Provided by: python-mpi4py-doc_3.1.5-5ubuntu2_all 
NAME
mpi4py - MPI for Python
Author Lisandro Dalcin
Contact
dalcinl@gmail.com
Date April 01, 2024
Abstract
This document describes the MPI for Python package. MPI for Python provides Python
bindings for the Message Passing Interface (MPI) standard, allowing Python applications to
exploit multiple processors on workstations, clusters and supercomputers.
This package builds on the MPI specification and provides an object oriented interface
resembling the MPI-2 C++ bindings. It supports point-to-point (sends, receives) and
collective (broadcasts, scatters, gathers) communication of any picklable Python object,
as well as efficient communication of Python objects exposing the Python buffer interface
(e.g. NumPy arrays and builtin bytes/array/memoryview objects).
INTRODUCTION
Over the last years, high performance computing has become an affordable resource to many
more researchers in the scientific community than ever before. The conjunction of quality
open source software and commodity hardware strongly influenced the now widespread
popularity of Beowulf class clusters and cluster of workstations.
Among many parallel computational models, message-passing has proven to be an effective
one. This paradigm is specially suited for (but not limited to) distributed memory
architectures and is used in today’s most demanding scientific and engineering application
related to modeling, simulation, design, and signal processing. However, portable
message-passing parallel programming used to be a nightmare in the past because of the
many incompatible options developers were faced to. Fortunately, this situation
definitely changed after the MPI Forum released its standard specification.
High performance computing is traditionally associated with software development using
compiled languages. However, in typical applications programs, only a small part of the
code is time-critical enough to require the efficiency of compiled languages. The rest of
the code is generally related to memory management, error handling, input/output, and user
interaction, and those are usually the most error prone and time-consuming lines of code
to write and debug in the whole development process. Interpreted high-level languages can
be really advantageous for this kind of tasks.
For implementing general-purpose numerical computations, MATLAB [1] is the dominant
interpreted programming language. In the open source side, Octave and Scilab are well
known, freely distributed software packages providing compatibility with the MATLAB
language. In this work, we present MPI for Python, a new package enabling applications to
exploit multiple processors using standard MPI “look and feel” in Python scripts.
[1] MATLAB is a registered trademark of The MathWorks, Inc.
What is MPI?
MPI, [mpi-using] [mpi-ref] the Message Passing Interface, is a standardized and portable
message-passing system designed to function on a wide variety of parallel computers. The
standard defines the syntax and semantics of library routines and allows users to write
portable programs in the main scientific programming languages (Fortran, C, or C++).
Since its release, the MPI specification [mpi-std1] [mpi-std2] has become the leading
standard for message-passing libraries for parallel computers. Implementations are
available from vendors of high-performance computers and from well known open source
projects like MPICH [mpi-mpich] and Open MPI [mpi-openmpi].
What is Python?
Python is a modern, easy to learn, powerful programming language. It has efficient
high-level data structures and a simple but effective approach to object-oriented
programming with dynamic typing and dynamic binding. It supports modules and packages,
which encourages program modularity and code reuse. Python’s elegant syntax, together with
its interpreted nature, make it an ideal language for scripting and rapid application
development in many areas on most platforms.
The Python interpreter and the extensive standard library are available in source or
binary form without charge for all major platforms, and can be freely distributed. It is
easily extended with new functions and data types implemented in C or C++. Python is also
suitable as an extension language for customizable applications.
Python is an ideal candidate for writing the higher-level parts of large-scale scientific
applications [Hinsen97] and driving simulations in parallel architectures [Beazley97] like
clusters of PC’s or SMP’s. Python codes are quickly developed, easily maintained, and can
achieve a high degree of integration with other libraries written in compiled languages.
Related Projects
As this work started and evolved, some ideas were borrowed from well known MPI and Python
related open source projects from the Internet.
• OOMPI
• It has no relation with Python, but is an excellent object oriented approach to MPI.
• It is a C++ class library specification layered on top of the C bindings that
encapsulates MPI into a functional class hierarchy.
• It provides a flexible and intuitive interface by adding some abstractions, like Ports
and Messages, which enrich and simplify the syntax.
• Pypar
• Its interface is rather minimal. There is no support for communicators or process
topologies.
• It does not require the Python interpreter to be modified or recompiled, but does not
permit interactive parallel runs.
• General (picklable) Python objects of any type can be communicated. There is good
support for numeric arrays, practically full MPI bandwidth can be achieved.
• pyMPI
• It rebuilds the Python interpreter providing a built-in module for message passing. It
does permit interactive parallel runs, which are useful for learning and debugging.
• It provides an interface suitable for basic parallel programing. There is not full
support for defining new communicators or process topologies.
• General (picklable) Python objects can be messaged between processors. There is not
support for numeric arrays.
• Scientific Python
• It provides a collection of Python modules that are useful for scientific computing.
• There is an interface to MPI and BSP (Bulk Synchronous Parallel programming).
• The interface is simple but incomplete and does not resemble the MPI specification.
There is support for numeric arrays.
Additionally, we would like to mention some available tools for scientific computing and
software development with Python.
• NumPy is a package that provides array manipulation and computational capabilities
similar to those found in IDL, MATLAB, or Octave. Using NumPy, it is possible to write
many efficient numerical data processing applications directly in Python without using
any C, C++ or Fortran code.
• SciPy is an open source library of scientific tools for Python, gathering a variety of
high level science and engineering modules together as a single package. It includes
modules for graphics and plotting, optimization, integration, special functions, signal
and image processing, genetic algorithms, ODE solvers, and others.
• Cython is a language that makes writing C extensions for the Python language as easy as
Python itself. The Cython language is very close to the Python language, but Cython
additionally supports calling C functions and declaring C types on variables and class
attributes. This allows the compiler to generate very efficient C code from Cython code.
This makes Cython the ideal language for wrapping for external C libraries, and for fast
C modules that speed up the execution of Python code.
• SWIG is a software development tool that connects programs written in C and C++ with a
variety of high-level programming languages like Perl, Tcl/Tk, Ruby and Python. Issuing
header files to SWIG is the simplest approach to interfacing C/C++ libraries from a
Python module.
[mpi-std1]
MPI Forum. MPI: A Message Passing Interface Standard. International Journal of
Supercomputer Applications, volume 8, number 3-4, pages 159-416, 1994.
[mpi-std2]
MPI Forum. MPI: A Message Passing Interface Standard. High Performance Computing
Applications, volume 12, number 1-2, pages 1-299, 1998.
[mpi-using]
William Gropp, Ewing Lusk, and Anthony Skjellum. Using MPI: portable parallel
programming with the message-passing interface. MIT Press, 1994.
[mpi-ref]
Mark Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack Dongarra. MPI -
The Complete Reference, volume 1, The MPI Core. MIT Press, 2nd. edition, 1998.
[mpi-mpich]
W. Gropp, E. Lusk, N. Doss, and A. Skjellum. A high-performance, portable
implementation of the MPI message passing interface standard. Parallel Computing,
22(6):789-828, September 1996.
[mpi-openmpi]
Edgar Gabriel, Graham E. Fagg, George Bosilca, Thara Angskun, Jack J. Dongarra,
Jeffrey M. Squyres, Vishal Sahay, Prabhanjan Kambadur, Brian Barrett, Andrew
Lumsdaine, Ralph H. Castain, David J. Daniel, Richard L. Graham, and Timothy S.
Woodall. Open MPI: Goals, Concept, and Design of a Next Generation MPI
Implementation. In Proceedings, 11th European PVM/MPI Users’ Group Meeting, Budapest,
Hungary, September 2004.
[Hinsen97]
Konrad Hinsen. The Molecular Modelling Toolkit: a case study of a large scientific
application in Python. In Proceedings of the 6th International Python Conference,
pages 29-35, San Jose, Ca., October 1997.
[Beazley97]
David M. Beazley and Peter S. Lomdahl. Feeding a large-scale physics application to
Python. In Proceedings of the 6th International Python Conference, pages 21-29, San
Jose, Ca., October 1997.
OVERVIEW
MPI for Python provides an object oriented approach to message passing which grounds on
the standard MPI-2 C++ bindings. The interface was designed with focus in translating MPI
syntax and semantics of standard MPI-2 bindings for C++ to Python. Any user of the
standard C/C++ MPI bindings should be able to use this module without need of learning a
new interface.
Communicating Python Objects and Array Data
The Python standard library supports different mechanisms for data persistence. Many of
them rely on disk storage, but pickling and marshaling can also work with memory buffers.
The pickle modules provide user-extensible facilities to serialize general Python objects
using ASCII or binary formats. The marshal module provides facilities to serialize
built-in Python objects using a binary format specific to Python, but independent of
machine architecture issues.
MPI for Python can communicate any built-in or user-defined Python object taking advantage
of the features provided by the pickle module. These facilities will be routinely used to
build binary representations of objects to communicate (at sending processes), and
restoring them back (at receiving processes).
Although simple and general, the serialization approach (i.e., pickling and unpickling)
previously discussed imposes important overheads in memory as well as processor usage,
especially in the scenario of objects with large memory footprints being communicated.
Pickling general Python objects, ranging from primitive or container built-in types to
user-defined classes, necessarily requires computer resources. Processing is also needed
for dispatching the appropriate serialization method (that depends on the type of the
object) and doing the actual packing. Additional memory is always needed, and if its total
amount is not known a priori, many reallocations can occur. Indeed, in the case of large
numeric arrays, this is certainly unacceptable and precludes communication of objects
occupying half or more of the available memory resources.
MPI for Python supports direct communication of any object exporting the single-segment
buffer interface. This interface is a standard Python mechanism provided by some types
(e.g., strings and numeric arrays), allowing access in the C side to a contiguous memory
buffer (i.e., address and length) containing the relevant data. This feature, in
conjunction with the capability of constructing user-defined MPI datatypes describing
complicated memory layouts, enables the implementation of many algorithms involving
multidimensional numeric arrays (e.g., image processing, fast Fourier transforms, finite
difference schemes on structured Cartesian grids) directly in Python, with negligible
overhead, and almost as fast as compiled Fortran, C, or C++ codes.
Communicators
In MPI for Python, Comm is the base class of communicators. The Intracomm and Intercomm
classes are sublcasses of the Comm class. The Comm.Is_inter method (and Comm.Is_intra,
provided for convenience but not part of the MPI specification) is defined for
communicator objects and can be used to determine the particular communicator class.
The two predefined intracommunicator instances are available: COMM_SELF and COMM_WORLD.
From them, new communicators can be created as needed.
The number of processes in a communicator and the calling process rank can be respectively
obtained with methods Comm.Get_size and Comm.Get_rank. The associated process group can be
retrieved from a communicator by calling the Comm.Get_group method, which returns an
instance of the Group class. Set operations with Group objects like like Group.Union,
Group.Intersection and Group.Difference are fully supported, as well as the creation of
new communicators from these groups using Comm.Create and Comm.Create_group.
New communicator instances can be obtained with the Comm.Clone, Comm.Dup and Comm.Split
methods, as well methods Intracomm.Create_intercomm and Intercomm.Merge.
Virtual topologies (Cartcomm, Graphcomm and Distgraphcomm classes, which are
specializations of the Intracomm class) are fully supported. New instances can be obtained
from intracommunicator instances with factory methods Intracomm.Create_cart and
Intracomm.Create_graph.
Point-to-Point Communications
Point to point communication is a fundamental capability of message passing systems. This
mechanism enables the transmission of data between a pair of processes, one side sending,
the other receiving.
MPI provides a set of send and receive functions allowing the communication of typed data
with an associated tag. The type information enables the conversion of data
representation from one architecture to another in the case of heterogeneous computing
environments; additionally, it allows the representation of non-contiguous data layouts
and user-defined datatypes, thus avoiding the overhead of (otherwise unavoidable)
packing/unpacking operations. The tag information allows selectivity of messages at the
receiving end.
Blocking Communications
MPI provides basic send and receive functions that are blocking. These functions block
the caller until the data buffers involved in the communication can be safely reused by
the application program.
In MPI for Python, the Comm.Send, Comm.Recv and Comm.Sendrecv methods of communicator
objects provide support for blocking point-to-point communications within Intracomm and
Intercomm instances. These methods can communicate memory buffers. The variants Comm.send,
Comm.recv and Comm.sendrecv can communicate general Python objects.
Nonblocking Communications
On many systems, performance can be significantly increased by overlapping communication
and computation. This is particularly true on systems where communication can be executed
autonomously by an intelligent, dedicated communication controller.
MPI provides nonblocking send and receive functions. They allow the possible overlap of
communication and computation. Non-blocking communication always come in two parts:
posting functions, which begin the requested operation; and test-for-completion functions,
which allow to discover whether the requested operation has completed.
In MPI for Python, the Comm.Isend and Comm.Irecv methods initiate send and receive
operations, respectively. These methods return a Request instance, uniquely identifying
the started operation. Its completion can be managed using the Request.Test, Request.Wait
and Request.Cancel methods. The management of Request objects and associated memory
buffers involved in communication requires a careful, rather low-level coordination. Users
must ensure that objects exposing their memory buffers are not accessed at the Python
level while they are involved in nonblocking message-passing operations.
Persistent Communications
Often a communication with the same argument list is repeatedly executed within an inner
loop. In such cases, communication can be further optimized by using persistent
communication, a particular case of nonblocking communication allowing the reduction of
the overhead between processes and communication controllers. Furthermore , this kind of
optimization can also alleviate the extra call overheads associated to interpreted,
dynamic languages like Python.
In MPI for Python, the Comm.Send_init and Comm.Recv_init methods create persistent
requests for a send and receive operation, respectively. These methods return an instance
of the Prequest class, a subclass of the Request class. The actual communication can be
effectively started using the Prequest.Start method, and its completion can be managed as
previously described.
Collective Communications
Collective communications allow the transmittal of data between multiple processes of a
group simultaneously. The syntax and semantics of collective functions is consistent with
point-to-point communication. Collective functions communicate typed data, but messages
are not paired with an associated tag; selectivity of messages is implied in the calling
order. Additionally, collective functions come in blocking versions only.
The more commonly used collective communication operations are the following.
• Barrier synchronization across all group members.
• Global communication functions
• Broadcast data from one member to all members of a group.
• Gather data from all members to one member of a group.
• Scatter data from one member to all members of a group.
• Global reduction operations such as sum, maximum, minimum, etc.
In MPI for Python, the Comm.Bcast, Comm.Scatter, Comm.Gather, Comm.Allgather,
Comm.Alltoall methods provide support for collective communications of memory buffers. The
lower-case variants Comm.bcast, Comm.scatter, Comm.gather, Comm.allgather and
Comm.alltoall can communicate general Python objects. The vector variants (which can
communicate different amounts of data to each process) Comm.Scatterv, Comm.Gatherv,
Comm.Allgatherv, Comm.Alltoallv and Comm.Alltoallw are also supported, they can only
communicate objects exposing memory buffers.
Global reducion operations on memory buffers are accessible through the Comm.Reduce,
Comm.Reduce_scatter, Comm.Allreduce, Intracomm.Scan and Intracomm.Exscan methods. The
lower-case variants Comm.reduce, Comm.allreduce, Intracomm.scan and Intracomm.exscan can
communicate general Python objects; however, the actual required reduction computations
are performed sequentially at some process. All the predefined (i.e., SUM, PROD, MAX,
etc.) reduction operations can be applied.
Support for GPU-aware MPI
Several MPI implementations, including Open MPI and MVAPICH, support passing GPU pointers
to MPI calls to avoid explict data movement between the host and the device. On the Python
side, GPU arrays have been implemented by many libraries that need GPU computation, such
as CuPy, Numba, PyTorch, and PyArrow. In order to increase library interoperability, two
kinds of zero-copy data exchange protocols are defined and agreed upon: DLPack and CUDA
Array Interface. For example, a CuPy array can be passed to a Numba CUDA-jit kernel.
MPI for Python provides an experimental support for GPU-aware MPI. This feature requires:
1. mpi4py is built against a GPU-aware MPI library.
2. The Python GPU arrays are compliant with either of the protocols.
See the Tutorial section for further information. We note that
• Whether or not a MPI call can work for GPU arrays depends on the underlying MPI
implementation, not on mpi4py.
• This support is currently experimental and subject to change in the future.
Dynamic Process Management
In the context of the MPI-1 specification, a parallel application is static; that is, no
processes can be added to or deleted from a running application after it has been started.
Fortunately, this limitation was addressed in MPI-2. The new specification added a process
management model providing a basic interface between an application and external resources
and process managers.
This MPI-2 extension can be really useful, especially for sequential applications built on
top of parallel modules, or parallel applications with a client/server model. The MPI-2
process model provides a mechanism to create new processes and establish communication
between them and the existing MPI application. It also provides mechanisms to establish
communication between two existing MPI applications, even when one did not start the
other.
In MPI for Python, new independent process groups can be created by calling the
Intracomm.Spawn method within an intracommunicator. This call returns a new
intercommunicator (i.e., an Intercomm instance) at the parent process group. The child
process group can retrieve the matching intercommunicator by calling the Comm.Get_parent
class method. At each side, the new intercommunicator can be used to perform point to
point and collective communications between the parent and child groups of processes.
Alternatively, disjoint groups of processes can establish communication using a
client/server approach. Any server application must first call the Open_port function to
open a port and the Publish_name function to publish a provided service, and next call the
Intracomm.Accept method. Any client applications can first find a published service by
calling the Lookup_name function, which returns the port where a server can be contacted;
and next call the Intracomm.Connect method. Both Intracomm.Accept and Intracomm.Connect
methods return an Intercomm instance. When connection between client/server processes is
no longer needed, all of them must cooperatively call the Comm.Disconnect method.
Additionally, server applications should release resources by calling the Unpublish_name
and Close_port functions.
One-Sided Communications
One-sided communications (also called Remote Memory Access, RMA) supplements the
traditional two-sided, send/receive based MPI communication model with a one-sided,
put/get based interface. One-sided communication that can take advantage of the
capabilities of highly specialized network hardware. Additionally, this extension lowers
latency and software overhead in applications written using a shared-memory-like paradigm.
The MPI specification revolves around the use of objects called windows; they intuitively
specify regions of a process’s memory that have been made available for remote read and
write operations. The published memory blocks can be accessed through three functions for
put (remote send), get (remote write), and accumulate (remote update or reduction) data
items. A much larger number of functions support different synchronization styles; the
semantics of these synchronization operations are fairly complex.
In MPI for Python, one-sided operations are available by using instances of the Win class.
New window objects are created by calling the Win.Create method at all processes within a
communicator and specifying a memory buffer . When a window instance is no longer needed,
the Win.Free method should be called.
The three one-sided MPI operations for remote write, read and reduction are available
through calling the methods Win.Put, Win.Get, and Win.Accumulate respectively within a Win
instance. These methods need an integer rank identifying the target process and an
integer offset relative the base address of the remote memory block being accessed.
The one-sided operations read, write, and reduction are implicitly nonblocking, and must
be synchronized by using two primary modes. Active target synchronization requires the
origin process to call the Win.Start and Win.Complete methods at the origin process, and
target process cooperates by calling the Win.Post and Win.Wait methods. There is also a
collective variant provided by the Win.Fence method. Passive target synchronization is
more lenient, only the origin process calls the Win.Lock and Win.Unlock methods. Locks are
used to protect remote accesses to the locked remote window and to protect local
load/store accesses to a locked local window.
Parallel Input/Output
The POSIX standard provides a model of a widely portable file system. However, the
optimization needed for parallel input/output cannot be achieved with this generic
interface. In order to ensure efficiency and scalability, the underlying parallel
input/output system must provide a high-level interface supporting partitioning of file
data among processes and a collective interface supporting complete transfers of global
data structures between process memories and files. Additionally, further efficiencies can
be gained via support for asynchronous input/output, strided accesses to data, and control
over physical file layout on storage devices. This scenario motivated the inclusion in the
MPI-2 standard of a custom interface in order to support more elaborated parallel
input/output operations.
The MPI specification for parallel input/output revolves around the use objects called
files. As defined by MPI, files are not just contiguous byte streams. Instead, they are
regarded as ordered collections of typed data items. MPI supports sequential or random
access to any integral set of these items. Furthermore, files are opened collectively by a
group of processes.
The common patterns for accessing a shared file (broadcast, scatter, gather, reduction) is
expressed by using user-defined datatypes. Compared to the communication patterns of
point-to-point and collective communications, this approach has the advantage of added
flexibility and expressiveness. Data access operations (read and write) are defined for
different kinds of positioning (using explicit offsets, individual file pointers, and
shared file pointers), coordination (non-collective and collective), and synchronism
(blocking, nonblocking, and split collective with begin/end phases).
In MPI for Python, all MPI input/output operations are performed through instances of the
File class. File handles are obtained by calling the File.Open method at all processes
within a communicator and providing a file name and the intended access mode. After use,
they must be closed by calling the File.Close method. Files even can be deleted by
calling method File.Delete.
After creation, files are typically associated with a per-process view. The view defines
the current set of data visible and accessible from an open file as an ordered set of
elementary datatypes. This data layout can be set and queried with the File.Set_view and
File.Get_view methods respectively.
Actual input/output operations are achieved by many methods combining read and write calls
with different behavior regarding positioning, coordination, and synchronism. Summing up,
MPI for Python provides the thirty (30) methods defined in MPI-2 for reading from or
writing to files using explicit offsets or file pointers (individual or shared), in
blocking or nonblocking and collective or noncollective versions.
Environmental Management
Initialization and Exit
Module functions Init or Init_thread and Finalize provide MPI initialization and
finalization respectively. Module functions Is_initialized and Is_finalized provide the
respective tests for initialization and finalization.
NOTE:
MPI_Init() or MPI_Init_thread() is actually called when you import the MPI module from
the mpi4py package, but only if MPI is not already initialized. In such case, calling
Init or Init_thread from Python is expected to generate an MPI error, and in turn an
exception will be raised.
NOTE:
MPI_Finalize() is registered (by using Python C/API function Py_AtExit()) for being
automatically called when Python processes exit, but only if mpi4py actually
initialized MPI. Therefore, there is no need to call Finalize from Python to ensure MPI
finalization.
Implementation Information
• The MPI version number can be retrieved from module function Get_version. It returns a
two-integer tuple (version, subversion).
• The Get_processor_name function can be used to access the processor name.
• The values of predefined attributes attached to the world communicator can be obtained
by calling the Comm.Get_attr method within the COMM_WORLD instance.
Timers
MPI timer functionalities are available through the Wtime and Wtick functions.
Error Handling
In order facilitate handle sharing with other Python modules interfacing MPI-based
parallel libraries, the predefined MPI error handlers ERRORS_RETURN and ERRORS_ARE_FATAL
can be assigned to and retrieved from communicators using methods Comm.Set_errhandler and
Comm.Get_errhandler, and similarly for windows and files.
When the predefined error handler ERRORS_RETURN is set, errors returned from MPI calls
within Python code will raise an instance of the exception class Exception, which is a
subclass of the standard Python exception RuntimeError.
NOTE:
After import, mpi4py overrides the default MPI rules governing inheritance of error
handlers. The ERRORS_RETURN error handler is set in the predefined COMM_SELF and
COMM_WORLD communicators, as well as any new Comm, Win, or File instance created
through mpi4py. If you ever pass such handles to C/C++/Fortran library code, it is
recommended to set the ERRORS_ARE_FATAL error handler on them to ensure MPI errors do
not pass silently.
WARNING:
Importing with from mpi4py.MPI import * will cause a name clashing with the standard
Python Exception base class.
TUTORIAL
WARNING:
Under construction. Contributions very welcome!
TIP:
Rolf Rabenseifner at HLRS developed a comprehensive MPI-3.1/4.0 course with slides and
a large set of exercises including solutions. This material is available online for
self-study. The slides and exercises show the C, Fortran, and Python (mpi4py)
interfaces. For performance reasons, most Python exercises use NumPy arrays and
communication routines involving buffer-like objects.
TIP:
Victor Eijkhout at TACC authored the book Parallel Programming for Science and
Engineering. This book is available online in PDF and HTML formats. The book covers
parallel programming with MPI and OpenMP in C/C++ and Fortran, and MPI in Python using
mpi4py.
MPI for Python supports convenient, pickle-based communication of generic Python object as
well as fast, near C-speed, direct array data communication of buffer-provider objects
(e.g., NumPy arrays).
• Communication of generic Python objects
You have to use methods with all-lowercase names, like Comm.send, Comm.recv, Comm.bcast,
Comm.scatter, Comm.gather . An object to be sent is passed as a parameter to the
communication call, and the received object is simply the return value.
The Comm.isend and Comm.irecv methods return Request instances; completion of these
methods can be managed using the Request.test and Request.wait methods.
The Comm.recv and Comm.irecv methods may be passed a buffer object that can be
repeatedly used to receive messages avoiding internal memory allocation. This buffer
must be sufficiently large to accommodate the transmitted messages; hence, any buffer
passed to Comm.recv or Comm.irecv must be at least as long as the pickled data
transmitted to the receiver.
Collective calls like Comm.scatter, Comm.gather, Comm.allgather, Comm.alltoall expect a
single value or a sequence of Comm.size elements at the root or all process. They return
a single value, a list of Comm.size elements, or None.
NOTE:
MPI for Python uses the highest protocol version available in the Python runtime (see
the HIGHEST_PROTOCOL constant in the pickle module). The default protocol can be
changed at import time by setting the MPI4PY_PICKLE_PROTOCOL environment variable, or
at runtime by assigning a different value to the PROTOCOL attribute of the pickle
object within the MPI module.
• Communication of buffer-like objects
You have to use method names starting with an upper-case letter, like Comm.Send,
Comm.Recv, Comm.Bcast, Comm.Scatter, Comm.Gather.
In general, buffer arguments to these calls must be explicitly specified by using a
2/3-list/tuple like [data, MPI.DOUBLE], or [data, count, MPI.DOUBLE] (the former one
uses the byte-size of data and the extent of the MPI datatype to define count).
For vector collectives communication operations like Comm.Scatterv and Comm.Gatherv,
buffer arguments are specified as [data, count, displ, datatype], where count and displ
are sequences of integral values.
Automatic MPI datatype discovery for NumPy/GPU arrays and PEP-3118 buffers is supported,
but limited to basic C types (all C/C99-native signed/unsigned integral types and
single/double precision real/complex floating types) and availability of matching
datatypes in the underlying MPI implementation. In this case, the buffer-provider object
can be passed directly as a buffer argument, the count and MPI datatype will be
inferred.
If mpi4py is built against a GPU-aware MPI implementation, GPU arrays can be passed to
upper-case methods as long as they have either the __dlpack__ and __dlpack_device__
methods or the __cuda_array_interface__ attribute that are compliant with the respective
standard specifications. Moreover, only C-contiguous or Fortran-contiguous GPU arrays
are supported. It is important to note that GPU buffers must be fully ready before any
MPI routines operate on them to avoid race conditions. This can be ensured by using the
synchronization API of your array library. mpi4py does not have access to any
GPU-specific functionality and thus cannot perform this operation automatically for
users.
Running Python scripts with MPI
Most MPI programs can be run with the command mpiexec. In practice, running Python
programs looks like:
$ mpiexec -n 4 python script.py
to run the program with 4 processors.
Point-to-Point Communication
• Python objects (pickle under the hood):
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
data = {'a': 7, 'b': 3.14}
comm.send(data, dest=1, tag=11)
elif rank == 1:
data = comm.recv(source=0, tag=11)
• Python objects with non-blocking communication:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
data = {'a': 7, 'b': 3.14}
req = comm.isend(data, dest=1, tag=11)
req.wait()
elif rank == 1:
req = comm.irecv(source=0, tag=11)
data = req.wait()
• NumPy arrays (the fast way!):
from mpi4py import MPI
import numpy
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
# passing MPI datatypes explicitly
if rank == 0:
data = numpy.arange(1000, dtype='i')
comm.Send([data, MPI.INT], dest=1, tag=77)
elif rank == 1:
data = numpy.empty(1000, dtype='i')
comm.Recv([data, MPI.INT], source=0, tag=77)
# automatic MPI datatype discovery
if rank == 0:
data = numpy.arange(100, dtype=numpy.float64)
comm.Send(data, dest=1, tag=13)
elif rank == 1:
data = numpy.empty(100, dtype=numpy.float64)
comm.Recv(data, source=0, tag=13)
Collective Communication
• Broadcasting a Python dictionary:
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
data = {'key1' : [7, 2.72, 2+3j],
'key2' : ( 'abc', 'xyz')}
else:
data = None
data = comm.bcast(data, root=0)
• Scattering Python objects:
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
if rank == 0:
data = [(i+1)**2 for i in range(size)]
else:
data = None
data = comm.scatter(data, root=0)
assert data == (rank+1)**2
• Gathering Python objects:
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
data = (rank+1)**2
data = comm.gather(data, root=0)
if rank == 0:
for i in range(size):
assert data[i] == (i+1)**2
else:
assert data is None
• Broadcasting a NumPy array:
from mpi4py import MPI
import numpy as np
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
data = np.arange(100, dtype='i')
else:
data = np.empty(100, dtype='i')
comm.Bcast(data, root=0)
for i in range(100):
assert data[i] == i
• Scattering NumPy arrays:
from mpi4py import MPI
import numpy as np
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
sendbuf = None
if rank == 0:
sendbuf = np.empty([size, 100], dtype='i')
sendbuf.T[:,:] = range(size)
recvbuf = np.empty(100, dtype='i')
comm.Scatter(sendbuf, recvbuf, root=0)
assert np.allclose(recvbuf, rank)
• Gathering NumPy arrays:
from mpi4py import MPI
import numpy as np
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
sendbuf = np.zeros(100, dtype='i') + rank
recvbuf = None
if rank == 0:
recvbuf = np.empty([size, 100], dtype='i')
comm.Gather(sendbuf, recvbuf, root=0)
if rank == 0:
for i in range(size):
assert np.allclose(recvbuf[i,:], i)
• Parallel matrix-vector product:
from mpi4py import MPI
import numpy
def matvec(comm, A, x):
m = A.shape[0] # local rows
p = comm.Get_size()
xg = numpy.zeros(m*p, dtype='d')
comm.Allgather([x, MPI.DOUBLE],
[xg, MPI.DOUBLE])
y = numpy.dot(A, xg)
return y
MPI-IO
• Collective I/O with NumPy arrays:
from mpi4py import MPI
import numpy as np
amode = MPI.MODE_WRONLY|MPI.MODE_CREATE
comm = MPI.COMM_WORLD
fh = MPI.File.Open(comm, "./datafile.contig", amode)
buffer = np.empty(10, dtype=np.int)
buffer[:] = comm.Get_rank()
offset = comm.Get_rank()*buffer.nbytes
fh.Write_at_all(offset, buffer)
fh.Close()
• Non-contiguous Collective I/O with NumPy arrays and datatypes:
from mpi4py import MPI
import numpy as np
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
amode = MPI.MODE_WRONLY|MPI.MODE_CREATE
fh = MPI.File.Open(comm, "./datafile.noncontig", amode)
item_count = 10
buffer = np.empty(item_count, dtype='i')
buffer[:] = rank
filetype = MPI.INT.Create_vector(item_count, 1, size)
filetype.Commit()
displacement = MPI.INT.Get_size()*rank
fh.Set_view(displacement, filetype=filetype)
fh.Write_all(buffer)
filetype.Free()
fh.Close()
Dynamic Process Management
• Compute Pi - Master (or parent, or client) side:
#!/usr/bin/env python
from mpi4py import MPI
import numpy
import sys
comm = MPI.COMM_SELF.Spawn(sys.executable,
args=['cpi.py'],
maxprocs=5)
N = numpy.array(100, 'i')
comm.Bcast([N, MPI.INT], root=MPI.ROOT)
PI = numpy.array(0.0, 'd')
comm.Reduce(None, [PI, MPI.DOUBLE],
op=MPI.SUM, root=MPI.ROOT)
print(PI)
comm.Disconnect()
• Compute Pi - Worker (or child, or server) side:
#!/usr/bin/env python
from mpi4py import MPI
import numpy
comm = MPI.Comm.Get_parent()
size = comm.Get_size()
rank = comm.Get_rank()
N = numpy.array(0, dtype='i')
comm.Bcast([N, MPI.INT], root=0)
h = 1.0 / N; s = 0.0
for i in range(rank, N, size):
x = h * (i + 0.5)
s += 4.0 / (1.0 + x**2)
PI = numpy.array(s * h, dtype='d')
comm.Reduce([PI, MPI.DOUBLE], None,
op=MPI.SUM, root=0)
comm.Disconnect()
CUDA-aware MPI + Python GPU arrays
• Reduce-to-all CuPy arrays:
from mpi4py import MPI
import cupy as cp
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
sendbuf = cp.arange(10, dtype='i')
recvbuf = cp.empty_like(sendbuf)
assert hasattr(sendbuf, '__cuda_array_interface__')
assert hasattr(recvbuf, '__cuda_array_interface__')
cp.cuda.get_current_stream().synchronize()
comm.Allreduce(sendbuf, recvbuf)
assert cp.allclose(recvbuf, sendbuf*size)
One-Sided Communications
• Read from (write to) the entire RMA window:
import numpy as np
from mpi4py import MPI
from mpi4py.util import dtlib
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
datatype = MPI.FLOAT
np_dtype = dtlib.to_numpy_dtype(datatype)
itemsize = datatype.Get_size()
N = 10
win_size = N * itemsize if rank == 0 else 0
win = MPI.Win.Allocate(win_size, comm=comm)
buf = np.empty(N, dtype=np_dtype)
if rank == 0:
buf.fill(42)
win.Lock(rank=0)
win.Put(buf, target_rank=0)
win.Unlock(rank=0)
comm.Barrier()
else:
comm.Barrier()
win.Lock(rank=0)
win.Get(buf, target_rank=0)
win.Unlock(rank=0)
assert np.all(buf == 42)
• Accessing a part of the RMA window using the target argument, which is defined as
(offset, count, datatype):
import numpy as np
from mpi4py import MPI
from mpi4py.util import dtlib
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
datatype = MPI.FLOAT
np_dtype = dtlib.to_numpy_dtype(datatype)
itemsize = datatype.Get_size()
N = comm.Get_size() + 1
win_size = N * itemsize if rank == 0 else 0
win = MPI.Win.Allocate(
size=win_size,
disp_unit=itemsize,
comm=comm,
)
if rank == 0:
mem = np.frombuffer(win, dtype=np_dtype)
mem[:] = np.arange(len(mem), dtype=np_dtype)
comm.Barrier()
buf = np.zeros(3, dtype=np_dtype)
target = (rank, 2, datatype)
win.Lock(rank=0)
win.Get(buf, target_rank=0, target=target)
win.Unlock(rank=0)
assert np.all(buf == [rank, rank+1, 0])
Wrapping with SWIG
• C source:
/* file: helloworld.c */
void sayhello(MPI_Comm comm)
{
int size, rank;
MPI_Comm_size(comm, &size);
MPI_Comm_rank(comm, &rank);
printf("Hello, World! "
"I am process %d of %d.\n",
rank, size);
}
• SWIG interface file:
// file: helloworld.i
%module helloworld
%{
#include <mpi.h>
#include "helloworld.c"
}%
%include mpi4py/mpi4py.i
%mpi4py_typemap(Comm, MPI_Comm);
void sayhello(MPI_Comm comm);
• Try it in the Python prompt:
>>> from mpi4py import MPI
>>> import helloworld
>>> helloworld.sayhello(MPI.COMM_WORLD)
Hello, World! I am process 0 of 1.
Wrapping with F2Py
• Fortran 90 source:
! file: helloworld.f90
subroutine sayhello(comm)
use mpi
implicit none
integer :: comm, rank, size, ierr
call MPI_Comm_size(comm, size, ierr)
call MPI_Comm_rank(comm, rank, ierr)
print *, 'Hello, World! I am process ',rank,' of ',size,'.'
end subroutine sayhello
• Compiling example using f2py
$ f2py -c --f90exec=mpif90 helloworld.f90 -m helloworld
• Try it in the Python prompt:
>>> from mpi4py import MPI
>>> import helloworld
>>> fcomm = MPI.COMM_WORLD.py2f()
>>> helloworld.sayhello(fcomm)
Hello, World! I am process 0 of 1.
MPI4PY
Runtime configuration options
mpi4py.rc
This object has attributes exposing runtime configuration options that become
effective at import time of the MPI module.
Attributes Summary
┌─────────────┬──────────────────────────────────┐
│initialize │ Automatic MPI initialization at │
│ │ import │
├─────────────┼──────────────────────────────────┤
│threads │ Request initialization with │
│ │ thread support │
├─────────────┼──────────────────────────────────┤
│thread_level │ Level of thread support to │
│ │ request │
├─────────────┼──────────────────────────────────┤
│finalize │ Automatic MPI finalization at │
│ │ exit │
├─────────────┼──────────────────────────────────┤
│fast_reduce │ Use tree-based reductions for │
│ │ objects │
├─────────────┼──────────────────────────────────┤
│recv_mprobe │ Use matched probes to receive │
│ │ objects │
├─────────────┼──────────────────────────────────┤
│errors │ Error handling policy │
└─────────────┴──────────────────────────────────┘
Attributes Documentation
mpi4py.rc.initialize
Automatic MPI initialization at import.
Type bool
Default
True
SEE ALSO:
MPI4PY_RC_INITIALIZE
mpi4py.rc.threads
Request initialization with thread support.
Type bool
Default
True
SEE ALSO:
MPI4PY_RC_THREADS
mpi4py.rc.thread_level
Level of thread support to request.
Type str
Default
"multiple"
Choices
"multiple", "serialized", "funneled", "single"
SEE ALSO:
MPI4PY_RC_THREAD_LEVEL
mpi4py.rc.finalize
Automatic MPI finalization at exit.
Type None or bool
Default
None
SEE ALSO:
MPI4PY_RC_FINALIZE
mpi4py.rc.fast_reduce
Use tree-based reductions for objects.
Type bool
Default
True
SEE ALSO:
MPI4PY_RC_FAST_REDUCE
mpi4py.rc.recv_mprobe
Use matched probes to receive objects.
Type bool
Default
True
SEE ALSO:
MPI4PY_RC_RECV_MPROBE
mpi4py.rc.errors
Error handling policy.
Type str
Default
"exception"
Choices
"exception", "default", "fatal"
SEE ALSO:
MPI4PY_RC_ERRORS
Example
MPI for Python features automatic initialization and finalization of the MPI execution
environment. By using the mpi4py.rc object, MPI initialization and finalization can be
handled programatically:
import mpi4py
mpi4py.rc.initialize = False # do not initialize MPI automatically
mpi4py.rc.finalize = False # do not finalize MPI automatically
from mpi4py import MPI # import the 'MPI' module
MPI.Init() # manual initialization of the MPI environment
... # your finest code here ...
MPI.Finalize() # manual finalization of the MPI environment
Environment variables
The following environment variables override the corresponding attributes of the mpi4py.rc
and MPI.pickle objects at import time of the MPI module.
NOTE:
For variables of boolean type, accepted values are 0 and 1 (interpreted as False and
True, respectively), and strings specifying a YAML boolean value (case-insensitive).
MPI4PY_RC_INITIALIZE
Type bool
Default
True
Whether to automatically initialize MPI at import time of the mpi4py.MPI module.
SEE ALSO:
mpi4py.rc.initialize
New in version 3.1.0.
MPI4PY_RC_FINALIZE
Type None | bool
Default
None
Choices
None, True, False
Whether to automatically finalize MPI at exit time of the Python process.
SEE ALSO:
mpi4py.rc.finalize
New in version 3.1.0.
MPI4PY_RC_THREADS
Type bool
Default
True
Whether to initialize MPI with thread support.
SEE ALSO:
mpi4py.rc.threads
New in version 3.1.0.
MPI4PY_RC_THREAD_LEVEL
Default
"multiple"
Choices
"single", "funneled", "serialized", "multiple"
The level of required thread support.
SEE ALSO:
mpi4py.rc.thread_level
New in version 3.1.0.
MPI4PY_RC_FAST_REDUCE
Type bool
Default
True
Whether to use tree-based reductions for objects.
SEE ALSO:
mpi4py.rc.fast_reduce
New in version 3.1.0.
MPI4PY_RC_RECV_MPROBE
Type bool
Default
True
Whether to use matched probes to receive objects.
SEE ALSO:
mpi4py.rc.recv_mprobe
MPI4PY_RC_ERRORS
Default
"exception"
Choices
"exception", "default", "fatal"
Controls default MPI error handling policy.
SEE ALSO:
mpi4py.rc.errors
New in version 3.1.0.
MPI4PY_PICKLE_PROTOCOL
Type int
Default
pickle.HIGHEST_PROTOCOL
Controls the default pickle protocol to use when communicating Python objects.
SEE ALSO:
PROTOCOL attribute of the MPI.pickle object within the MPI module.
New in version 3.1.0.
MPI4PY_PICKLE_THRESHOLD
Type int
Default
262144
Controls the default buffer size threshold for switching from in-band to
out-of-band buffer handling when using pickle protocol version 5 or higher.
SEE ALSO:
Module mpi4py.util.pkl5.
New in version 3.1.2.
Miscellaneous functions
mpi4py.profile()
mpi4py.get_config()
mpi4py.get_include()
MPI4PY.MPI
Classes
Ancillary
┌─────────┬───┐
│Datatype │ │
├─────────┼───┤
│Status │ │
├─────────┼───┤
│Request │ │
├─────────┼───┤
│Prequest │ │
├─────────┼───┤
│Grequest │ │
├─────────┼───┤
│Op │ │
├─────────┼───┤
│Group │ │
├─────────┼───┤
│Info │ │
└─────────┴───┘
Communication
┌──────────────┬───┐
│Comm │ │
├──────────────┼───┤
│Intracomm │ │
├──────────────┼───┤
│Topocomm │ │
├──────────────┼───┤
│Cartcomm │ │
├──────────────┼───┤
│Graphcomm │ │
├──────────────┼───┤
│Distgraphcomm │ │
├──────────────┼───┤
│Intercomm │ │
├──────────────┼───┤
│Message │ │
└──────────────┴───┘
One-sided operations
┌────┬───┐
│Win │ │
└────┴───┘
Input/Output
┌─────┬───┐
│File │ │
└─────┴───┘
Error handling
┌───────────┬───┐
│Errhandler │ │
├───────────┼───┤
│Exception │ │
└───────────┴───┘
Auxiliary
┌───────┬───┐
│Pickle │ │
├───────┼───┤
│memory │ │
└───────┴───┘
Functions
Version inquiry
┌────────────────────┬───┐
│Get_version │ │
├────────────────────┼───┤
│Get_library_version │ │
└────────────────────┴───┘
Initialization and finalization
┌───────────────┬───┐
│Init │ │
├───────────────┼───┤
│Init_thread │ │
├───────────────┼───┤
│Finalize │ │
├───────────────┼───┤
│Is_initialized │ │
├───────────────┼───┤
│Is_finalized │ │
├───────────────┼───┤
│Query_thread │ │
├───────────────┼───┤
│Is_thread_main │ │
└───────────────┴───┘
Memory allocation
┌──────────┬───┐
│Alloc_mem │ │
├──────────┼───┤
│Free_mem │ │
└──────────┴───┘
Address manipulation
┌────────────┬───┐
│Get_address │ │
├────────────┼───┤
│Aint_add │ │
├────────────┼───┤
│Aint_diff │ │
└────────────┴───┘
Timer
┌──────┬───┐
│Wtick │ │
├──────┼───┤
│Wtime │ │
└──────┴───┘
Error handling
┌─────────────────┬───┐
│Get_error_class │ │
├─────────────────┼───┤
│Get_error_string │ │
├─────────────────┼───┤
│Add_error_class │ │
├─────────────────┼───┤
│Add_error_code │ │
├─────────────────┼───┤
│Add_error_string │ │
└─────────────────┴───┘
Dynamic process management
┌───────────────┬───┐
│Open_port │ │
├───────────────┼───┤
│Close_port │ │
└───────────────┴───┘
│Publish_name │ │
├───────────────┼───┤
│Unpublish_name │ │
├───────────────┼───┤
│Lookup_name │ │
└───────────────┴───┘
Miscellanea
┌───────────────────┬───┐
│Attach_buffer │ │
├───────────────────┼───┤
│Detach_buffer │ │
├───────────────────┼───┤
│Compute_dims │ │
├───────────────────┼───┤
│Get_processor_name │ │
├───────────────────┼───┤
│Register_datarep │ │
├───────────────────┼───┤
│Pcontrol │ │
└───────────────────┴───┘
Utilities
┌───────────┬───┐
│get_vendor │ │
└───────────┴───┘
Attributes
┌───────────────────────────┬───┐
│UNDEFINED │ │
├───────────────────────────┼───┤
│ANY_SOURCE │ │
├───────────────────────────┼───┤
│ANY_TAG │ │
├───────────────────────────┼───┤
│PROC_NULL │ │
├───────────────────────────┼───┤
│ROOT │ │
├───────────────────────────┼───┤
│BOTTOM │ │
├───────────────────────────┼───┤
│IN_PLACE │ │
├───────────────────────────┼───┤
│KEYVAL_INVALID │ │
├───────────────────────────┼───┤
│TAG_UB │ │
├───────────────────────────┼───┤
│HOST │ │
├───────────────────────────┼───┤
│IO │ │
├───────────────────────────┼───┤
│WTIME_IS_GLOBAL │ │
├───────────────────────────┼───┤
│UNIVERSE_SIZE │ │
├───────────────────────────┼───┤
│APPNUM │ │
├───────────────────────────┼───┤
│LASTUSEDCODE │ │
├───────────────────────────┼───┤
│WIN_BASE │ │
├───────────────────────────┼───┤
│WIN_SIZE │ │
├───────────────────────────┼───┤
│WIN_DISP_UNIT │ │
├───────────────────────────┼───┤
│WIN_CREATE_FLAVOR │ │
├───────────────────────────┼───┤
│WIN_FLAVOR │ │
└───────────────────────────┴───┘
│WIN_MODEL │ │
├───────────────────────────┼───┤
│SUCCESS │ │
├───────────────────────────┼───┤
│ERR_LASTCODE │ │
├───────────────────────────┼───┤
│ERR_COMM │ │
├───────────────────────────┼───┤
│ERR_GROUP │ │
├───────────────────────────┼───┤
│ERR_TYPE │ │
├───────────────────────────┼───┤
│ERR_REQUEST │ │
├───────────────────────────┼───┤
│ERR_OP │ │
├───────────────────────────┼───┤
│ERR_BUFFER │ │
├───────────────────────────┼───┤
│ERR_COUNT │ │
├───────────────────────────┼───┤
│ERR_TAG │ │
├───────────────────────────┼───┤
│ERR_RANK │ │
├───────────────────────────┼───┤
│ERR_ROOT │ │
├───────────────────────────┼───┤
│ERR_TRUNCATE │ │
├───────────────────────────┼───┤
│ERR_IN_STATUS │ │
├───────────────────────────┼───┤
│ERR_PENDING │ │
├───────────────────────────┼───┤
│ERR_TOPOLOGY │ │
├───────────────────────────┼───┤
│ERR_DIMS │ │
├───────────────────────────┼───┤
│ERR_ARG │ │
├───────────────────────────┼───┤
│ERR_OTHER │ │
├───────────────────────────┼───┤
│ERR_UNKNOWN │ │
├───────────────────────────┼───┤
│ERR_INTERN │ │
├───────────────────────────┼───┤
│ERR_INFO │ │
├───────────────────────────┼───┤
│ERR_FILE │ │
├───────────────────────────┼───┤
│ERR_WIN │ │
├───────────────────────────┼───┤
│ERR_KEYVAL │ │
├───────────────────────────┼───┤
│ERR_INFO_KEY │ │
├───────────────────────────┼───┤
│ERR_INFO_VALUE │ │
├───────────────────────────┼───┤
│ERR_INFO_NOKEY │ │
├───────────────────────────┼───┤
│ERR_ACCESS │ │
├───────────────────────────┼───┤
│ERR_AMODE │ │
├───────────────────────────┼───┤
│ERR_BAD_FILE │ │
├───────────────────────────┼───┤
│ERR_FILE_EXISTS │ │
├───────────────────────────┼───┤
│ERR_FILE_IN_USE │ │
├───────────────────────────┼───┤
│ERR_NO_SPACE │ │
└───────────────────────────┴───┘
│ERR_NO_SUCH_FILE │ │
├───────────────────────────┼───┤
│ERR_IO │ │
├───────────────────────────┼───┤
│ERR_READ_ONLY │ │
├───────────────────────────┼───┤
│ERR_CONVERSION │ │
├───────────────────────────┼───┤
│ERR_DUP_DATAREP │ │
├───────────────────────────┼───┤
│ERR_UNSUPPORTED_DATAREP │ │
├───────────────────────────┼───┤
│ERR_UNSUPPORTED_OPERATION │ │
├───────────────────────────┼───┤
│ERR_NAME │ │
├───────────────────────────┼───┤
│ERR_NO_MEM │ │
├───────────────────────────┼───┤
│ERR_NOT_SAME │ │
├───────────────────────────┼───┤
│ERR_PORT │ │
├───────────────────────────┼───┤
│ERR_QUOTA │ │
├───────────────────────────┼───┤
│ERR_SERVICE │ │
├───────────────────────────┼───┤
│ERR_SPAWN │ │
├───────────────────────────┼───┤
│ERR_BASE │ │
├───────────────────────────┼───┤
│ERR_SIZE │ │
├───────────────────────────┼───┤
│ERR_DISP │ │
├───────────────────────────┼───┤
│ERR_ASSERT │ │
├───────────────────────────┼───┤
│ERR_LOCKTYPE │ │
├───────────────────────────┼───┤
│ERR_RMA_CONFLICT │ │
├───────────────────────────┼───┤
│ERR_RMA_SYNC │ │
├───────────────────────────┼───┤
│ERR_RMA_RANGE │ │
├───────────────────────────┼───┤
│ERR_RMA_ATTACH │ │
├───────────────────────────┼───┤
│ERR_RMA_SHARED │ │
├───────────────────────────┼───┤
│ERR_RMA_FLAVOR │ │
├───────────────────────────┼───┤
│ORDER_C │ │
├───────────────────────────┼───┤
│ORDER_F │ │
├───────────────────────────┼───┤
│ORDER_FORTRAN │ │
├───────────────────────────┼───┤
│TYPECLASS_INTEGER │ │
├───────────────────────────┼───┤
│TYPECLASS_REAL │ │
├───────────────────────────┼───┤
│TYPECLASS_COMPLEX │ │
├───────────────────────────┼───┤
│DISTRIBUTE_NONE │ │
├───────────────────────────┼───┤
│DISTRIBUTE_BLOCK │ │
├───────────────────────────┼───┤
│DISTRIBUTE_CYCLIC │ │
├───────────────────────────┼───┤
│DISTRIBUTE_DFLT_DARG │ │
└───────────────────────────┴───┘
│COMBINER_NAMED │ │
├───────────────────────────┼───┤
│COMBINER_DUP │ │
├───────────────────────────┼───┤
│COMBINER_CONTIGUOUS │ │
├───────────────────────────┼───┤
│COMBINER_VECTOR │ │
├───────────────────────────┼───┤
│COMBINER_HVECTOR │ │
├───────────────────────────┼───┤
│COMBINER_INDEXED │ │
├───────────────────────────┼───┤
│COMBINER_HINDEXED │ │
├───────────────────────────┼───┤
│COMBINER_INDEXED_BLOCK │ │
├───────────────────────────┼───┤
│COMBINER_HINDEXED_BLOCK │ │
├───────────────────────────┼───┤
│COMBINER_STRUCT │ │
├───────────────────────────┼───┤
│COMBINER_SUBARRAY │ │
├───────────────────────────┼───┤
│COMBINER_DARRAY │ │
├───────────────────────────┼───┤
│COMBINER_RESIZED │ │
├───────────────────────────┼───┤
│COMBINER_F90_REAL │ │
├───────────────────────────┼───┤
│COMBINER_F90_COMPLEX │ │
├───────────────────────────┼───┤
│COMBINER_F90_INTEGER │ │
├───────────────────────────┼───┤
│IDENT │ │
├───────────────────────────┼───┤
│CONGRUENT │ │
├───────────────────────────┼───┤
│SIMILAR │ │
├───────────────────────────┼───┤
│UNEQUAL │ │
├───────────────────────────┼───┤
│CART │ │
├───────────────────────────┼───┤
│GRAPH │ │
├───────────────────────────┼───┤
│DIST_GRAPH │ │
├───────────────────────────┼───┤
│UNWEIGHTED │ │
├───────────────────────────┼───┤
│WEIGHTS_EMPTY │ │
├───────────────────────────┼───┤
│COMM_TYPE_SHARED │ │
├───────────────────────────┼───┤
│BSEND_OVERHEAD │ │
├───────────────────────────┼───┤
│WIN_FLAVOR_CREATE │ │
├───────────────────────────┼───┤
│WIN_FLAVOR_ALLOCATE │ │
├───────────────────────────┼───┤
│WIN_FLAVOR_DYNAMIC │ │
├───────────────────────────┼───┤
│WIN_FLAVOR_SHARED │ │
├───────────────────────────┼───┤
│WIN_SEPARATE │ │
├───────────────────────────┼───┤
│WIN_UNIFIED │ │
├───────────────────────────┼───┤
│MODE_NOCHECK │ │
├───────────────────────────┼───┤
│MODE_NOSTORE │ │
└───────────────────────────┴───┘
│MODE_NOPUT │ │
├───────────────────────────┼───┤
│MODE_NOPRECEDE │ │
├───────────────────────────┼───┤
│MODE_NOSUCCEED │ │
├───────────────────────────┼───┤
│LOCK_EXCLUSIVE │ │
├───────────────────────────┼───┤
│LOCK_SHARED │ │
├───────────────────────────┼───┤
│MODE_RDONLY │ │
├───────────────────────────┼───┤
│MODE_WRONLY │ │
├───────────────────────────┼───┤
│MODE_RDWR │ │
├───────────────────────────┼───┤
│MODE_CREATE │ │
├───────────────────────────┼───┤
│MODE_EXCL │ │
├───────────────────────────┼───┤
│MODE_DELETE_ON_CLOSE │ │
├───────────────────────────┼───┤
│MODE_UNIQUE_OPEN │ │
├───────────────────────────┼───┤
│MODE_SEQUENTIAL │ │
├───────────────────────────┼───┤
│MODE_APPEND │ │
├───────────────────────────┼───┤
│SEEK_SET │ │
├───────────────────────────┼───┤
│SEEK_CUR │ │
├───────────────────────────┼───┤
│SEEK_END │ │
├───────────────────────────┼───┤
│DISPLACEMENT_CURRENT │ │
├───────────────────────────┼───┤
│DISP_CUR │ │
├───────────────────────────┼───┤
│THREAD_SINGLE │ │
├───────────────────────────┼───┤
│THREAD_FUNNELED │ │
├───────────────────────────┼───┤
│THREAD_SERIALIZED │ │
├───────────────────────────┼───┤
│THREAD_MULTIPLE │ │
├───────────────────────────┼───┤
│VERSION │ │
├───────────────────────────┼───┤
│SUBVERSION │ │
├───────────────────────────┼───┤
│MAX_PROCESSOR_NAME │ │
├───────────────────────────┼───┤
│MAX_ERROR_STRING │ │
├───────────────────────────┼───┤
│MAX_PORT_NAME │ │
├───────────────────────────┼───┤
│MAX_INFO_KEY │ │
├───────────────────────────┼───┤
│MAX_INFO_VAL │ │
├───────────────────────────┼───┤
│MAX_OBJECT_NAME │ │
├───────────────────────────┼───┤
│MAX_DATAREP_STRING │ │
├───────────────────────────┼───┤
│MAX_LIBRARY_VERSION_STRING │ │
├───────────────────────────┼───┤
│DATATYPE_NULL │ │
├───────────────────────────┼───┤
│UB │ │
└───────────────────────────┴───┘
│LB │ │
├───────────────────────────┼───┤
│PACKED │ │
├───────────────────────────┼───┤
│BYTE │ │
├───────────────────────────┼───┤
│AINT │ │
├───────────────────────────┼───┤
│OFFSET │ │
├───────────────────────────┼───┤
│COUNT │ │
├───────────────────────────┼───┤
│CHAR │ │
├───────────────────────────┼───┤
│WCHAR │ │
├───────────────────────────┼───┤
│SIGNED_CHAR │ │
├───────────────────────────┼───┤
│SHORT │ │
├───────────────────────────┼───┤
│INT │ │
├───────────────────────────┼───┤
│LONG │ │
├───────────────────────────┼───┤
│LONG_LONG │ │
├───────────────────────────┼───┤
│UNSIGNED_CHAR │ │
├───────────────────────────┼───┤
│UNSIGNED_SHORT │ │
├───────────────────────────┼───┤
│UNSIGNED │ │
├───────────────────────────┼───┤
│UNSIGNED_LONG │ │
├───────────────────────────┼───┤
│UNSIGNED_LONG_LONG │ │
├───────────────────────────┼───┤
│FLOAT │ │
├───────────────────────────┼───┤
│DOUBLE │ │
├───────────────────────────┼───┤
│LONG_DOUBLE │ │
├───────────────────────────┼───┤
│C_BOOL │ │
├───────────────────────────┼───┤
│INT8_T │ │
├───────────────────────────┼───┤
│INT16_T │ │
├───────────────────────────┼───┤
│INT32_T │ │
├───────────────────────────┼───┤
│INT64_T │ │
├───────────────────────────┼───┤
│UINT8_T │ │
├───────────────────────────┼───┤
│UINT16_T │ │
├───────────────────────────┼───┤
│UINT32_T │ │
├───────────────────────────┼───┤
│UINT64_T │ │
├───────────────────────────┼───┤
│C_COMPLEX │ │
├───────────────────────────┼───┤
│C_FLOAT_COMPLEX │ │
├───────────────────────────┼───┤
│C_DOUBLE_COMPLEX │ │
├───────────────────────────┼───┤
│C_LONG_DOUBLE_COMPLEX │ │
├───────────────────────────┼───┤
│CXX_BOOL │ │
└───────────────────────────┴───┘
│CXX_FLOAT_COMPLEX │ │
├───────────────────────────┼───┤
│CXX_DOUBLE_COMPLEX │ │
├───────────────────────────┼───┤
│CXX_LONG_DOUBLE_COMPLEX │ │
├───────────────────────────┼───┤
│SHORT_INT │ │
├───────────────────────────┼───┤
│INT_INT │ │
├───────────────────────────┼───┤
│TWOINT │ │
├───────────────────────────┼───┤
│LONG_INT │ │
├───────────────────────────┼───┤
│FLOAT_INT │ │
├───────────────────────────┼───┤
│DOUBLE_INT │ │
├───────────────────────────┼───┤
│LONG_DOUBLE_INT │ │
├───────────────────────────┼───┤
│CHARACTER │ │
├───────────────────────────┼───┤
│LOGICAL │ │
├───────────────────────────┼───┤
│INTEGER │ │
├───────────────────────────┼───┤
│REAL │ │
├───────────────────────────┼───┤
│DOUBLE_PRECISION │ │
├───────────────────────────┼───┤
│COMPLEX │ │
├───────────────────────────┼───┤
│DOUBLE_COMPLEX │ │
├───────────────────────────┼───┤
│LOGICAL1 │ │
├───────────────────────────┼───┤
│LOGICAL2 │ │
├───────────────────────────┼───┤
│LOGICAL4 │ │
├───────────────────────────┼───┤
│LOGICAL8 │ │
├───────────────────────────┼───┤
│INTEGER1 │ │
├───────────────────────────┼───┤
│INTEGER2 │ │
├───────────────────────────┼───┤
│INTEGER4 │ │
├───────────────────────────┼───┤
│INTEGER8 │ │
├───────────────────────────┼───┤
│INTEGER16 │ │
├───────────────────────────┼───┤
│REAL2 │ │
├───────────────────────────┼───┤
│REAL4 │ │
├───────────────────────────┼───┤
│REAL8 │ │
├───────────────────────────┼───┤
│REAL16 │ │
├───────────────────────────┼───┤
│COMPLEX4 │ │
├───────────────────────────┼───┤
│COMPLEX8 │ │
├───────────────────────────┼───┤
│COMPLEX16 │ │
├───────────────────────────┼───┤
│COMPLEX32 │ │
├───────────────────────────┼───┤
│UNSIGNED_INT │ │
└───────────────────────────┴───┘
│SIGNED_SHORT │ │
├───────────────────────────┼───┤
│SIGNED_INT │ │
├───────────────────────────┼───┤
│SIGNED_LONG │ │
├───────────────────────────┼───┤
│SIGNED_LONG_LONG │ │
├───────────────────────────┼───┤
│BOOL │ │
├───────────────────────────┼───┤
│SINT8_T │ │
├───────────────────────────┼───┤
│SINT16_T │ │
├───────────────────────────┼───┤
│SINT32_T │ │
├───────────────────────────┼───┤
│SINT64_T │ │
├───────────────────────────┼───┤
│F_BOOL │ │
├───────────────────────────┼───┤
│F_INT │ │
├───────────────────────────┼───┤
│F_FLOAT │ │
├───────────────────────────┼───┤
│F_DOUBLE │ │
├───────────────────────────┼───┤
│F_COMPLEX │ │
├───────────────────────────┼───┤
│F_FLOAT_COMPLEX │ │
├───────────────────────────┼───┤
│F_DOUBLE_COMPLEX │ │
├───────────────────────────┼───┤
│REQUEST_NULL │ │
├───────────────────────────┼───┤
│MESSAGE_NULL │ │
├───────────────────────────┼───┤
│MESSAGE_NO_PROC │ │
├───────────────────────────┼───┤
│OP_NULL │ │
├───────────────────────────┼───┤
│MAX │ │
├───────────────────────────┼───┤
│MIN │ │
├───────────────────────────┼───┤
│SUM │ │
├───────────────────────────┼───┤
│PROD │ │
├───────────────────────────┼───┤
│LAND │ │
├───────────────────────────┼───┤
│BAND │ │
├───────────────────────────┼───┤
│LOR │ │
├───────────────────────────┼───┤
│BOR │ │
├───────────────────────────┼───┤
│LXOR │ │
├───────────────────────────┼───┤
│BXOR │ │
├───────────────────────────┼───┤
│MAXLOC │ │
├───────────────────────────┼───┤
│MINLOC │ │
├───────────────────────────┼───┤
│REPLACE │ │
├───────────────────────────┼───┤
│NO_OP │ │
├───────────────────────────┼───┤
│GROUP_NULL │ │
└───────────────────────────┴───┘
│GROUP_EMPTY │ │
├───────────────────────────┼───┤
│INFO_NULL │ │
├───────────────────────────┼───┤
│INFO_ENV │ │
├───────────────────────────┼───┤
│ERRHANDLER_NULL │ │
├───────────────────────────┼───┤
│ERRORS_RETURN │ │
├───────────────────────────┼───┤
│ERRORS_ARE_FATAL │ │
├───────────────────────────┼───┤
│COMM_NULL │ │
├───────────────────────────┼───┤
│COMM_SELF │ │
├───────────────────────────┼───┤
│COMM_WORLD │ │
├───────────────────────────┼───┤
│WIN_NULL │ │
├───────────────────────────┼───┤
│FILE_NULL │ │
├───────────────────────────┼───┤
│pickle │ │
└───────────────────────────┴───┘
MPI4PY.FUTURES
New in version 3.0.0.
This package provides a high-level interface for asynchronously executing callables on a
pool of worker processes using MPI for inter-process communication.
concurrent.futures
The mpi4py.futures package is based on concurrent.futures from the Python standard
library. More precisely, mpi4py.futures provides the MPIPoolExecutor class as a concrete
implementation of the abstract class Executor. The submit() interface schedules a
callable to be executed asynchronously and returns a Future object representing the
execution of the callable. Future instances can be queried for the call result or
exception. Sets of Future instances can be passed to the wait() and as_completed()
functions.
NOTE:
The concurrent.futures package was introduced in Python 3.2. A backport targeting
Python 2.7 is available on PyPI. The mpi4py.futures package uses concurrent.futures if
available, either from the Python 3 standard library or the Python 2.7 backport if
installed. Otherwise, mpi4py.futures uses a bundled copy of core functionality
backported from Python 3.5 to work with Python 2.7.
SEE ALSO:
Module concurrent.futures
Documentation of the concurrent.futures standard module.
MPIPoolExecutor
The MPIPoolExecutor class uses a pool of MPI processes to execute calls asynchronously. By
performing computations in separate processes, it allows to side-step the global
interpreter lock but also means that only picklable objects can be executed and returned.
The __main__ module must be importable by worker processes, thus MPIPoolExecutor instances
may not work in the interactive interpreter.
MPIPoolExecutor takes advantage of the dynamic process management features introduced in
the MPI-2 standard. In particular, the MPI.Intracomm.Spawn method of MPI.COMM_SELF is used
in the master (or parent) process to spawn new worker (or child) processes running a
Python interpreter. The master process uses a separate thread (one for each
MPIPoolExecutor instance) to communicate back and forth with the workers. The worker
processes serve the execution of tasks in the main (and only) thread until they are
signaled for completion.
NOTE:
The worker processes must import the main script in order to unpickle any callable
defined in the __main__ module and submitted from the master process. Furthermore, the
callables may need access to other global variables. At the worker processes,
mpi4py.futures executes the main script code (using the runpy module) under the
__worker__ namespace to define the __main__ module. The __main__ and __worker__ modules
are added to sys.modules (both at the master and worker processes) to ensure proper
pickling and unpickling.
WARNING:
During the initial import phase at the workers, the main script cannot create and use
new MPIPoolExecutor instances. Otherwise, each worker would attempt to spawn a new pool
of workers, leading to infinite recursion. mpi4py.futures detects such recursive
attempts to spawn new workers and aborts the MPI execution environment. As the main
script code is run under the __worker__ namespace, the easiest way to avoid spawn
recursion is using the idiom if __name__ == '__main__': ... in the main script.
class mpi4py.futures.MPIPoolExecutor(max_workers=None, initializer=None, initargs=(),
**kwargs)
An Executor subclass that executes calls asynchronously using a pool of at most
max_workers processes. If max_workers is None or not given, its value is
determined from the MPI4PY_FUTURES_MAX_WORKERS environment variable if set, or the
MPI universe size if set, otherwise a single worker process is spawned. If
max_workers is lower than or equal to 0, then a ValueError will be raised.
initializer is an optional callable that is called at the start of each worker
process before executing any tasks; initargs is a tuple of arguments passed to the
initializer. If initializer raises an exception, all pending tasks and any attempt
to submit new tasks to the pool will raise a BrokenExecutor exception.
Other parameters:
• python_exe: Path to the Python interpreter executable used to spawn worker
processes, otherwise sys.executable is used.
• python_args: list or iterable with additional command line flags to pass to the
Python executable. Command line flags determined from inspection of sys.flags,
sys.warnoptions and sys._xoptions in are passed unconditionally.
• mpi_info: dict or iterable yielding (key, value) pairs. These (key, value) pairs
are passed (through an MPI.Info object) to the MPI.Intracomm.Spawn call used to
spawn worker processes. This mechanism allows telling the MPI runtime system
where and how to start the processes. Check the documentation of the backend MPI
implementation about the set of keys it interprets and the corresponding format
for values.
• globals: dict or iterable yielding (name, value) pairs to initialize the main
module namespace in worker processes.
• main: If set to False, do not import the __main__ module in worker processes.
Setting main to False prevents worker processes from accessing definitions in the
parent __main__ namespace.
• path: list or iterable with paths to append to sys.path in worker processes to
extend the module search path.
• wdir: Path to set the current working directory in worker processes using
os.chdir(). The initial working directory is set by the MPI implementation.
Quality MPI implementations should honor a wdir info key passed through mpi_info,
although such feature is not mandatory.
• env: dict or iterable yielding (name, value) pairs with environment variables to
update os.environ in worker processes. The initial environment is set by the MPI
implementation. MPI implementations may allow setting the initial environment
through mpi_info, however such feature is not required nor recommended by the MPI
standard.
submit(func, *args, **kwargs)
Schedule the callable, func, to be executed as func(*args, **kwargs) and
returns a Future object representing the execution of the callable.
executor = MPIPoolExecutor(max_workers=1)
future = executor.submit(pow, 321, 1234)
print(future.result())
map(func, *iterables, timeout=None, chunksize=1, **kwargs)
Equivalent to map(func, *iterables) except func is executed asynchronously
and several calls to func may be made concurrently, out-of-order, in
separate processes. The returned iterator raises a TimeoutError if
__next__() is called and the result isn’t available after timeout seconds
from the original call to map(). timeout can be an int or a float. If
timeout is not specified or None, there is no limit to the wait time. If a
call raises an exception, then that exception will be raised when its value
is retrieved from the iterator. This method chops iterables into a number of
chunks which it submits to the pool as separate tasks. The (approximate)
size of these chunks can be specified by setting chunksize to a positive
integer. For very long iterables, using a large value for chunksize can
significantly improve performance compared to the default size of one. By
default, the returned iterator yields results in-order, waiting for
successive tasks to complete . This behavior can be changed by passing the
keyword argument unordered as True, then the result iterator will yield a
result as soon as any of the tasks complete.
executor = MPIPoolExecutor(max_workers=3)
for result in executor.map(pow, [2]*32, range(32)):
print(result)
starmap(func, iterable, timeout=None, chunksize=1, **kwargs)
Equivalent to itertools.starmap(func, iterable). Used instead of map() when
argument parameters are already grouped in tuples from a single iterable
(the data has been “pre-zipped”). map(func, *iterable) is equivalent to
starmap(func, zip(*iterable)).
executor = MPIPoolExecutor(max_workers=3)
iterable = ((2, n) for n in range(32))
for result in executor.starmap(pow, iterable):
print(result)
shutdown(wait=True, cancel_futures=False)
Signal the executor that it should free any resources that it is using when
the currently pending futures are done executing. Calls to submit() and
map() made after shutdown() will raise RuntimeError.
If wait is True then this method will not return until all the pending
futures are done executing and the resources associated with the executor
have been freed. If wait is False then this method will return immediately
and the resources associated with the executor will be freed when all
pending futures are done executing. Regardless of the value of wait, the
entire Python program will not exit until all pending futures are done
executing.
If cancel_futures is True, this method will cancel all pending futures that
the executor has not started running. Any futures that are completed or
running won’t be cancelled, regardless of the value of cancel_futures.
You can avoid having to call this method explicitly if you use the with
statement, which will shutdown the executor instance (waiting as if
shutdown() were called with wait set to True).
import time
with MPIPoolExecutor(max_workers=1) as executor:
future = executor.submit(time.sleep, 2)
assert future.done()
bootup(wait=True)
Signal the executor that it should allocate eagerly any required resources
(in particular, MPI worker processes). If wait is True, then bootup() will
not return until the executor resources are ready to process submissions.
Resources are automatically allocated in the first call to submit(), thus
calling bootup() explicitly is seldom needed.
MPI4PY_FUTURES_MAX_WORKERS
If the max_workers parameter to MPIPoolExecutor is None or not given, the
MPI4PY_FUTURES_MAX_WORKERS environment variable provides fallback value for the
maximum number of MPI worker processes to spawn.
NOTE:
As the master process uses a separate thread to perform MPI communication with the
workers, the backend MPI implementation should provide support for MPI.THREAD_MULTIPLE.
However, some popular MPI implementations do not support yet concurrent MPI calls from
multiple threads. Additionally, users may decide to initialize MPI with a lower level
of thread support. If the level of thread support in the backend MPI is less than
MPI.THREAD_MULTIPLE, mpi4py.futures will use a global lock to serialize MPI calls. If
the level of thread support is less than MPI.THREAD_SERIALIZED, mpi4py.futures will
emit a RuntimeWarning.
WARNING:
If the level of thread support in the backend MPI is less than MPI.THREAD_SERIALIZED
(i.e, it is either MPI.THREAD_SINGLE or MPI.THREAD_FUNNELED), in theory mpi4py.futures
cannot be used. Rather than raising an exception, mpi4py.futures emits a warning and
takes a “cross-fingers” attitude to continue execution in the hope that serializing MPI
calls with a global lock will actually work.
MPICommExecutor
Legacy MPI-1 implementations (as well as some vendor MPI-2 implementations) do not support
the dynamic process management features introduced in the MPI-2 standard. Additionally,
job schedulers and batch systems in supercomputing facilities may pose additional
complications to applications using the MPI_Comm_spawn() routine.
With these issues in mind, mpi4py.futures supports an additonal, more traditional,
SPMD-like usage pattern requiring MPI-1 calls only. Python applications are started the
usual way, e.g., using the mpiexec command. Python code should make a collective call to
the MPICommExecutor context manager to partition the set of MPI processes within a MPI
communicator in one master processes and many workers processes. The master process gets
access to an MPIPoolExecutor instance to submit tasks. Meanwhile, the worker process
follow a different execution path and team-up to execute the tasks submitted from the
master.
Besides alleviating the lack of dynamic process managment features in legacy MPI-1 or
partial MPI-2 implementations, the MPICommExecutor context manager may be useful in
classic MPI-based Python applications willing to take advantage of the simple, task-based,
master/worker approach available in the mpi4py.futures package.
class mpi4py.futures.MPICommExecutor(comm=None, root=0)
Context manager for MPIPoolExecutor. This context manager splits a MPI
(intra)communicator comm (defaults to MPI.COMM_WORLD if not provided or None) in
two disjoint sets: a single master process (with rank root in comm) and the
remaining worker processes. These sets are then connected through an
intercommunicator. The target of the with statement is assigned either an
MPIPoolExecutor instance (at the master) or None (at the workers).
from mpi4py import MPI
from mpi4py.futures import MPICommExecutor
with MPICommExecutor(MPI.COMM_WORLD, root=0) as executor:
if executor is not None:
future = executor.submit(abs, -42)
assert future.result() == 42
answer = set(executor.map(abs, [-42, 42]))
assert answer == {42}
WARNING:
If MPICommExecutor is passed a communicator of size one (e.g., MPI.COMM_SELF), then the
executor instace assigned to the target of the with statement will execute all
submitted tasks in a single worker thread, thus ensuring that task execution still
progress asynchronously. However, the GIL will prevent the main and worker threads from
running concurrently in multicore processors. Moreover, the thread context switching
may harm noticeably the performance of CPU-bound tasks. In case of I/O-bound tasks, the
GIL is not usually an issue, however, as a single worker thread is used, it progress
one task at a time. We advice against using MPICommExecutor with communicators of size
one and suggest refactoring your code to use instead a ThreadPoolExecutor.
Command line
Recalling the issues related to the lack of support for dynamic process managment features
in MPI implementations, mpi4py.futures supports an alternative usage pattern where Python
code (either from scripts, modules, or zip files) is run under command line control of the
mpi4py.futures package by passing -m mpi4py.futures to the python executable. The
mpi4py.futures invocation should be passed a pyfile path to a script (or a
zipfile/directory containing a __main__.py file). Additionally, mpi4py.futures accepts -m
mod to execute a module named mod, -c cmd to execute a command string cmd, or even - to
read commands from standard input (sys.stdin). Summarizing, mpi4py.futures can be invoked
in the following ways:
• $ mpiexec -n numprocs python -m mpi4py.futures pyfile [arg] ...
• $ mpiexec -n numprocs python -m mpi4py.futures -m mod [arg] ...
• $ mpiexec -n numprocs python -m mpi4py.futures -c cmd [arg] ...
• $ mpiexec -n numprocs python -m mpi4py.futures - [arg] ...
Before starting the main script execution, mpi4py.futures splits MPI.COMM_WORLD in one
master (the process with rank 0 in MPI.COMM_WORLD) and numprocs - 1 workers and connects
them through an MPI intercommunicator. Afterwards, the master process proceeds with the
execution of the user script code, which eventually creates MPIPoolExecutor instances to
submit tasks. Meanwhile, the worker processes follow a different execution path to serve
the master. Upon successful termination of the main script at the master, the entire MPI
execution environment exists gracefully. In case of any unhandled exception in the main
script, the master process calls MPI.COMM_WORLD.Abort(1) to prevent deadlocks and force
termination of entire MPI execution environment.
WARNING:
Running scripts under command line control of mpi4py.futures is quite similar to
executing a single-process application that spawn additional workers as required.
However, there is a very important difference users should be aware of. All
MPIPoolExecutor instances created at the master will share the pool of workers. Tasks
submitted at the master from many different executors will be scheduled for execution
in random order as soon as a worker is idle. Any executor can easily starve all the
workers (e.g., by calling MPIPoolExecutor.map() with long iterables). If that ever
happens, submissions from other executors will not be serviced until free workers are
available.
SEE ALSO:
Command line
Documentation on Python command line interface.
Examples
The following julia.py script computes the Julia set and dumps an image to disk in binary
PGM format. The code starts by importing MPIPoolExecutor from the mpi4py.futures package.
Next, some global constants and functions implement the computation of the Julia set. The
computations are protected with the standard if __name__ == '__main__':... idiom. The
image is computed by whole scanlines submitting all these tasks at once using the map
method. The result iterator yields scanlines in-order as the tasks complete. Finally, each
scanline is dumped to disk.
julia.py
from mpi4py.futures import MPIPoolExecutor
x0, x1, w = -2.0, +2.0, 640*2
y0, y1, h = -1.5, +1.5, 480*2
dx = (x1 - x0) / w
dy = (y1 - y0) / h
c = complex(0, 0.65)
def julia(x, y):
z = complex(x, y)
n = 255
while abs(z) < 3 and n > 1:
z = z**2 + c
n -= 1
return n
def julia_line(k):
line = bytearray(w)
y = y1 - k * dy
for j in range(w):
x = x0 + j * dx
line[j] = julia(x, y)
return line
if __name__ == '__main__':
with MPIPoolExecutor() as executor:
image = executor.map(julia_line, range(h))
with open('julia.pgm', 'wb') as f:
f.write(b'P5 %d %d %d\n' % (w, h, 255))
for line in image:
f.write(line)
The recommended way to execute the script is by using the mpiexec command specifying one
MPI process (master) and (optional but recommended) the desired MPI universe size, which
determines the number of additional dynamically spawned processes (workers). The MPI
universe size is provided either by a batch system or set by the user via command-line
arguments to mpiexec or environment variables. Below we provide examples for MPICH and
Open MPI implementations [1]. In all of these examples, the mpiexec command launches a
single master process running the Python interpreter and executing the main script. When
required, mpi4py.futures spawns the pool of 16 worker processes. The master submits tasks
to the workers and waits for the results. The workers receive incoming tasks, execute
them, and send back the results to the master.
When using MPICH implementation or its derivatives based on the Hydra process manager,
users can set the MPI universe size via the -usize argument to mpiexec:
$ mpiexec -n 1 -usize 17 python julia.py
or, alternatively, by setting the MPIEXEC_UNIVERSE_SIZE environment variable:
$ MPIEXEC_UNIVERSE_SIZE=17 mpiexec -n 1 python julia.py
In the Open MPI implementation, the MPI universe size can be set via the -host argument to
mpiexec:
$ mpiexec -n 1 -host <hostname>:17 python julia.py
Another way to specify the number of workers is to use the mpi4py.futures-specific
environment variable MPI4PY_FUTURES_MAX_WORKERS:
$ MPI4PY_FUTURES_MAX_WORKERS=16 mpiexec -n 1 python julia.py
Note that in this case, the MPI universe size is ignored.
Alternatively, users may decide to execute the script in a more traditional way, that is,
all the MPI processes are started at once. The user script is run under command-line
control of mpi4py.futures passing the -m flag to the python executable:
$ mpiexec -n 17 python -m mpi4py.futures julia.py
As explained previously, the 17 processes are partitioned in one master and 16 workers.
The master process executes the main script while the workers execute the tasks submitted
by the master.
[1] When using an MPI implementation other than MPICH or Open MPI, please check the
documentation of the implementation and/or batch system for the ways to specify the
desired MPI universe size.
GIL See global interpreter lock.
MPI4PY.UTIL
New in version 3.1.0.
The mpi4py.util package collects miscellaneous utilities within the intersection of Python
and MPI.
mpi4py.util.pkl5
New in version 3.1.0.
pickle protocol 5 (see PEP 574) introduced support for out-of-band buffers, allowing for
more efficient handling of certain object types with large memory footprints.
MPI for Python uses the traditional in-band handling of buffers. This approach is
appropriate for communicating non-buffer Python objects, or buffer-like objects with small
memory footprints. For point-to-point communication, in-band buffer handling allows for
the communication of a pickled stream with a single MPI message, at the expense of
additional CPU and memory overhead in the pickling and unpickling steps.
The mpi4py.util.pkl5 module provides communicator wrapper classes reimplementing
pickle-based point-to-point communication methods using pickle protocol 5. Handling
out-of-band buffers necessarily involve multiple MPI messages, thus increasing latency and
hurting performance in case of small size data. However, in case of large size data, the
zero-copy savings of out-of-band buffer handling more than offset the extra latency costs.
Additionally, these wrapper methods overcome the infamous 2 GiB message count limit (MPI-1
to MPI-3).
NOTE:
Support for pickle protocol 5 is available in the pickle module within the Python
standard library since Python 3.8. Previous Python 3 releases can use the pickle5
backport, which is available on PyPI and can be installed with:
python -m pip install pickle5
class mpi4py.util.pkl5.Request(*args, **kwargs)
Custom request class for nonblocking communications.
NOTE:
Request is not a subclass of mpi4py.MPI.Request
Free()
cancel()
get_status()
test()
wait()
testall()
Classmethod
waitall()
Classmethod
class mpi4py.util.pkl5.Message(*args, **kwargs)
Custom message class for matching probes.
NOTE:
Message is not a subclass of mpi4py.MPI.Message
recv()
irecv()
probe()
Classmethod
iprobe()
Classmethod
class mpi4py.util.pkl5.Comm(*args, **kwargs)
Base communicator wrapper class.
send()
bsend()
ssend()
isend()
ibsend()
issend()
recv()
irecv()
WARNING:
This method cannot be supported reliably and raises RuntimeError.
sendrecv()
mprobe()
improbe()
bcast()
class mpi4py.util.pkl5.Intracomm(*args, **kwargs)
Intracommunicator wrapper class.
class mpi4py.util.pkl5.Intercomm(*args, **kwargs)
Intercommunicator wrapper class.
Examples
test-pkl5-1.py
import numpy as np
from mpi4py import MPI
from mpi4py.util import pkl5
comm = pkl5.Intracomm(MPI.COMM_WORLD) # comm wrapper
size = comm.Get_size()
rank = comm.Get_rank()
dst = (rank + 1) % size
src = (rank - 1) % size
sobj = np.full(1024**3, rank, dtype='i4') # > 4 GiB
sreq = comm.isend(sobj, dst, tag=42)
robj = comm.recv (None, src, tag=42)
sreq.Free()
assert np.min(robj) == src
assert np.max(robj) == src
test-pkl5-2.py
import numpy as np
from mpi4py import MPI
from mpi4py.util import pkl5
comm = pkl5.Intracomm(MPI.COMM_WORLD) # comm wrapper
size = comm.Get_size()
rank = comm.Get_rank()
dst = (rank + 1) % size
src = (rank - 1) % size
sobj = np.full(1024**3, rank, dtype='i4') # > 4 GiB
sreq = comm.isend(sobj, dst, tag=42)
status = MPI.Status()
rmsg = comm.mprobe(status=status)
assert status.Get_source() == src
assert status.Get_tag() == 42
rreq = rmsg.irecv()
robj = rreq.wait()
sreq.Free()
assert np.max(robj) == src
assert np.min(robj) == src
mpi4py.util.dtlib
New in version 3.1.0.
The mpi4py.util.dtlib module provides converter routines between NumPy and MPI datatypes.
mpi4py.util.dtlib.from_numpy_dtype()
Parameters
dtype -- NumPy dtype-like object.
mpi4py.util.dtlib.to_numpy_dtype()
Parameters
datatype -- MPI datatype.
MPI4PY.RUN
New in version 3.0.0.
At import time, mpi4py initializes the MPI execution environment calling MPI_Init_thread()
and installs an exit hook to automatically call MPI_Finalize() just before the Python
process terminates. Additionally, mpi4py overrides the default ERRORS_ARE_FATAL error
handler in favor of ERRORS_RETURN, which allows translating MPI errors in Python
exceptions. These departures from standard MPI behavior may be controversial, but are
quite convenient within the highly dynamic Python programming environment. Third-party
code using mpi4py can just from mpi4py import MPI and perform MPI calls without the
tedious initialization/finalization handling. MPI errors, once translated automatically
to Python exceptions, can be dealt with the common try…except…finally clauses; unhandled
MPI exceptions will print a traceback which helps in locating problems in source code.
Unfortunately, the interplay of automatic MPI finalization and unhandled exceptions may
lead to deadlocks. In unattended runs, these deadlocks will drain the battery of your
laptop, or burn precious allocation hours in your supercomputing facility.
Consider the following snippet of Python code. Assume this code is stored in a standard
Python script file and run with mpiexec in two or more processes.
from mpi4py import MPI
assert MPI.COMM_WORLD.Get_size() > 1
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
1/0
MPI.COMM_WORLD.send(None, dest=1, tag=42)
elif rank == 1:
MPI.COMM_WORLD.recv(source=0, tag=42)
Process 0 raises ZeroDivisionError exception before performing a send call to process 1.
As the exception is not handled, the Python interpreter running in process 0 will proceed
to exit with non-zero status. However, as mpi4py installed a finalizer hook to call
MPI_Finalize() before exit, process 0 will block waiting for other processes to also enter
the MPI_Finalize() call. Meanwhile, process 1 will block waiting for a message to arrive
from process 0, thus never reaching to MPI_Finalize(). The whole MPI execution environment
is irremediably in a deadlock state.
To alleviate this issue, mpi4py offers a simple, alternative command line execution
mechanism based on using the -m flag and implemented with the runpy module. To use this
features, Python code should be run passing -m mpi4py in the command line invoking the
Python interpreter. In case of unhandled exceptions, the finalizer hook will call
MPI_Abort() on the MPI_COMM_WORLD communicator, thus effectively aborting the MPI
execution environment.
WARNING:
When a process is forced to abort, resources (e.g. open files) are not cleaned-up and
any registered finalizers (either with the atexit module, the Python C/API function
Py_AtExit(), or even the C standard library function atexit()) will not be executed.
Thus, aborting execution is an extremely impolite way of ensuring process termination.
However, MPI provides no other mechanism to recover from a deadlock state.
Interface options
The use of -m mpi4py to execute Python code on the command line resembles that of the
Python interpreter.
• mpiexec -n numprocs python -m mpi4py pyfile [arg] ...
• mpiexec -n numprocs python -m mpi4py -m mod [arg] ...
• mpiexec -n numprocs python -m mpi4py -c cmd [arg] ...
• mpiexec -n numprocs python -m mpi4py - [arg] ...
<pyfile>
Execute the Python code contained in pyfile, which must be a filesystem path
referring to either a Python file, a directory containing a __main__.py file, or a
zipfile containing a __main__.py file.
-m <mod>
Search sys.path for the named module mod and execute its contents.
-c <cmd>
Execute the Python code in the cmd string command.
- Read commands from standard input (sys.stdin).
SEE ALSO:
Command line
Documentation on Python command line interface.
REFERENCE
┌───────────┬───┐
│mpi4py.MPI │ │
└───────────┴───┘
mpi4py.MPI
mpi4py.MPI
CITATION
If MPI for Python been significant to a project that leads to an academic publication,
please acknowledge that fact by citing the project.
• L. Dalcin and Y.-L. L. Fang, mpi4py: Status Update After 12 Years of Development,
Computing in Science & Engineering, 23(4):47-54, 2021.
https://doi.org/10.1109/MCSE.2021.3083216
• L. Dalcin, P. Kler, R. Paz, and A. Cosimo, Parallel Distributed Computing using Python,
Advances in Water Resources, 34(9):1124-1139, 2011.
https://doi.org/10.1016/j.advwatres.2011.04.013
• L. Dalcin, R. Paz, M. Storti, and J. D’Elia, MPI for Python: performance improvements
and MPI-2 extensions, Journal of Parallel and Distributed Computing, 68(5):655-662,
2008. https://doi.org/10.1016/j.jpdc.2007.09.005
• L. Dalcin, R. Paz, and M. Storti, MPI for Python, Journal of Parallel and Distributed
Computing, 65(9):1108-1115, 2005. https://doi.org/10.1016/j.jpdc.2005.03.010
INSTALLATION
Requirements
You need to have the following software properly installed in order to build MPI for
Python:
• A working MPI implementation, preferably supporting MPI-3 and built with shared/dynamic
libraries.
NOTE:
If you want to build some MPI implementation from sources, check the instructions at
Building MPI from sources in the appendix.
• Python 2.7, 3.5 or above.
NOTE:
Some MPI-1 implementations do require the actual command line arguments to be passed
in MPI_Init(). In this case, you will need to use a rebuilt, MPI-enabled, Python
interpreter executable. MPI for Python has some support for alleviating you from this
task. Check the instructions at MPI-enabled Python interpreter in the appendix.
Using pip
If you already have a working MPI (either if you installed it from sources or by using a
pre-built package from your favourite GNU/Linux distribution) and the mpicc compiler
wrapper is on your search path, you can use pip:
$ python -m pip install mpi4py
NOTE:
If the mpicc compiler wrapper is not on your search path (or if it has a different
name) you can use env to pass the environment variable MPICC providing the full path to
the MPI compiler wrapper executable:
$ env MPICC=/path/to/mpicc python -m pip install mpi4py
WARNING:
pip keeps previouly built wheel files on its cache for future reuse. If you want to
reinstall the mpi4py package using a different or updated MPI implementation, you have
to either first remove the cached wheel file with:
$ python -m pip cache remove mpi4py
or ask pip to disable the cache:
$ python -m pip install --no-cache-dir mpi4py
Using distutils
The MPI for Python package is available for download at the project website generously
hosted by GitHub. You can use curl or wget to get a release tarball.
• Using curl:
$ curl -O https://github.com/mpi4py/mpi4py/releases/download/X.Y.Z/mpi4py-X.Y.Z.tar.gz
• Using wget:
$ wget https://github.com/mpi4py/mpi4py/releases/download/X.Y.Z/mpi4py-X.Y.Z.tar.gz
After unpacking the release tarball:
$ tar -zxf mpi4py-X.Y.Z.tar.gz
$ cd mpi4py-X.Y.Z
the package is ready for building.
MPI for Python uses a standard distutils-based build system. However, some distutils
commands (like build) have additional options:
--mpicc=
Lets you specify a special location or name for the mpicc compiler wrapper.
--mpi= Lets you pass a section with MPI configuration within a special configuration file.
--configure
Runs exhaustive tests for checking about missing MPI types, constants, and
functions. This option should be passed in order to build MPI for Python against
old MPI-1 or MPI-2 implementations, possibly providing a subset of MPI-3.
If you use a MPI implementation providing a mpicc compiler wrapper (e.g., MPICH, Open
MPI), it will be used for compilation and linking. This is the preferred and easiest way
of building MPI for Python.
If mpicc is located somewhere in your search path, simply run the build command:
$ python setup.py build
If mpicc is not in your search path or the compiler wrapper has a different name, you can
run the build command specifying its location:
$ python setup.py build --mpicc=/where/you/have/mpicc
Alternatively, you can provide all the relevant information about your MPI implementation
by editing the file called mpi.cfg. You can use the default section [mpi] or add a new,
custom section, for example [other_mpi] (see the examples provided in the mpi.cfg file as
a starting point to write your own section):
[mpi]
include_dirs = /usr/local/mpi/include
libraries = mpi
library_dirs = /usr/local/mpi/lib
runtime_library_dirs = /usr/local/mpi/lib
[other_mpi]
include_dirs = /opt/mpi/include ...
libraries = mpi ...
library_dirs = /opt/mpi/lib ...
runtime_library_dirs = /op/mpi/lib ...
...
and then run the build command, perhaps specifying you custom configuration section:
$ python setup.py build --mpi=other_mpi
After building, the package is ready for install.
If you have root privileges (either by log-in as the root user of by using sudo) and you
want to install MPI for Python in your system for all users, just do:
$ python setup.py install
The previous steps will install the mpi4py package at standard location
prefix/lib/pythonX.X/site-packages.
If you do not have root privileges or you want to install MPI for Python for your private
use, just do:
$ python setup.py install --user
Testing
To quickly test the installation:
$ mpiexec -n 5 python -m mpi4py.bench helloworld
Hello, World! I am process 0 of 5 on localhost.
Hello, World! I am process 1 of 5 on localhost.
Hello, World! I am process 2 of 5 on localhost.
Hello, World! I am process 3 of 5 on localhost.
Hello, World! I am process 4 of 5 on localhost.
If you installed from source, issuing at the command line:
$ mpiexec -n 5 python demo/helloworld.py
or (in the case of ancient MPI-1 implementations):
$ mpirun -np 5 python `pwd`/demo/helloworld.py
will launch a five-process run of the Python interpreter and run the test script
demo/helloworld.py from the source distribution.
You can also run all the unittest scripts:
$ mpiexec -n 5 python test/runtests.py
or, if you have nose unit testing framework installed:
$ mpiexec -n 5 nosetests -w test
or, if you have py.test unit testing framework installed:
$ mpiexec -n 5 py.test test/
APPENDIX
MPI-enabled Python interpreter
WARNING:
These days it is no longer required to use the MPI-enabled Python interpreter in
most cases, and, therefore, it is not built by default anymore because it is too
difficult to reliably build a Python interpreter across different distributions.
If you know that you still really need it, see below on how to use the build_exe
and install_exe commands.
Some MPI-1 implementations (notably, MPICH 1) do require the actual command line arguments
to be passed at the time MPI_Init() is called. In this case, you will need to use a
re-built, MPI-enabled, Python interpreter binary executable. A basic implementation
(targeting Python 2.X) of what is required is shown below:
#include <Python.h>
#include <mpi.h>
int main(int argc, char *argv[])
{
int status, flag;
MPI_Init(&argc, &argv);
status = Py_Main(argc, argv);
MPI_Finalized(&flag);
if (!flag) MPI_Finalize();
return status;
}
The source code above is straightforward; compiling it should also be. However, the
linking step is more tricky: special flags have to be passed to the linker depending on
your platform. In order to alleviate you for such low-level details, MPI for Python
provides some pure-distutils based support to build and install an MPI-enabled Python
interpreter executable:
$ cd mpi4py-X.X.X
$ python setup.py build_exe [--mpi=<name>|--mpicc=/path/to/mpicc]
$ [sudo] python setup.py install_exe [--install-dir=$HOME/bin]
After the above steps you should have the MPI-enabled interpreter installed as
prefix/bin/pythonX.X-mpi (or $HOME/bin/pythonX.X-mpi). Assuming that prefix/bin (or
$HOME/bin) is listed on your PATH, you should be able to enter your MPI-enabled Python
interactively, for example:
$ python2.7-mpi
Python 2.7.8 (default, Nov 10 2014, 08:19:18)
[GCC 4.9.2 20141101 (Red Hat 4.9.2-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> sys.executable
'/usr/bin/python2.7-mpi'
>>>
Building MPI from sources
In the list below you have some executive instructions for building some of the
open-source MPI implementations out there with support for shared/dynamic libraries on
POSIX environments.
• MPICH
$ tar -zxf mpich-X.X.X.tar.gz
$ cd mpich-X.X.X
$ ./configure --enable-shared --prefix=/usr/local/mpich
$ make
$ make install
• Open MPI
$ tar -zxf openmpi-X.X.X tar.gz
$ cd openmpi-X.X.X
$ ./configure --prefix=/usr/local/openmpi
$ make all
$ make install
• MPICH 1
$ tar -zxf mpich-X.X.X.tar.gz
$ cd mpich-X.X.X
$ ./configure --enable-sharedlib --prefix=/usr/local/mpich1
$ make
$ make install
Perhaps you will need to set the LD_LIBRARY_PATH environment variable (using export,
setenv or what applies to your system) pointing to the directory containing the MPI
libraries . In case of getting runtime linking errors when running MPI programs, the
following lines can be added to the user login shell script (.profile, .bashrc, etc.).
• MPICH
MPI_DIR=/usr/local/mpich
export LD_LIBRARY_PATH=$MPI_DIR/lib:$LD_LIBRARY_PATH
• Open MPI
MPI_DIR=/usr/local/openmpi
export LD_LIBRARY_PATH=$MPI_DIR/lib:$LD_LIBRARY_PATH
• MPICH 1
MPI_DIR=/usr/local/mpich1
export LD_LIBRARY_PATH=$MPI_DIR/lib/shared:$LD_LIBRARY_PATH:
export MPICH_USE_SHLIB=yes
WARNING:
MPICH 1 support for dynamic libraries is not completely transparent. Users should set
the environment variable MPICH_USE_SHLIB to yes in order to avoid link problems when
using the mpicc compiler wrapper.
AUTHOR
Lisandro Dalcin
COPYRIGHT
2024, Lisandro Dalcin