
The Boost.MPI Python bindings, built on top of the C++ Boost.MPI using the
provide nearly all of the functionality of Boost.MPI within a dynamic, object-oriented
The Boost.MPI Python module can be built and installed from the
Just follow the configuration and installation
instructions for the C++ Boost.MPI. Once you have installed the Python module,
be sure that the installation location is in your
Getting started with the Boost.MPI Python module is as easy as importing
boost.mpi. Our first "Hello, World!"
program is just two lines long:
import boost.mpi as mpi print "I am process %d of %d." % (mpi.rank, mpi.size)
Go ahead and run this program with several processes. Be sure to invoke the
python interpreter from
mpirun -np 5 python hello_world.py
This will return output such as:
I am process 1 of 5. I am process 3 of 5. I am process 2 of 5. I am process 4 of 5. I am process 0 of 5.
Point-to-point operations in Boost.MPI have nearly the same syntax in Python as in C++. We can write a simple two-process Python program that prints "Hello, world!" by transmitting Python strings:
import boost.mpi as mpi if mpi.world.rank == 0: mpi.world.send(1, 0, 'Hello') msg = mpi.world.recv(1, 1) print msg,'!' else: msg = mpi.world.recv(0, 0) print (msg + ', '), mpi.world.send(0, 1, 'world')
There are only a few notable differences between this Python code and the
example in the C++ tutorial. First
of all, we don't need to write any initialization code in Python: just loading
boost.mpi module makes the appropriate
calls. Second, we're passing Python objects from one process to another through
MPI. Any Python object that can be pickled can be transmitted; the next section
will describe in more detail how the Boost.MPI Python layer transmits objects.
Finally, when we receive objects with
we don't need to specify the type because transmission of Python objects
When experimenting with Boost.MPI in Python, don't forget that help is always
pass the name of the module or module entity on the command line (e.g.,
pydoc boost.mpi.communicator) to receive complete reference
documentation. When in doubt, try it!
Boost.MPI can transmit user-defined data in several different ways. Most importantly, it can transmit arbitrary Python objects by pickling them at the sender and unpickling them at the receiver, allowing arbitrarily complex Python data structures to interoperate with MPI.
Boost.MPI also supports efficient serialization and transmission of C++ objects
(that have been exposed to Python) through its C++ interface. Any C++ type
that provides (de-)serialization routines that meet the requirements of the
Boost.Serialization library is eligible for this optimization, but the type
must be registered in advance. To register a C++ type, invoke the C++ function
If your C++ types come from other Python modules (they probably will!), those
modules will need to link against the
as described in the installation section.
Note that you do not need to link against
the Boost.MPI Python extension module.
Finally, Boost.MPI supports separation of the structure of an object from the data it stores, allowing the two pieces to be transmitted separately. This "skeleton/content" mechanism, described in more detail in a later section, is a communication optimization suitable for problems with fixed data structures whose internal data changes frequently.
Boost.MPI supports all of the MPI collectives (
broadcast, etc.) for any
type of data that can be transmitted with the point-to-point communication
operations. For the MPI collectives that require a user-specified operation
scan), the operation can be an arbitrary
Python function. For instance, one could concatenate strings with
mpi.all_reduce(my_string, lambda x,y: x + y)
The following module-level functions implement MPI collectives: all_gather Gather the values from all processes. all_reduce Combine the results from all processes. all_to_all Every process sends data to every other process. broadcast Broadcast data from one process to all other processes. gather Gather the values from all processes to the root. reduce Combine the results from all processes to the root. scan Prefix reduction of the values from all processes. scatter Scatter the values stored at the root to all processes.
Boost.MPI provides a skeleton/content mechanism that allows the transfer of large data structures to be split into two separate stages, with the skeleton (or, "shape") of the data structure sent first and the content (or, "data") of the data structure sent later, potentially several times, so long as the structure has not changed since the skeleton was transferred. The skeleton/content mechanism can improve performance when the data structure is large and its shape is fixed, because while the skeleton requires serialization (it has an unknown size), the content transfer is fixed-size and can be done without extra copies.
To use the skeleton/content mechanism from Python, you must first register
the type of your data structure with the skeleton/content mechanism from C++. The registration function is
and resides in the
Once you have registered your C++ data structures, you can extract the skeleton
for an instance of that data structure with
skeleton(). The resulting
can be transmitted via the normal send routine, e.g.,
mpi.world.send(1, 0, skeleton(my_data_structure))
skeleton_proxy objects can
be received on the other end via
recv(), which stores a newly-created instance
of your data structure with the same "shape" as the sender in its
shape = mpi.world.recv(0, 0) my_data_structure = shape.object
Once the skeleton has been transmitted, the content (accessed via
get_content) can be transmitted in much
the same way. Note, however, that the receiver also specifies
in its call to receive:
if mpi.rank == 0: mpi.world.send(1, 0, get_content(my_data_structure)) else: mpi.world.recv(0, 0, get_content(my_data_structure))
Of course, this transmission of content can occur repeatedly, if the values in the data structure--but not its shape--changes.
The skeleton/content mechanism is a structured way to exploit the interaction
between custom-built MPI datatypes and
to eliminate extra buffer copies.
Boost.MPI is a C++ library whose facilities have been exposed to Python via the Boost.Python library. Since the Boost.MPI Python bindings are build directly on top of the C++ library, and nearly every feature of C++ library is available in Python, hybrid C++/Python programs using Boost.MPI can interact, e.g., sending a value from Python but receiving that value in C++ (or vice versa). However, doing so requires some care. Because Python objects are dynamically typed, Boost.MPI transfers type information along with the serialized form of the object, so that the object can be received even when its type is not known. This mechanism differs from its C++ counterpart, where the static types of transmitted values are always known.
The only way to communicate between the C++ and Python views on Boost.MPI
is to traffic entirely in Python objects. For Python, this is the normal
state of affairs, so nothing will change. For C++, this means sending and
receiving values of type
from the Boost.Python
library. For instance, say we want to transmit an integer value from Python:
comm.send(1, 0, 17)
In C++, we would receive that value into a Python object and then
extract an integer value:
boost::python::object value; comm.recv(0, 0, value); int int_value = boost::python::extract<int>(value);
In the future, Boost.MPI will be extended to allow improved interoperability with the C++ Boost.MPI and the C MPI bindings.
The Boost.MPI Python module,
its own reference documentation, which
is also available using
(from the command line) or
(from the Python interpreter).