Man page - mpi4py(3)

Packages contains this manual

Package: python-mpi4py-doc
apt-get install python-mpi4py-doc

Manuals in package:

mpi4py(3)

Documentations in package:

python-mpi4py-doc

Manual

MPI4PY

NAME

mpi4py - MPI for Python

Author

Lisandro Dalcin

Contact

dalcinl@gmail.com

Date

March 27, 2025

Abstract

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 programming. There is not full support for defining new communicators or process topologies.
	•		General (picklable) Python objects can be messaged between processors. There is native 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 subclasses 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 Intracomm.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 reduction 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 explicit data movement between host and device. On the Python side, support for handling GPU arrays have been implemented in many libraries related GPU computation such as CuPy , Numba , PyTorch , and PyArrow . To maximize interoperability across library boundaries, two kinds of zero-copy data exchange protocols have been defined and agreed upon: DLPack and CUDA Array Interface (CAI) .

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 to 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. New custom error handlers can be created with Comm.Create_errhandler .

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

Input/Output (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()

GPU-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)
cp.cuda.get_current_stream().synchronize()
comm.Allreduce(sendbuf, recvbuf)

assert cp.allclose(recvbuf, sendbuf*size)

One-Sided Communication (RMA)

•

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

The MPI for Python package.

The Message Passing Interface (MPI) is a standardized and portable message-passing system designed to function on a wide variety of parallel computers. The MPI 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 has become the leading standard for message-passing libraries for parallel computers.

MPI for Python provides MPI bindings for the Python programming language, allowing any Python program to exploit multiple processors. This package build on the MPI specification and provides an object oriented interface which closely follows MPI-2 C++ bindings.

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

Image grohtml-122803-1.png

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.irecv_bufsz

Default buffer size in bytes for irecv() .

Type

int

Default

32768

SEE ALSO:

MPI4PY_RC_IRECV_BUFSZ

Added in version 4.0.0.

mpi4py.rc.errors

Error handling policy.

Type

str

Default

"exception"

Choices

"exception" , "default" , "abort" , "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 programmatically:

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

Added in version 4.0.0.

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

Added in version 4.0.0.

MPI4PY_RC_THREADS

Type

bool

Default

True

Whether to initialize MPI with thread support.

SEE ALSO:

mpi4py.rc.threads

Added 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

Added 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

Added 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_IRECV_BUFSZ

Type

int

Default

32768

Default buffer size in bytes for irecv() .

SEE ALSO:

mpi4py.rc.irecv_bufsz

Added in version 4.0.0.

MPI4PY_RC_ERRORS

Default

"exception"

Choices

"exception" , "default" , "abort" , "fatal"

Controls default MPI error handling policy.

SEE ALSO:

mpi4py.rc.errors

Added 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.

Added 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:

THRESHOLD attribute of the MPI.pickle object within the MPI module.

Added in version 3.1.2.

Miscellaneous functions

mpi4py.profile(name, *, path=None)

Support for the MPI profiling interface.
Parameters

	•		name ( str ) – Name of the profiler library to load.
	•		path ( sequence of str , optional ) – Additional paths to search for the profiler.

Return type

None

mpi4py.get_include()

Return the directory in the package that contains header files.

Extension modules that need to compile against mpi4py should use this function to locate the appropriate include directory. Using Python distutils (or perhaps NumPy distutils):

import mpi4py
Extension('extension_name', ...
include_dirs=[..., mpi4py.get_include()])

Return type

str

mpi4py.get_config()

Return a dictionary with information about MPI.

Changed in version 4.0.0: By default, this function returns an empty dictionary. However, downstream packagers and distributors may alter such behavior. To that end, MPI information must be provided under an mpi section within a UTF-8 encoded INI-style configuration file mpi.cfg located at the top-level package directory. The configuration file is read and parsed using the configparser module.
Return type

dict [ str , str ]

MPI4PY.MPI

Classes

Ancillary

Image grohtml-122803-2.png

Communication

Image grohtml-122803-3.png

One-sided operations

Image grohtml-122803-4.png

Input/Output

Image grohtml-122803-5.png

Error handling

Image grohtml-122803-6.png

Auxiliary

Image grohtml-122803-7.png

Functions

Version inquiry

Image grohtml-122803-8.png

Initialization and finalization

Image grohtml-122803-9.png

Memory allocation

Image grohtml-122803-10.png

Address manipulation

Image grohtml-122803-11.png

Timer

Image grohtml-122803-12.png

Error handling

Image grohtml-122803-13.png

Dynamic process management

Image grohtml-122803-14.png

Miscellanea

Image grohtml-122803-15.png

Utilities

Image grohtml-122803-16.png

Attributes

Image grohtml-122803-17.png

MPI4PY.TYPING

Added in version 4.0.0.

This module provides type aliases used to add type hints to the various functions and methods within the MPI module.

SEE ALSO:

Module typing

Documentation of the typing standard module.

Types Summary

Image grohtml-122803-18.png

Types Documentation
mpi4py.typing.SupportsBuffer = <class 'mpi4py.typing.SupportsBuffer'>

Python buffer protocol.

SEE ALSO:

Buffer Protocol

mpi4py.typing.SupportsDLPack = <class 'mpi4py.typing.SupportsDLPack'>

DLPack data interchange protocol.

SEE ALSO:

dlpack:python-spec

mpi4py.typing.SupportsCAI = <class 'mpi4py.typing.SupportsCAI'>

CUDA Array Interface (CAI) protocol.

SEE ALSO:

numba:cuda-array-interface

mpi4py.typing.Buffer

Buffer-like object.

alias of SupportsBuffer | SupportsDLPack | SupportsCAI

mpi4py.typing.Bottom

Start of the address range.

alias of BottomType | None

mpi4py.typing.InPlace

In-place buffer argument.

alias of InPlaceType | None

mpi4py.typing.Aint = <class 'numbers.Integral'>

Address-sized integral type.

alias of numbers.Integral

mpi4py.typing.Count = <class 'numbers.Integral'>

Integral type for counts.

alias of numbers.Integral

mpi4py.typing.Displ = <class 'numbers.Integral'>

Integral type for displacements.

alias of numbers.Integral

mpi4py.typing.Offset = <class 'numbers.Integral'>

Integral type for offsets.

alias of numbers.Integral

mpi4py.typing.TypeSpec

Datatype specification.

alias of Datatype | str

mpi4py.typing.BufSpec

Buffer specification.

	•		Buffer
	•		Tuple[ Buffer , Count ]
	•		Tuple[ Buffer , TypeSpec ]
	•		Tuple[ Buffer , Count , TypeSpec ]
	•		Tuple[ Bottom , Count , Datatype ]

mpi4py.typing.BufSpecB

Buffer specification (block).

	•		Buffer
	•		Tuple[ Buffer , Count ]
	•		Tuple[ Buffer , TypeSpec ]
	•		Tuple[ Buffer , Count , TypeSpec ]

mpi4py.typing.BufSpecV

Buffer specification (vector).

	•		Buffer
	•		Tuple[ Buffer , Sequence[ Count ]]
	•		Tuple[ Buffer , Tuple[Sequence[ Count ], Sequence[ Displ ]]]
	•		Tuple[ Buffer , TypeSpec ]
	•		Tuple[ Buffer , Sequence[ Count ], TypeSpec ]
	•		Tuple[ Buffer , Tuple[Sequence[ Count ], Sequence[ Displ ]], TypeSpec ]
	•		Tuple[ Buffer , Sequence[ Count ], Sequence[ Displ ], TypeSpec ]
	•		Tuple[ Bottom , Tuple[Sequence[ Count ], Sequence[ Displ ]], Datatype ]
	•		Tuple[ Bottom , Sequence[ Count ], Sequence[ Displ ], Datatype ]

mpi4py.typing.BufSpecW

Buffer specification (generalized).

	•		Tuple[ Buffer , Sequence[ Datatype ]]
	•		Tuple[ Buffer , Tuple[Sequence[ Count ], Sequence[ Displ ]], Sequence[ Datatype ]]
	•		Tuple[ Buffer , Sequence[ Count ], Sequence[ Displ ], Sequence[- Datatype ]]
	•		Tuple[ Bottom , Tuple[Sequence[ Count ], Sequence[ Displ ]], Sequence[ Datatype ]]
	•		Tuple[ Bottom , Sequence[ Count ], Sequence[ Displ ], Sequence[- Datatype ]]

alias of Tuple [ SupportsBuffer | SupportsDLPack | SupportsCAI , - Sequence [ Datatype ]] | Tuple [ SupportsBuffer | SupportsDLPack | SupportsCAI , Tuple [ Sequence [ Integral ], Sequence [ Integral ]], - Sequence [ Datatype ]] | Tuple [ SupportsBuffer | SupportsDLPack | SupportsCAI , Sequence [ Integral ], Sequence [ Integral ], Sequence [- Datatype ]] | Tuple [ BottomType | None , Tuple [ Sequence [ Integral ], Sequence [ Integral ]], Sequence [ Datatype ]] | Tuple [ BottomType | - None , Sequence [ Integral ], Sequence [ Integral ], Sequence [- Datatype ]] | List [ Any ]

mpi4py.typing.TargetSpec

Target specification.

	•		Displ
	•		Tuple[()]
	•		Tuple[ Displ ]
	•		Tuple[ Displ , Count ]
	•		Tuple[ Displ , Count , Datatype ]

mpi4py.typing.S = TypeVar("S")

Invariant TypeVar .

mpi4py.typing.T = TypeVar("T")

Invariant TypeVar .

mpi4py.typing.U = TypeVar("U")

Invariant TypeVar .

mpi4py.typing.V = TypeVar("V")

Invariant TypeVar .

MPI4PY.FUTURES

Added 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.

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.

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.
	•		use_pkl5 : If set to True , use pickle5 with out-of-band buffers for interprocess communication. If use_pkl5 is set to None or not given, its value is determined from the MPI4PY_FUTURES_USE_PKL5 environment variable. Using pickle5 with out-of-band buffers may benefit applications dealing with large buffer-like objects like NumPy arrays. See mpi4py.util.pkl5 for additional information.
	•		backoff : float value specifying the maximum number of seconds a worker thread or process suspends execution with - time.sleep() while idle-waiting. If not set, its value is determined from the MPI4PY_FUTURES_BACKOFF environment variable if set, otherwise the default value of 0.001 seconds is used. Lower values will reduce latency and increase execution throughput for very short-lived tasks, albeit at the expense of spinning CPU cores and increased energy consumption.

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.

num_workers

Number or worker processes in the pool.

MPI4PY_FUTURES_MAX_WORKERS

If the max_workers parameter to MPIPoolExecutor is None or not given, the MPI4PY_FUTURES_MAX_WORKERS environment variable provides a fallback value for the maximum number of MPI worker processes to spawn.

Added in version 3.1.0.

MPI4PY_FUTURES_USE_PKL5

If the use_pkl5 keyword argument to MPIPoolExecutor is None or not given, the MPI4PY_FUTURES_USE_PKL5 environment variable provides a fallback value for whether the executor should use pickle5 with out-of-band buffers for interprocess communication. Accepted values are 0 and 1 (interpreted as False and True , respectively), and strings specifying a YAML boolean value (case-insensitive). Using pickle5 with out-of-band buffers may benefit applications dealing with large buffer-like objects like NumPy arrays. See mpi4py.util.pkl5 for additional information.

Added in version 4.0.0.

MPI4PY_FUTURES_BACKOFF

If the backoff keyword argument to MPIPoolExecutor is not given, the MPI4PY_FUTURES_BACKOFF environment variable can be set to a float value specifying the maximum number of seconds a worker thread or process suspends execution with time.sleep() while idle-waiting. If not set, the default backoff value is 0.001 seconds. Lower values will reduce latency and increase execution throughput for very short-lived tasks, albeit at the expense of spinning CPU cores and increased energy consumption.

Added in version 4.0.0.

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 additional, 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 management 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 instance 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 management 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.

Parallel tasks

The mpi4py.futures package favors an embarrassingly parallel execution model involving a series of sequential tasks independent of each other and executed asynchronously. Albeit unnatural, MPIPoolExecutor can still be used for handling workloads involving parallel tasks, where worker processes communicate and coordinate each other via MPI.
mpi4py.futures.get_comm_workers()

Access an intracommunicator grouping MPI worker processes.

Executing parallel tasks with mpi4py.futures requires following some rules, cf. highlighted lines in example cpi.py :

	•		Use MPIPoolExecutor.num_workers to determine the number of worker processes in the executor and submit exactly one callable per worker process using the MPIPoolExecutor.submit() method.
	•		The submitted callable must use get_comm_workers() to access an intracommunicator grouping MPI worker processes. Afterwards, it is highly recommended calling the Barrier() method on the communicator. The barrier synchronization ensures that every worker process is executing the submitted callable exactly once. Afterwards, the parallel task can safely perform any kind of point-to-point or collective operation using the returned communicator.
	•		The Future instances returned by MPIPoolExecutor.submit() should be collected in a sequence. Use wait() with the sequence of Future instances to ensure logical completion of the parallel task.

Utilities

The mpi4py.futures package provides additional utilities for handling - Future instances.
mpi4py.futures.collect(fs)

Gather a collection of futures in a new future.
Parameters

fs – Collection of futures.

Returns

New future producing as result a list with results from fs .

mpi4py.futures.compose(future, resulthook=None, excepthook=None)

Compose the completion of a future with result and exception handlers.
Parameters

	•		future – Input future instance.
	•		resulthook – Function to be called once the input future completes with success. Once the input future finish running with success, its result value is the input argument for resulthook . The result of resulthook is set as the result of the output future. If resulthook is None , the output future is completed directly with the result of the input future.
	•		excepthook – Function to be called once the input future completes with failure. Once the input future finish running with failure, its exception value is the input argument for excepthook . If excepthook returns an Exception instance, it is set as the exception of the output future. Otherwise, the result of excepthook is set as the result of the output future. If excepthook is None , the output future is set as failed with the exception from the input future.

Returns

Output future instance to be completed once the input future is completed and either resulthook or excepthook finish executing.

Examples

Computing the Julia set

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:

$ env 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 localhost: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 :

$ env 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.

Computing Pi (parallel task)

The number \pi can be approximated via numerical integration with the simple midpoint rule, that is:

\pi = \int_{0}ˆ{1} \frac{4}{1+xˆ2} \,dx \approx \frac{1}{n} \sum_{i=1}ˆ{n} \frac{4}{1 + \left[\frac{1}{n} \left(i-\frac{1}{2}\right) \right]ˆ2} .

The following cpi.py script computes such approximations using mpi4py.futures with a parallel task involving a collective reduction operation. Highlighted lines correspond to the rules discussed in Parallel tasks .

cpi.py

import math
import sys
from mpi4py.futures import MPIPoolExecutor, wait
from mpi4py.futures import get_comm_workers

def compute_pi(n):
# Access intracommunicator and synchronize
comm = get_comm_workers()
comm.Barrier()

rank = comm.Get_rank()
size = comm.Get_size()

# Local computation
h = 1.0 / n
s = 0.0
for i in range(rank + 1, n + 1, size):
x = h * (i - 0.5)
s += 4.0 / (1.0 + x**2)
pi_partial = s * h

# Parallel reduce-to-all
pi = comm.allreduce(pi_partial)

# All workers return the same value
return pi

if __name__ == '__main__':
n = int(sys.argv[1]) if len(sys.argv) > 1 else 256

with MPIPoolExecutor() as executor:
# Submit exactly one callable per worker
P = executor.num_workers
fs = [executor.submit(compute_pi, n) for _ in range(P)]

# Wait for all workers to finish
wait(fs)

# Get result from the first future object.
# In this particular example, due to using reduce-to-all,
# all the other future objects hold the same result value.
pi = fs[0].result()
print(
f"pi: {pi:.16f}, error: {abs(pi - math.pi):.3e}",
f"({n:d} intervals, {P:d} workers)",
)

To run in modern MPI-2 mode:

$ env MPI4PY_FUTURES_MAX_WORKERS=4 mpiexec -n 1 python cpi.py 128
pi: 3.1415977398528137, error: 5.086e-06 (128 intervals, 4 workers)

$ env MPI4PY_FUTURES_MAX_WORKERS=8 mpiexec -n 1 python cpi.py 512
pi: 3.1415929714812316, error: 3.179e-07 (512 intervals, 8 workers)

To run in legacy MPI-1 mode:

$ mpiexec -n 5 python -m mpi4py.futures cpi.py 128
pi: 3.1415977398528137, error: 5.086e-06 (128 intervals, 4 workers)

$ mpiexec -n 9 python -m mpi4py.futures cpi.py 512
pi: 3.1415929714812316, error: 3.179e-07 (512 intervals, 8 workers)

Citation

If mpi4py.futures been significant to a project that leads to an academic publication, please acknowledge our work by citing the following article [mpi4py-futures] :
[mpi4py-futures]

M. Rogowski, S. Aseeri, D. Keyes, and L. Dalcin, mpi4py.futures: MPI-Based Asynchronous Task Execution for Python , IEEE Transactions on Parallel and Distributed Systems, 34(2):611-622, 2023. https://doi.org/10.1109/TPDS.2022.3225481

MPI4PY.UTIL

Added in version 3.1.0.

The mpi4py.util package collects miscellaneous utilities within the intersection of Python and MPI.

mpi4py.util.dtlib

Added in version 3.1.0.

The mpi4py.util.dtlib module provides converter routines between NumPy and MPI datatypes.
mpi4py.util.dtlib.from_numpy_dtype(dtype)

Convert NumPy datatype to MPI datatype.
Parameters

dtype ( DTypeLike ) – NumPy dtype-like object.

Return type

Datatype

mpi4py.util.dtlib.to_numpy_dtype(datatype)

Convert MPI datatype to NumPy datatype.
Parameters

datatype ( Datatype ) – MPI datatype.

Return type

dtype [ Any ]

mpi4py.util.pkl5

Added 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 and collective communication methods using pickle protocol 5. Handling out-of-band buffers necessarily involves 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

Request.

Custom request class for nonblocking communications.

NOTE:

Request is not a subclass of mpi4py.MPI.Request

Free()

Free a communication request.

Return type

None

free()

Free a communication request.

Return type

None

cancel()

Cancel a communication request.
Return type

None

get_status(status=None)

Non-destructive test for the completion of a request.
Parameters

status ( Status | None )

Return type

bool

test(status=None)

Test for the completion of a request.
Parameters

status ( Status | None )

Return type

tuple [ bool , Any | None ]

wait(status=None)

Wait for a request to complete.
Parameters

status ( Status | None )

Return type

Any

classmethod get_status_all(requests, statuses=None)

Non-destructive test for the completion of all requests.
Classmethod

classmethod testall(requests, statuses=None)

Test for the completion of all requests.
Classmethod

classmethod waitall(requests, statuses=None)

Wait for all requests to complete.
Classmethod

class mpi4py.util.pkl5.Message

Message.

Custom message class for matching probes.

NOTE:

Message is not a subclass of mpi4py.MPI.Message

free()

Do nothing.

Return type

None

recv(status=None)

Blocking receive of matched message.
Parameters

status ( Status | None )

Return type

Any

irecv()

Nonblocking receive of matched message.
Return type

Request

classmethod probe(comm, source=ANY_SOURCE, tag=ANY_TAG,
status=None)

Blocking test for a matched message.
Classmethod

classmethod iprobe(comm, source=ANY_SOURCE, tag=ANY_TAG,
status=None)

Nonblocking test for a matched message.
Classmethod

class mpi4py.util.pkl5.Comm

Communicator.

Base communicator wrapper class.
send(obj, dest, tag=0)

Blocking send in standard mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

None

bsend(obj, dest, tag=0)

Blocking send in buffered mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

None

ssend(obj, dest, tag=0)

Blocking send in synchronous mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

None

isend(obj, dest, tag=0)

Nonblocking send in standard mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

Request

ibsend(obj, dest, tag=0)

Nonblocking send in buffered mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

Request

issend(obj, dest, tag=0)

Nonblocking send in synchronous mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

Request

recv(buf=None, source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking receive.
Parameters

	•		buf ( Buffer \| None )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Any

irecv(buf=None, source=ANY_SOURCE, tag=ANY_TAG)

Nonblocking receive.

WARNING:

This method cannot be supported reliably and raises - RuntimeError .

Parameters

	•		buf ( Buffer \| None )
	•		source ( int )
	•		tag ( int )

Return type

Request

sendrecv(sendobj, dest, sendtag=0, recvbuf=None,
source=ANY_SOURCE, recvtag=ANY_TAG, status=None)

Send and receive.
Parameters

	•		sendobj ( Any )
	•		dest ( int )
	•		sendtag ( int )
	•		recvbuf ( Buffer \| None )
	•		source ( int )
	•		recvtag ( int )
	•		status ( Status \| None )

Return type

Any

mprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking test for a matched message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Message

improbe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Nonblocking test for a matched message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Message | None

bcast(obj, root=0)

Broadcast.

Added in version 3.1.0.
Parameters

	•		obj ( Any )
	•		root ( int )

Return type

Any

gather(sendobj, root=0)

Gather.

Added in version 4.0.0.
Parameters

	•		sendobj ( Any )
	•		root ( int )

Return type

list [ Any ] | None

scatter(sendobj, root=0)

Scatter.

Added in version 4.0.0.
Parameters

	•		sendobj ( Sequence[Any] \| None )
	•		root ( int )

Return type

Any

allgather(sendobj)

Gather to All.

Added in version 4.0.0.
Parameters

sendobj ( Any )

Return type

list [ Any ]

alltoall(sendobj)

All to All Scatter/Gather.

Added in version 4.0.0.
Parameters

sendobj ( Sequence[Any] )

Return type

list [ Any ]

class mpi4py.util.pkl5.Intracomm

Intracommunicator.

Intracommunicator wrapper class.

class mpi4py.util.pkl5.Intercomm

Intercommunicator.

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.pool

Added in version 4.0.0.

SEE ALSO:

This module intends to be a drop-in replacement for the - multiprocessing.pool interface from the Python standard library. The Pool class exposed here is implemented as a thin wrapper around MPIPoolExecutor .

NOTE:

The mpi4py.futures package offers a higher level interface for asynchronously pushing tasks to MPI worker process, allowing for a clear separation between submitting tasks and waiting for the results.

class mpi4py.util.pool.Pool

Pool using MPI processes as workers.
__init__(processes=None, initializer=None, initargs=(),
**kwargs)

Initialize a new Pool instance.
Parameters

	•		processes ( int \| None ) – Number of worker processes.
	•		initializer ( Callable[[...], object] \| None ) – An callable used to initialize workers processes.
	•		initargs ( Iterable[Any] ) – A tuple of arguments to pass to the initializer.
	•		kwargs ( Any )

Return type

None

NOTE:

Additional keyword arguments are passed down to the MPIPoolExecutor constructor.

WARNING:

The maxtasksperchild and context arguments of - multiprocessing.pool.Pool are not supported. Specifying maxtasksperchild or context with a value other than None will issue a warning of category - UserWarning .

apply(func, args=(), kwds={})

Call func with arguments args and keyword arguments kwds .

Equivalent to func(*args, **kwds) .
Parameters

	•		func ( Callable[[...], T] )
	•		args ( Iterable[Any] )
	•		kwds ( Mapping[str, Any] )

Return type

apply_async(func, args=(), kwds={}, callback=None,
error_callback=None)

Asynchronous version of apply() returning ApplyResult .
Parameters

	•		func ( Callable[..., T] )
	•		args ( Iterable[Any] )
	•		kwds ( Mapping[str, Any] )
	•		callback ( Callable[[T], object] \| None )
	•		error_callback ( Callable[[BaseException], - object] \| None )

Return type

AsyncResult [ T ]

map(func, iterable, chunksize=None)

Apply func to each element in iterable .

Equivalent to list(map(func, iterable)) .

Block until all results are ready and return them in a - list .

The iterable is choped into a number of chunks which are submitted as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.

Consider using imap() or imap_unordered() with explicit chunksize for better efficiency.
Parameters

	•		func ( Callable[[S], T] )
	•		iterable ( Iterable[S] )
	•		chunksize ( int \| None )

Return type

list [ T ]

map_async(func, iterable, chunksize=None, callback=None,
error_callback=None)

Asynchronous version of map() returning MapResult .
Parameters

	•		func ( Callable[[S], T] )
	•		iterable ( Iterable[S] )
	•		chunksize ( int \| None )
	•		callback ( Callable[[T], None] \| None )
	•		error_callback ( Callable[[BaseException], None] \| None )

Return type

MapResult [ T ]

imap(func, iterable, chunksize=1)

Like map() but return an iterator .

Equivalent to map(func, iterable) .
Parameters

	•		func ( Callable[[S], T] )
	•		iterable ( Iterable[S] )
	•		chunksize ( int )

Return type

Iterator [ T ]

imap_unordered(func, iterable, chunksize=1)

Like imap() but ordering of results is arbitrary.
Parameters

	•		func ( Callable[[S], T] )
	•		iterable ( Iterable[S] )
	•		chunksize ( int )

Return type

Iterator [ T ]

starmap(func, iterable, chunksize=None)

Apply func to each argument tuple in iterable .

Equivalent to list(itertools.starmap(func, iterable)) .

Block until all results are ready and return them in a - list .

The iterable is choped into a number of chunks which are submitted as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.

Consider using istarmap() or istarmap_unordered() with explicit chunksize for better efficiency.
Parameters

	•		func ( Callable[[...], T] )
	•		iterable ( Iterable[Iterable[Any]] )
	•		chunksize ( int \| None )

Return type

list [ T ]

starmap_async(func, iterable, chunksize=None, callback=None,
error_callback=None)

Asynchronous version of starmap() returning MapResult .
Parameters

	•		func ( Callable[..., T] )
	•		iterable ( Iterable[Iterable[Any]] )
	•		chunksize ( int \| None )
	•		callback ( Callable[[T], None] \| None )
	•		error_callback ( Callable[[BaseException], None] \| None )

Return type

MapResult [ T ]

istarmap(func, iterable, chunksize=1)

Like starmap() but return an iterator .

Equivalent to itertools.starmap(func, iterable) .
Parameters

	•		func ( Callable[[...], T] )
	•		iterable ( Iterable[Iterable[Any]] )
	•		chunksize ( int )

Return type

Iterator [ T ]

istarmap_unordered(func, iterable, chunksize=1)

Like istarmap() but ordering of results is arbitrary.
Parameters

	•		func ( Callable[[...], T] )
	•		iterable ( Iterable[Iterable[Any]] )
	•		chunksize ( int )

Return type

Iterator [ T ]

close()

Prevent any more tasks from being submitted to the pool.
Return type

None

terminate()

Stop the worker processes without completing pending tasks.
Return type

None

join()

Wait for the worker processes to exit.

Return type

None

class mpi4py.util.pool.ThreadPool

Bases: Pool

Pool using threads as workers.

class mpi4py.util.pool.AsyncResult

Asynchronous result.
get(timeout=None)

Return the result when it arrives.

If timeout is not None and the result does not arrive within timeout seconds then raise TimeoutError .

If the remote call raised an exception then that exception will be reraised.
Parameters

timeout ( float | None )

Return type

wait(timeout=None)

Wait until the result is available or timeout seconds pass.
Parameters

timeout ( float | None )

Return type

None

ready()

Return whether the call has completed.
Return type

bool

successful()

Return whether the call completed without raising an exception.

If the result is not ready then raise ValueError .
Return type

bool

class mpi4py.util.pool.ApplyResult

Bases: AsyncResult

Result type of apply_async() .

class mpi4py.util.pool.MapResult

Bases: AsyncResult

Result type of map_async() and starmap_async() .

mpi4py.util.sync

Added in version 4.0.0.

The mpi4py.util.sync module provides parallel synchronization utilities.

Sequential execution

class mpi4py.util.sync.Sequential

Sequential execution.

Context manager for sequential execution within a group of MPI processes.

The implementation is based in MPI-1 point-to-point communication. A process with rank i waits in a blocking receive until the previous process rank i-1 finish executing and signals the next rank i with a send.
__init__(comm, tag=0)

Initialize sequential execution.
Parameters

	•		comm ( Intracomm ) – Intracommunicator context.
	•		tag ( int ) – Tag for point-to-point communication.

Return type

None

__enter__()

Enter sequential execution.
Return type

Self

__exit__(*exc)

Exit sequential execution.
Parameters

exc ( object )

Return type

None

begin()

Begin sequential execution.
Return type

None

end()

End sequential execution.

Return type

None

Global counter

class mpi4py.util.sync.Counter

Global counter.

Produce consecutive values within a group of MPI processes. The counter interface is close to that of itertools.count .

The implementation is based in MPI-3 one-sided operations. A root process (typically rank 0 ) holds the counter, and its value is queried and incremented with an atomic RMA fetch-and-add operation.
__init__(start=0, step=1, *, typecode='i', comm=COMM_SELF,
info=INFO_NULL, root=0)

Initialize global counter.
Parameters

	•		start ( int ) – Start value.
	•		step ( int ) – Increment value.
	•		typecode ( str ) – Type code as defined in the - array module.
	•		comm ( Intracomm ) – Intracommunicator context.
	•		info ( Info ) – Info object for RMA context creation.
	•		root ( int ) – Process rank holding the counter memory.

Return type

None

__iter__()

Implement iter(self) .
Return type

Self

__next__()

Implement next(self) .
Return type

int

next(incr=None)

Return current value and increment.
Parameters

incr ( int | None ) – Increment value.

Returns

The counter value before incrementing.

Return type

int

free()

Free counter resources.

Return type

None

Mutual exclusion

class mpi4py.util.sync.Mutex

Mutual exclusion.

Establish a critical section or mutual exclusion among MPI processes.

The mutex interface is close to that of threading.Lock and - threading.RLock , allowing the use of either recursive or non-recursive mutual exclusion. However, a mutex should be used within a group of MPI processes, not threads.

In non-recursive mode, the semantics of Mutex are somewhat different than these of threading.Lock :

	•		Once acquired, a mutex is held and owned by a process until released.
	•		Trying to acquire a mutex already held raises RuntimeError .
	•		Trying to release a mutex not yet held raises RuntimeError .

This mutex implementation uses the scalable and fair spinlock algorithm from [mcs-paper] and took inspiration from the MPI-3 RMA implementation of [uam-book] .
__init__(*, recursive=False, comm=COMM_SELF, info=INFO_NULL)

Initialize mutex object.
Parameters

	•		comm ( Intracomm ) – Intracommunicator context.
	•		recursive ( bool ) – Whether to allow recursive acquisition.
	•		info ( Info ) – Info object for RMA context creation.

Return type

None

__enter__()

Acquire mutex.
Return type

Self

__exit__(*exc)

Release mutex.
Parameters

exc ( object )

Return type

None

acquire(blocking=True)

Acquire mutex, blocking or non-blocking.
Parameters

blocking ( bool ) – If True , block until the mutex is held.

Returns

True if the mutex is held, False otherwise.

Return type

bool

release()

Release mutex.
Return type

None

locked()

Return whether the mutex is held.
Return type

bool

count()

Return the recursion count.
Return type

int

free()

Free mutex resources.

Return type

None

[mcs-paper]

John M. Mellor-Crummey and Michael L. Scott. Algorithms for scalable synchronization on shared-memory multiprocessors. ACM Transactions on Computer Systems, 9(1):21-65, February 1991. - https://doi.org/10.1145/103727.103729

[uam-book]

William Gropp, Torsten Hoefler, Rajeev Thakur, Ewing Lusk. Using Advanced MPI - Modern Features of the Message-Passing Interface. Chapter 4, Section 4.7, Pages 130-131. The MIT Press, November 2014. https://mitpress.mit.edu/9780262527637/using-advanced-mpi/

Condition variable

class mpi4py.util.sync.Condition

Condition variable.

A condition variable allows one or more MPI processes to wait until they are notified by another processes.

The condition variable interface is close to that of - threading.Condition , allowing the use of either recursive or non-recursive mutual exclusion. However, the condition variable should be used within a group of MPI processes, not threads.

This condition variable implementation uses a MPI-3 RMA-based scalable and fair circular queue algorithm to track the set of waiting processes.
__init__(mutex=None, *, recursive=True, comm=COMM_SELF,
info=INFO_NULL)

Initialize condition variable.
Parameters

	•		mutex ( Mutex \| None ) – Mutual exclusion object.
	•		recursive ( bool ) – Whether to allow recursive acquisition.
	•		comm ( Intracomm ) – Intracommunicator context.
	•		info ( Info ) – Info object for RMA context creation.

Return type

None

__enter__()

Acquire the underlying mutex.
Return type

Self

__exit__(*exc)

Release the underlying mutex.
Parameters

exc ( object )

Return type

None

acquire(blocking=True)

Acquire the underlying mutex.
Parameters

blocking ( bool )

Return type

bool

release()

Release the underlying mutex.
Return type

None

locked()

Return whether the underlying mutex is held.
Return type

bool

wait()

Wait until notified by another process.

Returns

Always True .

Return type

Literal [True]

wait_for(predicate)

Wait until a predicate evaluates to True .
Parameters

predicate ( Callable[[], T] ) – callable returning a boolean.

Returns

The result of predicate once it evaluates to True .

Return type

notify(n=1)

Wake up one or more processes waiting on this condition.
Parameters

n ( int ) – Maximum number of processes to wake up.

Returns

The actual number of processes woken up.

Return type

int

notify_all()

Wake up all processes waiting on this condition.
Returns

The actual number of processes woken up.

Return type

int

free()

Free condition resources.

Return type

None

Semaphore object

class mpi4py.util.sync.Semaphore

Semaphore object.

A semaphore object manages an internal counter which is decremented by each acquire() call and incremented by each release() call. The internal counter never reaches a value below zero; when acquire() finds that it is zero, it blocks and waits until some other process calls release() .

The semaphore interface is close to that of threading.Semaphore and threading.BoundedSemaphore , allowing the use of either bounded (default) or unbounded semaphores. With a bounded semaphore, the internal counter never exceeds its initial value; otherwise release() raises ValueError .

This semaphore implementation uses a global Counter and a Condition variable to handle waiting and and notification.
__init__(value=1, *, bounded=True, comm=COMM_SELF,
info=INFO_NULL)

Initialize semaphore object.
Parameters

	•		value ( int ) – Initial value for internal counter.
	•		bounded ( bool ) – Bound internal counter to initial value.
	•		comm ( Intracomm ) – Intracommunicator context.
	•		info ( Info ) – Info object for RMA context creation.

Return type

None

__enter__()

Acquire semaphore.
Return type

Self

__exit__(*exc)

Release semaphore.
Parameters

exc ( object )

Return type

None

acquire(blocking=True)

Acquire semaphore, decrementing the internal counter by one.
Parameters

blocking ( bool ) – If True , block until the semaphore is acquired.

Returns

True if the semaphore is acquired, False otherwise.

Return type

bool

release(n=1)

Release semaphore, incrementing the internal counter by one or more.
Parameters

n ( int ) – Increment for the internal counter.

Return type

None

free()

Free semaphore resources.

Return type

None

Examples

test-sync-1.py

from mpi4py import MPI
from mpi4py.util.sync import Counter, Sequential

comm = MPI.COMM_WORLD

counter = Counter(comm)
with Sequential(comm):
value = next(counter)
counter.free()

assert comm.rank == value

test-sync-2.py

from mpi4py import MPI
from mpi4py.util.sync import Counter, Mutex

comm = MPI.COMM_WORLD

mutex = Mutex(comm)
counter = Counter(comm)
with mutex:
value = next(counter)
counter.free()
mutex.free()

assert (
list(range(comm.size)) ==
sorted(comm.allgather(value))
)

MPI4PY.RUN

Added 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.

Exceptions and deadlocks

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.

deadlock.py

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.

Command line

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.

MPI4PY.BENCH

Added in version 3.0.0.

REFERENCE

Image grohtml-122803-19.png

mpi4py.MPI

Message Passing Interface.

Classes

Image grohtml-122803-20.png

mpi4py.MPI.BottomType

class mpi4py.MPI.BottomType

Bases: int

Type of BOTTOM .
static __new__(cls)

Return type

Self

mpi4py.MPI.BufferAutomaticType

class mpi4py.MPI.BufferAutomaticType

Bases: int

Type of BUFFER_AUTOMATIC .
static __new__(cls)

Return type

Self

mpi4py.MPI.Cartcomm

class mpi4py.MPI.Cartcomm

Bases: Topocomm

Cartesian topology intracommunicator.
static __new__(cls, comm=None)

Parameters

comm ( Cartcomm | None )

Return type

Self

Methods Summary

Image grohtml-122803-21.png

Attributes Summary

Image grohtml-122803-22.png

Methods Documentation
Get_cart_rank(coords)

Translate logical coordinates to ranks.
Parameters

coords ( Sequence[int] )

Return type

int

Get_coords(rank)

Translate ranks to logical coordinates.
Parameters

rank ( int )

Return type

list [ int ]

Get_dim()

Return number of dimensions.
Return type

int

Get_topo()

Return information on the cartesian topology.
Return type

tuple [ list [ int ], list [ int ], list [ int ]]

Shift(direction, disp)

Return a process ranks for data shifting with Sendrecv .
Parameters

	•		direction ( int )
	•		disp ( int )

Return type

tuple [ int , int ]

Sub(remain_dims)

Return a lower-dimensional Cartesian topology.
Parameters

remain_dims ( Sequence[bool] )

Return type

Cartcomm

Attributes Documentation

	coords		Coordinates.
	dim		Number of dimensions.
	dims		Dimensions.
	ndim		Number of dimensions.

periods

Periodicity.

topo

Topology information.

mpi4py.MPI.Comm

class mpi4py.MPI.Comm

Bases: object

Communication context.
static __new__(cls, comm=None)

Parameters

comm ( Comm | None )

Return type

Self

Methods Summary

Image grohtml-122803-23.png

Attributes Summary

Image grohtml-122803-24.png

Methods Documentation
Abort(errorcode=0)

Terminate the MPI execution environment.

WARNING:

The invocation of this method prevents the execution of various Python exit and cleanup mechanisms. Use this method as a last resort to prevent parallel deadlocks in case of unrecoverable errors.

Parameters

errorcode ( int )

Return type

NoReturn

Ack_failed(num_to_ack=None)

Acknowledge failures on a communicator.
Parameters

num_to_ack ( int | None )

Return type

int

Agree(flag)

Blocking agreement.
Parameters

flag ( int )

Return type

int

Allgather(sendbuf, recvbuf)

Gather to All.

Gather data from all processes and broadcast the combined data to all other processes.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecB )

Return type

None

Allgather_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Gather to All.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecB )
	•		info ( Info )

Return type

Prequest

Allgatherv(sendbuf, recvbuf)

Gather to All Vector.

Gather data from all processes and send it to all other processes providing different amounts of data and displacements.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecV )

Return type

None

Allgatherv_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Gather to All Vector.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecV )
	•		info ( Info )

Return type

Prequest

Allreduce(sendbuf, recvbuf, op=SUM)

Reduce to All.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )

Return type

None

Allreduce_init(sendbuf, recvbuf, op=SUM, info=INFO_NULL)

Persistent Reduce to All.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )
	•		info ( Info )

Return type

Prequest

Alltoall(sendbuf, recvbuf)

All to All Scatter/Gather.

Send data to all processes and recv data from all processes.
Parameters

	•		sendbuf ( BufSpecB \| InPlace )
	•		recvbuf ( BufSpecB )

Return type

None

Alltoall_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent All to All Scatter/Gather.
Parameters

	•		sendbuf ( BufSpecB \| InPlace )
	•		recvbuf ( BufSpecB )
	•		info ( Info )

Return type

Prequest

Alltoallv(sendbuf, recvbuf)

All to All Scatter/Gather Vector.

Send data to all processes and recv data from all processes providing different amounts of data and displacements.
Parameters

	•		sendbuf ( BufSpecV \| InPlace )
	•		recvbuf ( BufSpecV )

Return type

None

Alltoallv_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent All to All Scatter/Gather Vector.
Parameters

	•		sendbuf ( BufSpecV \| InPlace )
	•		recvbuf ( BufSpecV )
	•		info ( Info )

Return type

Prequest

Alltoallw(sendbuf, recvbuf)

All to All Scatter/Gather General.

Send/recv data to/from all processes allowing the specification of different counts, displacements, and datatypes for each dest/source.
Parameters

	•		sendbuf ( BufSpecW \| InPlace )
	•		recvbuf ( BufSpecW )

Return type

None

Alltoallw_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent All to All Scatter/Gather General.
Parameters

	•		sendbuf ( BufSpecW \| InPlace )
	•		recvbuf ( BufSpecW )
	•		info ( Info )

Return type

Prequest

Attach_buffer(buf)

Attach a user-provided buffer for sending in buffered mode.
Parameters

buf ( Buffer | None )

Return type

None

Barrier()

Barrier synchronization.
Return type

None

Barrier_init(info=INFO_NULL)

Persistent Barrier.
Parameters

info ( Info )

Return type

Prequest

Bcast(buf, root=0)

Broadcast data from one process to all other processes.
Parameters

	•		buf ( BufSpec )
	•		root ( int )

Return type

None

Bcast_init(buf, root=0, info=INFO_NULL)

Persistent Broadcast.
Parameters

	•		buf ( BufSpec )
	•		root ( int )
	•		info ( Info )

Return type

Prequest

Bsend(buf, dest, tag=0)

Blocking send in buffered mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

None

Bsend_init(buf, dest, tag=0)

Persistent request for a send in buffered mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

Call_errhandler(errorcode)

Call the error handler installed on a communicator.
Parameters

errorcode ( int )

Return type

None

Clone()

Clone an existing communicator.
Return type

Self

Compare(comm)

Compare two communicators.
Parameters

comm ( Comm )

Return type

int

Create(group)

Create communicator from group.
Parameters

group ( Group )

Return type

Comm

classmethod Create_errhandler(errhandler_fn)

Create a new error handler for communicators.
Parameters

errhandler_fn ( Callable[[Comm, int], None] )

Return type

Errhandler

classmethod Create_keyval(copy_fn=None, delete_fn=None,
nopython=False)

Create a new attribute key for communicators.
Parameters

	•		copy_fn ( Callable[[Comm, int, Any], Any] \| None )
	•		delete_fn ( Callable[[Comm, int, Any], None] \| - None )
	•		nopython ( bool )

Return type

int

Delete_attr(keyval)

Delete attribute value associated with a key.
Parameters

keyval ( int )

Return type

None

Detach_buffer()

Remove an existing attached buffer.
Return type

Buffer | None

Disconnect()

Disconnect from a communicator.
Return type

None

Dup(info=None)

Duplicate a communicator.
Parameters

info ( Info | None )

Return type

Self

Dup_with_info(info)

Duplicate a communicator with hints.
Parameters

info ( Info )

Return type

Self

Flush_buffer()

Block until all buffered messages have been transmitted.
Return type

None

Free()

Free a communicator.

Return type

None

classmethod Free_keyval(keyval)

Free an attribute key for communicators.
Parameters

keyval ( int )

Return type

int

Gather(sendbuf, recvbuf, root=0)

Gather data to one process from all other processes.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecB \| None )
	•		root ( int )

Return type

None

Gather_init(sendbuf, recvbuf, root=0, info=INFO_NULL)

Persistent Gather.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecB \| None )
	•		root ( int )
	•		info ( Info )

Return type

Prequest

Gatherv(sendbuf, recvbuf, root=0)

Gather Vector.

Gather data to one process from all other processes providing different amounts of data and displacements.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecV \| None )
	•		root ( int )

Return type

None

Gatherv_init(sendbuf, recvbuf, root=0, info=INFO_NULL)

Persistent Gather Vector.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecV \| None )
	•		root ( int )
	•		info ( Info )

Return type

Prequest

Get_attr(keyval)

Retrieve attribute value by key.
Parameters

keyval ( int )

Return type

int | Any | None

Get_errhandler()

Get the error handler for a communicator.
Return type

Errhandler

Get_failed()

Extract the group of failed processes.
Return type

Group

Get_group()

Access the group associated with a communicator.
Return type

Group

Get_info()

Return the current hints for a communicator.
Return type

Info

Get_name()

Get the print name for this communicator.
Return type

str

classmethod Get_parent()

Return the parent intercommunicator for this process.
Return type

Intercomm

Get_rank()

Return the rank of this process in a communicator.
Return type

int

Get_size()

Return the number of processes in a communicator.
Return type

int

Get_topology()

Return the type of topology (if any) associated with a communicator.
Return type

int

Iagree(flag)

Nonblocking agreement.
Parameters

flag ( Buffer )

Return type

Request

Iallgather(sendbuf, recvbuf)

Nonblocking Gather to All.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecB )

Return type

Request

Iallgatherv(sendbuf, recvbuf)

Nonblocking Gather to All Vector.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecV )

Return type

Request

Iallreduce(sendbuf, recvbuf, op=SUM)

Nonblocking Reduce to All.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )

Return type

Request

Ialltoall(sendbuf, recvbuf)

Nonblocking All to All Scatter/Gather.
Parameters

	•		sendbuf ( BufSpecB \| InPlace )
	•		recvbuf ( BufSpecB )

Return type

Request

Ialltoallv(sendbuf, recvbuf)

Nonblocking All to All Scatter/Gather Vector.
Parameters

	•		sendbuf ( BufSpecV \| InPlace )
	•		recvbuf ( BufSpecV )

Return type

Request

Ialltoallw(sendbuf, recvbuf)

Nonblocking All to All Scatter/Gather General.
Parameters

	•		sendbuf ( BufSpecW \| InPlace )
	•		recvbuf ( BufSpecW )

Return type

Request

Ibarrier()

Nonblocking Barrier.
Return type

Request

Ibcast(buf, root=0)

Nonblocking Broadcast.
Parameters

	•		buf ( BufSpec )
	•		root ( int )

Return type

Request

Ibsend(buf, dest, tag=0)

Nonblocking send in buffered mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

Idup(info=None)

Nonblocking duplicate a communicator.
Parameters

info ( Info | None )

Return type

tuple [ Self , Request ]

Idup_with_info(info)

Nonblocking duplicate a communicator with hints.
Parameters

info ( Info )

Return type

tuple [ Self , Request ]

Iflush_buffer()

Nonblocking flush for buffered messages.
Return type

Request

Igather(sendbuf, recvbuf, root=0)

Nonblocking Gather.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecB \| None )
	•		root ( int )

Return type

Request

Igatherv(sendbuf, recvbuf, root=0)

Nonblocking Gather Vector.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpecV \| None )
	•		root ( int )

Return type

Request

Improbe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Nonblocking test for a matched message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Message | None

Iprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Nonblocking test for a message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

bool

Irecv(buf, source=ANY_SOURCE, tag=ANY_TAG)

Nonblocking receive.
Parameters

	•		buf ( BufSpec )
	•		source ( int )
	•		tag ( int )

Return type

Request

Ireduce(sendbuf, recvbuf, op=SUM, root=0)

Nonblocking Reduce to Root.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec \| None )
	•		op ( Op )
	•		root ( int )

Return type

Request

Ireduce_scatter(sendbuf, recvbuf, recvcounts=None, op=SUM)

Nonblocking Reduce-Scatter (vector version).
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		recvcounts ( Sequence[int] \| None )
	•		op ( Op )

Return type

Request

Ireduce_scatter_block(sendbuf, recvbuf, op=SUM)

Nonblocking Reduce-Scatter Block (regular, non-vector version).
Parameters

	•		sendbuf ( BufSpecB \| InPlace )
	•		recvbuf ( BufSpec \| BufSpecB )
	•		op ( Op )

Return type

Request

Irsend(buf, dest, tag=0)

Nonblocking send in ready mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

Is_inter()

Return whether the communicator is an intercommunicator.
Return type

bool

Is_intra()

Return whether the communicator is an intracommunicator.
Return type

bool

Is_revoked()

Indicate whether the communicator has been revoked.
Return type

bool

Iscatter(sendbuf, recvbuf, root=0)

Nonblocking Scatter.
Parameters

	•		sendbuf ( BufSpecB \| None )
	•		recvbuf ( BufSpec \| InPlace )
	•		root ( int )

Return type

Request

Iscatterv(sendbuf, recvbuf, root=0)

Nonblocking Scatter Vector.
Parameters

	•		sendbuf ( BufSpecV \| None )
	•		recvbuf ( BufSpec \| InPlace )
	•		root ( int )

Return type

Request

Isend(buf, dest, tag=0)

Nonblocking send.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

Isendrecv(sendbuf, dest, sendtag=0, recvbuf=None,
source=ANY_SOURCE, recvtag=ANY_TAG)

Nonblocking send and receive.
Parameters

	•		sendbuf ( BufSpec )
	•		dest ( int )
	•		sendtag ( int )
	•		recvbuf ( BufSpec \| None )
	•		source ( int )
	•		recvtag ( int )

Return type

Request

Isendrecv_replace(buf, dest, sendtag=0, source=ANY_SOURCE,
recvtag=ANY_TAG)

Send and receive a message.

NOTE:

This function is guaranteed not to deadlock in situations where pairs of blocking sends and receives may deadlock.

CAUTION:

A common mistake when using this function is to mismatch the tags with the source and destination ranks, which can result in deadlock.

Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		sendtag ( int )
	•		source ( int )
	•		recvtag ( int )

Return type

Request

Ishrink()

Nonblocking shrink a communicator to remove all failed processes.
Return type

tuple [ Comm , Request ]

Issend(buf, dest, tag=0)

Nonblocking send in synchronous mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

classmethod Join(fd)

Interconnect two processes connected by a socket.
Parameters

fd ( int )

Return type

Intercomm

Mprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking test for a matched message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Message

Precv_init(buf, partitions, source=ANY_SOURCE, tag=ANY_TAG,
info=INFO_NULL)

Create request for a partitioned recv operation.
Parameters

	•		buf ( BufSpec )
	•		partitions ( int )
	•		source ( int )
	•		tag ( int )
	•		info ( Info )

Return type

Prequest

Probe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking test for a message.

NOTE:

This function blocks until the message arrives.

Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Literal [True]

Psend_init(buf, partitions, dest, tag=0, info=INFO_NULL)

Create request for a partitioned send operation.
Parameters

	•		buf ( BufSpec )
	•		partitions ( int )
	•		dest ( int )
	•		tag ( int )
	•		info ( Info )

Return type

Prequest

Recv(buf, source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking receive.

NOTE:

This function blocks until the message is received.

Parameters

	•		buf ( BufSpec )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

None

Recv_init(buf, source=ANY_SOURCE, tag=ANY_TAG)

Create a persistent request for a receive.
Parameters

	•		buf ( BufSpec )
	•		source ( int )
	•		tag ( int )

Return type

Prequest

Reduce(sendbuf, recvbuf, op=SUM, root=0)

Reduce to Root.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec \| None )
	•		op ( Op )
	•		root ( int )

Return type

None

Reduce_init(sendbuf, recvbuf, op=SUM, root=0, info=INFO_NULL)

Persistent Reduce to Root.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec \| None )
	•		op ( Op )
	•		root ( int )
	•		info ( Info )

Return type

Prequest

Reduce_scatter(sendbuf, recvbuf, recvcounts=None, op=SUM)

Reduce-Scatter (vector version).
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		recvcounts ( Sequence[int] \| None )
	•		op ( Op )

Return type

None

Reduce_scatter_block(sendbuf, recvbuf, op=SUM)

Reduce-Scatter Block (regular, non-vector version).
Parameters

	•		sendbuf ( BufSpecB \| InPlace )
	•		recvbuf ( BufSpec \| BufSpecB )
	•		op ( Op )

Return type

None

Reduce_scatter_block_init(sendbuf, recvbuf, op=SUM,
info=INFO_NULL)

Persistent Reduce-Scatter Block (regular, non-vector version).
Parameters

	•		sendbuf ( BufSpecB \| InPlace )
	•		recvbuf ( BufSpec \| BufSpecB )
	•		op ( Op )
	•		info ( Info )

Return type

Prequest

Reduce_scatter_init(sendbuf, recvbuf, recvcounts=None, op=SUM,
info=INFO_NULL)

Persistent Reduce-Scatter (vector version).
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		recvcounts ( Sequence[int] \| None )
	•		op ( Op )
	•		info ( Info )

Return type

Prequest

Revoke()

Revoke a communicator.
Return type

None

Rsend(buf, dest, tag=0)

Blocking send in ready mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

None

Rsend_init(buf, dest, tag=0)

Persistent request for a send in ready mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

Scatter(sendbuf, recvbuf, root=0)

Scatter data from one process to all other processes.
Parameters

	•		sendbuf ( BufSpecB \| None )
	•		recvbuf ( BufSpec \| InPlace )
	•		root ( int )

Return type

None

Scatter_init(sendbuf, recvbuf, root=0, info=INFO_NULL)

Persistent Scatter.
Parameters

	•		sendbuf ( BufSpecB \| None )
	•		recvbuf ( BufSpec \| InPlace )
	•		root ( int )
	•		info ( Info )

Return type

Prequest

Scatterv(sendbuf, recvbuf, root=0)

Scatter Vector.

Scatter data from one process to all other processes providing different amounts of data and displacements.
Parameters

	•		sendbuf ( BufSpecV \| None )
	•		recvbuf ( BufSpec \| InPlace )
	•		root ( int )

Return type

None

Scatterv_init(sendbuf, recvbuf, root=0, info=INFO_NULL)

Persistent Scatter Vector.
Parameters

	•		sendbuf ( BufSpecV \| None )
	•		recvbuf ( BufSpec \| InPlace )
	•		root ( int )
	•		info ( Info )

Return type

Prequest

Send(buf, dest, tag=0)

Blocking send.

NOTE:

This function may block until the message is received. Whether Send blocks or not depends on several factors and is implementation dependent.

Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

None

Send_init(buf, dest, tag=0)

Create a persistent request for a standard send.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Prequest

Sendrecv(sendbuf, dest, sendtag=0, recvbuf=None,
source=ANY_SOURCE, recvtag=ANY_TAG, status=None)

Send and receive a message.

NOTE:

This function is guaranteed not to deadlock in situations where pairs of blocking sends and receives may deadlock.

CAUTION:

A common mistake when using this function is to mismatch the tags with the source and destination ranks, which can result in deadlock.

Parameters

	•		sendbuf ( BufSpec )
	•		dest ( int )
	•		sendtag ( int )
	•		recvbuf ( BufSpec \| None )
	•		source ( int )
	•		recvtag ( int )
	•		status ( Status \| None )

Return type

None

Sendrecv_replace(buf, dest, sendtag=0, source=ANY_SOURCE,
recvtag=ANY_TAG, status=None)

Send and receive a message.

NOTE:

This function is guaranteed not to deadlock in situations where pairs of blocking sends and receives may deadlock.

CAUTION:

A common mistake when using this function is to mismatch the tags with the source and destination ranks, which can result in deadlock.

Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		sendtag ( int )
	•		source ( int )
	•		recvtag ( int )
	•		status ( Status \| None )

Return type

None

Set_attr(keyval, attrval)

Store attribute value associated with a key.
Parameters

	•		keyval ( int )
	•		attrval ( Any )

Return type

None

Set_errhandler(errhandler)

Set the error handler for a communicator.
Parameters

errhandler ( Errhandler )

Return type

None

Set_info(info)

Set new values for the hints associated with a communicator.
Parameters

info ( Info )

Return type

None

Set_name(name)

Set the print name for this communicator.
Parameters

name ( str )

Return type

None

Shrink()

Shrink a communicator to remove all failed processes.
Return type

Comm

Split(color=0, key=0)

Split communicator by color and key.
Parameters

	•		color ( int )
	•		key ( int )

Return type

Comm

Split_type(split_type, key=0, info=INFO_NULL)

Split communicator by split type.
Parameters

	•		split_type ( int )
	•		key ( int )
	•		info ( Info )

Return type

Comm

Ssend(buf, dest, tag=0)

Blocking send in synchronous mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

None

Ssend_init(buf, dest, tag=0)

Persistent request for a send in synchronous mode.
Parameters

	•		buf ( BufSpec )
	•		dest ( int )
	•		tag ( int )

Return type

Request

allgather(sendobj)

Gather to All.
Parameters

sendobj ( Any )

Return type

list [ Any ]

allreduce(sendobj, op=SUM)

Reduce to All.
Parameters

	•		sendobj ( Any )
	•		op ( Op \| Callable[[Any, Any], Any] )

Return type

Any

alltoall(sendobj)

All to All Scatter/Gather.
Parameters

sendobj ( Sequence[Any] )

Return type

list [ Any ]

barrier()

Barrier synchronization.

NOTE:

This method is equivalent to Barrier .

Return type

None

bcast(obj, root=0)

Broadcast.
Parameters

	•		obj ( Any )
	•		root ( int )

Return type

Any

bsend(obj, dest, tag=0)

Send in buffered mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Comm

free()

Call Free if not null or predefined.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Comm

gather(sendobj, root=0)

Gather.
Parameters

	•		sendobj ( Any )
	•		root ( int )

Return type

list [ Any ] | None

ibsend(obj, dest, tag=0)

Nonblocking send in buffered mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

Request

improbe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Nonblocking test for a matched message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Message | None

iprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Nonblocking test for a message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

bool

irecv(buf=None, source=ANY_SOURCE, tag=ANY_TAG)

Nonblocking receive.
Parameters

	•		buf ( Buffer \| None )
	•		source ( int )
	•		tag ( int )

Return type

Request

isend(obj, dest, tag=0)

Nonblocking send.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

Request

issend(obj, dest, tag=0)

Nonblocking send in synchronous mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

Request

mprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking test for a matched message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Message

probe(source=ANY_SOURCE, tag=ANY_TAG, status=None)

Blocking test for a message.
Parameters

	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Literal [True]

py2f()

Return type

int

recv(buf=None, source=ANY_SOURCE, tag=ANY_TAG, status=None)

Receive.
Parameters

	•		buf ( Buffer \| None )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Any

reduce(sendobj, op=SUM, root=0)

Reduce to Root.
Parameters

	•		sendobj ( Any )
	•		op ( Op \| Callable[[Any, Any], Any] )
	•		root ( int )

Return type

Any | None

scatter(sendobj, root=0)

Scatter.
Parameters

	•		sendobj ( Sequence[Any] \| None )
	•		root ( int )

Return type

Any

send(obj, dest, tag=0)

Send in standard mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

None

sendrecv(sendobj, dest, sendtag=0, recvbuf=None,
source=ANY_SOURCE, recvtag=ANY_TAG, status=None)

Send and Receive.
Parameters

	•		sendobj ( Any )
	•		dest ( int )
	•		sendtag ( int )
	•		recvbuf ( Buffer \| None )
	•		source ( int )
	•		recvtag ( int )
	•		status ( Status \| None )

Return type

Any

ssend(obj, dest, tag=0)

Send in synchronous mode.
Parameters

	•		obj ( Any )
	•		dest ( int )
	•		tag ( int )

Return type

None

Attributes Documentation

	group		Group.
	handle		MPI handle.
	info		Info hints.

is_inter

Is intercommunicator.

is_intra

Is intracommunicator.

is_topo

Is a topology.

	name		Print name.
	rank		Rank of this process.
	size		Number of processes.

topology

Topology type.

mpi4py.MPI.Datatype

class mpi4py.MPI.Datatype

Bases: object

Datatype object.
static __new__(cls, datatype=None)

Parameters

datatype ( Datatype | None )

Return type

Self

Methods Summary

Image grohtml-122803-25.png

Attributes Summary

Image grohtml-122803-26.png

Methods Documentation
Commit()

Commit the datatype.
Return type

Self

Create_contiguous(count)

Create a contiguous datatype.
Parameters

count ( int )

Return type

Self

Create_darray(size, rank, gsizes, distribs, dargs, psizes,
order=ORDER_C)

Create a datatype for a distributed array on Cartesian process grids.
Parameters

	•		size ( int )
	•		rank ( int )
	•		gsizes ( Sequence[int] )
	•		distribs ( Sequence[int] )
	•		dargs ( Sequence[int] )
	•		psizes ( Sequence[int] )
	•		order ( int )

Return type

Self

classmethod Create_f90_complex(p, r)

Return a bounded complex datatype.
Parameters

	•		p ( int )
	•		r ( int )

Return type

Self

classmethod Create_f90_integer(r)

Return a bounded integer datatype.
Parameters

r ( int )

Return type

Self

classmethod Create_f90_real(p, r)

Return a bounded real datatype.
Parameters

	•		p ( int )
	•		r ( int )

Return type

Self

Create_hindexed(blocklengths, displacements)

Create an indexed datatype.

NOTE:

Displacements are measured in bytes.

Parameters

	•		blocklengths ( Sequence[int] )
	•		displacements ( Sequence[int] )

Return type

Self

Create_hindexed_block(blocklength, displacements)

Create an indexed datatype with constant-sized blocks.

NOTE:

Displacements are measured in bytes.

Parameters

	•		blocklength ( int )
	•		displacements ( Sequence[int] )

Return type

Self

Create_hvector(count, blocklength, stride)

Create a vector (strided) datatype with stride in bytes.
Parameters

	•		count ( int )
	•		blocklength ( int )
	•		stride ( int )

Return type

Self

Create_indexed(blocklengths, displacements)

Create an indexed datatype.
Parameters

	•		blocklengths ( Sequence[int] )
	•		displacements ( Sequence[int] )

Return type

Self

Create_indexed_block(blocklength, displacements)

Create an indexed datatype with constant-sized blocks.
Parameters

	•		blocklength ( int )
	•		displacements ( Sequence[int] )

Return type

Self

classmethod Create_keyval(copy_fn=None, delete_fn=None,
nopython=False)

Create a new attribute key for datatypes.
Parameters

	•		copy_fn ( Callable[[Datatype, int, Any], Any] \| - None )
	•		delete_fn ( Callable[[Datatype, int, Any], None] \| None )
	•		nopython ( bool )

Return type

int

Create_resized(lb, extent)

Create a datatype with a new lower bound and extent.
Parameters

	•		lb ( int )
	•		extent ( int )

Return type

Self

classmethod Create_struct(blocklengths, displacements,
datatypes)

Create a general composite (struct) datatype.

NOTE:

Displacements are measured in bytes.

Parameters

	•		blocklengths ( Sequence[int] )
	•		displacements ( Sequence[int] )
	•		datatypes ( Sequence[Datatype] )

Return type

Self

Create_subarray(sizes, subsizes, starts, order=ORDER_C)

Create a datatype for a subarray of a multidimensional array.
Parameters

	•		sizes ( Sequence[int] )
	•		subsizes ( Sequence[int] )
	•		starts ( Sequence[int] )
	•		order ( int )

Return type

Self

Create_vector(count, blocklength, stride)

Create a vector (strided) datatype.
Parameters

	•		count ( int )
	•		blocklength ( int )
	•		stride ( int )

Return type

Self

Delete_attr(keyval)

Delete attribute value associated with a key.
Parameters

keyval ( int )

Return type

None

Dup()

Duplicate a datatype.

Return type

Self

Free()

Free the datatype.

Return type

None

classmethod Free_keyval(keyval)

Free an attribute key for datatypes.
Parameters

keyval ( int )

Return type

int

Get_attr(keyval)

Retrieve attribute value by key.
Parameters

keyval ( int )

Return type

int | Any | None

Get_contents()

Return the input arguments used to create a datatype.
Return type

tuple [ list [ int ], list [ int ], list [ int ], list [- Datatype ]]

Get_envelope()

Return the number of input arguments used to create a datatype.
Return type

tuple [ int , int , int , int , int ]

Get_extent()

Return lower bound and extent of datatype.
Return type

tuple [ int , int ]

Get_name()

Get the print name for this datatype.
Return type

str

Get_size()

Return the number of bytes occupied by entries in the datatype.
Return type

int

Get_true_extent()

Return the true lower bound and extent of a datatype.
Return type

tuple [ int , int ]

classmethod Get_value_index(value, index)

Return a predefined pair datatype.
Parameters

	•		value ( Datatype )
	•		index ( Datatype )

Return type

Self

classmethod Match_size(typeclass, size)

Find a datatype matching a specified size in bytes.
Parameters

	•		typeclass ( int )
	•		size ( int )

Return type

Self

Pack(inbuf, outbuf, position, comm)

Pack into contiguous memory according to datatype.
Parameters

	•		inbuf ( BufSpec )
	•		outbuf ( BufSpec )
	•		position ( int )
	•		comm ( Comm )

Return type

int

Pack_external(datarep, inbuf, outbuf, position)

Pack into contiguous memory according to datatype.

Uses the portable data representation external32 .
Parameters

	•		datarep ( str )
	•		inbuf ( BufSpec )
	•		outbuf ( BufSpec )
	•		position ( int )

Return type

int

Pack_external_size(datarep, count)

Determine the amount of space needed to pack a message.

Uses the portable data representation external32 .

NOTE:

Returns an upper bound measured in bytes.

Parameters

	•		datarep ( str )
	•		count ( int )

Return type

int

Pack_size(count, comm)

Determine the amount of space needed to pack a message.

NOTE:

Returns an upper bound measured in bytes.

Parameters

	•		count ( int )
	•		comm ( Comm )

Return type

int

Set_attr(keyval, attrval)

Store attribute value associated with a key.
Parameters

	•		keyval ( int )
	•		attrval ( Any )

Return type

None

Set_name(name)

Set the print name for this datatype.
Parameters

name ( str )

Return type

None

Unpack(inbuf, position, outbuf, comm)

Unpack from contiguous memory according to datatype.
Parameters

	•		inbuf ( BufSpec )
	•		position ( int )
	•		outbuf ( BufSpec )
	•		comm ( Comm )

Return type

int

Unpack_external(datarep, inbuf, position, outbuf)

Unpack from contiguous memory according to datatype.

Uses the portable data representation external32 .
Parameters

	•		datarep ( str )
	•		inbuf ( BufSpec )
	•		position ( int )
	•		outbuf ( BufSpec )

Return type

int

decode()

Convenience method for decoding a datatype.
Return type

tuple [ Datatype , str , dict [ str , Any ]]

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Datatype

free()

Call Free if not null or predefined.

Return type

None

classmethod fromcode(code)

Get predefined MPI datatype from character code or type string.
Parameters

code ( str )

Return type

Datatype

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Datatype

py2f()

Return type

int

tocode()

Get character code or type string from predefined MPI datatype.
Return type

str

Attributes Documentation
combiner

Combiner.

contents

Contents.

envelope

Envelope.

	extent		Extent.
	handle		MPI handle.

is_named

Is a named datatype.

is_predefined

Is a predefined datatype.

	lb		Lower bound.
	name		Print name.
	size		Size (in bytes).

true_extent

True extent.

true_lb

True lower bound.

true_ub

True upper bound.

typechar

Character code.

typestr

Type string.

Upper bound.

mpi4py.MPI.Distgraphcomm

class mpi4py.MPI.Distgraphcomm

Bases: Topocomm

Distributed graph topology intracommunicator.
static __new__(cls, comm=None)

Parameters

comm ( Distgraphcomm | None )

Return type

Self

Methods Summary

Image grohtml-122803-27.png

Methods Documentation
Get_dist_neighbors()

Return adjacency information for a distributed graph topology.
Return type

tuple [ list [ int ], list [ int ], tuple [ list [ int ], - list [ int ]] | None ]

Get_dist_neighbors_count()

Return adjacency information for a distributed graph topology.
Return type

int

mpi4py.MPI.Errhandler

class mpi4py.MPI.Errhandler

Bases: object

Error handler.
static __new__(cls, errhandler=None)

Parameters

errhandler ( Errhandler | None )

Return type

Self

Methods Summary

Image grohtml-122803-28.png

Attributes Summary

Image grohtml-122803-29.png

Methods Documentation

Free()

Free an error handler.

Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Errhandler

free()

Call Free if not null.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Errhandler

py2f()

Return type

int

Attributes Documentation

handle

MPI handle.

mpi4py.MPI.File

class mpi4py.MPI.File

Bases: object

File I/O context.
static __new__(cls, file=None)

Parameters

file ( File | None )

Return type

Self

Methods Summary

Image grohtml-122803-30.png

Attributes Summary

Image grohtml-122803-31.png

Methods Documentation
Call_errhandler(errorcode)

Call the error handler installed on a file.
Parameters

errorcode ( int )

Return type

None

Close()

Close a file.
Return type

None

classmethod Create_errhandler(errhandler_fn)

Create a new error handler for files.
Parameters

errhandler_fn ( Callable[[File, int], None] )

Return type

Errhandler

classmethod Delete(filename, info=INFO_NULL)

Delete a file.
Parameters

	•		filename ( PathLike \| str \| bytes )
	•		info ( Info )

Return type

None

Get_amode()

Return the file access mode.
Return type

int

Get_atomicity()

Return the atomicity mode.
Return type

bool

Get_byte_offset(offset)

Return the absolute byte position in the file.

NOTE:

Input offset is measured in etype units relative to the current file view.

Parameters

offset ( int )

Return type

int

Get_errhandler()

Get the error handler for a file.
Return type

Errhandler

Get_group()

Access the group of processes that opened the file.
Return type

Group

Get_info()

Return the current hints for a file.
Return type

Info

Get_position()

Return the current position of the individual file pointer.

NOTE:

Position is measured in etype units relative to the current file view.

Return type

int

Get_position_shared()

Return the current position of the shared file pointer.

NOTE:

Position is measured in etype units relative to the current view.

Return type

int

Get_size()

Return the file size.
Return type

int

Get_type_extent(datatype)

Return the extent of datatype in the file.
Parameters

datatype ( Datatype )

Return type

int

Get_view()

Return the file view.
Return type

tuple [ int , Datatype , Datatype , str ]

Iread(buf)

Nonblocking read using individual file pointer.
Parameters

buf ( BufSpec )

Return type

Request

Iread_all(buf)

Nonblocking collective read using individual file pointer.
Parameters

buf ( BufSpec )

Return type

Request

Iread_at(offset, buf)

Nonblocking read using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )

Return type

Request

Iread_at_all(offset, buf)

Nonblocking collective read using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )

Return type

Request

Iread_shared(buf)

Nonblocking read using shared file pointer.
Parameters

buf ( BufSpec )

Return type

Request

Iwrite(buf)

Nonblocking write using individual file pointer.
Parameters

buf ( BufSpec )

Return type

Request

Iwrite_all(buf)

Nonblocking collective write using individual file pointer.
Parameters

buf ( BufSpec )

Return type

Request

Iwrite_at(offset, buf)

Nonblocking write using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )

Return type

Request

Iwrite_at_all(offset, buf)

Nonblocking collective write using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )

Return type

Request

Iwrite_shared(buf)

Nonblocking write using shared file pointer.
Parameters

buf ( BufSpec )

Return type

Request

classmethod Open(comm, filename, amode=MODE_RDONLY,
info=INFO_NULL)

Open a file.
Parameters

	•		comm ( Intracomm )
	•		filename ( PathLike \| str \| bytes )
	•		amode ( int )
	•		info ( Info )

Return type

Self

Preallocate(size)

Preallocate storage space for a file.
Parameters

size ( int )

Return type

None

Read(buf, status=None)

Read using individual file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_all(buf, status=None)

Collective read using individual file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_all_begin(buf)

Start a split collective read using individual file pointer.
Parameters

buf ( BufSpec )

Return type

None

Read_all_end(buf, status=None)

Complete a split collective read using individual file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_at(offset, buf, status=None)

Read using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_at_all(offset, buf, status=None)

Collective read using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_at_all_begin(offset, buf)

Start a split collective read using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )

Return type

None

Read_at_all_end(buf, status=None)

Complete a split collective read using explicit offset.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_ordered(buf, status=None)

Collective read using shared file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_ordered_begin(buf)

Start a split collective read using shared file pointer.
Parameters

buf ( BufSpec )

Return type

None

Read_ordered_end(buf, status=None)

Complete a split collective read using shared file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Read_shared(buf, status=None)

Read using shared file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Seek(offset, whence=SEEK_SET)

Update the individual file pointer.
Parameters

	•		offset ( int )
	•		whence ( int )

Return type

None

Seek_shared(offset, whence=SEEK_SET)

Update the shared file pointer.
Parameters

	•		offset ( int )
	•		whence ( int )

Return type

None

Set_atomicity(flag)

Set the atomicity mode.
Parameters

flag ( bool )

Return type

None

Set_errhandler(errhandler)

Set the error handler for a file.
Parameters

errhandler ( Errhandler )

Return type

None

Set_info(info)

Set new values for the hints associated with a file.
Parameters

info ( Info )

Return type

None

Set_size(size)

Set the file size.
Parameters

size ( int )

Return type

None

Set_view(disp=0, etype=BYTE, filetype=None, datarep='native',
info=INFO_NULL)

Set the file view.
Parameters

	•		disp ( int )
	•		etype ( Datatype )
	•		filetype ( Datatype \| None )
	•		datarep ( str )
	•		info ( Info )

Return type

None

Sync()

Causes all previous writes to be transferred to the storage device.

Return type

None

Write(buf, status=None)

Write using individual file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_all(buf, status=None)

Collective write using individual file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_all_begin(buf)

Start a split collective write using individual file pointer.
Parameters

buf ( BufSpec )

Return type

None

Write_all_end(buf, status=None)

Complete a split collective write using individual file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_at(offset, buf, status=None)

Write using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_at_all(offset, buf, status=None)

Collective write using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_at_all_begin(offset, buf)

Start a split collective write using explicit offset.
Parameters

	•		offset ( int )
	•		buf ( BufSpec )

Return type

None

Write_at_all_end(buf, status=None)

Complete a split collective write using explicit offset.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_ordered(buf, status=None)

Collective write using shared file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_ordered_begin(buf)

Start a split collective write using shared file pointer.
Parameters

buf ( BufSpec )

Return type

None

Write_ordered_end(buf, status=None)

Complete a split collective write using shared file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

Write_shared(buf, status=None)

Write using shared file pointer.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

File

free()

Call Close if not null.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

File

py2f()

Return type

int

Attributes Documentation

amode

Access mode.

atomicity

Atomicity mode.

group

Group.

group_rank

Group rank.

group_size

Group size.

	handle		MPI handle.
	info		Info hints.
	size		Size (in bytes).

mpi4py.MPI.Graphcomm

class mpi4py.MPI.Graphcomm

Bases: Topocomm

General graph topology intracommunicator.
static __new__(cls, comm=None)

Parameters

comm ( Graphcomm | None )

Return type

Self

Methods Summary

Image grohtml-122803-32.png

Attributes Summary

Image grohtml-122803-33.png

Methods Documentation
Get_dims()

Return the number of nodes and edges.
Return type

tuple [ int , int ]

Get_neighbors(rank)

Return list of neighbors of a process.
Parameters

rank ( int )

Return type

list [ int ]

Get_neighbors_count(rank)

Return number of neighbors of a process.
Parameters

rank ( int )

Return type

int

Get_topo()

Return index and edges.
Return type

tuple [ list [ int ], list [ int ]]

Attributes Documentation

	dims		Number of nodes and edges.
	edges		Edges.
	index		Index.
	nedges		Number of edges.

neighbors

Neighbors.

nneighbors

Number of neighbors.

	nnodes		Number of nodes.
	topo		Topology information.

mpi4py.MPI.Grequest

class mpi4py.MPI.Grequest

Bases: Request

Generalized request handler.
static __new__(cls, request=None)

Parameters

request ( Grequest | None )

Return type

Self

Methods Summary

Image grohtml-122803-34.png

Methods Documentation
Complete()

Notify that a user-defined request is complete.
Return type

None

classmethod Start(query_fn=None, free_fn=None, cancel_fn=None,
args=None, kwargs=None)

Create and return a user-defined request.
Parameters

	•		query_fn ( Callable[[...], None] \| None )
	•		free_fn ( Callable[[...], None] \| None )
	•		cancel_fn ( Callable[[...], None] \| None )
	•		args ( tuple[Any] \| None )
	•		kwargs ( dict[str, Any] \| None )

Return type

Grequest

complete(obj=None)

Notify that a user-defined request is complete.
Parameters

obj ( Any )

Return type

None

mpi4py.MPI.Group

class mpi4py.MPI.Group

Bases: object

Group of processes.
static __new__(cls, group=None)

Parameters

group ( Group | None )

Return type

Self

Methods Summary

Image grohtml-122803-35.png

Attributes Summary

Image grohtml-122803-36.png

Methods Documentation
Compare(group)

Compare two groups.
Parameters

group ( Group )

Return type

int

classmethod Create_from_session_pset(session, pset_name)

Create a new group from session and process set.
Parameters

	•		session ( Session )
	•		pset_name ( str )

Return type

Self

classmethod Difference(group1, group2)

Create a new group from the difference of two existing groups.
Parameters

	•		group1 ( Group )
	•		group2 ( Group )

Return type

Self

Dup()

Duplicate a group.

Return type

Self

Excl(ranks)

Create a new group by excluding listed members.
Parameters

ranks ( Sequence[int] )

Return type

Self

Free()

Free a group.

Return type

None

Get_rank()

Return the rank of this process in a group.
Return type

int

Get_size()

Return the number of processes in a group.
Return type

int

Incl(ranks)

Create a new group by including listed members.
Parameters

ranks ( Sequence[int] )

Return type

Self

classmethod Intersection(group1, group2)

Create a new group from the intersection of two existing groups.
Parameters

	•		group1 ( Group )
	•		group2 ( Group )

Return type

Self

Range_excl(ranks)

Create a new group by excluding ranges of members.
Parameters

ranks ( Sequence[tuple[int, int, int]] )

Return type

Self

Range_incl(ranks)

Create a new group by including ranges of members.
Parameters

ranks ( Sequence[tuple[int, int, int]] )

Return type

Self

Translate_ranks(ranks=None, group=None)

Translate ranks in a group to those in another group.
Parameters

	•		ranks ( Sequence[int] \| None )
	•		group ( Group \| None )

Return type

list [ int ]

classmethod Union(group1, group2)

Create a new group from the union of two existing groups.
Parameters

	•		group1 ( Group )
	•		group2 ( Group )

Return type

Self

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Group

free()

Call Free if not null or predefined.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Group

py2f()

Return type

int

Attributes Documentation

	handle		MPI handle.
	rank		Rank of this process.
	size		Number of processes.

mpi4py.MPI.InPlaceType

class mpi4py.MPI.InPlaceType

Bases: int

Type of IN_PLACE .
static __new__(cls)

Return type

Self

mpi4py.MPI.Info

class mpi4py.MPI.Info

Bases: object

Info object.
static __new__(cls, info=None)

Parameters

info ( Info | None )

Return type

Self

Methods Summary

Image grohtml-122803-37.png

Attributes Summary

Image grohtml-122803-38.png

Methods Documentation
classmethod Create(items=None)

Create a new info object.
Parameters

items ( Info | Mapping[str, str] | Iterable[tuple[ - str, str]] | None )

Return type

Self

classmethod Create_env(args=None)

Create a new environment info object.
Parameters

args ( Sequence[str] | None )

Return type

Self

Delete(key)

Remove a (key, value) pair from info.
Parameters

key ( str )

Return type

None

Dup()

Duplicate an existing info object.

Return type

Self

Free()

Free an info object.

Return type

None

Get(key)

Retrieve the value associated with a key.
Parameters

key ( str )

Return type

str | None

Get_nkeys()

Return the number of currently defined keys in info.
Return type

int

Get_nthkey(n)

Return the n -th defined key in info.
Parameters

n ( int )

Return type

str

Set(key, value)

Store a value associated with a key.
Parameters

	•		key ( str )
	•		value ( str )

Return type

None

clear()

Clear contents.
Return type

None

copy()

Copy contents.

Return type

Self

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Info

free()

Call Free if not null or predefined.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Info

get(key, default=None)

Retrieve value by key.
Parameters

	•		key ( str )
	•		default ( str \| None )

Return type

str | None

items()

Return list of items.
Return type

list [ tuple [ str , str ]]

keys()

Return list of keys.

Return type

list [ str ]

pop(key, *default)

Pop value by key.
Parameters

	•		key ( str )
	•		default ( str )

Return type

str

popitem()

Pop first item.
Return type

tuple [ str , str ]

py2f()

Return type

int

update(items=(), **kwds)

Update contents.
Parameters

	•		items ( Info \| Mapping[str, str] \| Iterable[ - tuple[str, str]] )
	•		kwds ( str )

Return type

None

values()

Return list of values.
Return type

list [ str ]

Attributes Documentation

handle

MPI handle.

mpi4py.MPI.Intercomm

class mpi4py.MPI.Intercomm

Bases: Comm

Intercommunicator.
static __new__(cls, comm=None)

Parameters

comm ( Intercomm | None )

Return type

Self

Methods Summary

Image grohtml-122803-39.png

Attributes Summary

Image grohtml-122803-40.png

Methods Documentation
classmethod Create_from_groups(local_group, local_leader,
remote_group, remote_leader, stringtag='org.mpi4py',
info=INFO_NULL, errhandler=None)

Create communicator from group.
Parameters

	•		local_group ( Group )
	•		local_leader ( int )
	•		remote_group ( Group )
	•		remote_leader ( int )
	•		stringtag ( str )
	•		info ( Info )
	•		errhandler ( Errhandler \| None )

Return type

Intracomm

Get_remote_group()

Access the remote group associated with the inter-communicator.
Return type

Group

Get_remote_size()

Intercommunicator remote size.
Return type

int

Merge(high=False)

Merge intercommunicator into an intracommunicator.
Parameters

high ( bool )

Return type

Intracomm

Attributes Documentation
remote_group

Remote group.

remote_size

Number of remote processes.

mpi4py.MPI.Intracomm

class mpi4py.MPI.Intracomm

Bases: Comm

Intracommunicator.
static __new__(cls, comm=None)

Parameters

comm ( Intracomm | None )

Return type

Self

Methods Summary

Image grohtml-122803-41.png

Methods Documentation
Accept(port_name, info=INFO_NULL, root=0)

Accept a request to form a new intercommunicator.
Parameters

	•		port_name ( str )
	•		info ( Info )
	•		root ( int )

Return type

Intercomm

Cart_map(dims, periods=None)

Determine optimal process placement on a Cartesian topology.
Parameters

	•		dims ( Sequence[int] )
	•		periods ( Sequence[bool] \| None )

Return type

int

Connect(port_name, info=INFO_NULL, root=0)

Make a request to form a new intercommunicator.
Parameters

	•		port_name ( str )
	•		info ( Info )
	•		root ( int )

Return type

Intercomm

Create_cart(dims, periods=None, reorder=False)

Create cartesian communicator.
Parameters

	•		dims ( Sequence[int] )
	•		periods ( Sequence[bool] \| None )
	•		reorder ( bool )

Return type

Cartcomm

Create_dist_graph(sources, degrees, destinations, weights=None,
info=INFO_NULL, reorder=False)

Create distributed graph communicator.
Parameters

	•		sources ( Sequence[int] )
	•		degrees ( Sequence[int] )
	•		destinations ( Sequence[int] )
	•		weights ( Sequence[int] \| None )
	•		info ( Info )
	•		reorder ( bool )

Return type

Distgraphcomm

Create_dist_graph_adjacent(sources, destinations,
sourceweights=None, destweights=None, info=INFO_NULL,
reorder=False)

Create distributed graph communicator.
Parameters

	•		sources ( Sequence[int] )
	•		destinations ( Sequence[int] )
	•		sourceweights ( Sequence[int] \| None )
	•		destweights ( Sequence[int] \| None )
	•		info ( Info )
	•		reorder ( bool )

Return type

Distgraphcomm

classmethod Create_from_group(group, stringtag='org.mpi4py',
info=INFO_NULL, errhandler=None)

Create communicator from group.
Parameters

	•		group ( Group )
	•		stringtag ( str )
	•		info ( Info )
	•		errhandler ( Errhandler \| None )

Return type

Intracomm

Create_graph(index, edges, reorder=False)

Create graph communicator.
Parameters

	•		index ( Sequence[int] )
	•		edges ( Sequence[int] )
	•		reorder ( bool )

Return type

Graphcomm

Create_group(group, tag=0)

Create communicator from group.
Parameters

	•		group ( Group )
	•		tag ( int )

Return type

Intracomm

Create_intercomm(local_leader, peer_comm, remote_leader, tag=0)

Create intercommunicator.
Parameters

	•		local_leader ( int )
	•		peer_comm ( Intracomm )
	•		remote_leader ( int )
	•		tag ( int )

Return type

Intercomm

Exscan(sendbuf, recvbuf, op=SUM)

Exclusive Scan.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )

Return type

None

Exscan_init(sendbuf, recvbuf, op=SUM, info=INFO_NULL)

Persistent Exclusive Scan.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )
	•		info ( Info )

Return type

Prequest

Graph_map(index, edges)

Determine optimal process placement on a graph topology.
Parameters

	•		index ( Sequence[int] )
	•		edges ( Sequence[int] )

Return type

int

Iexscan(sendbuf, recvbuf, op=SUM)

Inclusive Scan.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )

Return type

Request

Iscan(sendbuf, recvbuf, op=SUM)

Inclusive Scan.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )

Return type

Request

Scan(sendbuf, recvbuf, op=SUM)

Inclusive Scan.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )

Return type

None

Scan_init(sendbuf, recvbuf, op=SUM, info=INFO_NULL)

Persistent Inclusive Scan.
Parameters

	•		sendbuf ( BufSpec \| InPlace )
	•		recvbuf ( BufSpec )
	•		op ( Op )
	•		info ( Info )

Return type

Prequest

Spawn(command, args=None, maxprocs=1, info=INFO_NULL, root=0,
errcodes=None)

Spawn instances of a single MPI application.
Parameters

	•		command ( str )
	•		args ( Sequence[str] \| None )
	•		maxprocs ( int )
	•		info ( Info )
	•		root ( int )
	•		errcodes ( list[int] \| None )

Return type

Intercomm

Spawn_multiple(command, args=None, maxprocs=None,
info=INFO_NULL, root=0, errcodes=None)

Spawn instances of multiple MPI applications.
Parameters

	•		command ( Sequence[str] )
	•		args ( Sequence[Sequence[str]] \| None )
	•		maxprocs ( Sequence[int] \| None )
	•		info ( Sequence[Info] \| Info )
	•		root ( int )
	•		errcodes ( list[list[int]] \| None )

Return type

Intercomm

exscan(sendobj, op=SUM)

Exclusive Scan.
Parameters

	•		sendobj ( Any )
	•		op ( Op \| Callable[[Any, Any], Any] )

Return type

Any

scan(sendobj, op=SUM)

Inclusive Scan.
Parameters

	•		sendobj ( Any )
	•		op ( Op \| Callable[[Any, Any], Any] )

Return type

Any

mpi4py.MPI.Message

class mpi4py.MPI.Message

Bases: object

Matched message.
static __new__(cls, message=None)

Parameters

message ( Message | None )

Return type

Self

Methods Summary

Image grohtml-122803-42.png

Attributes Summary

Image grohtml-122803-43.png

Methods Documentation
classmethod Iprobe(comm, source=ANY_SOURCE, tag=ANY_TAG,
status=None)

Nonblocking test for a matched message.
Parameters

	•		comm ( Comm )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Self | None

Irecv(buf)

Nonblocking receive of matched message.
Parameters

buf ( BufSpec )

Return type

Request

classmethod Probe(comm, source=ANY_SOURCE, tag=ANY_TAG,
status=None)

Blocking test for a matched message.
Parameters

	•		comm ( Comm )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Self

Recv(buf, status=None)

Blocking receive of matched message.
Parameters

	•		buf ( BufSpec )
	•		status ( Status \| None )

Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Message

free()

Do nothing.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Message

classmethod iprobe(comm, source=ANY_SOURCE, tag=ANY_TAG,
status=None)

Nonblocking test for a matched message.
Parameters

	•		comm ( Comm )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Self | None

irecv()

Nonblocking receive of matched message.
Return type

Request

classmethod probe(comm, source=ANY_SOURCE, tag=ANY_TAG,
status=None)

Blocking test for a matched message.
Parameters

	•		comm ( Comm )
	•		source ( int )
	•		tag ( int )
	•		status ( Status \| None )

Return type

Self

py2f()

Return type

int

recv(status=None)

Blocking receive of matched message.
Parameters

status ( Status | None )

Return type

Any

Attributes Documentation

handle

MPI handle.

mpi4py.MPI.Op

class mpi4py.MPI.Op

Bases: object

Reduction operation.
static __new__(cls, op=None)

Parameters

op ( Op | None )

Return type

Self

Methods Summary

Image grohtml-122803-44.png

Attributes Summary

Image grohtml-122803-45.png

Methods Documentation
classmethod Create(function, commute=False)

Create a user-defined reduction operation.
Parameters

	•		function ( Callable[[Buffer, Buffer, Datatype], - None] )
	•		commute ( bool )

Return type

Self

Free()

Free a user-defined reduction operation.

Return type

None

Is_commutative()

Query reduction operations for their commutativity.
Return type

bool

Reduce_local(inbuf, inoutbuf)

Apply a reduction operation to local data.
Parameters

	•		inbuf ( BufSpec )
	•		inoutbuf ( BufSpec )

Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

free()

Call Free if not null or predefined.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

py2f()

Return type

int

Attributes Documentation

handle

MPI handle.

is_commutative

Is a commutative operation.

is_predefined

Is a predefined operation.

mpi4py.MPI.Pickle

class mpi4py.MPI.Pickle

Bases: object

Pickle/unpickle Python objects.
static __new__(cls, pickle=None)

Parameters

pickle ( Pickle | None )

Return type

Self

Methods Summary

Image grohtml-122803-46.png

Attributes Summary

Image grohtml-122803-47.png

Methods Documentation
dumps(obj)

Serialize object to pickle data stream.
Parameters

obj ( Any )

Return type

bytes

dumps_oob(obj)

Serialize object to pickle data stream and out-of-band buffers.
Parameters

obj ( Any )

Return type

tuple [ bytes , list [ buffer ]]

loads(data)

Deserialize object from pickle data stream.
Parameters

data ( Buffer )

Return type

Any

loads_oob(data, buffers)

Deserialize object from pickle data stream and out-of-band buffers.
Parameters

	•		data ( Buffer )
	•		buffers ( Iterable[Buffer] )

Return type

Any

Attributes Documentation
PROTOCOL

Protocol version.

THRESHOLD

Out-of-band threshold.

mpi4py.MPI.Prequest

class mpi4py.MPI.Prequest

Bases: Request

Persistent request handler.
static __new__(cls, request=None)

Parameters

request ( Prequest | None )

Return type

Self

Methods Summary

Image grohtml-122803-48.png

Methods Documentation
Parrived(partition)

Test partial completion of a partitioned receive operation.
Parameters

partition ( int )

Return type

bool

Pready(partition)

Mark a given partition as ready.
Parameters

partition ( int )

Return type

None

Pready_list(partitions)

Mark a sequence of partitions as ready.
Parameters

partitions ( Sequence[int] )

Return type

None

Pready_range(partition_low, partition_high)

Mark a range of partitions as ready.
Parameters

	•		partition_low ( int )
	•		partition_high ( int )

Return type

None

Start()

Initiate a communication with a persistent request.
Return type

None

classmethod Startall(requests)

Start a collection of persistent requests.
Parameters

requests ( list[Prequest] )

Return type

None

mpi4py.MPI.Request

class mpi4py.MPI.Request

Bases: object

Request handler.
static __new__(cls, request=None)

Parameters

request ( Request | None )

Return type

Self

Methods Summary

Image grohtml-122803-49.png

Attributes Summary

Image grohtml-122803-50.png

Methods Documentation
Cancel()

Cancel a request.
Return type

None

Free()

Free a communication request.

Return type

None

Get_status(status=None)

Non-destructive test for the completion of a request.
Parameters

status ( Status | None )

Return type

bool

classmethod Get_status_all(requests, statuses=None)

Non-destructive test for the completion of all requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

bool

classmethod Get_status_any(requests, status=None)

Non-destructive test for the completion of any requests.
Parameters

	•		requests ( Sequence[Request] )
	•		status ( Status \| None )

Return type

tuple [ int , bool ]

classmethod Get_status_some(requests, statuses=None)

Non-destructive test for completion of some requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

list [ int ] | None

Test(status=None)

Test for the completion of a non-blocking operation.
Parameters

status ( Status | None )

Return type

bool

classmethod Testall(requests, statuses=None)

Test for completion of all previously initiated requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

bool

classmethod Testany(requests, status=None)

Test for completion of any previously initiated request.
Parameters

	•		requests ( Sequence[Request] )
	•		status ( Status \| None )

Return type

tuple [ int , bool ]

classmethod Testsome(requests, statuses=None)

Test for completion of some previously initiated requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

list [ int ] | None

Wait(status=None)

Wait for a non-blocking operation to complete.
Parameters

status ( Status | None )

Return type

Literal [True]

classmethod Waitall(requests, statuses=None)

Wait for all previously initiated requests to complete.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

Literal [True]

classmethod Waitany(requests, status=None)

Wait for any previously initiated request to complete.
Parameters

	•		requests ( Sequence[Request] )
	•		status ( Status \| None )

Return type

int

classmethod Waitsome(requests, statuses=None)

Wait for some previously initiated requests to complete.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

list [ int ] | None

cancel()

Cancel a request.
Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Request

free()

Call Free if not null.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Request

get_status(status=None)

Non-destructive test for the completion of a request.
Parameters

status ( Status | None )

Return type

bool

classmethod get_status_all(requests, statuses=None)

Non-destructive test for the completion of all requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

bool

classmethod get_status_any(requests, status=None)

Non-destructive test for the completion of any requests.
Parameters

	•		requests ( Sequence[Request] )
	•		status ( Status \| None )

Return type

tuple [ int , bool ]

classmethod get_status_some(requests, statuses=None)

Non-destructive test for completion of some requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

list [ int ] | None

py2f()

Return type

int

test(status=None)

Test for the completion of a non-blocking operation.
Parameters

status ( Status | None )

Return type

tuple [ bool , Any | None ]

classmethod testall(requests, statuses=None)

Test for completion of all previously initiated requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

tuple [ bool , list [ Any ] | None ]

classmethod testany(requests, status=None)

Test for completion of any previously initiated request.
Parameters

	•		requests ( Sequence[Request] )
	•		status ( Status \| None )

Return type

tuple [ int , bool , Any | None ]

classmethod testsome(requests, statuses=None)

Test for completion of some previously initiated requests.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

tuple [ list [ int ] | None , list [ Any ] | None ]

wait(status=None)

Wait for a non-blocking operation to complete.
Parameters

status ( Status | None )

Return type

Any

classmethod waitall(requests, statuses=None)

Wait for all previously initiated requests to complete.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

list [ Any ]

classmethod waitany(requests, status=None)

Wait for any previously initiated request to complete.
Parameters

	•		requests ( Sequence[Request] )
	•		status ( Status \| None )

Return type

tuple [ int , Any ]

classmethod waitsome(requests, statuses=None)

Wait for some previously initiated requests to complete.
Parameters

	•		requests ( Sequence[Request] )
	•		statuses ( list[Status] \| None )

Return type

tuple [ list [ int ] | None , list [ Any ] | None ]

Attributes Documentation

handle

MPI handle.

mpi4py.MPI.Session

class mpi4py.MPI.Session

Bases: object

Session context.
static __new__(cls, session=None)

Parameters

session ( Session | None )

Return type

Self

Methods Summary

Image grohtml-122803-51.png

Attributes Summary

Image grohtml-122803-52.png

Methods Documentation
Attach_buffer(buf)

Attach a user-provided buffer for sending in buffered mode.
Parameters

buf ( Buffer | None )

Return type

None

Call_errhandler(errorcode)

Call the error handler installed on a session.
Parameters

errorcode ( int )

Return type

None

classmethod Create_errhandler(errhandler_fn)

Create a new error handler for sessions.
Parameters

errhandler_fn ( Callable[[Session, int], None] )

Return type

Errhandler

Create_group(pset_name)

Create a new group from session and process set.
Parameters

pset_name ( str )

Return type

Group

Detach_buffer()

Remove an existing attached buffer.
Return type

Buffer | None

Finalize()

Finalize a session.
Return type

None

Flush_buffer()

Block until all buffered messages have been transmitted.
Return type

None

Get_errhandler()

Get the error handler for a session.
Return type

Errhandler

Get_info()

Return the current hints for a session.
Return type

Info

Get_nth_pset(n, info=INFO_NULL)

Name of the n -th process set.
Parameters

	•		n ( int )
	•		info ( Info )

Return type

str

Get_num_psets(info=INFO_NULL)

Number of available process sets.
Parameters

info ( Info )

Return type

int

Get_pset_info(pset_name)

Return the current hints for a session and process set.
Parameters

pset_name ( str )

Return type

Info

Iflush_buffer()

Nonblocking flush for buffered messages.
Return type

Request

classmethod Init(info=INFO_NULL, errhandler=None)

Create a new session.
Parameters

	•		info ( Info )
	•		errhandler ( Errhandler \| None )

Return type

Self

Set_errhandler(errhandler)

Set the error handler for a session.
Parameters

errhandler ( Errhandler )

Return type

None

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Session

free()

Call Finalize if not null.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Session

py2f()

Return type

int

Attributes Documentation

handle

MPI handle.

mpi4py.MPI.Status

class mpi4py.MPI.Status

Bases: object

Status object.
static __new__(cls, status=None)

Parameters

status ( Status | None )

Return type

Self

Methods Summary

Image grohtml-122803-53.png

Attributes Summary

Image grohtml-122803-54.png

Methods Documentation
Get_count(datatype=BYTE)

Get the number of top level elements.
Parameters

datatype ( Datatype )

Return type

int

Get_elements(datatype)

Get the number of basic elements in a datatype.
Parameters

datatype ( Datatype )

Return type

int

Get_error()

Get message error.
Return type

int

Get_source()

Get message source.
Return type

int

Get_tag()

Get message tag.
Return type

int

Is_cancelled()

Test to see if a request was cancelled.
Return type

bool

Set_cancelled(flag)

Set the cancelled state associated with a status.

NOTE:

This method should be used only when implementing query callback functions for generalized requests.

Parameters

flag ( bool )

Return type

None

Set_elements(datatype, count)

Set the number of elements in a status.

NOTE:

This method should be only used when implementing query callback functions for generalized requests.

Parameters

	•		datatype ( Datatype )
	•		count ( int )

Return type

None

Set_error(error)

Set message error.
Parameters

error ( int )

Return type

None

Set_source(source)

Set message source.
Parameters

source ( int )

Return type

None

Set_tag(tag)

Set message tag.
Parameters

tag ( int )

Return type

None

classmethod f2py(arg)

Parameters

arg ( list[int] )

Return type

Self

py2f()

Return type

list [ int ]

Attributes Documentation
cancelled

Cancelled state.

	count		Byte count.
	error		Message error.
	source		Message source.
	tag		Message tag.

mpi4py.MPI.Topocomm

class mpi4py.MPI.Topocomm

Bases: Intracomm

Topology intracommunicator.
static __new__(cls, comm=None)

Parameters

comm ( Topocomm | None )

Return type

Self

Methods Summary

Image grohtml-122803-55.png

Attributes Summary

Image grohtml-122803-56.png

Methods Documentation
Ineighbor_allgather(sendbuf, recvbuf)

Nonblocking Neighbor Gather to All.
Parameters

	•		sendbuf ( BufSpec )
	•		recvbuf ( BufSpecB )

Return type

Request

Ineighbor_allgatherv(sendbuf, recvbuf)

Nonblocking Neighbor Gather to All Vector.
Parameters

	•		sendbuf ( BufSpec )
	•		recvbuf ( BufSpecV )

Return type

Request

Ineighbor_alltoall(sendbuf, recvbuf)

Nonblocking Neighbor All to All.
Parameters

	•		sendbuf ( BufSpecB )
	•		recvbuf ( BufSpecB )

Return type

Request

Ineighbor_alltoallv(sendbuf, recvbuf)

Nonblocking Neighbor All to All Vector.
Parameters

	•		sendbuf ( BufSpecV )
	•		recvbuf ( BufSpecV )

Return type

Request

Ineighbor_alltoallw(sendbuf, recvbuf)

Nonblocking Neighbor All to All General.
Parameters

	•		sendbuf ( BufSpecW )
	•		recvbuf ( BufSpecW )

Return type

Request

Neighbor_allgather(sendbuf, recvbuf)

Neighbor Gather to All.
Parameters

	•		sendbuf ( BufSpec )
	•		recvbuf ( BufSpecB )

Return type

None

Neighbor_allgather_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Neighbor Gather to All.
Parameters

	•		sendbuf ( BufSpec )
	•		recvbuf ( BufSpecB )
	•		info ( Info )

Return type

Prequest

Neighbor_allgatherv(sendbuf, recvbuf)

Neighbor Gather to All Vector.
Parameters

	•		sendbuf ( BufSpec )
	•		recvbuf ( BufSpecV )

Return type

None

Neighbor_allgatherv_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Neighbor Gather to All Vector.
Parameters

	•		sendbuf ( BufSpec )
	•		recvbuf ( BufSpecV )
	•		info ( Info )

Return type

Prequest

Neighbor_alltoall(sendbuf, recvbuf)

Neighbor All to All.
Parameters

	•		sendbuf ( BufSpecB )
	•		recvbuf ( BufSpecB )

Return type

None

Neighbor_alltoall_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Neighbor All to All.
Parameters

	•		sendbuf ( BufSpecB )
	•		recvbuf ( BufSpecB )
	•		info ( Info )

Return type

Prequest

Neighbor_alltoallv(sendbuf, recvbuf)

Neighbor All to All Vector.
Parameters

	•		sendbuf ( BufSpecV )
	•		recvbuf ( BufSpecV )

Return type

None

Neighbor_alltoallv_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Neighbor All to All Vector.
Parameters

	•		sendbuf ( BufSpecV )
	•		recvbuf ( BufSpecV )
	•		info ( Info )

Return type

Prequest

Neighbor_alltoallw(sendbuf, recvbuf)

Neighbor All to All General.
Parameters

	•		sendbuf ( BufSpecW )
	•		recvbuf ( BufSpecW )

Return type

None

Neighbor_alltoallw_init(sendbuf, recvbuf, info=INFO_NULL)

Persistent Neighbor All to All General.
Parameters

	•		sendbuf ( BufSpecW )
	•		recvbuf ( BufSpecW )
	•		info ( Info )

Return type

Prequest

neighbor_allgather(sendobj)

Neighbor Gather to All.
Parameters

sendobj ( Any )

Return type

list [ Any ]

neighbor_alltoall(sendobj)

Neighbor All to All.
Parameters

sendobj ( list[Any] )

Return type

list [ Any ]

Attributes Documentation
degrees

Number of incoming and outgoing neighbors.

indegree

Number of incoming neighbors.

inedges

Incoming neighbors.

inoutedges

Incoming and outgoing neighbors.

outdegree

Number of outgoing neighbors.

outedges

Outgoing neighbors.

mpi4py.MPI.Win

class mpi4py.MPI.Win

Bases: object

Remote memory access context.
static __new__(cls, win=None)

Parameters

win ( Win | None )

Return type

Self

Methods Summary

Image grohtml-122803-57.png

Attributes Summary

Image grohtml-122803-58.png

Methods Documentation
Accumulate(origin, target_rank, target=None, op=SUM)

Accumulate data into the target process.
Parameters

	•		origin ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )
	•		op ( Op )

Return type

None

classmethod Allocate(size, disp_unit=1, info=INFO_NULL,
comm=COMM_SELF)

Create an window object for one-sided communication.
Parameters

	•		size ( int )
	•		disp_unit ( int )
	•		info ( Info )
	•		comm ( Intracomm )

Return type

Self

classmethod Allocate_shared(size, disp_unit=1, info=INFO_NULL,
comm=COMM_SELF)

Create an window object for one-sided communication.
Parameters

	•		size ( int )
	•		disp_unit ( int )
	•		info ( Info )
	•		comm ( Intracomm )

Return type

Self

Attach(memory)

Attach a local memory region.
Parameters

memory ( Buffer )

Return type

None

Call_errhandler(errorcode)

Call the error handler installed on a window.
Parameters

errorcode ( int )

Return type

None

Compare_and_swap(origin, compare, result, target_rank,
target_disp=0)

Perform one-sided atomic compare-and-swap.
Parameters

	•		origin ( BufSpec )
	•		compare ( BufSpec )
	•		result ( BufSpec )
	•		target_rank ( int )
	•		target_disp ( int )

Return type

None

Complete()

Complete an RMA operation begun after an Start .
Return type

None

classmethod Create(memory, disp_unit=1, info=INFO_NULL,
comm=COMM_SELF)

Create an window object for one-sided communication.
Parameters

	•		memory ( Buffer \| Bottom )
	•		disp_unit ( int )
	•		info ( Info )
	•		comm ( Intracomm )

Return type

Self

classmethod Create_dynamic(info=INFO_NULL, comm=COMM_SELF)

Create an window object for one-sided communication.
Parameters

	•		info ( Info )
	•		comm ( Intracomm )

Return type

Self

classmethod Create_errhandler(errhandler_fn)

Create a new error handler for windows.
Parameters

errhandler_fn ( Callable[[Win, int], None] )

Return type

Errhandler

classmethod Create_keyval(copy_fn=None, delete_fn=None,
nopython=False)

Create a new attribute key for windows.
Parameters

	•		copy_fn ( Callable[[Win, int, Any], Any] \| None )
	•		delete_fn ( Callable[[Win, int, Any], None] \| - None )
	•		nopython ( bool )

Return type

int

Delete_attr(keyval)

Delete attribute value associated with a key.
Parameters

keyval ( int )

Return type

None

Detach(memory)

Detach a local memory region.
Parameters

memory ( Buffer )

Return type

None

Fence(assertion=0)

Perform an MPI fence synchronization on a window.
Parameters

assertion ( int )

Return type

None

Fetch_and_op(origin, result, target_rank, target_disp=0, op=SUM)

Perform one-sided read-modify-write.
Parameters

	•		origin ( BufSpec )
	•		result ( BufSpec )
	•		target_rank ( int )
	•		target_disp ( int )
	•		op ( Op )

Return type

None

Flush(rank)

Complete all outstanding RMA operations at a target.
Parameters

rank ( int )

Return type

None

Flush_all()

Complete all outstanding RMA operations at all targets.
Return type

None

Flush_local(rank)

Complete locally all outstanding RMA operations at a target.
Parameters

rank ( int )

Return type

None

Flush_local_all()

Complete locally all outstanding RMA operations at all targets.
Return type

None

Free()

Free a window.

Return type

None

classmethod Free_keyval(keyval)

Free an attribute key for windows.
Parameters

keyval ( int )

Return type

int

Get(origin, target_rank, target=None)

Get data from a memory window on a remote process.
Parameters

	•		origin ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )

Return type

None

Get_accumulate(origin, result, target_rank, target=None, op=SUM)

Fetch-and-accumulate data into the target process.
Parameters

	•		origin ( BufSpec )
	•		result ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )
	•		op ( Op )

Return type

None

Get_attr(keyval)

Retrieve attribute value by key.
Parameters

keyval ( int )

Return type

int | Any | None

Get_errhandler()

Get the error handler for a window.
Return type

Errhandler

Get_group()

Access the group of processes that created the window.
Return type

Group

Get_info()

Return the current hints for a window.
Return type

Info

Get_name()

Get the print name for this window.
Return type

str

Lock(rank, lock_type=LOCK_EXCLUSIVE, assertion=0)

Begin an RMA access epoch at the target process.
Parameters

	•		rank ( int )
	•		lock_type ( int )
	•		assertion ( int )

Return type

None

Lock_all(assertion=0)

Begin an RMA access epoch at all processes.
Parameters

assertion ( int )

Return type

None

Post(group, assertion=0)

Start an RMA exposure epoch.
Parameters

	•		group ( Group )
	•		assertion ( int )

Return type

None

Put(origin, target_rank, target=None)

Put data into a memory window on a remote process.
Parameters

	•		origin ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )

Return type

None

Raccumulate(origin, target_rank, target=None, op=SUM)

Fetch-and-accumulate data into the target process.
Parameters

	•		origin ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )
	•		op ( Op )

Return type

Request

Rget(origin, target_rank, target=None)

Get data from a memory window on a remote process.
Parameters

	•		origin ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )

Return type

Request

Rget_accumulate(origin, result, target_rank, target=None,
op=SUM)

Accumulate data into the target process using remote memory access.
Parameters

	•		origin ( BufSpec )
	•		result ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )
	•		op ( Op )

Return type

Request

Rput(origin, target_rank, target=None)

Put data into a memory window on a remote process.
Parameters

	•		origin ( BufSpec )
	•		target_rank ( int )
	•		target ( TargetSpec \| None )

Return type

Request

Set_attr(keyval, attrval)

Store attribute value associated with a key.
Parameters

	•		keyval ( int )
	•		attrval ( Any )

Return type

None

Set_errhandler(errhandler)

Set the error handler for a window.
Parameters

errhandler ( Errhandler )

Return type

None

Set_info(info)

Set new values for the hints associated with a window.
Parameters

info ( Info )

Return type

None

Set_name(name)

Set the print name for this window.
Parameters

name ( str )

Return type

None

Shared_query(rank)

Query the process-local address for remote memory segments.
Parameters

rank ( int )

Return type

tuple [ buffer , int ]

Start(group, assertion=0)

Start an RMA access epoch for MPI.
Parameters

	•		group ( Group )
	•		assertion ( int )

Return type

None

Sync()

Synchronize public and private copies of the window.

Return type

None

Test()

Test whether an RMA exposure epoch has completed.

Return type

bool

Unlock(rank)

Complete an RMA access epoch at the target process.
Parameters

rank ( int )

Return type

None

Unlock_all()

Complete an RMA access epoch at all processes.
Return type

None

Wait()

Complete an RMA exposure epoch begun with Post .

Return type

Literal [True]

classmethod f2py(arg)

Parameters

arg ( int )

Return type

Win

free()

Call Free if not null.

Return type

None

classmethod fromhandle(handle)

Create object from MPI handle.
Parameters

handle ( int )

Return type

Win

py2f()

Return type

int

tomemory()

Return window memory buffer.
Return type

buffer

Attributes Documentation

	attrs		Attributes.
	flavor		Create flavor.
	group		Group.

group_rank

Group rank.

group_size

Group size.

	handle		MPI handle.
	info		Info hints.
	model		Memory model.
	name		Print name.

mpi4py.MPI.buffer

class mpi4py.MPI.buffer

Bases: object

Buffer.
static __new__(cls, buf)

Parameters

buf ( Buffer )

Return type

Self

Methods Summary

Image grohtml-122803-59.png

Attributes Summary

Image grohtml-122803-60.png

Methods Documentation
static allocate(nbytes, clear=False)

Buffer allocation.
Parameters

	•		nbytes ( int )
	•		clear ( bool )

Return type

buffer

cast(format, shape=Ellipsis)

Cast to a memoryview with new format or shape.
Parameters

	•		format ( str )
	•		shape ( list[int] \| tuple[int, ...] )

Return type

memoryview

static fromaddress(address, nbytes, readonly=False)

Buffer from address and size in bytes.
Parameters

	•		address ( int )
	•		nbytes ( int )
	•		readonly ( bool )

Return type

buffer

static frombuffer(obj, readonly=False)

Buffer from buffer-like object.
Parameters

	•		obj ( Buffer )
	•		readonly ( bool )

Return type

buffer

release()

Release the underlying buffer exposed by the buffer object.
Return type

None

tobytes(order=None)

Return the data in the buffer as a byte string.
Parameters

order ( str | None )

Return type

bytes

toreadonly()

Return a readonly version of the buffer object.
Return type

buffer

Attributes Documentation
address

Buffer address.

format

Format of each element.

itemsize

Size (in bytes) of each element.

	nbytes		Buffer size (in bytes).
	obj		Object exposing buffer.

readonly

Buffer is read-only.

mpi4py.MPI.memory

mpi4py.MPI.memory

alias of buffer

Exceptions

Image grohtml-122803-61.png

mpi4py.MPI.Exception

exception mpi4py.MPI.Exception

Bases: RuntimeError

Exception class.
static __new__(cls, ierr=SUCCESS)

Parameters

ierr ( int )

Return type

Self

Methods Summary

Image grohtml-122803-62.png

Attributes Summary

Image grohtml-122803-63.png

Methods Documentation
Get_error_class()

Error class.
Return type

int

Get_error_code()

Error code.
Return type

int

Get_error_string()

Error string.
Return type

str

Attributes Documentation
error_class

Error class.

error_code

Error code.

error_string

Error string.

Functions

Image grohtml-122803-64.png

mpi4py.MPI.Add_error_class

mpi4py.MPI.Add_error_class()

Add an error class to the known error classes.
Return type

int

mpi4py.MPI.Add_error_code

mpi4py.MPI.Add_error_code(errorclass)

Add an error code to an error class .
Parameters

errorclass ( int )

Return type

int

mpi4py.MPI.Add_error_string

mpi4py.MPI.Add_error_string(errorcode, string)

Associate an error string with an error class or error code .
Parameters

	•		errorcode ( int )
	•		string ( str )

Return type

None

mpi4py.MPI.Aint_add

mpi4py.MPI.Aint_add(base, disp)

Return the sum of base address and displacement.
Parameters

	•		base ( int )
	•		disp ( int )

Return type

int

mpi4py.MPI.Aint_diff

mpi4py.MPI.Aint_diff(addr1, addr2)

Return the difference between absolute addresses.
Parameters

	•		addr1 ( int )
	•		addr2 ( int )

Return type

int

mpi4py.MPI.Alloc_mem

mpi4py.MPI.Alloc_mem(size, info=INFO_NULL)

Allocate memory for message passing and remote memory access.
Parameters

	•		size ( int )
	•		info ( Info )

Return type

buffer

mpi4py.MPI.Attach_buffer

mpi4py.MPI.Attach_buffer(buf)

Attach a user-provided buffer for sending in buffered mode.
Parameters

buf ( Buffer | None )

Return type

None

mpi4py.MPI.Close_port

mpi4py.MPI.Close_port(port_name)

Close a port.
Parameters

port_name ( str )

Return type

None

mpi4py.MPI.Compute_dims

mpi4py.MPI.Compute_dims(nnodes, dims)

Return a balanced distribution of processes per coordinate direction.
Parameters

	•		nnodes ( int )
	•		dims ( int \| Sequence[int] )

Return type

list [ int ]

mpi4py.MPI.Detach_buffer

mpi4py.MPI.Detach_buffer()

Remove an existing attached buffer.
Return type

Buffer | None

mpi4py.MPI.Finalize

mpi4py.MPI.Finalize()

Terminate the MPI execution environment.
Return type

None

mpi4py.MPI.Flush_buffer

mpi4py.MPI.Flush_buffer()

Block until all buffered messages have been transmitted.
Return type

None

mpi4py.MPI.Free_mem

mpi4py.MPI.Free_mem(mem)

Free memory allocated with Alloc_mem .
Parameters

mem ( buffer )

Return type

None

mpi4py.MPI.Get_address

mpi4py.MPI.Get_address(location)

Get the address of a location in memory.
Parameters

location ( Buffer | Bottom )

Return type

int

mpi4py.MPI.Get_error_class

mpi4py.MPI.Get_error_class(errorcode)

Convert an error code into an error class .
Parameters

errorcode ( int )

Return type

int

mpi4py.MPI.Get_error_string

mpi4py.MPI.Get_error_string(errorcode)

Return the error string for a given error class or error code .
Parameters

errorcode ( int )

Return type

str

mpi4py.MPI.Get_hw_resource_info

mpi4py.MPI.Get_hw_resource_info()

Obtain information about the hardware platform of the calling processor.
Return type

Info

mpi4py.MPI.Get_library_version

mpi4py.MPI.Get_library_version()

Obtain the version string of the MPI library.
Return type

str

mpi4py.MPI.Get_processor_name

mpi4py.MPI.Get_processor_name()

Obtain the name of the calling processor.
Return type

str

mpi4py.MPI.Get_version

mpi4py.MPI.Get_version()

Obtain the version number of the MPI standard.
Return type

tuple [ int , int ]

mpi4py.MPI.Iflush_buffer

mpi4py.MPI.Iflush_buffer()

Nonblocking flush for buffered messages.
Return type

Request

mpi4py.MPI.Init

mpi4py.MPI.Init()

Initialize the MPI execution environment.
Return type

None

mpi4py.MPI.Init_thread

mpi4py.MPI.Init_thread(required=THREAD_MULTIPLE)

Initialize the MPI execution environment.
Parameters

required ( int )

Return type

int

mpi4py.MPI.Is_finalized

mpi4py.MPI.Is_finalized()

Indicate whether Finalize has completed.
Return type

bool

mpi4py.MPI.Is_initialized

mpi4py.MPI.Is_initialized()

Indicate whether Init has been called.
Return type

bool

mpi4py.MPI.Is_thread_main

mpi4py.MPI.Is_thread_main()

Indicate whether this thread called Init or Init_thread .
Return type

bool

mpi4py.MPI.Lookup_name

mpi4py.MPI.Lookup_name(service_name, info=INFO_NULL)

Lookup a port name given a service name.
Parameters

	•		service_name ( str )
	•		info ( Info )

Return type

str

mpi4py.MPI.Open_port

mpi4py.MPI.Open_port(info=INFO_NULL)

Return an address used to connect group of processes.
Parameters

info ( Info )

Return type

str

mpi4py.MPI.Pcontrol

mpi4py.MPI.Pcontrol(level)

Control profiling.
Parameters

level ( int )

Return type

None

mpi4py.MPI.Publish_name

mpi4py.MPI.Publish_name(service_name, port_name, info=INFO_NULL)

Publish a service name.
Parameters

	•		service_name ( str )
	•		port_name ( str )
	•		info ( Info )

Return type

None

mpi4py.MPI.Query_thread

mpi4py.MPI.Query_thread()

Return the level of thread support provided by the MPI library.
Return type

int

mpi4py.MPI.Register_datarep

mpi4py.MPI.Register_datarep(datarep, read_fn, write_fn, extent_fn)

	•		datarep ( str )
	•		read_fn ( Callable[[Buffer, Datatype, int, Buffer, int], None] )
	•		write_fn ( Callable[[Buffer, Datatype, int, Buffer, - int], None] )
	•		extent_fn ( Callable[[Datatype], int] )

Return type

None

mpi4py.MPI.Remove_error_class

mpi4py.MPI.Remove_error_class(errorclass)

Remove an error class from the known error classes.
Parameters

errorclass ( int )

Return type

None

mpi4py.MPI.Remove_error_code

mpi4py.MPI.Remove_error_code(errorcode)

Remove an error code from the known error codes.
Parameters

errorcode ( int )

Return type

None

mpi4py.MPI.Remove_error_string

mpi4py.MPI.Remove_error_string(errorcode)

Remove error string association from error class or error code .
Parameters

errorcode ( int )

Return type

None

mpi4py.MPI.Unpublish_name

mpi4py.MPI.Unpublish_name(service_name, port_name, info=INFO_NULL)

Unpublish a service name.
Parameters

	•		service_name ( str )
	•		port_name ( str )
	•		info ( Info )

Return type

None

mpi4py.MPI.Wtick

mpi4py.MPI.Wtick()

Return the resolution of Wtime .
Return type

float

mpi4py.MPI.Wtime

mpi4py.MPI.Wtime()

Return an elapsed time on the calling processor.
Return type

float

mpi4py.MPI.get_vendor

mpi4py.MPI.get_vendor()

Information about the underlying MPI implementation.
Returns

	•		string with the name of the MPI implementation.
	•		integer 3-tuple version number (major, minor, micro) .

Return type

tuple [ str , tuple [ int , int , int ]]

Attributes

Image grohtml-122803-65.png

mpi4py.MPI.UNDEFINED

mpi4py.MPI.UNDEFINED: int = UNDEFINED

Constant UNDEFINED of type int

mpi4py.MPI.ANY_SOURCE

mpi4py.MPI.ANY_SOURCE: int = ANY_SOURCE

Constant ANY_SOURCE of type int

mpi4py.MPI.ANY_TAG

mpi4py.MPI.ANY_TAG: int = ANY_TAG

Constant ANY_TAG of type int

mpi4py.MPI.PROC_NULL

mpi4py.MPI.PROC_NULL: int = PROC_NULL

Constant PROC_NULL of type int

mpi4py.MPI.ROOT

mpi4py.MPI.ROOT: int = ROOT

Constant ROOT of type int

mpi4py.MPI.BOTTOM

mpi4py.MPI.BOTTOM: BottomType = BOTTOM

Constant BOTTOM of type BottomType

mpi4py.MPI.IN_PLACE

mpi4py.MPI.IN_PLACE: InPlaceType = IN_PLACE

Constant IN_PLACE of type InPlaceType

mpi4py.MPI.KEYVAL_INVALID

mpi4py.MPI.KEYVAL_INVALID: int = KEYVAL_INVALID

Constant KEYVAL_INVALID of type int

mpi4py.MPI.TAG_UB

mpi4py.MPI.TAG_UB: int = TAG_UB

Constant TAG_UB of type int

mpi4py.MPI.IO

mpi4py.MPI.IO: int = IO

Constant IO of type int

mpi4py.MPI.WTIME_IS_GLOBAL

mpi4py.MPI.WTIME_IS_GLOBAL: int = WTIME_IS_GLOBAL

Constant WTIME_IS_GLOBAL of type int

mpi4py.MPI.UNIVERSE_SIZE

mpi4py.MPI.UNIVERSE_SIZE: int = UNIVERSE_SIZE

Constant UNIVERSE_SIZE of type int

mpi4py.MPI.APPNUM

mpi4py.MPI.APPNUM: int = APPNUM

Constant APPNUM of type int

mpi4py.MPI.LASTUSEDCODE

mpi4py.MPI.LASTUSEDCODE: int = LASTUSEDCODE

Constant LASTUSEDCODE of type int

mpi4py.MPI.WIN_BASE

mpi4py.MPI.WIN_BASE: int = WIN_BASE

Constant WIN_BASE of type int

mpi4py.MPI.WIN_SIZE

mpi4py.MPI.WIN_SIZE: int = WIN_SIZE

Constant WIN_SIZE of type int

mpi4py.MPI.WIN_DISP_UNIT

mpi4py.MPI.WIN_DISP_UNIT: int = WIN_DISP_UNIT

Constant WIN_DISP_UNIT of type int

mpi4py.MPI.WIN_CREATE_FLAVOR

mpi4py.MPI.WIN_CREATE_FLAVOR: int = WIN_CREATE_FLAVOR

Constant WIN_CREATE_FLAVOR of type int

mpi4py.MPI.WIN_FLAVOR

mpi4py.MPI.WIN_FLAVOR: int = WIN_FLAVOR

Constant WIN_FLAVOR of type int

mpi4py.MPI.WIN_MODEL

mpi4py.MPI.WIN_MODEL: int = WIN_MODEL

Constant WIN_MODEL of type int

mpi4py.MPI.SUCCESS

mpi4py.MPI.SUCCESS: int = SUCCESS

Constant SUCCESS of type int

mpi4py.MPI.ERR_LASTCODE

mpi4py.MPI.ERR_LASTCODE: int = ERR_LASTCODE

Constant ERR_LASTCODE of type int

mpi4py.MPI.ERR_TYPE

mpi4py.MPI.ERR_TYPE: int = ERR_TYPE

Constant ERR_TYPE of type int

mpi4py.MPI.ERR_REQUEST

mpi4py.MPI.ERR_REQUEST: int = ERR_REQUEST

Constant ERR_REQUEST of type int

mpi4py.MPI.ERR_OP

mpi4py.MPI.ERR_OP: int = ERR_OP

Constant ERR_OP of type int

mpi4py.MPI.ERR_GROUP

mpi4py.MPI.ERR_GROUP: int = ERR_GROUP

Constant ERR_GROUP of type int

mpi4py.MPI.ERR_INFO

mpi4py.MPI.ERR_INFO: int = ERR_INFO

Constant ERR_INFO of type int

mpi4py.MPI.ERR_ERRHANDLER

mpi4py.MPI.ERR_ERRHANDLER: int = ERR_ERRHANDLER

Constant ERR_ERRHANDLER of type int

mpi4py.MPI.ERR_SESSION

mpi4py.MPI.ERR_SESSION: int = ERR_SESSION

Constant ERR_SESSION of type int

mpi4py.MPI.ERR_COMM

mpi4py.MPI.ERR_COMM: int = ERR_COMM

Constant ERR_COMM of type int

mpi4py.MPI.ERR_WIN

mpi4py.MPI.ERR_WIN: int = ERR_WIN

Constant ERR_WIN of type int

mpi4py.MPI.ERR_FILE

mpi4py.MPI.ERR_FILE: int = ERR_FILE

Constant ERR_FILE of type int

mpi4py.MPI.ERR_BUFFER

mpi4py.MPI.ERR_BUFFER: int = ERR_BUFFER

Constant ERR_BUFFER of type int

mpi4py.MPI.ERR_COUNT

mpi4py.MPI.ERR_COUNT: int = ERR_COUNT

Constant ERR_COUNT of type int

mpi4py.MPI.ERR_TAG

mpi4py.MPI.ERR_TAG: int = ERR_TAG

Constant ERR_TAG of type int

mpi4py.MPI.ERR_RANK

mpi4py.MPI.ERR_RANK: int = ERR_RANK

Constant ERR_RANK of type int

mpi4py.MPI.ERR_ROOT

mpi4py.MPI.ERR_ROOT: int = ERR_ROOT

Constant ERR_ROOT of type int

mpi4py.MPI.ERR_TRUNCATE

mpi4py.MPI.ERR_TRUNCATE: int = ERR_TRUNCATE

Constant ERR_TRUNCATE of type int

mpi4py.MPI.ERR_IN_STATUS

mpi4py.MPI.ERR_IN_STATUS: int = ERR_IN_STATUS

Constant ERR_IN_STATUS of type int

mpi4py.MPI.ERR_PENDING

mpi4py.MPI.ERR_PENDING: int = ERR_PENDING

Constant ERR_PENDING of type int

mpi4py.MPI.ERR_TOPOLOGY

mpi4py.MPI.ERR_TOPOLOGY: int = ERR_TOPOLOGY

Constant ERR_TOPOLOGY of type int

mpi4py.MPI.ERR_DIMS

mpi4py.MPI.ERR_DIMS: int = ERR_DIMS

Constant ERR_DIMS of type int

mpi4py.MPI.ERR_ARG

mpi4py.MPI.ERR_ARG: int = ERR_ARG

Constant ERR_ARG of type int

mpi4py.MPI.ERR_OTHER

mpi4py.MPI.ERR_OTHER: int = ERR_OTHER

Constant ERR_OTHER of type int

mpi4py.MPI.ERR_UNKNOWN

mpi4py.MPI.ERR_UNKNOWN: int = ERR_UNKNOWN

Constant ERR_UNKNOWN of type int

mpi4py.MPI.ERR_INTERN

mpi4py.MPI.ERR_INTERN: int = ERR_INTERN

Constant ERR_INTERN of type int

mpi4py.MPI.ERR_KEYVAL

mpi4py.MPI.ERR_KEYVAL: int = ERR_KEYVAL

Constant ERR_KEYVAL of type int

mpi4py.MPI.ERR_NO_MEM

mpi4py.MPI.ERR_NO_MEM: int = ERR_NO_MEM

Constant ERR_NO_MEM of type int

mpi4py.MPI.ERR_INFO_KEY

mpi4py.MPI.ERR_INFO_KEY: int = ERR_INFO_KEY

Constant ERR_INFO_KEY of type int

mpi4py.MPI.ERR_INFO_VALUE

mpi4py.MPI.ERR_INFO_VALUE: int = ERR_INFO_VALUE

Constant ERR_INFO_VALUE of type int

mpi4py.MPI.ERR_INFO_NOKEY

mpi4py.MPI.ERR_INFO_NOKEY: int = ERR_INFO_NOKEY

Constant ERR_INFO_NOKEY of type int

mpi4py.MPI.ERR_SPAWN

mpi4py.MPI.ERR_SPAWN: int = ERR_SPAWN

Constant ERR_SPAWN of type int

mpi4py.MPI.ERR_PORT

mpi4py.MPI.ERR_PORT: int = ERR_PORT

Constant ERR_PORT of type int

mpi4py.MPI.ERR_SERVICE

mpi4py.MPI.ERR_SERVICE: int = ERR_SERVICE

Constant ERR_SERVICE of type int

mpi4py.MPI.ERR_NAME

mpi4py.MPI.ERR_NAME: int = ERR_NAME

Constant ERR_NAME of type int

mpi4py.MPI.ERR_PROC_ABORTED

mpi4py.MPI.ERR_PROC_ABORTED: int = ERR_PROC_ABORTED

Constant ERR_PROC_ABORTED of type int

mpi4py.MPI.ERR_BASE

mpi4py.MPI.ERR_BASE: int = ERR_BASE

Constant ERR_BASE of type int

mpi4py.MPI.ERR_SIZE

mpi4py.MPI.ERR_SIZE: int = ERR_SIZE

Constant ERR_SIZE of type int

mpi4py.MPI.ERR_DISP

mpi4py.MPI.ERR_DISP: int = ERR_DISP

Constant ERR_DISP of type int

mpi4py.MPI.ERR_ASSERT

mpi4py.MPI.ERR_ASSERT: int = ERR_ASSERT

Constant ERR_ASSERT of type int

mpi4py.MPI.ERR_LOCKTYPE

mpi4py.MPI.ERR_LOCKTYPE: int = ERR_LOCKTYPE

Constant ERR_LOCKTYPE of type int

mpi4py.MPI.ERR_RMA_CONFLICT

mpi4py.MPI.ERR_RMA_CONFLICT: int = ERR_RMA_CONFLICT

Constant ERR_RMA_CONFLICT of type int

mpi4py.MPI.ERR_RMA_SYNC

mpi4py.MPI.ERR_RMA_SYNC: int = ERR_RMA_SYNC

Constant ERR_RMA_SYNC of type int

mpi4py.MPI.ERR_RMA_RANGE

mpi4py.MPI.ERR_RMA_RANGE: int = ERR_RMA_RANGE

Constant ERR_RMA_RANGE of type int

mpi4py.MPI.ERR_RMA_ATTACH

mpi4py.MPI.ERR_RMA_ATTACH: int = ERR_RMA_ATTACH

Constant ERR_RMA_ATTACH of type int

mpi4py.MPI.ERR_RMA_SHARED

mpi4py.MPI.ERR_RMA_SHARED: int = ERR_RMA_SHARED

Constant ERR_RMA_SHARED of type int

mpi4py.MPI.ERR_RMA_FLAVOR

mpi4py.MPI.ERR_RMA_FLAVOR: int = ERR_RMA_FLAVOR

Constant ERR_RMA_FLAVOR of type int

mpi4py.MPI.ERR_BAD_FILE

mpi4py.MPI.ERR_BAD_FILE: int = ERR_BAD_FILE

Constant ERR_BAD_FILE of type int

mpi4py.MPI.ERR_NO_SUCH_FILE

mpi4py.MPI.ERR_NO_SUCH_FILE: int = ERR_NO_SUCH_FILE

Constant ERR_NO_SUCH_FILE of type int

mpi4py.MPI.ERR_FILE_EXISTS

mpi4py.MPI.ERR_FILE_EXISTS: int = ERR_FILE_EXISTS

Constant ERR_FILE_EXISTS of type int

mpi4py.MPI.ERR_FILE_IN_USE

mpi4py.MPI.ERR_FILE_IN_USE: int = ERR_FILE_IN_USE

Constant ERR_FILE_IN_USE of type int

mpi4py.MPI.ERR_AMODE

mpi4py.MPI.ERR_AMODE: int = ERR_AMODE

Constant ERR_AMODE of type int

mpi4py.MPI.ERR_ACCESS

mpi4py.MPI.ERR_ACCESS: int = ERR_ACCESS

Constant ERR_ACCESS of type int

mpi4py.MPI.ERR_READ_ONLY

mpi4py.MPI.ERR_READ_ONLY: int = ERR_READ_ONLY

Constant ERR_READ_ONLY of type int

mpi4py.MPI.ERR_NO_SPACE

mpi4py.MPI.ERR_NO_SPACE: int = ERR_NO_SPACE

Constant ERR_NO_SPACE of type int

mpi4py.MPI.ERR_QUOTA

mpi4py.MPI.ERR_QUOTA: int = ERR_QUOTA

Constant ERR_QUOTA of type int

mpi4py.MPI.ERR_NOT_SAME

mpi4py.MPI.ERR_NOT_SAME: int = ERR_NOT_SAME

Constant ERR_NOT_SAME of type int

mpi4py.MPI.ERR_IO

mpi4py.MPI.ERR_IO: int = ERR_IO

Constant ERR_IO of type int

mpi4py.MPI.ERR_UNSUPPORTED_OPERATION

mpi4py.MPI.ERR_UNSUPPORTED_OPERATION: int = ERR_UNSUPPORTED_OPERATION

Constant ERR_UNSUPPORTED_OPERATION of type int

mpi4py.MPI.ERR_UNSUPPORTED_DATAREP

mpi4py.MPI.ERR_UNSUPPORTED_DATAREP: int = ERR_UNSUPPORTED_DATAREP

Constant ERR_UNSUPPORTED_DATAREP of type int

mpi4py.MPI.ERR_CONVERSION

mpi4py.MPI.ERR_CONVERSION: int = ERR_CONVERSION

Constant ERR_CONVERSION of type int

mpi4py.MPI.ERR_DUP_DATAREP

mpi4py.MPI.ERR_DUP_DATAREP: int = ERR_DUP_DATAREP

Constant ERR_DUP_DATAREP of type int

mpi4py.MPI.ERR_VALUE_TOO_LARGE

mpi4py.MPI.ERR_VALUE_TOO_LARGE: int = ERR_VALUE_TOO_LARGE

Constant ERR_VALUE_TOO_LARGE of type int

mpi4py.MPI.ERR_REVOKED

mpi4py.MPI.ERR_REVOKED: int = ERR_REVOKED

Constant ERR_REVOKED of type int

mpi4py.MPI.ERR_PROC_FAILED

mpi4py.MPI.ERR_PROC_FAILED: int = ERR_PROC_FAILED

Constant ERR_PROC_FAILED of type int

mpi4py.MPI.ERR_PROC_FAILED_PENDING

mpi4py.MPI.ERR_PROC_FAILED_PENDING: int = ERR_PROC_FAILED_PENDING

Constant ERR_PROC_FAILED_PENDING of type int

mpi4py.MPI.ORDER_C

mpi4py.MPI.ORDER_C: int = ORDER_C

Constant ORDER_C of type int

mpi4py.MPI.ORDER_FORTRAN

mpi4py.MPI.ORDER_FORTRAN: int = ORDER_FORTRAN

Constant ORDER_FORTRAN of type int

mpi4py.MPI.ORDER_F

mpi4py.MPI.ORDER_F: int = ORDER_F

Constant ORDER_F of type int

mpi4py.MPI.TYPECLASS_INTEGER

mpi4py.MPI.TYPECLASS_INTEGER: int = TYPECLASS_INTEGER

Constant TYPECLASS_INTEGER of type int

mpi4py.MPI.TYPECLASS_REAL

mpi4py.MPI.TYPECLASS_REAL: int = TYPECLASS_REAL

Constant TYPECLASS_REAL of type int

mpi4py.MPI.TYPECLASS_COMPLEX

mpi4py.MPI.TYPECLASS_COMPLEX: int = TYPECLASS_COMPLEX

Constant TYPECLASS_COMPLEX of type int

mpi4py.MPI.DISTRIBUTE_NONE

mpi4py.MPI.DISTRIBUTE_NONE: int = DISTRIBUTE_NONE

Constant DISTRIBUTE_NONE of type int

mpi4py.MPI.DISTRIBUTE_BLOCK

mpi4py.MPI.DISTRIBUTE_BLOCK: int = DISTRIBUTE_BLOCK

Constant DISTRIBUTE_BLOCK of type int

mpi4py.MPI.DISTRIBUTE_CYCLIC

mpi4py.MPI.DISTRIBUTE_CYCLIC: int = DISTRIBUTE_CYCLIC

Constant DISTRIBUTE_CYCLIC of type int

mpi4py.MPI.DISTRIBUTE_DFLT_DARG

mpi4py.MPI.DISTRIBUTE_DFLT_DARG: int = DISTRIBUTE_DFLT_DARG

Constant DISTRIBUTE_DFLT_DARG of type int

mpi4py.MPI.COMBINER_NAMED

mpi4py.MPI.COMBINER_NAMED: int = COMBINER_NAMED

Constant COMBINER_NAMED of type int

mpi4py.MPI.COMBINER_DUP

mpi4py.MPI.COMBINER_DUP: int = COMBINER_DUP

Constant COMBINER_DUP of type int

mpi4py.MPI.COMBINER_CONTIGUOUS

mpi4py.MPI.COMBINER_CONTIGUOUS: int = COMBINER_CONTIGUOUS

Constant COMBINER_CONTIGUOUS of type int

mpi4py.MPI.COMBINER_VECTOR

mpi4py.MPI.COMBINER_VECTOR: int = COMBINER_VECTOR

Constant COMBINER_VECTOR of type int

mpi4py.MPI.COMBINER_HVECTOR

mpi4py.MPI.COMBINER_HVECTOR: int = COMBINER_HVECTOR

Constant COMBINER_HVECTOR of type int

mpi4py.MPI.COMBINER_INDEXED

mpi4py.MPI.COMBINER_INDEXED: int = COMBINER_INDEXED

Constant COMBINER_INDEXED of type int

mpi4py.MPI.COMBINER_HINDEXED

mpi4py.MPI.COMBINER_HINDEXED: int = COMBINER_HINDEXED

Constant COMBINER_HINDEXED of type int

mpi4py.MPI.COMBINER_INDEXED_BLOCK

mpi4py.MPI.COMBINER_INDEXED_BLOCK: int = COMBINER_INDEXED_BLOCK

Constant COMBINER_INDEXED_BLOCK of type int

mpi4py.MPI.COMBINER_HINDEXED_BLOCK

mpi4py.MPI.COMBINER_HINDEXED_BLOCK: int = COMBINER_HINDEXED_BLOCK

Constant COMBINER_HINDEXED_BLOCK of type int

mpi4py.MPI.COMBINER_STRUCT

mpi4py.MPI.COMBINER_STRUCT: int = COMBINER_STRUCT

Constant COMBINER_STRUCT of type int

mpi4py.MPI.COMBINER_SUBARRAY

mpi4py.MPI.COMBINER_SUBARRAY: int = COMBINER_SUBARRAY

Constant COMBINER_SUBARRAY of type int

mpi4py.MPI.COMBINER_DARRAY

mpi4py.MPI.COMBINER_DARRAY: int = COMBINER_DARRAY

Constant COMBINER_DARRAY of type int

mpi4py.MPI.COMBINER_RESIZED

mpi4py.MPI.COMBINER_RESIZED: int = COMBINER_RESIZED

Constant COMBINER_RESIZED of type int

mpi4py.MPI.COMBINER_VALUE_INDEX

mpi4py.MPI.COMBINER_VALUE_INDEX: int = COMBINER_VALUE_INDEX

Constant COMBINER_VALUE_INDEX of type int

mpi4py.MPI.COMBINER_F90_INTEGER

mpi4py.MPI.COMBINER_F90_INTEGER: int = COMBINER_F90_INTEGER

Constant COMBINER_F90_INTEGER of type int

mpi4py.MPI.COMBINER_F90_REAL

mpi4py.MPI.COMBINER_F90_REAL: int = COMBINER_F90_REAL

Constant COMBINER_F90_REAL of type int

mpi4py.MPI.COMBINER_F90_COMPLEX

mpi4py.MPI.COMBINER_F90_COMPLEX: int = COMBINER_F90_COMPLEX

Constant COMBINER_F90_COMPLEX of type int

mpi4py.MPI.F_SOURCE

mpi4py.MPI.F_SOURCE: int = F_SOURCE

Constant F_SOURCE of type int

mpi4py.MPI.F_TAG

mpi4py.MPI.F_TAG: int = F_TAG

Constant F_TAG of type int

mpi4py.MPI.F_ERROR

mpi4py.MPI.F_ERROR: int = F_ERROR

Constant F_ERROR of type int

mpi4py.MPI.F_STATUS_SIZE

mpi4py.MPI.F_STATUS_SIZE: int = F_STATUS_SIZE

Constant F_STATUS_SIZE of type int

mpi4py.MPI.IDENT

mpi4py.MPI.IDENT: int = IDENT

Constant IDENT of type int

mpi4py.MPI.CONGRUENT

mpi4py.MPI.CONGRUENT: int = CONGRUENT

Constant CONGRUENT of type int

mpi4py.MPI.SIMILAR

mpi4py.MPI.SIMILAR: int = SIMILAR

Constant SIMILAR of type int

mpi4py.MPI.UNEQUAL

mpi4py.MPI.UNEQUAL: int = UNEQUAL

Constant UNEQUAL of type int

mpi4py.MPI.CART

mpi4py.MPI.CART: int = CART

Constant CART of type int

mpi4py.MPI.GRAPH

mpi4py.MPI.GRAPH: int = GRAPH

Constant GRAPH of type int

mpi4py.MPI.DIST_GRAPH

mpi4py.MPI.DIST_GRAPH: int = DIST_GRAPH

Constant DIST_GRAPH of type int

mpi4py.MPI.UNWEIGHTED

mpi4py.MPI.UNWEIGHTED: int = UNWEIGHTED

Constant UNWEIGHTED of type int

mpi4py.MPI.WEIGHTS_EMPTY

mpi4py.MPI.WEIGHTS_EMPTY: int = WEIGHTS_EMPTY

Constant WEIGHTS_EMPTY of type int

mpi4py.MPI.COMM_TYPE_SHARED

mpi4py.MPI.COMM_TYPE_SHARED: int = COMM_TYPE_SHARED

Constant COMM_TYPE_SHARED of type int

mpi4py.MPI.COMM_TYPE_HW_GUIDED

mpi4py.MPI.COMM_TYPE_HW_GUIDED: int = COMM_TYPE_HW_GUIDED

Constant COMM_TYPE_HW_GUIDED of type int

mpi4py.MPI.COMM_TYPE_HW_UNGUIDED

mpi4py.MPI.COMM_TYPE_HW_UNGUIDED: int = COMM_TYPE_HW_UNGUIDED

Constant COMM_TYPE_HW_UNGUIDED of type int

mpi4py.MPI.COMM_TYPE_RESOURCE_GUIDED

mpi4py.MPI.COMM_TYPE_RESOURCE_GUIDED: int = COMM_TYPE_RESOURCE_GUIDED

Constant COMM_TYPE_RESOURCE_GUIDED of type int

mpi4py.MPI.BSEND_OVERHEAD

mpi4py.MPI.BSEND_OVERHEAD: int = BSEND_OVERHEAD

Constant BSEND_OVERHEAD of type int

mpi4py.MPI.BUFFER_AUTOMATIC

mpi4py.MPI.BUFFER_AUTOMATIC: BufferAutomaticType = BUFFER_AUTOMATIC

Constant BUFFER_AUTOMATIC of type BufferAutomaticType

mpi4py.MPI.WIN_FLAVOR_CREATE

mpi4py.MPI.WIN_FLAVOR_CREATE: int = WIN_FLAVOR_CREATE

Constant WIN_FLAVOR_CREATE of type int

mpi4py.MPI.WIN_FLAVOR_ALLOCATE

mpi4py.MPI.WIN_FLAVOR_ALLOCATE: int = WIN_FLAVOR_ALLOCATE

Constant WIN_FLAVOR_ALLOCATE of type int

mpi4py.MPI.WIN_FLAVOR_DYNAMIC

mpi4py.MPI.WIN_FLAVOR_DYNAMIC: int = WIN_FLAVOR_DYNAMIC

Constant WIN_FLAVOR_DYNAMIC of type int

mpi4py.MPI.WIN_FLAVOR_SHARED

mpi4py.MPI.WIN_FLAVOR_SHARED: int = WIN_FLAVOR_SHARED

Constant WIN_FLAVOR_SHARED of type int

mpi4py.MPI.WIN_SEPARATE

mpi4py.MPI.WIN_SEPARATE: int = WIN_SEPARATE

Constant WIN_SEPARATE of type int

mpi4py.MPI.WIN_UNIFIED

mpi4py.MPI.WIN_UNIFIED: int = WIN_UNIFIED

Constant WIN_UNIFIED of type int

mpi4py.MPI.MODE_NOCHECK

mpi4py.MPI.MODE_NOCHECK: int = MODE_NOCHECK

Constant MODE_NOCHECK of type int

mpi4py.MPI.MODE_NOSTORE

mpi4py.MPI.MODE_NOSTORE: int = MODE_NOSTORE

Constant MODE_NOSTORE of type int

mpi4py.MPI.MODE_NOPUT

mpi4py.MPI.MODE_NOPUT: int = MODE_NOPUT

Constant MODE_NOPUT of type int

mpi4py.MPI.MODE_NOPRECEDE

mpi4py.MPI.MODE_NOPRECEDE: int = MODE_NOPRECEDE

Constant MODE_NOPRECEDE of type int

mpi4py.MPI.MODE_NOSUCCEED

mpi4py.MPI.MODE_NOSUCCEED: int = MODE_NOSUCCEED

Constant MODE_NOSUCCEED of type int

mpi4py.MPI.LOCK_EXCLUSIVE

mpi4py.MPI.LOCK_EXCLUSIVE: int = LOCK_EXCLUSIVE

Constant LOCK_EXCLUSIVE of type int

mpi4py.MPI.LOCK_SHARED

mpi4py.MPI.LOCK_SHARED: int = LOCK_SHARED

Constant LOCK_SHARED of type int

mpi4py.MPI.MODE_RDONLY

mpi4py.MPI.MODE_RDONLY: int = MODE_RDONLY

Constant MODE_RDONLY of type int

mpi4py.MPI.MODE_WRONLY

mpi4py.MPI.MODE_WRONLY: int = MODE_WRONLY

Constant MODE_WRONLY of type int

mpi4py.MPI.MODE_RDWR

mpi4py.MPI.MODE_RDWR: int = MODE_RDWR

Constant MODE_RDWR of type int

mpi4py.MPI.MODE_CREATE

mpi4py.MPI.MODE_CREATE: int = MODE_CREATE

Constant MODE_CREATE of type int

mpi4py.MPI.MODE_EXCL

mpi4py.MPI.MODE_EXCL: int = MODE_EXCL

Constant MODE_EXCL of type int

mpi4py.MPI.MODE_DELETE_ON_CLOSE

mpi4py.MPI.MODE_DELETE_ON_CLOSE: int = MODE_DELETE_ON_CLOSE

Constant MODE_DELETE_ON_CLOSE of type int

mpi4py.MPI.MODE_UNIQUE_OPEN

mpi4py.MPI.MODE_UNIQUE_OPEN: int = MODE_UNIQUE_OPEN

Constant MODE_UNIQUE_OPEN of type int

mpi4py.MPI.MODE_SEQUENTIAL

mpi4py.MPI.MODE_SEQUENTIAL: int = MODE_SEQUENTIAL

Constant MODE_SEQUENTIAL of type int

mpi4py.MPI.MODE_APPEND

mpi4py.MPI.MODE_APPEND: int = MODE_APPEND

Constant MODE_APPEND of type int

mpi4py.MPI.SEEK_SET

mpi4py.MPI.SEEK_SET: int = SEEK_SET

Constant SEEK_SET of type int

mpi4py.MPI.SEEK_CUR

mpi4py.MPI.SEEK_CUR: int = SEEK_CUR

Constant SEEK_CUR of type int

mpi4py.MPI.SEEK_END

mpi4py.MPI.SEEK_END: int = SEEK_END

Constant SEEK_END of type int

mpi4py.MPI.DISPLACEMENT_CURRENT

mpi4py.MPI.DISPLACEMENT_CURRENT: int = DISPLACEMENT_CURRENT

Constant DISPLACEMENT_CURRENT of type int

mpi4py.MPI.DISP_CUR

mpi4py.MPI.DISP_CUR: int = DISP_CUR

Constant DISP_CUR of type int

mpi4py.MPI.THREAD_SINGLE

mpi4py.MPI.THREAD_SINGLE: int = THREAD_SINGLE

Constant THREAD_SINGLE of type int

mpi4py.MPI.THREAD_FUNNELED

mpi4py.MPI.THREAD_FUNNELED: int = THREAD_FUNNELED

Constant THREAD_FUNNELED of type int

mpi4py.MPI.THREAD_SERIALIZED

mpi4py.MPI.THREAD_SERIALIZED: int = THREAD_SERIALIZED

Constant THREAD_SERIALIZED of type int

mpi4py.MPI.THREAD_MULTIPLE

mpi4py.MPI.THREAD_MULTIPLE: int = THREAD_MULTIPLE

Constant THREAD_MULTIPLE of type int

mpi4py.MPI.VERSION

mpi4py.MPI.VERSION: int = VERSION

Constant VERSION of type int

mpi4py.MPI.SUBVERSION

mpi4py.MPI.SUBVERSION: int = SUBVERSION

Constant SUBVERSION of type int

mpi4py.MPI.MAX_PROCESSOR_NAME

mpi4py.MPI.MAX_PROCESSOR_NAME: int = MAX_PROCESSOR_NAME

Constant MAX_PROCESSOR_NAME of type int

mpi4py.MPI.MAX_ERROR_STRING

mpi4py.MPI.MAX_ERROR_STRING: int = MAX_ERROR_STRING

Constant MAX_ERROR_STRING of type int

mpi4py.MPI.MAX_PORT_NAME

mpi4py.MPI.MAX_PORT_NAME: int = MAX_PORT_NAME

Constant MAX_PORT_NAME of type int

mpi4py.MPI.MAX_INFO_KEY

mpi4py.MPI.MAX_INFO_KEY: int = MAX_INFO_KEY

Constant MAX_INFO_KEY of type int

mpi4py.MPI.MAX_INFO_VAL

mpi4py.MPI.MAX_INFO_VAL: int = MAX_INFO_VAL

Constant MAX_INFO_VAL of type int

mpi4py.MPI.MAX_OBJECT_NAME

mpi4py.MPI.MAX_OBJECT_NAME: int = MAX_OBJECT_NAME

Constant MAX_OBJECT_NAME of type int

mpi4py.MPI.MAX_DATAREP_STRING

mpi4py.MPI.MAX_DATAREP_STRING: int = MAX_DATAREP_STRING

Constant MAX_DATAREP_STRING of type int

mpi4py.MPI.MAX_LIBRARY_VERSION_STRING

mpi4py.MPI.MAX_LIBRARY_VERSION_STRING: int = MAX_LIBRARY_VERSION_STRING

Constant MAX_LIBRARY_VERSION_STRING of type int

mpi4py.MPI.MAX_PSET_NAME_LEN

mpi4py.MPI.MAX_PSET_NAME_LEN: int = MAX_PSET_NAME_LEN

Constant MAX_PSET_NAME_LEN of type int

mpi4py.MPI.MAX_STRINGTAG_LEN

mpi4py.MPI.MAX_STRINGTAG_LEN: int = MAX_STRINGTAG_LEN

Constant MAX_STRINGTAG_LEN of type int

mpi4py.MPI.DATATYPE_NULL

mpi4py.MPI.DATATYPE_NULL: Datatype = DATATYPE_NULL

Object DATATYPE_NULL of type Datatype

mpi4py.MPI.PACKED

mpi4py.MPI.PACKED: Datatype = PACKED

Object PACKED of type Datatype

mpi4py.MPI.BYTE

mpi4py.MPI.BYTE: Datatype = BYTE

Object BYTE of type Datatype

mpi4py.MPI.AINT

mpi4py.MPI.AINT: Datatype = AINT

Object AINT of type Datatype

mpi4py.MPI.OFFSET

mpi4py.MPI.OFFSET: Datatype = OFFSET

Object OFFSET of type Datatype

mpi4py.MPI.COUNT

mpi4py.MPI.COUNT: Datatype = COUNT

Object COUNT of type Datatype

mpi4py.MPI.CHAR

mpi4py.MPI.CHAR: Datatype = CHAR

Object CHAR of type Datatype

mpi4py.MPI.WCHAR

mpi4py.MPI.WCHAR: Datatype = WCHAR

Object WCHAR of type Datatype

mpi4py.MPI.SIGNED_CHAR

mpi4py.MPI.SIGNED_CHAR: Datatype = SIGNED_CHAR

Object SIGNED_CHAR of type Datatype

mpi4py.MPI.SHORT

mpi4py.MPI.SHORT: Datatype = SHORT

Object SHORT of type Datatype

mpi4py.MPI.INT

mpi4py.MPI.INT: Datatype = INT

Object INT of type Datatype

mpi4py.MPI.LONG

mpi4py.MPI.LONG: Datatype = LONG

Object LONG of type Datatype

mpi4py.MPI.LONG_LONG

mpi4py.MPI.LONG_LONG: Datatype = LONG_LONG

Object LONG_LONG of type Datatype

mpi4py.MPI.UNSIGNED_CHAR

mpi4py.MPI.UNSIGNED_CHAR: Datatype = UNSIGNED_CHAR

Object UNSIGNED_CHAR of type Datatype

mpi4py.MPI.UNSIGNED_SHORT

mpi4py.MPI.UNSIGNED_SHORT: Datatype = UNSIGNED_SHORT

Object UNSIGNED_SHORT of type Datatype

mpi4py.MPI.UNSIGNED

mpi4py.MPI.UNSIGNED: Datatype = UNSIGNED

Object UNSIGNED of type Datatype

mpi4py.MPI.UNSIGNED_LONG

mpi4py.MPI.UNSIGNED_LONG: Datatype = UNSIGNED_LONG

Object UNSIGNED_LONG of type Datatype

mpi4py.MPI.UNSIGNED_LONG_LONG

mpi4py.MPI.UNSIGNED_LONG_LONG: Datatype = UNSIGNED_LONG_LONG

Object UNSIGNED_LONG_LONG of type Datatype

mpi4py.MPI.FLOAT

mpi4py.MPI.FLOAT: Datatype = FLOAT

Object FLOAT of type Datatype

mpi4py.MPI.DOUBLE

mpi4py.MPI.DOUBLE: Datatype = DOUBLE

Object DOUBLE of type Datatype

mpi4py.MPI.LONG_DOUBLE

mpi4py.MPI.LONG_DOUBLE: Datatype = LONG_DOUBLE

Object LONG_DOUBLE of type Datatype

mpi4py.MPI.C_BOOL

mpi4py.MPI.C_BOOL: Datatype = C_BOOL

Object C_BOOL of type Datatype

mpi4py.MPI.INT8_T

mpi4py.MPI.INT8_T: Datatype = INT8_T

Object INT8_T of type Datatype

mpi4py.MPI.INT16_T

mpi4py.MPI.INT16_T: Datatype = INT16_T

Object INT16_T of type Datatype

mpi4py.MPI.INT32_T

mpi4py.MPI.INT32_T: Datatype = INT32_T

Object INT32_T of type Datatype

mpi4py.MPI.INT64_T

mpi4py.MPI.INT64_T: Datatype = INT64_T

Object INT64_T of type Datatype

mpi4py.MPI.UINT8_T

mpi4py.MPI.UINT8_T: Datatype = UINT8_T

Object UINT8_T of type Datatype

mpi4py.MPI.UINT16_T

mpi4py.MPI.UINT16_T: Datatype = UINT16_T

Object UINT16_T of type Datatype

mpi4py.MPI.UINT32_T

mpi4py.MPI.UINT32_T: Datatype = UINT32_T

Object UINT32_T of type Datatype

mpi4py.MPI.UINT64_T

mpi4py.MPI.UINT64_T: Datatype = UINT64_T

Object UINT64_T of type Datatype

mpi4py.MPI.C_COMPLEX

mpi4py.MPI.C_COMPLEX: Datatype = C_COMPLEX

Object C_COMPLEX of type Datatype

mpi4py.MPI.C_FLOAT_COMPLEX

mpi4py.MPI.C_FLOAT_COMPLEX: Datatype = C_FLOAT_COMPLEX

Object C_FLOAT_COMPLEX of type Datatype

mpi4py.MPI.C_DOUBLE_COMPLEX

mpi4py.MPI.C_DOUBLE_COMPLEX: Datatype = C_DOUBLE_COMPLEX

Object C_DOUBLE_COMPLEX of type Datatype

mpi4py.MPI.C_LONG_DOUBLE_COMPLEX

mpi4py.MPI.C_LONG_DOUBLE_COMPLEX: Datatype = C_LONG_DOUBLE_COMPLEX

Object C_LONG_DOUBLE_COMPLEX of type Datatype

mpi4py.MPI.CXX_BOOL

mpi4py.MPI.CXX_BOOL: Datatype = CXX_BOOL

Object CXX_BOOL of type Datatype

mpi4py.MPI.CXX_FLOAT_COMPLEX

mpi4py.MPI.CXX_FLOAT_COMPLEX: Datatype = CXX_FLOAT_COMPLEX

Object CXX_FLOAT_COMPLEX of type Datatype

mpi4py.MPI.CXX_DOUBLE_COMPLEX

mpi4py.MPI.CXX_DOUBLE_COMPLEX: Datatype = CXX_DOUBLE_COMPLEX

Object CXX_DOUBLE_COMPLEX of type Datatype

mpi4py.MPI.CXX_LONG_DOUBLE_COMPLEX

mpi4py.MPI.CXX_LONG_DOUBLE_COMPLEX: Datatype = CXX_LONG_DOUBLE_COMPLEX

Object CXX_LONG_DOUBLE_COMPLEX of type Datatype

mpi4py.MPI.SHORT_INT

mpi4py.MPI.SHORT_INT: Datatype = SHORT_INT

Object SHORT_INT of type Datatype

mpi4py.MPI.INT_INT

mpi4py.MPI.INT_INT: Datatype = INT_INT

Object INT_INT of type Datatype

mpi4py.MPI.TWOINT

mpi4py.MPI.TWOINT: Datatype = TWOINT

Object TWOINT of type Datatype

mpi4py.MPI.LONG_INT

mpi4py.MPI.LONG_INT: Datatype = LONG_INT

Object LONG_INT of type Datatype

mpi4py.MPI.FLOAT_INT

mpi4py.MPI.FLOAT_INT: Datatype = FLOAT_INT

Object FLOAT_INT of type Datatype

mpi4py.MPI.DOUBLE_INT

mpi4py.MPI.DOUBLE_INT: Datatype = DOUBLE_INT

Object DOUBLE_INT of type Datatype

mpi4py.MPI.LONG_DOUBLE_INT

mpi4py.MPI.LONG_DOUBLE_INT: Datatype = LONG_DOUBLE_INT

Object LONG_DOUBLE_INT of type Datatype

mpi4py.MPI.CHARACTER

mpi4py.MPI.CHARACTER: Datatype = CHARACTER

Object CHARACTER of type Datatype

mpi4py.MPI.LOGICAL

mpi4py.MPI.LOGICAL: Datatype = LOGICAL

Object LOGICAL of type Datatype

mpi4py.MPI.INTEGER

mpi4py.MPI.INTEGER: Datatype = INTEGER

Object INTEGER of type Datatype

mpi4py.MPI.REAL

mpi4py.MPI.REAL: Datatype = REAL

Object REAL of type Datatype

mpi4py.MPI.DOUBLE_PRECISION

mpi4py.MPI.DOUBLE_PRECISION: Datatype = DOUBLE_PRECISION

Object DOUBLE_PRECISION of type Datatype

mpi4py.MPI.COMPLEX

mpi4py.MPI.COMPLEX: Datatype = COMPLEX

Object COMPLEX of type Datatype

mpi4py.MPI.DOUBLE_COMPLEX

mpi4py.MPI.DOUBLE_COMPLEX: Datatype = DOUBLE_COMPLEX

Object DOUBLE_COMPLEX of type Datatype

mpi4py.MPI.LOGICAL1

mpi4py.MPI.LOGICAL1: Datatype = LOGICAL1

Object LOGICAL1 of type Datatype

mpi4py.MPI.LOGICAL2

mpi4py.MPI.LOGICAL2: Datatype = LOGICAL2

Object LOGICAL2 of type Datatype

mpi4py.MPI.LOGICAL4

mpi4py.MPI.LOGICAL4: Datatype = LOGICAL4

Object LOGICAL4 of type Datatype

mpi4py.MPI.LOGICAL8

mpi4py.MPI.LOGICAL8: Datatype = LOGICAL8

Object LOGICAL8 of type Datatype

mpi4py.MPI.INTEGER1

mpi4py.MPI.INTEGER1: Datatype = INTEGER1

Object INTEGER1 of type Datatype

mpi4py.MPI.INTEGER2

mpi4py.MPI.INTEGER2: Datatype = INTEGER2

Object INTEGER2 of type Datatype

mpi4py.MPI.INTEGER4

mpi4py.MPI.INTEGER4: Datatype = INTEGER4

Object INTEGER4 of type Datatype

mpi4py.MPI.INTEGER8

mpi4py.MPI.INTEGER8: Datatype = INTEGER8

Object INTEGER8 of type Datatype

mpi4py.MPI.INTEGER16

mpi4py.MPI.INTEGER16: Datatype = INTEGER16

Object INTEGER16 of type Datatype

mpi4py.MPI.REAL2

mpi4py.MPI.REAL2: Datatype = REAL2

Object REAL2 of type Datatype

mpi4py.MPI.REAL4

mpi4py.MPI.REAL4: Datatype = REAL4

Object REAL4 of type Datatype

mpi4py.MPI.REAL8

mpi4py.MPI.REAL8: Datatype = REAL8

Object REAL8 of type Datatype

mpi4py.MPI.REAL16

mpi4py.MPI.REAL16: Datatype = REAL16

Object REAL16 of type Datatype

mpi4py.MPI.COMPLEX4

mpi4py.MPI.COMPLEX4: Datatype = COMPLEX4

Object COMPLEX4 of type Datatype

mpi4py.MPI.COMPLEX8

mpi4py.MPI.COMPLEX8: Datatype = COMPLEX8

Object COMPLEX8 of type Datatype

mpi4py.MPI.COMPLEX16

mpi4py.MPI.COMPLEX16: Datatype = COMPLEX16

Object COMPLEX16 of type Datatype

mpi4py.MPI.COMPLEX32

mpi4py.MPI.COMPLEX32: Datatype = COMPLEX32

Object COMPLEX32 of type Datatype

mpi4py.MPI.UNSIGNED_INT

mpi4py.MPI.UNSIGNED_INT: Datatype = UNSIGNED_INT

Object UNSIGNED_INT of type Datatype

mpi4py.MPI.SIGNED_SHORT

mpi4py.MPI.SIGNED_SHORT: Datatype = SIGNED_SHORT

Object SIGNED_SHORT of type Datatype

mpi4py.MPI.SIGNED_INT

mpi4py.MPI.SIGNED_INT: Datatype = SIGNED_INT

Object SIGNED_INT of type Datatype

mpi4py.MPI.SIGNED_LONG

mpi4py.MPI.SIGNED_LONG: Datatype = SIGNED_LONG

Object SIGNED_LONG of type Datatype

mpi4py.MPI.SIGNED_LONG_LONG

mpi4py.MPI.SIGNED_LONG_LONG: Datatype = SIGNED_LONG_LONG

Object SIGNED_LONG_LONG of type Datatype

mpi4py.MPI.BOOL

mpi4py.MPI.BOOL: Datatype = BOOL

Object BOOL of type Datatype

mpi4py.MPI.SINT8_T

mpi4py.MPI.SINT8_T: Datatype = SINT8_T

Object SINT8_T of type Datatype

mpi4py.MPI.SINT16_T

mpi4py.MPI.SINT16_T: Datatype = SINT16_T

Object SINT16_T of type Datatype

mpi4py.MPI.SINT32_T

mpi4py.MPI.SINT32_T: Datatype = SINT32_T

Object SINT32_T of type Datatype

mpi4py.MPI.SINT64_T

mpi4py.MPI.SINT64_T: Datatype = SINT64_T

Object SINT64_T of type Datatype

mpi4py.MPI.F_BOOL

mpi4py.MPI.F_BOOL: Datatype = F_BOOL

Object F_BOOL of type Datatype

mpi4py.MPI.F_INT

mpi4py.MPI.F_INT: Datatype = F_INT

Object F_INT of type Datatype

mpi4py.MPI.F_FLOAT

mpi4py.MPI.F_FLOAT: Datatype = F_FLOAT

Object F_FLOAT of type Datatype

mpi4py.MPI.F_DOUBLE

mpi4py.MPI.F_DOUBLE: Datatype = F_DOUBLE

Object F_DOUBLE of type Datatype

mpi4py.MPI.F_COMPLEX

mpi4py.MPI.F_COMPLEX: Datatype = F_COMPLEX

Object F_COMPLEX of type Datatype

mpi4py.MPI.F_FLOAT_COMPLEX

mpi4py.MPI.F_FLOAT_COMPLEX: Datatype = F_FLOAT_COMPLEX

Object F_FLOAT_COMPLEX of type Datatype

mpi4py.MPI.F_DOUBLE_COMPLEX

mpi4py.MPI.F_DOUBLE_COMPLEX: Datatype = F_DOUBLE_COMPLEX

Object F_DOUBLE_COMPLEX of type Datatype

mpi4py.MPI.REQUEST_NULL

mpi4py.MPI.REQUEST_NULL: Request = REQUEST_NULL

Object REQUEST_NULL of type Request

mpi4py.MPI.MESSAGE_NULL

mpi4py.MPI.MESSAGE_NULL: Message = MESSAGE_NULL

Object MESSAGE_NULL of type Message

mpi4py.MPI.MESSAGE_NO_PROC

mpi4py.MPI.MESSAGE_NO_PROC: Message = MESSAGE_NO_PROC

Object MESSAGE_NO_PROC of type Message

mpi4py.MPI.OP_NULL

mpi4py.MPI.OP_NULL: Op = OP_NULL

Object OP_NULL of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.MAX

mpi4py.MPI.MAX: Op = MAX

Object MAX of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.MIN

mpi4py.MPI.MIN: Op = MIN

Object MIN of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.SUM

mpi4py.MPI.SUM: Op = SUM

Object SUM of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.PROD

mpi4py.MPI.PROD: Op = PROD

Object PROD of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.LAND

mpi4py.MPI.LAND: Op = LAND

Object LAND of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.BAND

mpi4py.MPI.BAND: Op = BAND

Object BAND of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.LOR

mpi4py.MPI.LOR: Op = LOR

Object LOR of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.BOR

mpi4py.MPI.BOR: Op = BOR

Object BOR of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.LXOR

mpi4py.MPI.LXOR: Op = LXOR

Object LXOR of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.BXOR

mpi4py.MPI.BXOR: Op = BXOR

Object BXOR of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.MAXLOC

mpi4py.MPI.MAXLOC: Op = MAXLOC

Object MAXLOC of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.MINLOC

mpi4py.MPI.MINLOC: Op = MINLOC

Object MINLOC of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.REPLACE

mpi4py.MPI.REPLACE: Op = REPLACE

Object REPLACE of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.NO_OP

mpi4py.MPI.NO_OP: Op = NO_OP

Object NO_OP of type Op
Parameters

	•		x ( Any )
	•		y ( Any )

Return type

Any

mpi4py.MPI.GROUP_NULL

mpi4py.MPI.GROUP_NULL: Group = GROUP_NULL

Object GROUP_NULL of type Group

mpi4py.MPI.GROUP_EMPTY

mpi4py.MPI.GROUP_EMPTY: Group = GROUP_EMPTY

Object GROUP_EMPTY of type Group

mpi4py.MPI.INFO_NULL

mpi4py.MPI.INFO_NULL: Info = INFO_NULL

Object INFO_NULL of type Info

mpi4py.MPI.INFO_ENV

mpi4py.MPI.INFO_ENV: Info = INFO_ENV

Object INFO_ENV of type Info

mpi4py.MPI.ERRHANDLER_NULL

mpi4py.MPI.ERRHANDLER_NULL: Errhandler = ERRHANDLER_NULL

Object ERRHANDLER_NULL of type Errhandler

mpi4py.MPI.ERRORS_RETURN

mpi4py.MPI.ERRORS_RETURN: Errhandler = ERRORS_RETURN

Object ERRORS_RETURN of type Errhandler

mpi4py.MPI.ERRORS_ABORT

mpi4py.MPI.ERRORS_ABORT: Errhandler = ERRORS_ABORT

Object ERRORS_ABORT of type Errhandler

mpi4py.MPI.ERRORS_ARE_FATAL

mpi4py.MPI.ERRORS_ARE_FATAL: Errhandler = ERRORS_ARE_FATAL

Object ERRORS_ARE_FATAL of type Errhandler

mpi4py.MPI.SESSION_NULL

mpi4py.MPI.SESSION_NULL: Session = SESSION_NULL

Object SESSION_NULL of type Session

mpi4py.MPI.COMM_NULL

mpi4py.MPI.COMM_NULL: Comm = COMM_NULL

Object COMM_NULL of type Comm

mpi4py.MPI.COMM_SELF

mpi4py.MPI.COMM_SELF: Intracomm = COMM_SELF

Object COMM_SELF of type Intracomm

mpi4py.MPI.COMM_WORLD

mpi4py.MPI.COMM_WORLD: Intracomm = COMM_WORLD

Object COMM_WORLD of type Intracomm

mpi4py.MPI.WIN_NULL

mpi4py.MPI.WIN_NULL: Win = WIN_NULL

Object WIN_NULL of type Win

mpi4py.MPI.FILE_NULL

mpi4py.MPI.FILE_NULL: File = FILE_NULL

Object FILE_NULL of type File

mpi4py.MPI.pickle

mpi4py.MPI.pickle: Pickle = <mpi4py.MPI.Pickle object>

Object pickle of type Pickle

CITATION

If MPI for Python been significant to a project that leads to an academic publication, please acknowledge that fact by citing the project.

	•		M. Rogowski, S. Aseeri, D. Keyes, and L. Dalcin, mpi4py.futures: MPI-Based Asynchronous Task Execution for Python , IEEE Transactions on Parallel and Distributed Systems, 34(2):611-622, 2023. - https://doi.org/10.1109/TPDS.2022.3225481
	•		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

Build backends

mpi4py supports three different build backends: setuptools (default), - scikit-build-core ( CMake -based), and meson-python ( Meson -based). The build backend can be selected by setting the MPI4PY_BUILD_BACKEND environment variable.
MPI4PY_BUILD_BACKEND

Choices

"setuptools" , "scikit-build-core" , "meson-python"

Default

"setuptools"

Request a build backend for building mpi4py from sources.

Using setuptools

TIP:

Set the MPI4PY_BUILD_BACKEND environment variable to "setuptools" to use the setuptools build backend.

When using the default setuptools build backend, mpi4py relies on the legacy Python distutils framework to build C extension modules. The following environment variables affect the build configuration.
MPI4PY_BUILD_MPICC

The mpicc compiler wrapper command is searched for in the executable search path ( PATH environment variable) and used to compile the mpi4py.MPI C extension module. Alternatively, use the MPI4PY_BUILD_MPICC environment variable to the full path or command corresponding to the MPI-aware C compiler.

MPI4PY_BUILD_MPILD

The mpicc compiler wrapper command is also used for linking the mpi4py.MPI C extension module. Alternatively, use the MPI4PY_BUILD_MPILD environment variable to specify the full path or command corresponding to the MPI-aware C linker.

MPI4PY_BUILD_MPICFG

If the MPI implementation does not provide a compiler wrapper, or it is not installed in a default system location, all relevant build information like include/library locations and library lists can be provided in an ini-style configuration file under a [mpi] section. mpi4py can then be asked to use the custom build information by setting the MPI4PY_BUILD_MPICFG environment variable to the full path of the configuration file. As an example, see the mpi.cfg file located in the top level mpi4py source directory.

MPI4PY_BUILD_CONFIGURE

Some vendor MPI implementations may not provide complete coverage of the MPI standard, or may provide partial features of newer MPI standard versions while advertising support for an older version. Setting the MPI4PY_BUILD_CONFIGURE environment variable to a non-empty string will trigger the run of exhaustive checks for the availability of all MPI constants, predefined handles, and routines.

The following environment variables are aliases for the ones described above. Having shorter names, they are convenient for occasional use in the command line. Its usage is not recommended in automation scenarios like packaging recipes, deployment scripts, and container image creation.

	MPICC		Convenience alias for MPI4PY_BUILD_MPICC .
	MPILD		Convenience alias for MPI4PY_BUILD_MPILD .
	MPICFG		Convenience alias for MPI4PY_BUILD_MPICFG .

Using scikit-build-core

TIP:

Set the MPI4PY_BUILD_BACKEND environment variable to "scikit-build-core" to use the scikit-build-core build backend.

When using the scikit-build-core build backend, mpi4py delegates all of MPI build configuration to CMake ’s FindMPI module. Besides the obvious advantage of cross-platform support, this delegation to CMake may be convenient in build environments exposing vendor software stacks via intricate module systems. Note however that mpi4py will not be able to look for MPI routines available beyond the MPI standard version the MPI implementation advertises to support (via the MPI_VERSION and MPI_SUBVERSION macro constants in the mpi.h header file), any missing MPI constant or symbol will prevent a successful build.

Using meson-python

TIP:

Set the MPI4PY_BUILD_BACKEND environment variable to "meson-python" to use the meson-python build backend.

When using the meson-python build backend, mpi4py delegates build tasks to the Meson build system.

WARNING:

mpi4py support for the meson-python build backend is experimental. For the time being, users must set the CC environment variable to the command or path corresponding to the mpicc C compiler wrapper.

Using pip

You can install the latest mpi4py release from its source distribution at PyPI using pip :

$ python -m pip install mpi4py

You can also install the in-development version with:

$ python -m pip install git+https://github.com/mpi4py/mpi4py

or:

$ python -m pip install https://github.com/mpi4py/mpi4py/tarball/master

NOTE:

Installing mpi4py from its source distribution (available at PyPI) or Git source code repository (available at GitHub) requires a C compiler and a working MPI implementation with development headers and libraries.

WARNING:

pip keeps previously 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 conda

The conda-forge community provides ready-to-use binary packages from an ever growing collection of software libraries built around the multi-platform conda package manager. Four MPI implementations are available on conda-forge: Open MPI (Linux and macOS), MPICH (Linux and macOS), Intel MPI (Linux and Windows) and Microsoft MPI (Windows). You can install mpi4py and your preferred MPI implementation using the conda package manager:

•

to use MPICH do:

$ conda install -c conda-forge mpi4py mpich

•

to use Open MPI do:

$ conda install -c conda-forge mpi4py openmpi

•

to use Intel MPI do:

$ conda install -c conda-forge mpi4py impi_rt

•

to use Microsoft MPI do:

$ conda install -c conda-forge mpi4py msmpi

MPICH and many of its derivatives are ABI-compatible. You can provide the package specification mpich=X.Y.*=external_* (where X and Y are the major and minor version numbers) to request the conda package manager to use system-provided MPICH (or derivative) libraries. Similarly, you can provide the package specification openmpi=X.Y.*=external_* to use system-provided Open MPI libraries.

The openmpi package on conda-forge has built-in CUDA support, but it is disabled by default. To enable it, follow the instruction outlined during conda install . Additionally, UCX support is also available once the ucx package is installed.

WARNING:

Binary conda-forge packages are built with a focus on compatibility. The MPICH and Open MPI packages are build in a constrained environment with relatively dated OS images. Therefore, they may lack support for high-performance features like cross-memory attach (XPMEM/CMA). In production scenarios, it is recommended to use external (either custom-built or system-provided) MPI installations. See the relevant conda-forge documentation about using external MPI libraries .

Linux

On Fedora Linux systems (as well as RHEL and their derivatives using the EPEL software repository), you can install binary packages with the system package manager:

•

using dnf and the mpich package:

$ sudo dnf install python3-mpi4py-mpich

•

using dnf and the openmpi package:

$ sudo dnf install python3-mpi4py-openmpi

Please remember to load the correct MPI module for your chosen MPI implementation:

•

for the mpich package do:

$ module load mpi/mpich-$(arch)
$ python -c "from mpi4py import MPI"

•

for the openmpi package do:

$ module load mpi/openmpi-$(arch)
$ python -c "from mpi4py import MPI"

On Ubuntu Linux and Debian Linux systems, binary packages are available for installation using the system package manager:

$ sudo apt install python3-mpi4py

Note that on Ubuntu/Debian systems, the mpi4py package uses Open MPI. To use MPICH, install the libmpich-dev and python3-dev packages (and any other required development tools). Afterwards, install mpi4py from sources using pip .

macOS

macOS users can install mpi4py using the Homebrew package manager:

$ brew install mpi4py

Note that the Homebrew mpi4py package uses Open MPI. Alternatively, install the mpich package and next install mpi4py from sources using pip .

Windows

Windows users can install mpi4py from binary wheels hosted on the Python Package Index (PyPI) using pip :

$ python -m pip install mpi4py

The Windows wheels available on PyPI are specially crafted to work with either the Intel MPI or the Microsoft MPI runtime, therefore requiring a separate installation of any one of these packages.

Intel MPI is under active development and supports recent version of the MPI standard. Intel MPI can be installed with pip (see the impi-rt package on PyPI), being therefore straightforward to get it up and running within a Python environment. Intel MPI can also be installed system-wide as part of the Intel HPC Toolkit for Windows or via standalone online/offline installers.

DEVELOPMENT

Prerequisites

You need to have the following software properly installed to develop MPI for Python :

	•		Python 3.6 or above.
	•		The Cython compiler.
	•		A working MPI implementation like MPICH or Open MPI , preferably supporting MPI-4 and built with shared/dynamic libraries.

Optionally, consider installing the following packages:

	•		NumPy for enabling comprehensive testing of MPI communication.
	•		CuPy for enabling comprehensive testing with a GPU-aware MPI.
	•		Sphinx to build the documentation.

TIP:

Most routine development tasks like building, installing in editable mode, testing, and generating documentation can be performed with the spin developer tool. Run spin at the top level source directory for a list of available subcommands.

Building

MPI for Python uses setuptools -based build system that relies on the setup.py file. Some setuptools commands (e.g., build ) accept additional options:

--mpi=

Lets you pass a section with MPI configuration within a special configuration file. Alternatively, you can use the MPICFG environment variable.

--mpicc=

Specify the path or name of the mpicc C compiler wrapper. Alternatively, use the MPICC environment variable.

--mpild=

Specify the full path or name for the MPI-aware C linker. Alternatively, use the MPILD environment variable. If not set, the mpicc C compiler wrapper is used for linking.

--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, MPI-2, or MPI-3 implementations, possibly providing a subset of MPI-4.

If you use a MPI implementation providing a mpicc C compiler wrapper (e.g., MPICH or Open MPI), it will be used for compilation and linking. This is the preferred and easiest way to build MPI for Python .

If mpicc is found in the executable search path ( PATH environment variable), 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, either via the --mpicc command option or using the MPICC environment variable:

$ python setup.py build --mpicc=/path/to/mpicc
$ env MPICC=/path/to/mpicc python setup.py build

Alternatively, you can provide all the relevant information about your MPI implementation by editing the mpi.cfg file located in the top level source directory. You can use the default section [mpi] or add a new custom section, for example [vendor_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

[vendor_mpi]
include_dirs = /opt/mpi/include ...
libraries = mpi ...
library_dirs = /opt/mpi/lib ...
runtime_library_dirs = /opt/mpi/lib ...

...

and then run the build command specifying you custom configuration section:

$ python setup.py build --mpi=vendor_mpi
$ env MPICFG=vendor_mpi python setup.py build

Installing

MPI for Python can be installed in editable mode:

$ python -m pip install --editable .

After modifying Cython sources, an in-place rebuild is needed:

$ python setup.py build --inplace

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.

$ mpiexec -n 5 python -m mpi4py.bench ringtest -l 10 -n 1048576
time for 10 loops = 0.00361614 seconds (5 processes, 1048576 bytes)

If you installed from a git clone or the source distribution, issuing at the command line:

$ mpiexec -n 5 python demo/helloworld.py

will launch a five-process run of the Python interpreter and run the demo script demo/helloworld.py from the source distribution.

You can also run all the unittest scripts:

$ mpiexec -n 5 python test/main.py

or, if you have the pytest unit testing framework installed:

$ mpiexec -n 5 pytest

GUIDELINES

Fair play

Summary

This section defines Rules of Play for companies and outside developers that engage with the mpi4py project. It covers:

	•		Restrictions on use of the mpi4py name.
	•		How and whether to publish a modified distribution.
	•		How to make us aware of patched versions.

After reading this section, companies and developers will know what kinds of behavior the mpi4py developers and contributors would like to see, and which we consider troublesome, bothersome, and unacceptable.

This document is a close adaptation of NumPy NEP 36 .

Motivation

Occasionally, we learn of modified mpi4py versions and binary distributions circulated by outsiders. These patched versions can cause problems to mpi4py users (see, e.g., mpi4py/mpi4py#508 ). When issues like these arise, our developers waste time identifying the problematic release, locating alterations, and determining an appropriate course of action.

In addition, packages on the Python Packaging Index are sometimes named such that users assume they are sanctioned or maintained by the mpi4py developers. We wish to reduce the number of such incidents.

Scope

This document aims to define a minimal set of rules that, when followed, will be considered good-faith efforts in line with the expectations of the mpi4py developers and contributors.

Our hope is that companies and outside developers who feel they need to modify mpi4py will first consider contributing to the project, or use alternative mechanisms for patching and extending mpi4py.

When in doubt, please talk to us first . We may suggest an alternative; at minimum, we’ll be informed and we may even grant an exception if deemed appropriate.

Fair play rules

Do not reuse the mpi4py name for projects not affiliated with the mpi4py project.

At time of writing, there are only a handful of mpi4py -named packages developed by the mpi4py project, including mpi4py and mpi4py-fft . We ask that outside packages not include the phrase mpi4py , i.e., avoid names such as mycompany-mpi4py or mpi4py-mycompany .

To be clear, this rule only applies to modules (package names); it is perfectly acceptable to have a submodule of your own package named mycompany.mpi4py .

Do not publish binary mpi4py wheels on PyPI ( https://pypi.org/ ).

We ask companies and outside developers to not publish binary mpi4py wheels in the main Python Package Index ( https://pypi.org/ ) under names such mpi4py-mpich , mpi4py-openmpi , or mpi4py-vendor_mpi .

The usual approaches to build binary Python wheels involve the embedding of dependent shared libraries. While such an approach may seem convenient and often is, in the particular case of MPI and mpi4py it is ultimately harmful to end users. Embedding the MPI shared libraries would prevent the use of external, system-provided MPI installations with hardware-specific optimizations and site-specific tweaks.

The MPI Forum is currently discussing the standardization of a proposal for an Application Binary Interface (ABI) for MPI, see [mpi-abi-paper] and [mpi-abi-issue] . Such standardization will allow for any binary dependent on the MPI library to be used with multiple MPI backends. Once this proposal becomes part of the MPI standard, the mpi4py project will consider publishing on PyPI binary wheels capable of using any backend MPI implementation supporting the new MPI ABI specification. In the mean time, mpi4py is currently distributing experimental MPI and mpi4py binary wheels on - https://anaconda.org/mpi4py .

[mpi-abi-paper]

J. Hammond, L. Dalcin, E. Schnetter, M. Pérache, J. B. Besnard, J. Brown, G. Brito Gadeschi, S. Byrne, J. Schuchart, and H. Zhou. MPI Application Binary Interface Standardization. EuroMPI 2023, Bristol, UK, September 2023. - https://doi.org/10.1145/3615318.3615319

[mpi-abi-issue]

MPI Forum GitHub Issue: MPI needs a standard ABI . - https://github.com/mpi-forum/mpi-issues/issues/751

Do not republish modified versions of mpi4py.

Modified versions of mpi4py make it very difficult for the developers to address bug reports, since we typically do not know which parts of mpi4py have been modified.

If you have to break this rule (and we implore you not to!), then make it clear in the __version__ tag that you have modified mpi4py, e.g.:

>>> print(mpi4py.__version__)
'4.0.0+mycompany.13`

We understand that minor patches are often required to make a library work inside of a package ecosystem. This is totally acceptable, but we ask that no substantive changes are made.

Do not extend or modify mpi4py’s API.

If you absolutely have to break the previous rule, please do not add additional functions to the namespace, or modify the API of existing functions. Having additional functions exposed in distributed versions is confusing for users and developers alike.

LICENSE

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

	1.		Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
	2.		Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
	3.		Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

CHANGES

Release 4.0.3 [2025-02-13]

•

Fix DLPack v1.0 support.

Release 4.0.2 [2025-02-01]

	•		Support MPI-4 features within Intel MPI 2021.14.
	•		Various fixes and updates to tests.
	•		Minor fixes to typing support.
	•		Minor fix to documentation.

Release 4.0.1 [2024-10-11]

•

Update support for Python 3.13:

•

			Enable Cython 3.1 support for free-threaded CPython.
	•		Allow compiling Cython-generated C sources with the full Python C-API.
	•		Fix MPI DLL path workarounds on Windows after changes to locals() .
	•

Enhancements to test suite:

	•		Support XML reports via unittest-xml-reporting .
	•		Add command line options to exclude tests by patterns and files.
	•		Refactor Python 2 code to use Python 3 constructs using pyupgrade .
	•

Miscellaneous:

	•		Minor and mostly inconsequential subclass fix in mpi4py.util.pkl5 .
	•		Update compatibility workarounds for legacy MPICH 3.0 release.

Release 4.0.0 [2024-07-28]

•

New features:

•

Add support for the MPI-4.0 standard.

•

			Use large count MPI-4 routines.
	•		Add persistent collective communication.
	•		Add partitioned point-to-point communication.
	•		Add new communicator constructors.
	•		Add the Session class and its methods.
	•

Add support for the MPI-4.1 standard.

	•		Add non-destructive completion test for multiple requests.
	•		Add value-index datatype constructor.
	•		Add communicator/session buffer attach/detach/flush.
	•		Support for removal of error classes/codes/strings.
	•		Support for querying hardware resource information.
	•

Add preliminary support for the upcoming MPI-5.0 standard.

	•		User-level failure mitigation (ULFM).
	•

mpi4py.util.pool : New drop-in replacement for multiprocessing.pool .

	•		mpi4py.util.sync : New synchronization utilities.
	•		Add runtime check for mismatch between mpiexec and MPI library.
	•		Support scikit-build-core as an alternative build backend.
	•		Support meson-python as an alternative build backend.
	•

Enhancements:

	•		mpi4py.futures : Support for parallel tasks.
	•		mpi4py.futures : Report exception tracebacks in workers.
	•		mpi4py.util.pkl5 : Add support for collective communication.
	•		Add methods Datatype.fromcode() , Datatype.tocode() and attributes Datatype.typestr , Datatype.typechar to simplify NumPy interoperability for simple cases.
	•		Add methods Comm.Create_errhandler() , Win.Create_errhandler() , and File.Create_errhandler() to create custom error handlers.
	•		Add support for pickle serialization of instances of MPI types. All instances of Datatype , Info , and Status can be serialized. Instances of Op can be serialized only if created through mpi4py by calling Op.Create() . Instances of other MPI types can be serialized only if they reference predefined handles.
	•		Add handle attribute and fromhandle() class method to MPI classes to ease interoperability with external code. The handle value is an unsigned integer guaranteed to fit on the platform’s uintptr_t C type.
	•		Add lowercase free() method to MPI classes to ease MPI object deallocation and cleanup. This method eventually attempts to call Free() , but only if the object’s MPI handle is not a null or predefined handle, and such call is allowed within the World Model init/finalize.
	•

Backward-incompatible changes:

	•		Python 2 is no longer supported, Python 3.6+ is required, but typing stubs are supported for Python 3.8+.
	•		The Intracomm.Create_group() method is no longer defined in the base Comm class.
	•		Group.Compare() and Comm.Compare() are no longer class methods but instance methods. Existing codes using the former class methods are expected to continue working.
	•		Group.Translate_ranks() is no longer a class method but an instance method. Existing codes using the former class method are expected to continue working.
	•		The LB and UB datatypes are no longer available, use Datatype.Create_resized() instead.
	•		The HOST predefined attribute key is no longer available.
	•		The MPI.memory class has been renamed to MPI.buffer . The old name is still available as an alias to the new name.
	•		The mpi4py.dl module is no longer available.
	•		The mpi4py.get_config function returns an empty dictionary.
	•

Miscellaneous:

	•		The project is now licensed under the BSD-3-Clause license. This change is fairly inconsequential for users and distributors. It simply adds an additional clause against using contributor names for promotional purposes without their consent.
	•		Add a new guidelines section to documentation laying out new fair play rules. These rules ask companies and outside developers to refrain from reusing the mpi4py name in unaffiliated projects, publishing binary mpi4py wheels on the main Python Package Index (PyPI), and distributing modified versions with incompatible or extended API changes. The primary motivation of these rules is to avoid fragmentation and end-user confusion.

Release 3.1.6 [2024-04-14]

WARNING:

This is the last release supporting Python 2.

•

Fix various build issues.

Release 3.1.5 [2023-10-04]

WARNING:

This is the last release supporting Python 2.

•

Rebuild C sources with Cython 0.29.36 to support Python 3.12.

Release 3.1.4 [2022-11-02]

WARNING:

This is the last release supporting Python 2.

	•		Rebuild C sources with Cython 0.29.32 to support Python 3.11.
	•		Fix contiguity check for DLPack and CAI buffers.
	•		Workaround build failures with setuptools v60.

Release 3.1.3 [2021-11-25]

WARNING:

This is the last release supporting Python 2.

•

Add missing support for MPI.BOTTOM to generalized all-to-all collectives.

Release 3.1.2 [2021-11-04]

WARNING:

This is the last release supporting Python 2.

	•		mpi4py.futures : Add _max_workers property to MPIPoolExecutor .
	•		mpi4py.util.dtlib : Fix computation of alignment for predefined datatypes.
	•		mpi4py.util.pkl5 : Fix deadlock when using ssend() + mprobe() .
	•		mpi4py.util.pkl5 : Add environment variable MPI4PY_PICKLE_THRESHOLD .
	•		mpi4py.rc : Interpret "y" and "n" strings as boolean values.
	•		Fix/add typemap/typestr for MPI.WCHAR / MPI.COUNT datatypes.
	•		Minor fixes and additions to documentation.
	•		Minor fixes to typing support.
	•		Support for local version identifier (PEP-440).

Release 3.1.1 [2021-08-14]

WARNING:

This is the last release supporting Python 2.

	•		Fix typo in Requires-Python package metadata.
	•		Regenerate C sources with Cython 0.29.24.

Release 3.1.0 [2021-08-12]

WARNING:

This is the last release supporting Python 2.

•

New features:

•

			mpi4py.util : New package collecting miscellaneous utilities.
	•		Enhancements:

•

			Add pickle-based Request.waitsome() and Request.testsome() .
	•		Add lowercase methods Request.get_status() and Request.cancel() .
	•		Support for passing Python GPU arrays compliant with the DLPack data interchange mechanism ( link ) and the __cuda_array_interface__ (CAI) standard ( link ) to uppercase methods. This support requires that mpi4py is built against CUDA-aware MPI implementations. This feature is currently experimental and subject to future changes.
	•		mpi4py.futures : Add support for initializers and canceling futures at shutdown. Environment variables names now follow the pattern MPI4PY_FUTURES_* , the previous MPI4PY_* names are deprecated.
	•		Add type annotations to Cython code. The first line of the docstring of functions and methods displays a signature including type annotations.
	•		Add companion stub files to support type checkers.
	•		Support for weak references.
	•

Miscellaneous:

•

Add a new mpi4py publication ( link ) to the citation listing.

Release 3.0.3 [2019-11-04]

•

Regenerate Cython wrappers to support Python 3.8.

Release 3.0.2 [2019-06-11]

•

Bug fixes:

•

			Fix handling of readonly buffers in support for Python 2 legacy buffer interface. The issue triggers only when using a buffer-like object that is readonly and does not export the new Python 3 buffer interface.
	•		Fix build issues with Open MPI 4.0.x series related to removal of many MPI-1 symbols deprecated in MPI-2 and removed in MPI-3.
	•		Minor documentation fixes.

Release 3.0.1 [2019-02-15]

•

Bug fixes:

•

			Fix Comm.scatter() and other collectives corrupting input send list. Add safety measures to prevent related issues in global reduction operations.
	•		Fix error-checking code for counts in Op.Reduce_local() .
	•

Enhancements:

	•		Map size-specific Python/NumPy typecodes to MPI datatypes.
	•		Allow partial specification of target list/tuple arguments in the various Win RMA methods.
	•		Workaround for removal of MPI_{LB\|UB} in Open MPI 4.0.
	•		Support for Microsoft MPI v10.0.

Release 3.0.0 [2017-11-08]

•

New features:

•

			mpi4py.futures : Execute computations asynchronously using a pool of MPI processes. This package is based on concurrent.futures from the Python standard library.
	•		mpi4py.run : Run Python code and abort execution in case of unhandled exceptions to prevent deadlocks.
	•		mpi4py.bench : Run basic MPI benchmarks and tests.
	•

Enhancements:

	•		Lowercase, pickle-based collective communication calls are now thread-safe through the use of fine-grained locking.
	•		The MPI module now exposes a memory type which is a lightweight variant of the builtin memoryview type, but exposes both the legacy Python 2 and the modern Python 3 buffer interface under a Python 2 runtime.
	•		The MPI.Comm.Alltoallw() method now uses count=1 and displ=0 as defaults, assuming that messages are specified through user-defined datatypes.
	•		The Request.Wait[all]() methods now return True to match the interface of Request.Test[all]() .
	•		The Win class now implements the Python buffer interface.
	•

Backward-incompatible changes:

	•		The buf argument of the MPI.Comm.recv() method is deprecated, passing anything but None emits a warning.
	•		The MPI.Win.memory property was removed, use the MPI.Win.tomemory() method instead.
	•		Executing python -m mpi4py in the command line is now equivalent to python -m mpi4py.run . For the former behavior, use python -m mpi4py.bench .
	•		Python 2.6 and 3.2 are no longer supported. The mpi4py.MPI module may still build and partially work, but other pure-Python modules under the mpi4py namespace will not.
	•		Windows: Remove support for legacy MPICH2, Open MPI, and DeinoMPI.

Release 2.0.0 [2015-10-18]

•

Support for MPI-3 features.

•

			Matched probes and receives.
	•		Nonblocking collectives.
	•		Neighborhood collectives.
	•		New communicator constructors.
	•		Request-based RMA operations.
	•		New RMA communication and synchronisation calls.
	•		New window constructors.
	•		New datatype constructor.
	•		New C++ boolean and floating complex datatypes.
	•

Support for MPI-2 features not included in previous releases.

	•		Generalized All-to-All collective ( Comm.Alltoallw() )
	•		User-defined data representations ( Register_datarep() )
	•

New scalable implementation of reduction operations for Python objects. This code is based on binomial tree algorithms using point-to-point communication and duplicated communicator contexts. To disable this feature, use mpi4py.rc.fast_reduce = False .

•

Backward-incompatible changes:

•

			Python 2.4, 2.5, 3.0 and 3.1 are no longer supported.
	•		Default MPI error handling policies are overridden. After import, mpi4py sets the ERRORS_RETURN error handler in COMM_SELF and COMM_WORLD , as well as any new Comm , Win , or File instance created through mpi4py, thus effectively ignoring the MPI rules about error handler inheritance. This way, MPI errors translate to Python exceptions. To disable this behavior and use the standard MPI error handling rules, use mpi4py.rc.errors = 'default' .
	•		Change signature of all send methods, dest is a required argument.
	•		Change signature of all receive and probe methods, source defaults to ANY_SOURCE , tag defaults to ANY_TAG .
	•		Change signature of send lowercase-spelling methods, obj arguments are not mandatory.
	•		Change signature of recv lowercase-spelling methods, renamed ‘obj’ arguments to ‘buf’.
	•		Change Request.Waitsome() and Request.Testsome() to return None or list .
	•		Change signature of all lowercase-spelling collectives, sendobj arguments are now mandatory, recvobj arguments were removed.
	•		Reduction operations MAXLOC and MINLOC are no longer special-cased in lowercase-spelling methods Comm.[all]reduce() and Comm.[ex]scan() , the input object must be specified as a tuple (obj, location) .
	•		Change signature of name publishing functions. The new signatures are Publish_name(service_name, port_name, info=INFO_NULL) and Unpublish_name(service_name, port_name, info=INFO_NULL)` .
	•		Win instances now cache Python objects exposing memory by keeping references instead of using MPI attribute caching.
	•		Change signature of Win.Lock() . The new signature is Win.Lock(rank, lock_type=LOCK_EXCLUSIVE, assertion=0) .
	•		Move Cartcomm.Map() to Intracomm.Cart_map() .
	•		Move Graphcomm.Map() to Intracomm.Graph_map() .
	•		Remove the mpi4py.MPE module.
	•		Rename the Cython definition file for use with cimport statement from mpi_c.pxd to libmpi.pxd .

Release 1.3.1 [2013-08-07]

	•		Regenerate C wrappers with Cython 0.19.1 to support Python 3.3.
	•		Install .pxd files in <site-packages>/mpi4py* to ease the support for Cython’s cimport statement in code requiring to access mpi4py internals.
	•		As a side-effect of using Cython 0.19.1, ancient Python 2.3 is no longer supported. If you really need it, you can install an older Cython and run python setup.py build_src --force .

Release 1.3 [2012-01-20]

	•		Now Comm.recv() accept a buffer to receive the message.
	•		Add Comm.irecv() and Request.{wait\|test}[any\|all]() .
	•		Add Intracomm.Spawn_multiple() .
	•		Better buffer handling for PEP 3118 and legacy buffer interfaces.
	•		Add support for attribute attribute caching on communicators, datatypes and windows.
	•		Install MPI-enabled Python interpreter as <path>/mpi4py/bin/python-mpi .
	•		Windows: Support for building with Open MPI.

Release 1.2.2 [2010-09-13]

	•		Add mpi4py.get_config() to retrieve information (compiler wrappers, includes, libraries, etc) about the MPI implementation employed to build mpi4py.
	•		Workaround Python libraries with missing GILState-related API calls in case of non-threaded Python builds.
	•		Windows: look for MPICH2, DeinoMPI, Microsoft HPC Pack at their default install locations under %ProgramFiles.
	•		MPE: fix hacks related to old API’s, these hacks are broken when MPE is built with a MPI implementations other than MPICH2.
	•		HP-MPI: fix for missing Fortran datatypes, use dlopen() to load the MPI shared library before MPI_Init()
	•		Many distutils-related fixes, cleanup, and enhancements, better logics to find MPI compiler wrappers.
	•		Support for pip install mpi4py .

Release 1.2.1 [2010-02-26]

	•		Fix declaration in Cython include file. This declaration, while valid for Cython, broke the simple-minded parsing used in conf/mpidistutils.py to implement configure-tests for availability of MPI symbols.
	•		Update SWIG support and make it compatible with Python 3. Also generate an warning for SWIG < 1.3.28.
	•		Fix distutils-related issues in Mac OS X. Now ARCHFLAGS environment variable is honored of all Python’s config/Makefile variables.
	•		Fix issues with Open MPI < 1.4.2 related to error checking and MPI_XXX_NULL handles.

Release 1.2 [2009-12-29]

	•		Automatic MPI datatype discovery for NumPy arrays and PEP-3118 buffers. Now buffer-like objects can be messaged directly, it is no longer required to explicitly pass a 2/3-list/tuple like [data, MPI.DOUBLE] , or [data, count, MPI.DOUBLE] . Only basic types are supported, i.e., all C/C99-native signed/unsigned integral types and single/double precision real/complex floating types. Many thanks to Eilif Muller for the initial feedback.
	•		Nonblocking send of pickled Python objects. Many thanks to Andreas Kloeckner for the initial patch and enlightening discussion about this enhancement.
	•		Request instances now hold a reference to the Python object exposing the buffer involved in point-to-point communication or parallel I/O. Many thanks to Andreas Kloeckner for the initial feedback.
	•		Support for logging of user-defined states and events using MPE . Runtime (i.e., without requiring a recompile!) activation of logging of all MPI calls is supported in POSIX platforms implementing dlopen() .
	•		Support for all the new features in MPI-2.2 (new C99 and F90 datatypes, distributed graph topology, local reduction operation, and other minor enhancements).
	•		Fix the annoying issues related to Open MPI and Python dynamic loading of extension modules in platforms supporting dlopen() .
	•		Fix SLURM dynamic loading issues on SiCortex. Many thanks to Ian Langmore for providing me shell access.

Release 1.1.0 [2009-06-06]

	•		Fix bug in Comm.Iprobe() that caused segfaults as Python C-API calls were issued with the GIL released (issue #2).
	•		Add Comm.bsend() and Comm.ssend() for buffered and synchronous send semantics when communicating general Python objects.
	•		Now the call Info.Get(key) return a single value (i.e, instead of a 2-tuple); this value is None if key is not in the Info object, or a string otherwise. Previously, the call redundantly returned (None, False) for missing key-value pairs; None is enough to signal a missing entry.
	•		Add support for parametrized Fortran datatypes.
	•		Add support for decoding user-defined datatypes.
	•		Add support for user-defined reduction operations on memory buffers. However, at most 16 user-defined reduction operations can be created. Ask the author for more room if you need it.

Release 1.0.0 [2009-03-20]

This is the fist release of the all-new, Cython-based, implementation of MPI for Python . Unfortunately, this implementation is not backward-compatible with the previous one. The list below summarizes the more important changes that can impact user codes.

•

Some communication calls had overloaded functionality. Now there is a clear distinction between communication of general Python object with pickle , and (fast, near C-speed) communication of buffer-like objects (e.g., NumPy arrays).

•

			for communicating general Python objects, you have to use all-lowercase methods, like send() , recv() , bcast() , etc.
	•		for communicating array data, you have to use Send() , Recv() , Bcast() , etc. methods. 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 the count ).
	•

Indexing a communicator with an integer returned a special object associating the communication with a target rank, alleviating you from specifying source/destination/root arguments in point-to-point and collective communications. This functionality is no longer available, expressions like:

MPI.COMM_WORLD[0].Send(...)
MPI.COMM_WORLD[0].Recv(...)
MPI.COMM_WORLD[0].Bcast(...)

have to be replaced by:

MPI.COMM_WORLD.Send(..., dest=0)
MPI.COMM_WORLD.Recv(..., source=0)
MPI.COMM_WORLD.Bcast(..., root=0)

	•		Automatic MPI initialization (i.e., at import time) requests the maximum level of MPI thread support (i.e., it is done by calling MPI_Init_thread() and passing MPI_THREAD_MULTIPLE ). In case you need to change this behavior, you can tweak the contents of the mpi4py.rc module.
	•		In order to obtain the values of predefined attributes attached to the world communicator, now you have to use the Get_attr() method on the MPI.COMM_WORLD instance:

tag_ub = MPI.COMM_WORLD.Get_attr(MPI.TAG_UB)

	•		In the previous implementation, MPI.COMM_WORLD and MPI.COMM_SELF were associated to duplicates of the (C-level) MPI_COMM_WORLD and MPI_COMM_SELF predefined communicator handles. Now this is no longer the case, MPI.COMM_WORLD and MPI.COMM_SELF proxies the actual MPI_COMM_WORLD and MPI_COMM_SELF handles.
	•		Convenience aliases MPI.WORLD and MPI.SELF were removed. Use instead MPI.COMM_WORLD and MPI.COMM_SELF .
	•		Convenience constants MPI.WORLD_SIZE and MPI.WORLD_RANK were removed. Use instead MPI.COMM_WORLD.Get_size() and MPI.COMM_WORLD.Get_rank() .

AUTHOR

Lisandro Dalcin

COPYRIGHT

2025, Lisandro Dalcin