MPI4PY(1)

NAME
mpi4py - MPI for Python

Author
    Lisandro Dalcin

Contact
    dalcinl@gmail.com

Date
    Nov 02, 2022

  Abstract

This document describes the MPI for Python package. MPI for Python provides Python bindings for the Message Passing Interface (MPI) standard, allowing Python applications to exploit multiple processors on workstations, clusters and supercomputers.

This package builds on the MPI specification and provides an object oriented interface resembling the MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communication of any picklable Python object, as well as efficient communication of Python objects exposing the Python buffer interface (e.g. NumPy arrays and builtin bytes/array/memoryview objects).

INTRODUCTION

Over the last years, high performance computing has become an affordable resource to many more researchers in the scientific community than ever before. The conjunction of quality open source software and commodity hardware strongly influenced the now widespread popularity of Beowulf class clusters and cluster of workstations.

Among many parallel computational models, message-passing has proven to be an effective one. This paradigm is specially suited for (but not limited to) distributed memory architectures and is used in today’s most demanding scientific and engineering application related to modeling, simulation, design, and signal processing. However, portable message-passing parallel programming used to be a nightmare in the past because of the many incompatible options developers were faced to. Fortunately, this situation definitely changed after the MPI Forum released its standard specification.

High performance computing is traditionally associated with software development using compiled languages. However, in typical applications programs, only a small part of the code is time-critical enough to require the efficiency of compiled languages. The rest of the code is generally related to memory management, error handling, input/output, and user interaction, and those are usually the most error prone and time-consuming lines of code to write and debug in the whole development process. Interpreted high-level languages can be really advantageous for this kind of tasks.

For implementing general-purpose numerical computations, MATLAB [1] is the dominant interpreted programming language. In the open source side, Octave and Scilab are well known, freely distributed software packages providing compatibility with the MATLAB language. In this work, we present MPI for Python, a new package enabling applications to exploit multiple processors using standard MPI “look and feel” in Python scripts.

[1] MATLAB is a registered trademark of The MathWorks, Inc.

  What is MPI?

MPI, [mpi-using] [mpi-ref] the Message Passing Interface, is a standardized and portable message-passing system designed to function on a wide variety of parallel computers. The standard defines the syntax and semantics of library routines and allows users to write portable programs in the main scientific programming languages (Fortran, C, or C++).

Since its release, the MPI specification [mpi-std1] [mpi-std2] has become the leading standard for message-passing libraries for parallel computers. Implementations are available from vendors of high-performance computers and from well known open source projects like MPICH [mpi-mpich] and Open MPI [mpi-openmpi].

  What is Python?

Python is a modern, easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming with dynamic typing and dynamic binding. It supports modules and packages, which encourages program modularity and code reuse. Python’s elegant syntax, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.

The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. It is easily extended with new functions and data types implemented in C or C++. Python is also suitable as an extension language for customizable applications.

Python is an ideal candidate for writing the higher-level parts of large-scale scientific applications [Hinsen97] and driving simulations in parallel architectures [Beazley97] like clusters of PC’s or SMP’s. Python codes are quickly developed, easily maintained, and can achieve a high degree of integration with other libraries written in compiled languages.

  Related Projects

As this work started and evolved, some ideas were borrowed from well known MPI and Python related open source projects from the Internet.

•   OOMPI

•   It has no relation with Python, but is an excellent object oriented approach to MPI.

•   It is a C++ class library specification layered on top of the C bindings that encapsulates MPI into a functional class hierarchy.

•   It provides a flexible and intuitive interface by adding some abstractions, like Ports and Messages, which enrich and simplify the syntax.

•   Pypar

•   Its interface is rather minimal. There is no support for communicators or process topologies.

•   It does not require the Python interpreter to be modified or recompiled, but does not permit interactive parallel runs.

•   General (picklable) Python objects of any type can be communicated. There is good support for numeric arrays, practically full MPI bandwidth can be achieved.

•   pyMPI

•   It rebuilds the Python interpreter providing a built-in module for message passing. It does permit interactive parallel runs, which are useful for learning and debugging.

•   It provides an interface suitable for basic parallel programing. There is not full support for defining new communicators or process topologies.

•   General (picklable) Python objects can be messaged between processors. There is not support for numeric arrays.

•   Scientific Python

•   It provides a collection of Python modules that are useful for scientific computing.

•   There is an interface to MPI and BSP (Bulk Synchronous Parallel programming).

•   The interface is simple but incomplete and does not resemble the MPI specification. There is support for numeric arrays.

    Additionally, we would like to mention some available tools for scientific computing and software development with Python.

•   NumPy is a package that provides array manipulation and computational capabilities similar to those found in IDL, MATLAB, or Octave. Using NumPy, it is possible to write many efficient numerical data processing applications directly in Python without using any C, C++ or Fortran code.

•   SciPy is an open source library of scientific tools for Python, gathering a variety of high level science and engineering modules together as a single package. It includes modules for graphics and plotting, optimization, integration, special functions, signal and image processing, genetic algorithms, ODE solvers, and others.

•   Cython is a language that makes writing C extensions for the Python language as easy as Python itself. The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. This makes Cython the ideal language for wrapping for external C libraries, and for fast C modules that speed up the execution of Python code.

•   SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages like Perl, Tcl/Tk, Ruby and Python. Issuing header files to SWIG is the simplest approach to interfacing C/C++ libraries from a Python module.

[mpi-std1]
    MPI Forum. MPI: A Message Passing Interface Standard. International Journal of Supercomputer Applications, volume 8, number 3-4, pages 159-416, 1994.

[mpi-std2]
    MPI Forum. MPI: A Message Passing Interface Standard. High Performance Computing Applications, volume 12, number 1-2, pages 1-299, 1998.

[mpi-using]
    William Gropp, Ewing Lusk, and Anthony Skjellum. Using MPI: portable parallel programming with the message-passing interface. MIT Press, 1994.

[mpi-ref]
    Mark Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack Dongarra. MPI - The Complete Reference, volume 1, The MPI Core. MIT Press, 2nd. edition, 1998.

[mpi-mpich]
    W. Gropp, E. Lusk, N. Doss, and A. Skjellum. A high-performance, portable implementation of the MPI message passing interface standard. Parallel Computing, 22(6):789-828, September 1996.

[mpi-openmpi]
    Edgar Gabriel, Graham E. Fagg, George Bosilca, Thara Angskun, Jack J. Dongarra, Jeffrey M. Squyres, Vishal Sahay, Prabhanjan Kambadur, Brian Barrett, Andrew Lumsdaine, Ralph H. Castain, David J. Daniel, Richard L. Graham, and Timothy S. Woodall. Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation. In Proceedings, 11th European PVM/MPI Users’ Group Meeting, Budapest, Hungary, September 2004.

[Hinsen97]
    Konrad Hinsen. The Molecular Modelling Toolkit: a case study of a large scientific application in Python. In Proceedings of the 6th International Python Conference, pages 29-35, San Jose, Ca., October 1997.

[Beazley97]
    David M. Beazley and Peter S. Lomdahl. Feeding a large-scale physics application to Python. In Proceedings of the 6th International Python Conference, pages 21-29, San Jose, Ca., October 1997.

OVERVIEW

MPI for Python provides an object oriented approach to message passing which grounds on the standard MPI-2 C++ bindings. The interface was designed with focus in translating MPI syntax and semantics of standard MPI-2 bindings for C++ to Python. Any user of the standard C/C++ MPI bindings should be able to use this module without need of learning a new interface.

  Communicating Python Objects and Array Data

The Python standard library supports different mechanisms for data persistence. Many of them rely on disk storage, but pickling and marshaling can also work with memory buffers.

The pickle modules provide user-extensible facilities to serialize general Python objects using ASCII or binary formats. The marshal module provides facilities to serialize built-in Python objects using a binary format specific to Python, but independent of machine architecture issues.

MPI for Python can communicate any built-in or user-defined Python object taking advantage of the features provided by the pickle module. These facilities will be routinely used to build binary representations of objects to communicate (at sending processes), and restoring them back (at receiving processes).

Although simple and general, the serialization approach (i.e., pickling and unpickling) previously discussed imposes important overheads in memory as well as processor usage, especially in the scenario of objects with large memory footprints being communicated. Pickling general Python objects, ranging from primitive or container built-in types to user-defined classes, necessarily requires computer resources. Processing is also needed for dispatching the appropriate serialization method (that depends on the type of the object) and doing the actual packing. Additional memory is always needed, and if its total amount is not known a priori, many reallocations can occur. Indeed, in the case of large numeric arrays, this is certainly unacceptable and precludes communication of objects occupying half or more of the available memory resources.

MPI for Python supports direct communication of any object exporting the single-segment buffer interface. This interface is a standard Python mechanism provided by some types (e.g., strings and numeric arrays), allowing access in the C side to a contiguous memory buffer (i.e., address and length) containing the relevant data. This feature, in conjunction with the capability of constructing user-defined MPI datatypes describing complicated memory layouts, enables the implementation of many algorithms involving multidimensional numeric arrays (e.g., image processing, fast Fourier transforms, finite difference schemes on structured Cartesian grids) directly in Python, with negligible overhead, and almost as fast as compiled Fortran, C, or C++ codes.

  Communicators

In MPI for Python, Comm is the base class of communicators. The Intracomm and Intercomm classes are sublcasses of the Comm class. The Comm.Is_inter method (and Comm.Is_intra, provided for convenience but not part of the MPI specification) is defined for communicator objects and can be used to determine the particular communicator class.

The two predefined intracommunicator instances are available: COMM_SELF and COMM_WORLD. From them, new communicators can be created as needed.

The number of processes in a communicator and the calling process rank can be respectively obtained with methods Comm.Get_size and Comm.Get_rank. The associated process group can be retrieved from a communicator by calling the Comm.Get_group method, which returns an instance of the Group class. Set operations with Group objects like like Group.Union, Group.Intersection and Group.Difference are fully supported, as well as the creation of new communicators from these groups using Comm.Create and Comm.Create_group.

New communicator instances can be obtained with the Comm.Clone, Comm.Dup and Comm.Split methods, as well methods Intracomm.Create_intercomm and Intercomm.Merge.

Virtual topologies (Cartcomm, Graphcomm and Distgraphcomm classes, which are specializations of the Intracomm class) are fully supported. New instances can be obtained from intracommunicator instances with factory methods Intracomm.Create_cart and Intracomm.Create_graph.

  Point-to-Point Communications

Point to point communication is a fundamental capability of message passing systems. This mechanism enables the transmission of data between a pair of processes, one side sending, the other receiving.

MPI provides a set of send and receive functions allowing the communication of typed data with an associated tag. The type information enables the conversion of data representation from one architecture to another in the case of heterogeneous computing environments; additionally, it allows the representation of non-contiguous data layouts and user-defined datatypes, thus avoiding the overhead of (otherwise unavoidable) packing/unpacking operations. The tag information allows selectivity of messages at the receiving end.

  Blocking Communications

MPI provides basic send and receive functions that are blocking. These functions block the caller until the data buffers involved in the communication can be safely reused by the application program.

In MPI for Python, the Comm.Send, Comm.Recv and Comm.Sendrecv methods of communicator objects provide support for blocking point-to-point communications within Intracomm and Intercomm instances. These methods can communicate memory buffers. The variants Comm.send, Comm.recv and Comm.sendrecv can communicate general Python objects.

  Nonblocking Communications

On many systems, performance can be significantly increased by overlapping communication and computation. This is particularly true on systems where communication can be executed autonomously by an intelligent, dedicated communication controller.

MPI provides nonblocking send and receive functions. They allow the possible overlap of communication and computation. Non-blocking communication always come in two parts: posting functions, which begin the requested operation; and test-for-completion functions, which allow to discover whether the requested operation has completed.

In MPI for Python, the Comm.Isend and Comm.Irecv methods initiate send and receive operations, respectively. These methods return a Request instance, uniquely identifying the started operation. Its completion can be managed using the Request.Test, Request.Wait and Request.Cancel methods. The management of Request objects and associated memory buffers involved in communication requires a careful, rather low-level coordination. Users must ensure that objects exposing their memory buffers are not accessed at the Python level while they are involved in nonblocking message-passing operations.

  Persistent Communications

Often a communication with the same argument list is repeatedly executed within an inner loop. In such cases, communication can be further optimized by using persistent communication, a particular case of nonblocking communication allowing the reduction of the overhead between processes and communication controllers. Furthermore , this kind of optimization can also alleviate the extra call overheads associated to interpreted, dynamic languages like Python.

In MPI for Python, the Comm.Send_init and Comm.Recv_init methods create persistent requests for a send and receive operation, respectively. These methods return an instance of the Prequest class, a subclass of the Request class. The actual communication can be effectively started using the Prequest.Start method, and its completion can be managed as previously described.

  Collective Communications

Collective communications allow the transmittal of data between multiple processes of a group simultaneously. The syntax and semantics of collective functions is consistent with point-to-point communication. Collective functions communicate typed data, but messages are not paired with an associated tag; selectivity of messages is implied in the calling order. Additionally, collective functions come in blocking versions only.

The more commonly used collective communication operations are the following.

•   Barrier synchronization across all group members.

•   Global communication functions

•   Broadcast data from one member to all members of a group.

•   Gather data from all members to one member of a group.

•   Scatter data from one member to all members of a group.

•   Global reduction operations such as sum, maximum, minimum, etc.

    In MPI for Python, the Comm.Bcast, Comm.Scatter, Comm.Gather, Comm.Allgather, Comm.Alltoall methods provide support for collective communications of memory buffers. The lower-case variants Comm.bcast, Comm.scatter, Comm.gather, Comm.allgather and Comm.alltoall can communicate general Python objects. The vector variants (which can communicate different amounts of data to each process) Comm.Scatterv, Comm.Gatherv, Comm.Allgatherv, Comm.Alltoallv and Comm.Alltoallw are also supported, they can only communicate objects exposing memory buffers.

    Global reducion operations on memory buffers are accessible through the Comm.Reduce, Comm.Reduce_scatter, Comm.Allreduce, Intracomm.Scan and Intracomm.Exscan methods. The lower-case variants Comm.reduce, Comm.allreduce, Intracomm.scan and Intracomm.exscan can communicate general Python objects; however, the actual required reduction computations are performed sequentially at some process. All the predefined (i.e., SUM, PROD, MAX, etc.) reduction operations can be applied.

  Support for GPU-aware MPI

Several MPI implementations, including Open MPI and MVAPICH, support passing GPU pointers to MPI calls to avoid explict data movement between the host and the device. On the Python side, GPU arrays have been implemented by many libraries that need GPU computation, such as CuPy, Numba, PyTorch, and PyArrow. In order to increase library interoperability, two kinds of zero-copy data exchange protocols are defined and agreed upon: DLPack and CUDA Array Interface. For example, a CuPy array can be passed to a Numba CUDA-jit kernel.

MPI for Python provides an experimental support for GPU-aware MPI. This feature requires:

1.  mpi4py is built against a GPU-aware MPI library.

2.  The Python GPU arrays are compliant with either of the protocols.

    See the Tutorial section for further information. We note that

•   Whether or not a MPI call can work for GPU arrays depends on the underlying MPI implementation, not on mpi4py.

•   This support is currently experimental and subject to change in the future.

  Dynamic Process Management

In the context of the MPI-1 specification, a parallel application is static; that is, no processes can be added to or deleted from a running application after it has been started. Fortunately, this limitation was addressed in MPI-2. The new specification added a process management model providing a basic interface between an application and external resources and process managers.

This MPI-2 extension can be really useful, especially for sequential applications built on top of parallel modules, or parallel applications with a client/server model. The MPI-2 process model provides a mechanism to create new processes and establish communication between them and the existing MPI application. It also provides mechanisms to establish communication between two existing MPI applications, even when one did not start the other.

In MPI for Python, new independent process groups can be created by calling the Intracomm.Spawn method within an intracommunicator. This call returns a new intercommunicator (i.e., an Intercomm instance) at the parent process group. The child process group can retrieve the matching intercommunicator by calling the Comm.Get_parent class method. At each side, the new intercommunicator can be used to perform point to point and collective communications between the parent and child groups of processes.

Alternatively, disjoint groups of processes can establish communication using a client/server approach. Any server application must first call the Open_port function to open a port and the Publish_name function to publish a provided service, and next call the Intracomm.Accept method. Any client applications can first find a published service by calling the Lookup_name function, which returns the port where a server can be contacted; and next call the Intracomm.Connect method. Both Intracomm.Accept and Intracomm.Connect methods return an Intercomm instance. When connection between client/server processes is no longer needed, all of them must cooperatively call the Comm.Disconnect method. Additionally, server applications should release resources by calling the Unpublish_name and Close_port functions.

  One-Sided Communications

One-sided communications (also called Remote Memory Access, RMA) supplements the traditional two-sided, send/receive based MPI communication model with a one-sided, put/get based interface. One-sided communication that can take advantage of the capabilities of highly specialized network hardware. Additionally, this extension lowers latency and software overhead in applications written using a shared-memory-like paradigm.

The MPI specification revolves around the use of objects called windows; they intuitively specify regions of a process’s memory that have been made available for remote read and write operations. The published memory blocks can be accessed through three functions for put (remote send), get (remote write), and accumulate (remote update or reduction) data items. A much larger number of functions support different synchronization styles; the semantics of these synchronization operations are fairly complex.

In MPI for Python, one-sided operations are available by using instances of the Win class. New window objects are created by calling the Win.Create method at all processes within a communicator and specifying a memory buffer . When a window instance is no longer needed, the Win.Free method should be called.

The three one-sided MPI operations for remote write, read and reduction are available through calling the methods Win.Put, Win.Get, and Win.Accumulate respectively within a Win instance. These methods need an integer rank identifying the target process and an integer offset relative the base address of the remote memory block being accessed.

The one-sided operations read, write, and reduction are implicitly nonblocking, and must be synchronized by using two primary modes. Active target synchronization requires the origin process to call the Win.Start and Win.Complete methods at the origin process, and target process cooperates by calling the Win.Post and Win.Wait methods. There is also a collective variant provided by the Win.Fence method. Passive target synchronization is more lenient, only the origin process calls the Win.Lock and Win.Unlock methods. Locks are used to protect remote accesses to the locked remote window and to protect local load/store accesses to a locked local window.

  Parallel Input/Output

The POSIX standard provides a model of a widely portable file system. However, the optimization needed for parallel input/output cannot be achieved with this generic interface. In order to ensure efficiency and scalability, the underlying parallel input/output system must provide a high-level interface supporting partitioning of file data among processes and a collective interface supporting complete transfers of global data structures between process memories and files. Additionally, further efficiencies can be gained via support for asynchronous input/output, strided accesses to data, and control over physical file layout on storage devices. This scenario motivated the inclusion in the MPI-2 standard of a custom interface in order to support more elaborated parallel input/output operations.

The MPI specification for parallel input/output revolves around the use objects called files. As defined by MPI, files are not just contiguous byte streams. Instead, they are regarded as ordered collections of typed data items. MPI supports sequential or random access to any integral set of these items. Furthermore, files are opened collectively by a group of processes.

The common patterns for accessing a shared file (broadcast, scatter, gather, reduction) is expressed by using user-defined datatypes. Compared to the communication patterns of point-to-point and collective communications, this approach has the advantage of added flexibility and expressiveness. Data access operations (read and write) are defined for different kinds of positioning (using explicit offsets, individual file pointers, and shared file pointers), coordination (non-collective and collective), and synchronism (blocking, nonblocking, and split collective with begin/end phases).

In MPI for Python, all MPI input/output operations are performed through instances of the File class. File handles are obtained by calling the File.Open method at all processes within a communicator and providing a file name and the intended access mode. After use, they must be closed by calling the File.Close method. Files even can be deleted by calling method File.Delete.

After creation, files are typically associated with a per-process view. The view defines the current set of data visible and accessible from an open file as an ordered set of elementary datatypes. This data layout can be set and queried with the File.Set_view and File.Get_view methods respectively.

Actual input/output operations are achieved by many methods combining read and write calls with different behavior regarding positioning, coordination, and synchronism. Summing up, MPI for Python provides the thirty (30) methods defined in MPI-2 for reading from or writing to files using explicit offsets or file pointers (individual or shared), in blocking or nonblocking and collective or noncollective versions.

  Environmental Management

  Initialization and Exit

Module functions Init or Init_thread and Finalize provide MPI initialization and finalization respectively. Module functions Is_initialized and Is_finalized provide the respective tests for initialization and finalization.

NOTE: MPI_Init() or MPI_Init_thread() is actually called when you import the MPI module from the mpi4py package, but only if MPI is not already initialized. In such case, calling Init or Init_thread from Python is expected to generate an MPI error, and in turn an exception will be raised.

NOTE: MPI_Finalize() is registered (by using Python C/API function Py_AtExit()) for being automatically called when Python processes exit, but only if mpi4py actually initialized MPI. Therefore, there is no need to call Finalize from Python to ensure MPI finalization.

  Implementation Information

•   The MPI version number can be retrieved from module function Get_version. It returns a two-integer tuple (version, subversion).

•   The Get_processor_name function can be used to access the processor name.

•   The values of predefined attributes attached to the world communicator can be obtained by calling the Comm.Get_attr method within the COMM_WORLD instance.

  Timers

MPI timer functionalities are available through the Wtime and Wtick functions.

  Error Handling

In order facilitate handle sharing with other Python modules interfacing MPI-based parallel libraries, the predefined MPI error handlers ERRORS_RETURN and ERRORS_ARE_FATAL can be assigned to and retrieved from communicators using methods Comm.Set_errhandler and Comm.Get_errhandler, and similarly for windows and files.

When the predefined error handler ERRORS_RETURN is set, errors returned from MPI calls within Python code will raise an instance of the exception class Exception, which is a subclass of the standard Python exception RuntimeError.

NOTE: After import, mpi4py overrides the default MPI rules governing inheritance of error handlers. The ERRORS_RETURN error handler is set in the predefined COMM_SELF and COMM_WORLD communicators, as well as any new Comm, Win, or File instance created through mpi4py. If you ever pass such handles to C/C++/Fortran library code, it is recommended to set the ERRORS_ARE_FATAL error handler on them to ensure MPI errors do not pass silently.

WARNING: Importing with from mpi4py.MPI import * will cause a name clashing with the standard Python Exception base class.

TUTORIAL

WARNING: Under construction. Contributions very welcome!

TIP: Rolf Rabenseifner at HLRS developed a comprehensive MPI-3.1/4.0 course with slides and a large set of exercises including solutions. This material is available online for self-study. The slides and exercises show the C, Fortran, and Python (mpi4py) interfaces. For performance reasons, most Python exercises use NumPy arrays and communication routines involving buffer-like objects.

TIP: Victor Eijkhout at TACC authored the book Parallel Programming for Science and Engineering. This book is available online in PDF and HTML formats. The book covers parallel programming with MPI and OpenMP in C/C++ and Fortran, and MPI in Python using mpi4py.

MPI for Python supports convenient, pickle-based communication of generic Python object as well as fast, near C-speed, direct array data communication of buffer-provider objects (e.g., NumPy arrays).

•   Communication of generic Python objects

    You have to use methods with all-lowercase names, like Comm.send, Comm.recv, Comm.bcast, Comm.scatter, Comm.gather . An object to be sent is passed as a parameter to the communication call, and the received object is simply the return value.

    The Comm.isend and Comm.irecv methods return Request instances; completion of these methods can be managed using the Request.test and Request.wait methods.

    The Comm.recv and Comm.irecv methods may be passed a buffer object that can be repeatedly used to receive messages avoiding internal memory allocation. This buffer must be sufficiently large to accommodate the transmitted messages; hence, any buffer passed to Comm.recv or Comm.irecv must be at least as long as the pickled data transmitted to the receiver.

    Collective calls like Comm.scatter, Comm.gather, Comm.allgather, Comm.alltoall expect a single value or a sequence of Comm.size elements at the root or all process. They return a single value, a list of Comm.size elements, or None.

    NOTE: MPI for Python uses the highest protocol version available in the Python runtime (see the HIGHEST_PROTOCOL constant in the pickle module). The default protocol can be changed at import time by setting the MPI4PY_PICKLE_PROTOCOL environment variable, or at runtime by assigning a different value to the PROTOCOL attribute of the pickle object within the MPI module.

•   Communication of buffer-like objects

    You have to use method names starting with an upper-case letter, like Comm.Send, Comm.Recv, Comm.Bcast, Comm.Scatter, Comm.Gather.

    In general, buffer arguments to these calls must be explicitly specified by using a 2/3-list/tuple like [data, MPI.DOUBLE], or [data, count, MPI.DOUBLE] (the former one uses the byte-size of data and the extent of the MPI datatype to define count).

    For vector collectives communication operations like Comm.Scatterv and Comm.Gatherv, buffer arguments are specified as [data, count, displ, datatype], where count and displ are sequences of integral values.

    Automatic MPI datatype discovery for NumPy/GPU arrays and PEP-3118 buffers is supported, but limited to basic C types (all C/C99-native signed/unsigned integral types and single/double precision real/complex floating types) and availability of matching datatypes in the underlying MPI implementation. In this case, the buffer-provider object can be passed directly as a buffer argument, the count and MPI datatype will be inferred.

    If mpi4py is built against a GPU-aware MPI implementation, GPU arrays can be passed to upper-case methods as long as they have either the __dlpack__ and __dlpack_device__ methods or the __cuda_array_interface__ attribute that are compliant with the respective standard specifications. Moreover, only C-contiguous or Fortran-contiguous GPU arrays are supported. It is important to note that GPU buffers must be fully ready before any MPI routines operate on them to avoid race conditions. This can be ensured by using the synchronization API of your array library. mpi4py does not have access to any GPU-specific functionality and thus cannot perform this operation automatically for users.

  Running Python scripts with MPI

Most MPI programs can be run with the command mpiexec. In practice, running Python programs looks like:

$ mpiexec -n 4 python script.py

to run the program with 4 processors.

  Point-to-Point Communication

•   Python objects (pickle under the hood):

    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    if rank == 0:
        data = {'a': 7, 'b': 3.14}
        comm.send(data, dest=1, tag=11)
    elif rank == 1:
        data = comm.recv(source=0, tag=11)

•   Python objects with non-blocking communication:

    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    if rank == 0:
        data = {'a': 7, 'b': 3.14}
        req = comm.isend(data, dest=1, tag=11)
        req.wait()
    elif rank == 1:
        req = comm.irecv(source=0, tag=11)
        data = req.wait()

•   NumPy arrays (the fast way!):

    from mpi4py import MPI
    import numpy
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    # passing MPI datatypes explicitly
    if rank == 0:
        data = numpy.arange(1000, dtype='i')
        comm.Send([data, MPI.INT], dest=1, tag=77)
    elif rank == 1:
        data = numpy.empty(1000, dtype='i')
        comm.Recv([data, MPI.INT], source=0, tag=77)
    # automatic MPI datatype discovery
    if rank == 0:
        data = numpy.arange(100, dtype=numpy.float64)
        comm.Send(data, dest=1, tag=13)
    elif rank == 1:
        data = numpy.empty(100, dtype=numpy.float64)
        comm.Recv(data, source=0, tag=13)

  Collective Communication

•   Broadcasting a Python dictionary:

    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    if rank == 0:
        data = {'key1' : [7, 2.72, 2+3j],
                'key2' : ( 'abc', 'xyz')}
    else:
        data = None
    data = comm.bcast(data, root=0)

•   Scattering Python objects:

    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    size = comm.Get_size()
    rank = comm.Get_rank()
    if rank == 0:
        data = [(i+1)**2 for i in range(size)]
    else:
        data = None
    data = comm.scatter(data, root=0)
    assert data == (rank+1)**2

•   Gathering Python objects:

    from mpi4py import MPI
    comm = MPI.COMM_WORLD
    size = comm.Get_size()
    rank = comm.Get_rank()
    data = (rank+1)**2
    data = comm.gather(data, root=0)
    if rank == 0:
        for i in range(size):
            assert data[i] == (i+1)**2
    else:
        assert data is None

•   Broadcasting a NumPy array:

    from mpi4py import MPI
    import numpy as np
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    if rank == 0:
        data = np.arange(100, dtype='i')
    else:
        data = np.empty(100, dtype='i')
    comm.Bcast(data, root=0)
    for i in range(100):
        assert data[i] == i

•   Scattering NumPy arrays:

    from mpi4py import MPI
    import numpy as np
    comm = MPI.COMM_WORLD
    size = comm.Get_size()
    rank = comm.Get_rank()
    sendbuf = None
    if rank == 0:
        sendbuf = np.empty([size, 100], dtype='i')
        sendbuf.T[:,:] = range(size)
    recvbuf = np.empty(100, dtype='i')
    comm.Scatter(sendbuf, recvbuf, root=0)
    assert np.allclose(recvbuf, rank)

•   Gathering NumPy arrays:

    from mpi4py import MPI
    import numpy as np
    comm = MPI.COMM_WORLD
    size = comm.Get_size()
    rank = comm.Get_rank()
    sendbuf = np.zeros(100, dtype='i') + rank
    recvbuf = None
    if rank == 0:
        recvbuf = np.empty([size, 100], dtype='i')
    comm.Gather(sendbuf, recvbuf, root=0)
    if rank == 0:
        for i in range(size):
            assert np.allclose(recvbuf[i,:], i)

•   Parallel matrix-vector product:

    from mpi4py import MPI
    import numpy
    def matvec(comm, A, x):
        m = A.shape[0] # local rows
        p = comm.Get_size()
        xg = numpy.zeros(m*p, dtype='d')
        comm.Allgather([x,  MPI.DOUBLE],
                       [xg, MPI.DOUBLE])
        y = numpy.dot(A, xg)
        return y

  MPI-IO

•   Collective I/O with NumPy arrays:

    from mpi4py import MPI
    import numpy as np
    amode = MPI.MODE_WRONLY|MPI.MODE_CREATE
    comm = MPI.COMM_WORLD
    fh = MPI.File.Open(comm, "./datafile.contig", amode)
    buffer = np.empty(10, dtype=np.int)
    buffer[:] = comm.Get_rank()
    offset = comm.Get_rank()*buffer.nbytes
    fh.Write_at_all(offset, buffer)
    fh.Close()

•   Non-contiguous Collective I/O with NumPy arrays and datatypes:

    from mpi4py import MPI
    import numpy as np
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    size = comm.Get_size()
    amode = MPI.MODE_WRONLY|MPI.MODE_CREATE
    fh = MPI.File.Open(comm, "./datafile.noncontig", amode)
    item_count = 10
    buffer = np.empty(item_count, dtype='i')
    buffer[:] = rank
    filetype = MPI.INT.Create_vector(item_count, 1, size)
    filetype.Commit()
    displacement = MPI.INT.Get_size()*rank
    fh.Set_view(displacement, filetype=filetype)
    fh.Write_all(buffer)
    filetype.Free()
    fh.Close()

  Dynamic Process Management

•   Compute Pi - Master (or parent, or client) side:

    #!/usr/bin/env python
    from mpi4py import MPI
    import numpy
    import sys
    comm = MPI.COMM_SELF.Spawn(sys.executable,
                               args=['cpi.py'],
                               maxprocs=5)
    N = numpy.array(100, 'i')
    comm.Bcast([N, MPI.INT], root=MPI.ROOT)
    PI = numpy.array(0.0, 'd')
    comm.Reduce(None, [PI, MPI.DOUBLE],
                op=MPI.SUM, root=MPI.ROOT)
    print(PI)
    comm.Disconnect()

•   Compute Pi - Worker (or child, or server) side:

    #!/usr/bin/env python
    from mpi4py import MPI
    import numpy
    comm = MPI.Comm.Get_parent()
    size = comm.Get_size()
    rank = comm.Get_rank()
    N = numpy.array(0, dtype='i')
    comm.Bcast([N, MPI.INT], root=0)
    h = 1.0 / N; s = 0.0
    for i in range(rank, N, size):
        x = h * (i + 0.5)
        s += 4.0 / (1.0 + x**2)
    PI = numpy.array(s * h, dtype='d')
    comm.Reduce([PI, MPI.DOUBLE], None,
                op=MPI.SUM, root=0)
    comm.Disconnect()

  CUDA-aware MPI + Python GPU arrays

•   Reduce-to-all CuPy arrays:

    from mpi4py import MPI
    import cupy as cp
    comm = MPI.COMM_WORLD
    size = comm.Get_size()
    rank = comm.Get_rank()
    sendbuf = cp.arange(10, dtype='i')
    recvbuf = cp.empty_like(sendbuf)
    assert hasattr(sendbuf, '__cuda_array_interface__')
    assert hasattr(recvbuf, '__cuda_array_interface__')
    cp.cuda.get_current_stream().synchronize()
    comm.Allreduce(sendbuf, recvbuf)
    assert cp.allclose(recvbuf, sendbuf*size)

  One-Sided Communications

•   Read from (write to) the entire RMA window:

    import numpy as np
    from mpi4py import MPI
    from mpi4py.util import dtlib
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    datatype = MPI.FLOAT
    np_dtype = dtlib.to_numpy_dtype(datatype)
    itemsize = datatype.Get_size()
    N = 10
    win_size = N * itemsize if rank == 0 else 0
    win = MPI.Win.Allocate(win_size, comm=comm)
    buf = np.empty(N, dtype=np_dtype)
    if rank == 0:
        buf.fill(42)
        win.Lock(rank=0)
        win.Put(buf, target_rank=0)
        win.Unlock(rank=0)
        comm.Barrier()
    else:
        comm.Barrier()
        win.Lock(rank=0)
        win.Get(buf, target_rank=0)
        win.Unlock(rank=0)
        assert np.all(buf == 42)

•   Accessing a part of the RMA window using the target argument, which is defined as (offset, count, datatype):

    import numpy as np
    from mpi4py import MPI
    from mpi4py.util import dtlib
    comm = MPI.COMM_WORLD
    rank = comm.Get_rank()
    datatype = MPI.FLOAT
    np_dtype = dtlib.to_numpy_dtype(datatype)
    itemsize = datatype.Get_size()
    N = comm.Get_size() + 1
    win_size = N * itemsize if rank == 0 else 0
    win = MPI.Win.Allocate(
        size=win_size,
        disp_unit=itemsize,
        comm=comm,
    )
    if rank == 0:
        mem = np.frombuffer(win, dtype=np_dtype)
        mem[:] = np.arange(len(mem), dtype=np_dtype)
    comm.Barrier()
    buf = np.zeros(3, dtype=np_dtype)
    target = (rank, 2, datatype)
    win.Lock(rank=0)
    win.Get(buf, target_rank=0, target=target)
    win.Unlock(rank=0)
    assert np.all(buf == [rank, rank+1, 0])

  Wrapping with SWIG

•   C source:

    /* file: helloworld.c */
    void sayhello(MPI_Comm comm)
    {
      int size, rank;
      MPI_Comm_size(comm, &size);
      MPI_Comm_rank(comm, &rank);
      printf("Hello, World! "
             "I am process %d of %d.\n",
             rank, size);
    }

•   SWIG interface file:

    // file: helloworld.i
    %module helloworld
    %{
    #include <mpi.h>
    #include "helloworld.c"
    }%
    %include mpi4py/mpi4py.i
    %mpi4py_typemap(Comm, MPI_Comm);
    void sayhello(MPI_Comm comm);

•   Try it in the Python prompt:

    >>> from mpi4py import MPI
    >>> import helloworld
    >>> helloworld.sayhello(MPI.COMM_WORLD)
    Hello, World! I am process 0 of 1.

  Wrapping with F2Py

•   Fortran 90 source:

    ! file: helloworld.f90
    subroutine sayhello(comm)
      use mpi
      implicit none
      integer :: comm, rank, size, ierr
      call MPI_Comm_size(comm, size, ierr)
      call MPI_Comm_rank(comm, rank, ierr)
      print *, 'Hello, World! I am process ',rank,' of ',size,'.'
    end subroutine sayhello

•   Compiling example using f2py

    $ f2py -c --f90exec=mpif90 helloworld.f90 -m helloworld

•   Try it in the Python prompt:

    >>> from mpi4py import MPI
    >>> import helloworld
    >>> fcomm = MPI.COMM_WORLD.py2f()
    >>> helloworld.sayhello(fcomm)
    Hello, World! I am process 0 of 1.

MPI4PY

This is 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
    initialize | Automatic MPI initialization at import
    threads | Request initialization with thread support
    thread_level | Level of thread support to request
    finalize | Automatic MPI finalization at exit
    fast_reduce | Use tree-based reductions for objects
    recv_mprobe | Use matched probes to receive objects
    errors | Error handling policy

    Attributes Documentation

mpi4py.rc.initialize
    Automatic MPI initialization at import.

Type
    bool

Default
    True

    SEE ALSO: MPI4PY_RC_INITIALIZE

mpi4py.rc.threads
    Request initialization with thread support.

Type
    bool

Default
    True

    SEE ALSO: MPI4PY_RC_THREADS

mpi4py.rc.thread_level
    Level of thread support to request.

Type
    str

Default
    "multiple"

Choices
    "multiple", "serialized", "funneled", "single"

    SEE ALSO: MPI4PY_RC_THREAD_LEVEL

mpi4py.rc.finalize
    Automatic MPI finalization at exit.

Type
    None or bool

Default
    None

    SEE ALSO: MPI4PY_RC_FINALIZE

mpi4py.rc.fast_reduce
    Use tree-based reductions for objects.

Type
    bool

Default
    True

    SEE ALSO: MPI4PY_RC_FAST_REDUCE

mpi4py.rc.recv_mprobe
    Use matched probes to receive objects.

Type
    bool

Default
    True

    SEE ALSO: MPI4PY_RC_RECV_MPROBE

mpi4py.rc.errors
    Error handling policy.

Type
    str

Default
    "exception"

Choices
    "exception", "default", "fatal"

    SEE ALSO: MPI4PY_RC_ERRORS

    Example

    MPI for Python features automatic initialization and finalization of the MPI execution environment. By using the mpi4py.rc object, MPI initialization and finalization can be handled programatically:

    import mpi4py
    mpi4py.rc.initialize = False  # do not initialize MPI automatically
    mpi4py.rc.finalize = False    # do not finalize MPI automatically
    from mpi4py import MPI # import the 'MPI' module
    MPI.Init()      # manual initialization of the MPI environment
    ...             # your finest code here ...
    MPI.Finalize()  # manual finalization of the MPI environment

  Environment variables

The following environment variables override the corresponding attributes of the mpi4py.rc and MPI.pickle objects at import time of the MPI module.

NOTE: For variables of boolean type, accepted values are 0 and 1 (interpreted as False and True, respectively), and strings specifying a YAML boolean value (case-insensitive).

MPI4PY_RC_INITIALIZE

Type
    bool

Default
    True

    Whether to automatically initialize MPI at import time of the mpi4py.MPI module.

    SEE ALSO: mpi4py.rc.initialize

    New in version 3.1.0.

MPI4PY_RC_FINALIZE

Type
    None | bool

Default
    None

Choices
    None, True, False

    Whether to automatically finalize MPI at exit time of the Python process.

    SEE ALSO: mpi4py.rc.finalize

    New in version 3.1.0.

MPI4PY_RC_THREADS

Type
    bool

Default
    True

    Whether to initialize MPI with thread support.

    SEE ALSO: mpi4py.rc.threads

    New in version 3.1.0.

MPI4PY_RC_THREAD_LEVEL

Default
    "multiple"

Choices
    "single", "funneled", "serialized", "multiple"

    The level of required thread support.

    SEE ALSO: mpi4py.rc.thread_level

    New in version 3.1.0.

MPI4PY_RC_FAST_REDUCE

Type
    bool

Default
    True

    Whether to use tree-based reductions for objects.

    SEE ALSO: mpi4py.rc.fast_reduce

    New in version 3.1.0.

MPI4PY_RC_RECV_MPROBE

Type
    bool

Default
    True

    Whether to use matched probes to receive objects.

    SEE ALSO: mpi4py.rc.recv_mprobe

MPI4PY_RC_ERRORS

Default
    "exception"

Choices
    "exception", "default", "fatal"

    Controls default MPI error handling policy.

    SEE ALSO: mpi4py.rc.errors

    New in version 3.1.0.

MPI4PY_PICKLE_PROTOCOL

Type
    int

Default
    pickle.HIGHEST_PROTOCOL

    Controls the default pickle protocol to use when communicating Python objects.

    SEE ALSO: PROTOCOL attribute of the MPI.pickle object within the MPI module.

    New in version 3.1.0.

MPI4PY_PICKLE_THRESHOLD

Type
    int

Default
    262144

    Controls the default buffer size threshold for switching from in-band to out-of-band buffer handling when using pickle protocol version 5 or higher.

    SEE ALSO: Module mpi4py.util.pkl5.

    New in version 3.1.2.

  Miscellaneous functions

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

•   logfile (str, optional) – Filename prefix for dumping profiler output.

Return type
    None

mpi4py.get_config()
    Return a dictionary with information about MPI.

Return type
    Dict[str, str]

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.MPI

  Classes

Ancillary
Datatype([datatype]) | Datatype object
Status([status]) | Status object
Request([request]) | Request handle
Prequest([request]) | Persistent request handle
Grequest([request]) | Generalized request handle
Op([op]) | Operation object
Group([group]) | Group of processes
Info([info]) | Info object

Communication
Comm([comm]) | Communicator
Intracomm([comm]) | Intracommunicator
Topocomm([comm]) | Topology intracommunicator
Cartcomm([comm]) | Cartesian topology intracommunicator
Graphcomm([comm]) | General graph topology intracommunicator
Distgraphcomm([comm]) | Distributed graph topology intracommunicator
Intercomm([comm]) | Intercommunicator
Message([message]) | Matched message handle

One-sided operations
Win([win]) | Window handle

Input/Output
File([file]) | File handle

Error handling
Errhandler([errhandler]) | Error handler
Exception([ierr]) | Exception class

Auxiliary
Pickle([dumps, loads, protocol]) | Pickle/unpickle Python objects
memory(buf) | Memory buffer

  Functions

Version inquiry
Get_version() | Obtain the version number of the MPI standard supported by the implementation as a tuple (version, subversion)
Get_library_version() | Obtain the version string of the MPI library

Initialization and finalization
Init() | Initialize the MPI execution environment
Init_thread([required]) | Initialize the MPI execution environment
Finalize() | Terminate the MPI execution environment
Is_initialized() | Indicates whether Init has been called
Is_finalized() | Indicates whether Finalize has completed
Query_thread() | Return the level of thread support provided by the MPI library
Is_thread_main() | Indicate whether this thread called Init or Init_thread

Memory allocation
Alloc_mem(size[, info]) | Allocate memory for message passing and RMA
Free_mem(mem) | Free memory allocated with Alloc_mem()

Address manipulation
Get_address(location) | Get the address of a location in memory
Aint_add(base, disp) | Return the sum of base address and displacement
Aint_diff(addr1, addr2) | Return the difference between absolute addresses

Timer
Wtick() | Return the resolution of Wtime
Wtime() | Return an elapsed time on the calling processor

Error handling
Get_error_class(errorcode) | Convert an error code into an error class
Get_error_string(errorcode) | Return the error string for a given error class or error code
Add_error_class() | Add an error class to the known error classes
Add_error_code(errorclass) | Add an error code to an error class
Add_error_string(errorcode, string) | Associate an error string with an error class or errorcode

Dynamic process management
Open_port([info]) | Return an address that can be used to establish connections between groups of MPI processes
Close_port(port_name) | Close a port
Publish_name(service_name, port_name[, info]) | Publish a service name
Unpublish_name(service_name, port_name[, info]) | Unpublish a service name
Lookup_name(service_name[, info]) | Lookup a port name given a service name

Miscellanea
Attach_buffer(buf) | Attach a user-provided buffer for sending in buffered mode
Detach_buffer() | Remove an existing attached buffer
Compute_dims(nnodes, dims) | Return a balanced distribution of processes per coordinate direction
Get_processor_name() | Obtain the name of the calling processor
Register_datarep(datarep, read_fn, write_fn, ...) | Register user-defined data representations
Pcontrol(level) | Control profiling

Utilities
get_vendor() | Infomation about the underlying MPI implementation

  Attributes
UNDEFINED | int UNDEFINED
ANY_SOURCE | int ANY_SOURCE
ANY_TAG | int ANY_TAG
PROC_NULL | int PROC_NULL
ROOT | int ROOT
BOTTOM | Bottom BOTTOM
IN_PLACE | InPlace IN_PLACE
KEYVAL_INVALID | int KEYVAL_INVALID
TAG_UB | int TAG_UB
HOST | int HOST
IO | int IO
WTIME_IS_GLOBAL | int WTIME_IS_GLOBAL
UNIVERSE_SIZE | int UNIVERSE_SIZE
APPNUM | int APPNUM
LASTUSEDCODE | int LASTUSEDCODE
WIN_BASE | int WIN_BASE
WIN_SIZE | int WIN_SIZE
WIN_DISP_UNIT | int WIN_DISP_UNIT
WIN_CREATE_FLAVOR | int WIN_CREATE_FLAVOR
WIN_FLAVOR | int WIN_FLAVOR
WIN_MODEL | int WIN_MODEL
SUCCESS | int SUCCESS
ERR_LASTCODE | int ERR_LASTCODE
ERR_COMM | int ERR_COMM
ERR_GROUP | int ERR_GROUP
ERR_TYPE | int ERR_TYPE
ERR_REQUEST | int ERR_REQUEST
ERR_OP | int ERR_OP
ERR_BUFFER | int ERR_BUFFER
ERR_COUNT | int ERR_COUNT
ERR_TAG | int ERR_TAG
ERR_RANK | int ERR_RANK
ERR_ROOT | int ERR_ROOT
ERR_TRUNCATE | int ERR_TRUNCATE
ERR_IN_STATUS | int ERR_IN_STATUS
ERR_PENDING | int ERR_PENDING
ERR_TOPOLOGY | int ERR_TOPOLOGY
ERR_DIMS | int ERR_DIMS
ERR_ARG | int ERR_ARG
ERR_OTHER | int ERR_OTHER
ERR_UNKNOWN | int ERR_UNKNOWN
ERR_INTERN | int ERR_INTERN
ERR_INFO | int ERR_INFO
ERR_FILE | int ERR_FILE
ERR_WIN | int ERR_WIN
ERR_KEYVAL | int ERR_KEYVAL
ERR_INFO_KEY | int ERR_INFO_KEY
ERR_INFO_VALUE | int ERR_INFO_VALUE
ERR_INFO_NOKEY | int ERR_INFO_NOKEY
ERR_ACCESS | int ERR_ACCESS
ERR_AMODE | int ERR_AMODE
ERR_BAD_FILE | int ERR_BAD_FILE
ERR_FILE_EXISTS | int ERR_FILE_EXISTS
ERR_FILE_IN_USE | int ERR_FILE_IN_USE
ERR_NO_SPACE | int ERR_NO_SPACE
ERR_NO_SUCH_FILE | int ERR_NO_SUCH_FILE
ERR_IO | int ERR_IO
ERR_READ_ONLY | int ERR_READ_ONLY
ERR_CONVERSION | int ERR_CONVERSION
ERR_DUP_DATAREP | int ERR_DUP_DATAREP
ERR_UNSUPPORTED_DATAREP | int ERR_UNSUPPORTED_DATAREP
ERR_UNSUPPORTED_OPERATION | int ERR_UNSUPPORTED_OPERATION
ERR_NAME | int ERR_NAME
ERR_NO_MEM | int ERR_NO_MEM
ERR_NOT_SAME | int ERR_NOT_SAME
ERR_PORT | int ERR_PORT
ERR_QUOTA | int ERR_QUOTA
ERR_SERVICE | int ERR_SERVICE
ERR_SPAWN | int ERR_SPAWN
ERR_BASE | int ERR_BASE
ERR_SIZE | int ERR_SIZE
ERR_DISP | int ERR_DISP
ERR_ASSERT | int ERR_ASSERT
ERR_LOCKTYPE | int ERR_LOCKTYPE
ERR_RMA_CONFLICT | int ERR_RMA_CONFLICT
ERR_RMA_SYNC | int ERR_RMA_SYNC
ERR_RMA_RANGE | int ERR_RMA_RANGE
ERR_RMA_ATTACH | int ERR_RMA_ATTACH
ERR_RMA_SHARED | int ERR_RMA_SHARED
ERR_RMA_FLAVOR | int ERR_RMA_FLAVOR
ORDER_C | int ORDER_C
ORDER_F | int ORDER_F
ORDER_FORTRAN | int ORDER_FORTRAN
TYPECLASS_INTEGER | int TYPECLASS_INTEGER
TYPECLASS_REAL | int TYPECLASS_REAL
TYPECLASS_COMPLEX | int TYPECLASS_COMPLEX
DISTRIBUTE_NONE | int DISTRIBUTE_NONE
DISTRIBUTE_BLOCK | int DISTRIBUTE_BLOCK
DISTRIBUTE_CYCLIC | int DISTRIBUTE_CYCLIC
DISTRIBUTE_DFLT_DARG | int DISTRIBUTE_DFLT_DARG
COMBINER_NAMED | int COMBINER_NAMED
COMBINER_DUP | int COMBINER_DUP
COMBINER_CONTIGUOUS | int COMBINER_CONTIGUOUS
COMBINER_VECTOR | int COMBINER_VECTOR
COMBINER_HVECTOR | int COMBINER_HVECTOR
COMBINER_INDEXED | int COMBINER_INDEXED
COMBINER_HINDEXED | int COMBINER_HINDEXED
COMBINER_INDEXED_BLOCK | int COMBINER_INDEXED_BLOCK
COMBINER_HINDEXED_BLOCK | int COMBINER_HINDEXED_BLOCK
COMBINER_STRUCT | int COMBINER_STRUCT
COMBINER_SUBARRAY | int COMBINER_SUBARRAY
COMBINER_DARRAY | int COMBINER_DARRAY
COMBINER_RESIZED | int COMBINER_RESIZED
COMBINER_F90_REAL | int COMBINER_F90_REAL
COMBINER_F90_COMPLEX | int COMBINER_F90_COMPLEX
COMBINER_F90_INTEGER | int COMBINER_F90_INTEGER
IDENT | int IDENT
CONGRUENT | int CONGRUENT
SIMILAR | int SIMILAR
UNEQUAL | int UNEQUAL
CART | int CART
GRAPH | int GRAPH
DIST_GRAPH | int DIST_GRAPH
UNWEIGHTED | int UNWEIGHTED
WEIGHTS_EMPTY | int WEIGHTS_EMPTY
COMM_TYPE_SHARED | int COMM_TYPE_SHARED
BSEND_OVERHEAD | int BSEND_OVERHEAD
WIN_FLAVOR_CREATE | int WIN_FLAVOR_CREATE
WIN_FLAVOR_ALLOCATE | int WIN_FLAVOR_ALLOCATE
WIN_FLAVOR_DYNAMIC | int WIN_FLAVOR_DYNAMIC
WIN_FLAVOR_SHARED | int WIN_FLAVOR_SHARED
WIN_SEPARATE | int WIN_SEPARATE
WIN_UNIFIED | int WIN_UNIFIED
MODE_NOCHECK | int MODE_NOCHECK
MODE_NOSTORE | int MODE_NOSTORE
MODE_NOPUT | int MODE_NOPUT
MODE_NOPRECEDE | int MODE_NOPRECEDE
MODE_NOSUCCEED | int MODE_NOSUCCEED
LOCK_EXCLUSIVE | int LOCK_EXCLUSIVE
LOCK_SHARED | int LOCK_SHARED
MODE_RDONLY | int MODE_RDONLY
MODE_WRONLY | int MODE_WRONLY
MODE_RDWR | int MODE_RDWR
MODE_CREATE | int MODE_CREATE
MODE_EXCL | int MODE_EXCL
MODE_DELETE_ON_CLOSE | int MODE_DELETE_ON_CLOSE
MODE_UNIQUE_OPEN | int MODE_UNIQUE_OPEN
MODE_SEQUENTIAL | int MODE_SEQUENTIAL
MODE_APPEND | int MODE_APPEND
SEEK_SET | int SEEK_SET
SEEK_CUR | int SEEK_CUR
SEEK_END | int SEEK_END
DISPLACEMENT_CURRENT | int DISPLACEMENT_CURRENT
DISP_CUR | int DISP_CUR
THREAD_SINGLE | int THREAD_SINGLE
THREAD_FUNNELED | int THREAD_FUNNELED
THREAD_SERIALIZED | int THREAD_SERIALIZED
THREAD_MULTIPLE | int THREAD_MULTIPLE
VERSION | int VERSION
SUBVERSION | int SUBVERSION
MAX_PROCESSOR_NAME | int MAX_PROCESSOR_NAME
MAX_ERROR_STRING | int MAX_ERROR_STRING
MAX_PORT_NAME | int MAX_PORT_NAME
MAX_INFO_KEY | int MAX_INFO_KEY
MAX_INFO_VAL | int MAX_INFO_VAL
MAX_OBJECT_NAME | int MAX_OBJECT_NAME
MAX_DATAREP_STRING | int MAX_DATAREP_STRING
MAX_LIBRARY_VERSION_STRING | int MAX_LIBRARY_VERSION_STRING
DATATYPE_NULL | Datatype DATATYPE_NULL
UB | Datatype UB
LB | Datatype LB
PACKED | Datatype PACKED
BYTE | Datatype BYTE
AINT | Datatype AINT
OFFSET | Datatype OFFSET
COUNT | Datatype COUNT
CHAR | Datatype CHAR
WCHAR | Datatype WCHAR
SIGNED_CHAR | Datatype SIGNED_CHAR
SHORT | Datatype SHORT
INT | Datatype INT
LONG | Datatype LONG
LONG_LONG | Datatype LONG_LONG
UNSIGNED_CHAR | Datatype UNSIGNED_CHAR
UNSIGNED_SHORT | Datatype UNSIGNED_SHORT
UNSIGNED | Datatype UNSIGNED
UNSIGNED_LONG | Datatype UNSIGNED_LONG
UNSIGNED_LONG_LONG | Datatype UNSIGNED_LONG_LONG
FLOAT | Datatype FLOAT
DOUBLE | Datatype DOUBLE
LONG_DOUBLE | Datatype LONG_DOUBLE
C_BOOL | Datatype C_BOOL
INT8_T | Datatype INT8_T
INT16_T | Datatype INT16_T
INT32_T | Datatype INT32_T
INT64_T | Datatype INT64_T
UINT8_T | Datatype UINT8_T
UINT16_T | Datatype UINT16_T
UINT32_T | Datatype UINT32_T
UINT64_T | Datatype UINT64_T
C_COMPLEX | Datatype C_COMPLEX
C_FLOAT_COMPLEX | Datatype C_FLOAT_COMPLEX
C_DOUBLE_COMPLEX | Datatype C_DOUBLE_COMPLEX
C_LONG_DOUBLE_COMPLEX | Datatype C_LONG_DOUBLE_COMPLEX
CXX_BOOL | Datatype CXX_BOOL
CXX_FLOAT_COMPLEX | Datatype CXX_FLOAT_COMPLEX
CXX_DOUBLE_COMPLEX | Datatype CXX_DOUBLE_COMPLEX
CXX_LONG_DOUBLE_COMPLEX | Datatype CXX_LONG_DOUBLE_COMPLEX
SHORT_INT | Datatype SHORT_INT
INT_INT | Datatype INT_INT
TWOINT | Datatype TWOINT
LONG_INT | Datatype LONG_INT
FLOAT_INT | Datatype FLOAT_INT
DOUBLE_INT | Datatype DOUBLE_INT
LONG_DOUBLE_INT | Datatype LONG_DOUBLE_INT
CHARACTER | Datatype CHARACTER
LOGICAL | Datatype LOGICAL
INTEGER | Datatype INTEGER
REAL | Datatype REAL
DOUBLE_PRECISION | Datatype DOUBLE_PRECISION
COMPLEX | Datatype COMPLEX
DOUBLE_COMPLEX | Datatype DOUBLE_COMPLEX
LOGICAL1 | Datatype LOGICAL1
LOGICAL2 | Datatype LOGICAL2
LOGICAL4 | Datatype LOGICAL4
LOGICAL8 | Datatype LOGICAL8
INTEGER1 | Datatype INTEGER1
INTEGER2 | Datatype INTEGER2
INTEGER4 | Datatype INTEGER4
INTEGER8 | Datatype INTEGER8
INTEGER16 | Datatype INTEGER16
REAL2 | Datatype REAL2
REAL4 | Datatype REAL4
REAL8 | Datatype REAL8
REAL16 | Datatype REAL16
COMPLEX4 | Datatype COMPLEX4
COMPLEX8 | Datatype COMPLEX8
COMPLEX16 | Datatype COMPLEX16
COMPLEX32 | Datatype COMPLEX32
UNSIGNED_INT | Datatype UNSIGNED_INT
SIGNED_SHORT | Datatype SIGNED_SHORT
SIGNED_INT | Datatype SIGNED_INT
SIGNED_LONG | Datatype SIGNED_LONG
SIGNED_LONG_LONG | Datatype SIGNED_LONG_LONG
BOOL | Datatype BOOL
SINT8_T | Datatype SINT8_T
SINT16_T | Datatype SINT16_T
SINT32_T | Datatype SINT32_T
SINT64_T | Datatype SINT64_T
F_BOOL | Datatype F_BOOL
F_INT | Datatype F_INT
F_FLOAT | Datatype F_FLOAT
F_DOUBLE | Datatype F_DOUBLE
F_COMPLEX | Datatype F_COMPLEX
F_FLOAT_COMPLEX | Datatype F_FLOAT_COMPLEX
F_DOUBLE_COMPLEX | Datatype F_DOUBLE_COMPLEX
REQUEST_NULL | Request REQUEST_NULL
MESSAGE_NULL | Message MESSAGE_NULL
MESSAGE_NO_PROC | Message MESSAGE_NO_PROC
OP_NULL | Op OP_NULL
MAX | Op MAX
MIN | Op MIN
SUM | Op SUM
PROD | Op PROD
LAND | Op LAND
BAND | Op BAND
LOR | Op LOR
BOR | Op BOR
LXOR | Op LXOR
BXOR | Op BXOR
MAXLOC | Op MAXLOC
MINLOC | Op MINLOC
REPLACE | Op REPLACE
NO_OP | Op NO_OP
GROUP_NULL | Group GROUP_NULL
GROUP_EMPTY | Group GROUP_EMPTY
INFO_NULL | Info INFO_NULL
INFO_ENV | Info INFO_ENV
ERRHANDLER_NULL | Errhandler ERRHANDLER_NULL
ERRORS_RETURN | Errhandler ERRORS_RETURN
ERRORS_ARE_FATAL | Errhandler ERRORS_ARE_FATAL
COMM_NULL | Comm COMM_NULL
COMM_SELF | Intracomm COMM_SELF
COMM_WORLD | Intracomm COMM_WORLD
WIN_NULL | Win WIN_NULL
FILE_NULL | File FILE_NULL
pickle | Pickle pickle

MPI4PY.FUTURES

New in version 3.0.0.

This package provides a high-level interface for asynchronously executing callables on a pool of worker processes using MPI for inter-process communication.

  concurrent.futures

The mpi4py.futures package is based on concurrent.futures from the Python standard library. More precisely, mpi4py.futures provides the MPIPoolExecutor class as a concrete implementation of the abstract class Executor. The submit() interface schedules a callable to be executed asynchronously and returns a Future object representing the execution of the callable. Future instances can be queried for the call result or exception. Sets of Future instances can be passed to the wait() and as_completed() functions.

NOTE: The concurrent.futures package was introduced in Python 3.2. A backport targeting Python 2.7 is available on PyPI. The mpi4py.futures package uses concurrent.futures if available, either from the Python 3 standard library or the Python 2.7 backport if installed. Otherwise, mpi4py.futures uses a bundled copy of core functionality backported from Python 3.5 to work with Python 2.7.

SEE ALSO:

Module concurrent.futures
    Documentation of the concurrent.futures standard module.

  MPIPoolExecutor

The MPIPoolExecutor class uses a pool of MPI processes to execute calls asynchronously. By performing computations in separate processes, it allows to side-step the global interpreter lock but also means that only picklable objects can be executed and returned. The __main__ module must be importable by worker processes, thus MPIPoolExecutor instances may not work in the interactive interpreter.

MPIPoolExecutor takes advantage of the dynamic process management features introduced in the MPI-2 standard. In particular, the MPI.Intracomm.Spawn method of MPI.COMM_SELF is used in the master (or parent) process to spawn new worker (or child) processes running a Python interpreter. The master process uses a separate thread (one for each MPIPoolExecutor instance) to communicate back and forth with the workers. The worker processes serve the execution of tasks in the main (and only) thread until they are signaled for completion.

NOTE: The worker processes must import the main script in order to unpickle any callable defined in the __main__ module and submitted from the master process. Furthermore, the callables may need access to other global variables. At the worker processes, mpi4py.futures executes the main script code (using the runpy module) under the __worker__ namespace to define the __main__ module. The __main__ and __worker__ modules are added to sys.modules (both at the master and worker processes) to ensure proper pickling and unpickling.

WARNING: During the initial import phase at the workers, the main script cannot create and use new MPIPoolExecutor instances. Otherwise, each worker would attempt to spawn a new pool of workers, leading to infinite recursion. mpi4py.futures detects such recursive attempts to spawn new workers and aborts the MPI execution environment. As the main script code is run under the __worker__ namespace, the easiest way to avoid spawn recursion is using the idiom if __name__ == '__main__': ... in the main script.

class mpi4py.futures.MPIPoolExecutor(max_workers=None, initializer=None, initargs=(), **kwargs)
    An Executor subclass that executes calls asynchronously using a pool of at most max_workers processes. If max_workers is None or not given, its value is determined from the MPI4PY_FUTURES_MAX_WORKERS environment variable if set, or the MPI universe size if set, otherwise a single worker process is spawned. If max_workers is lower than or equal to 0, then a ValueError will be raised.

    initializer is an optional callable that is called at the start of each worker process before executing any tasks; initargs is a tuple of arguments passed to the initializer. If initializer raises an exception, all pending tasks and any attempt to submit new tasks to the pool will raise a BrokenExecutor exception.

    Other parameters:

•   python_exe: Path to the Python interpreter executable used to spawn worker processes, otherwise sys.executable is used.

•   python_args: list or iterable with additional command line flags to pass to the Python executable. Command line flags determined from inspection of sys.flags, sys.warnoptions and sys._xoptions in are passed unconditionally.

•   mpi_info: dict or iterable yielding (key, value) pairs. These (key, value) pairs are passed (through an MPI.Info object) to the MPI.Intracomm.Spawn call used to spawn worker processes. This mechanism allows telling the MPI runtime system where and how to start the processes. Check the documentation of the backend MPI implementation about the set of keys it interprets and the corresponding format for values.

•   globals: dict or iterable yielding (name, value) pairs to initialize the main module namespace in worker processes.

•   main: If set to False, do not import the __main__ module in worker processes. Setting main to False prevents worker processes from accessing definitions in the parent __main__ namespace.

•   path: list or iterable with paths to append to sys.path in worker processes to extend the module search path.

•   wdir: Path to set the current working directory in worker processes using os.chdir(). The initial working directory is set by the MPI implementation. Quality MPI implementations should honor a wdir info key passed through mpi_info, although such feature is not mandatory.

•   env: dict or iterable yielding (name, value) pairs with environment variables to update os.environ in worker processes. The initial environment is set by the MPI implementation. MPI implementations may allow setting the initial environment through mpi_info, however such feature is not required nor recommended by the MPI standard.

submit(func, *args, **kwargs)
    Schedule the callable, func, to be executed as func(*args, **kwargs) and returns a Future object representing the execution of the callable.

    executor = MPIPoolExecutor(max_workers=1)
    future = executor.submit(pow, 321, 1234)
    print(future.result())

map(func, *iterables, timeout=None, chunksize=1, **kwargs)
    Equivalent to map(func, *iterables) except func is executed asynchronously and several calls to func may be made concurrently, out-of-order, in separate processes. The returned iterator raises a TimeoutError if __next__() is called and the result isn’t available after timeout seconds from the original call to map(). timeout can be an int or a float. If timeout is not specified or None, there is no limit to the wait time. If a call raises an exception, then that exception will be raised when its value is retrieved from the iterator. This method chops iterables into a number of chunks which it submits to the pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer. For very long iterables, using a large value for chunksize can significantly improve performance compared to the default size of one. By default, the returned iterator yields results in-order, waiting for successive tasks to complete . This behavior can be changed by passing the keyword argument unordered as True, then the result iterator will yield a result as soon as any of the tasks complete.

    executor = MPIPoolExecutor(max_workers=3)
    for result in executor.map(pow, [2]*32, range(32)):
        print(result)

starmap(func, iterable, timeout=None, chunksize=1, **kwargs)
    Equivalent to itertools.starmap(func, iterable). Used instead of map() when argument parameters are already grouped in tuples from a single iterable (the data has been “pre-zipped”). map(func, *iterable) is equivalent to starmap(func, zip(*iterable)).

    executor = MPIPoolExecutor(max_workers=3)
    iterable = ((2, n) for n in range(32))
    for result in executor.starmap(pow, iterable):
        print(result)

shutdown(wait=True, cancel_futures=False)
    Signal the executor that it should free any resources that it is using when the currently pending futures are done executing. Calls to submit() and map() made after shutdown() will raise RuntimeError.

    If wait is True then this method will not return until all the pending futures are done executing and the resources associated with the executor have been freed. If wait is False then this method will return immediately and the resources associated with the executor will be freed when all pending futures are done executing. Regardless of the value of wait, the entire Python program will not exit until all pending futures are done executing.

    If cancel_futures is True, this method will cancel all pending futures that the executor has not started running. Any futures that are completed or running won’t be cancelled, regardless of the value of cancel_futures.

    You can avoid having to call this method explicitly if you use the with statement, which will shutdown the executor instance (waiting as if shutdown() were called with wait set to True).

    import time
    with MPIPoolExecutor(max_workers=1) as executor:
        future = executor.submit(time.sleep, 2)
    assert future.done()

bootup(wait=True)
    Signal the executor that it should allocate eagerly any required resources (in particular, MPI worker processes). If wait is True, then bootup() will not return until the executor resources are ready to process submissions. Resources are automatically allocated in the first call to submit(), thus calling bootup() explicitly is seldom needed.

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

    NOTE: As the master process uses a separate thread to perform MPI communication with the workers, the backend MPI implementation should provide support for MPI.THREAD_MULTIPLE. However, some popular MPI implementations do not support yet concurrent MPI calls from multiple threads. Additionally, users may decide to initialize MPI with a lower level of thread support. If the level of thread support in the backend MPI is less than MPI.THREAD_MULTIPLE, mpi4py.futures will use a global lock to serialize MPI calls. If the level of thread support is less than MPI.THREAD_SERIALIZED, mpi4py.futures will emit a RuntimeWarning.

    WARNING: If the level of thread support in the backend MPI is less than MPI.THREAD_SERIALIZED (i.e, it is either MPI.THREAD_SINGLE or MPI.THREAD_FUNNELED), in theory mpi4py.futures cannot be used. Rather than raising an exception, mpi4py.futures emits a warning and takes a “cross-fingers” attitude to continue execution in the hope that serializing MPI calls with a global lock will actually work.

  MPICommExecutor

Legacy MPI-1 implementations (as well as some vendor MPI-2 implementations) do not support the dynamic process management features introduced in the MPI-2 standard. Additionally, job schedulers and batch systems in supercomputing facilities may pose additional complications to applications using the MPI_Comm_spawn() routine.

With these issues in mind, mpi4py.futures supports an additonal, more traditional, SPMD-like usage pattern requiring MPI-1 calls only. Python applications are started the usual way, e.g., using the mpiexec command. Python code should make a collective call to the MPICommExecutor context manager to partition the set of MPI processes within a MPI communicator in one master processes and many workers processes. The master process gets access to an MPIPoolExecutor instance to submit tasks. Meanwhile, the worker process follow a different execution path and team-up to execute the tasks submitted from the master.

Besides alleviating the lack of dynamic process managment features in legacy MPI-1 or partial MPI-2 implementations, the MPICommExecutor context manager may be useful in classic MPI-based Python applications willing to take advantage of the simple, task-based, master/worker approach available in the mpi4py.futures package.

class mpi4py.futures.MPICommExecutor(comm=None, root=0)
    Context manager for MPIPoolExecutor. This context manager splits a MPI (intra)communicator comm (defaults to MPI.COMM_WORLD if not provided or None) in two disjoint sets: a single master process (with rank root in comm) and the remaining worker processes. These sets are then connected through an intercommunicator. The target of the with statement is assigned either an MPIPoolExecutor instance (at the master) or None (at the workers).

    from mpi4py import MPI
    from mpi4py.futures import MPICommExecutor
    with MPICommExecutor(MPI.COMM_WORLD, root=0) as executor:
        if executor is not None:
           future = executor.submit(abs, -42)
           assert future.result() == 42
           answer = set(executor.map(abs, [-42, 42]))
           assert answer == {42}

    WARNING: If MPICommExecutor is passed a communicator of size one (e.g., MPI.COMM_SELF), then the executor instace assigned to the target of the with statement will execute all submitted tasks in a single worker thread, thus ensuring that task execution still progress asynchronously. However, the GIL will prevent the main and worker threads from running concurrently in multicore processors. Moreover, the thread context switching may harm noticeably the performance of CPU-bound tasks. In case of I/O-bound tasks, the GIL is not usually an issue, however, as a single worker thread is used, it progress one task at a time. We advice against using MPICommExecutor with communicators of size one and suggest refactoring your code to use instead a ThreadPoolExecutor.

  Command line

Recalling the issues related to the lack of support for dynamic process managment features in MPI implementations, mpi4py.futures supports an alternative usage pattern where Python code (either from scripts, modules, or zip files) is run under command line control of the mpi4py.futures package by passing -m mpi4py.futures to the python executable. The mpi4py.futures invocation should be passed a pyfile path to a script (or a zipfile/directory containing a __main__.py file). Additionally, mpi4py.futures accepts -m mod to execute a module named mod, -c cmd to execute a command string cmd, or even - to read commands from standard input (sys.stdin). Summarizing, mpi4py.futures can be invoked in the following ways:

•   $ mpiexec -n numprocs python -m mpi4py.futures pyfile [arg] ...

•   $ mpiexec -n numprocs python -m mpi4py.futures -m mod [arg] ...

•   $ mpiexec -n numprocs python -m mpi4py.futures -c cmd [arg] ...

•   $ mpiexec -n numprocs python -m mpi4py.futures - [arg] ...

    Before starting the main script execution, mpi4py.futures splits MPI.COMM_WORLD in one master (the process with rank 0 in MPI.COMM_WORLD) and numprocs - 1 workers and connects them through an MPI intercommunicator. Afterwards, the master process proceeds with the execution of the user script code, which eventually creates MPIPoolExecutor instances to submit tasks. Meanwhile, the worker processes follow a different execution path to serve the master. Upon successful termination of the main script at the master, the entire MPI execution environment exists gracefully. In case of any unhandled exception in the main script, the master process calls MPI.COMM_WORLD.Abort(1) to prevent deadlocks and force termination of entire MPI execution environment.

    WARNING: Running scripts under command line control of mpi4py.futures is quite similar to executing a single-process application that spawn additional workers as required. However, there is a very important difference users should be aware of. All MPIPoolExecutor instances created at the master will share the pool of workers. Tasks submitted at the master from many different executors will be scheduled for execution in random order as soon as a worker is idle. Any executor can easily starve all the workers (e.g., by calling MPIPoolExecutor.map() with long iterables). If that ever happens, submissions from other executors will not be serviced until free workers are available.

    SEE ALSO:

Command line
    Documentation on Python command line interface.

  Examples

The following julia.py script computes the Julia set and dumps an image to disk in binary PGM format. The code starts by importing MPIPoolExecutor from the mpi4py.futures package. Next, some global constants and functions implement the computation of the Julia set. The computations are protected with the standard if __name__ == '__main__': ... idiom. The image is computed by whole scanlines submitting all these tasks at once using the map method. The result iterator yields scanlines in-order as the tasks complete. Finally, each scanline is dumped to disk.

julia.py

from mpi4py.futures import MPIPoolExecutor
x0, x1, w = -2.0, +2.0, 640*2
y0, y1, h = -1.5, +1.5, 480*2
dx = (x1 - x0) / w
dy = (y1 - y0) / h
c = complex(0, 0.65)
def julia(x, y):
    z = complex(x, y)
    n = 255
    while abs(z) < 3 and n > 1:
        z = z**2 + c
        n -= 1
    return n
def julia_line(k):
    line = bytearray(w)
    y = y1 - k * dy
    for j in range(w):
        x = x0 + j * dx
        line[j] = julia(x, y)
    return line
if __name__ == '__main__':
    with MPIPoolExecutor() as executor:
        image = executor.map(julia_line, range(h))
        with open('julia.pgm', 'wb') as f:
            f.write(b'P5 %d %d %d\n' % (w, h, 255))
            for line in image:
                f.write(line)

The recommended way to execute the script is by using the mpiexec command specifying one MPI process (master) and (optional but recommended) the desired MPI universe size, which determines the number of additional dynamically spawned processes (workers). The MPI universe size is provided either by a batch system or set by the user via command-line arguments to mpiexec or environment variables. Below we provide examples for MPICH and Open MPI implementations [1]. In all of these examples, the mpiexec command launches a single master process running the Python interpreter and executing the main script. When required, mpi4py.futures spawns the pool of 16 worker processes. The master submits tasks to the workers and waits for the results. The workers receive incoming tasks, execute them, and send back the results to the master.

When using MPICH implementation or its derivatives based on the Hydra process manager, users can set the MPI universe size via the -usize argument to mpiexec:

$ mpiexec -n 1 -usize 17 python julia.py

or, alternatively, by setting the MPIEXEC_UNIVERSE_SIZE environment variable:

$ MPIEXEC_UNIVERSE_SIZE=17 mpiexec -n 1 python julia.py

In the Open MPI implementation, the MPI universe size can be set via the -host argument to mpiexec:

$ mpiexec -n 1 -host <hostname>:17 python julia.py

Another way to specify the number of workers is to use the mpi4py.futures-specific environment variable MPI4PY_FUTURES_MAX_WORKERS:

$ MPI4PY_FUTURES_MAX_WORKERS=16 mpiexec -n 1 python julia.py

Note that in this case, the MPI universe size is ignored.

Alternatively, users may decide to execute the script in a more traditional way, that is, all the MPI processes are started at once. The user script is run under command-line control of mpi4py.futures passing the -m flag to the python executable:

$ mpiexec -n 17 python -m mpi4py.futures julia.py

As explained previously, the 17 processes are partitioned in one master and 16 workers. The master process executes the main script while the workers execute the tasks submitted by the master.

[1] When using an MPI implementation other than MPICH or Open MPI, please check the documentation of the implementation and/or batch system for the ways to specify the desired MPI universe size.

GIL See global interpreter lock.

MPI4PY.UTIL

New in version 3.1.0.

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

  mpi4py.util.pkl5

New in version 3.1.0.

pickle protocol 5 (see PEP 574) introduced support for out-of-band buffers, allowing for more efficient handling of certain object types with large memory footprints.

MPI for Python uses the traditional in-band handling of buffers. This approach is appropriate for communicating non-buffer Python objects, or buffer-like objects with small memory footprints. For point-to-point communication, in-band buffer handling allows for the communication of a pickled stream with a single MPI message, at the expense of additional CPU and memory overhead in the pickling and unpickling steps.

The mpi4py.util.pkl5 module provides communicator wrapper classes reimplementing pickle-based point-to-point communication methods using pickle protocol 5. Handling out-of-band buffers necessarily involve multiple MPI messages, thus increasing latency and hurting performance in case of small size data. However, in case of large size data, the zero-copy savings of out-of-band buffer handling more than offset the extra latency costs. Additionally, these wrapper methods overcome the infamous 2 GiB message count limit (MPI-1 to MPI-3).

NOTE: Support for pickle protocol 5 is available in the pickle module within the Python standard library since Python 3.8. Previous Python 3 releases can use the pickle5 backport, which is available on PyPI and can be installed with:

python -m pip install pickle5

class mpi4py.util.pkl5.Request(request=None)
    Request.

    Custom request class for nonblocking communications.

    NOTE: Request is not a subclass of mpi4py.MPI.Request

Parameters
    request (Iterable[MPI.Request]) –

Return type
    Request

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 (Optional[Status]) –

Return type
    bool

test(status=None)
    Test for the completion of a request.

Parameters
    status (Optional[Status]) –

Return type
    Tuple[bool, Optional[Any]]

wait(status=None)
    Wait for a request to complete.

Parameters
    status (Optional[Status]) –

Return type
    Any

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=None)
    Message.

    Custom message class for matching probes.

    NOTE: Message is not a subclass of mpi4py.MPI.Message

Parameters
    message (Iterable[MPI.Message]) –

Return type
    Message

recv(status=None)
    Blocking receive of matched message.

Parameters
    status (Optional[Status]) –

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 (Optional[Buffer]) –

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

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 (Optional[Buffer]) –

•   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 (Optional[Buffer]) –

•   source (int) –

•   recvtag (int) –

•   status (Optional[Status]) –

Return type
    Any

mprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)
    Blocking test for a matched message.

Parameters

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

Return type
    Message

improbe(source=ANY_SOURCE, tag=ANY_TAG, status=None)
    Nonblocking test for a matched message.

Parameters

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

Return type
    Optional[Message]

bcast(obj, root=0)
    Broadcast.

Parameters

•   obj (Any) –

•   root (int) –

Return type
    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.dtlib

New in version 3.1.0.

The mpi4py.util.dtlib module provides converter routines between NumPy and MPI datatypes.

mpi4py.util.dtlib.from_numpy_dtype(dtype)
    Convert NumPy datatype to MPI datatype.

Parameters
    dtype (numpy.typing.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
    numpy.dtype

MPI4PY.RUN

New in version 3.0.0.

At import time, mpi4py initializes the MPI execution environment calling MPI_Init_thread() and installs an exit hook to automatically call MPI_Finalize() just before the Python process terminates. Additionally, mpi4py overrides the default ERRORS_ARE_FATAL error handler in favor of ERRORS_RETURN, which allows translating MPI errors in Python exceptions. These departures from standard MPI behavior may be controversial, but are quite convenient within the highly dynamic Python programming environment. Third-party code using mpi4py can just from mpi4py import MPI and perform MPI calls without the tedious initialization/finalization handling. MPI errors, once translated automatically to Python exceptions, can be dealt with the common try…except…finally clauses; unhandled MPI exceptions will print a traceback which helps in locating problems in source code.

Unfortunately, the interplay of automatic MPI finalization and unhandled exceptions may lead to deadlocks. In unattended runs, these deadlocks will drain the battery of your laptop, or burn precious allocation hours in your supercomputing facility.

Consider the following snippet of Python code. Assume this code is stored in a standard Python script file and run with mpiexec in two or more processes.

from mpi4py import MPI
assert MPI.COMM_WORLD.Get_size() > 1
rank = MPI.COMM_WORLD.Get_rank()
if rank == 0:
    1/0
    MPI.COMM_WORLD.send(None, dest=1, tag=42)
elif rank == 1:
    MPI.COMM_WORLD.recv(source=0, tag=42)

Process 0 raises ZeroDivisionError exception before performing a send call to process 1. As the exception is not handled, the Python interpreter running in process 0 will proceed to exit with non-zero status. However, as mpi4py installed a finalizer hook to call MPI_Finalize() before exit, process 0 will block waiting for other processes to also enter the MPI_Finalize() call. Meanwhile, process 1 will block waiting for a message to arrive from process 0, thus never reaching to MPI_Finalize(). The whole MPI execution environment is irremediably in a deadlock state.

To alleviate this issue, mpi4py offers a simple, alternative command line execution mechanism based on using the -m flag and implemented with the runpy module. To use this features, Python code should be run passing -m mpi4py in the command line invoking the Python interpreter. In case of unhandled exceptions, the finalizer hook will call MPI_Abort() on the MPI_COMM_WORLD communicator, thus effectively aborting the MPI execution environment.

WARNING: When a process is forced to abort, resources (e.g. open files) are not cleaned-up and any registered finalizers (either with the atexit module, the Python C/API function Py_AtExit(), or even the C standard library function atexit()) will not be executed. Thus, aborting execution is an extremely impolite way of ensuring process termination. However, MPI provides no other mechanism to recover from a deadlock state.

  Interface options

The use of -m mpi4py to execute Python code on the command line resembles that of the Python interpreter.

•   mpiexec -n numprocs python -m mpi4py pyfile [arg] ...

•   mpiexec -n numprocs python -m mpi4py -m mod [arg] ...

•   mpiexec -n numprocs python -m mpi4py -c cmd [arg] ...

•   mpiexec -n numprocs python -m mpi4py - [arg] ...

<pyfile>
    Execute the Python code contained in pyfile, which must be a filesystem path referring to either a Python file, a directory containing a __main__.py file, or a zipfile containing a __main__.py file.

-m <mod>
    Search sys.path for the named module mod and execute its contents.

-c <cmd>
    Execute the Python code in the cmd string command.

-   Read commands from standard input (sys.stdin).

    SEE ALSO:

Command line
    Documentation on Python command line interface.

REFERENCE
mpi4py.MPI | Message Passing Interface.

  mpi4py.MPI

Message Passing Interface.

Classes
Cartcomm([comm]) | Cartesian topology intracommunicator
Comm([comm]) | Communicator
Datatype([datatype]) | Datatype object
Distgraphcomm([comm]) | Distributed graph topology intracommunicator
Errhandler([errhandler]) | Error handler
File([file]) | File handle
Graphcomm([comm]) | General graph topology intracommunicator
Grequest([request]) | Generalized request handle
Group([group]) | Group of processes
Info([info]) | Info object
Intercomm([comm]) | Intercommunicator
Intracomm([comm]) | Intracommunicator
Message([message]) | Matched message handle
Op([op]) | Operation object
Pickle([dumps, loads, protocol]) | Pickle/unpickle Python objects
Prequest([request]) | Persistent request handle
Request([request]) | Request handle
Status([status]) | Status object
Topocomm([comm]) | Topology intracommunicator
Win([win]) | Window handle
memory(buf) | Memory buffer

  mpi4py.MPI.Cartcomm

class mpi4py.MPI.Cartcomm(comm=None)
    Bases: Topocomm

    Cartesian topology intracommunicator

Parameters
    comm (Optional[Cartcomm]) –

Return type
    Cartcomm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Cartcomm]) –

Return type
    Cartcomm

    Methods Summary
    Get_cart_rank(coords) | Translate logical coordinates to ranks
    Get_coords(rank) | Translate ranks to logical coordinates
    Get_dim() | Return number of dimensions
    Get_topo() | Return information on the cartesian topology
    Shift(direction, disp) | Return a tuple (source, dest) of process ranks for data shifting with Comm.Sendrecv()
    Sub(remain_dims) | Return cartesian communicators that form lower-dimensional subgrids

    Attributes Summary
    coords | coordinates
    dim | number of dimensions
    dims | dimensions
    ndim | number of dimensions
    periods | periodicity
    topo | topology information

    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 tuple (source, dest) of process ranks for data shifting with Comm.Sendrecv()

Parameters

•   direction (int) –

•   disp (int) –

Return type
    Tuple[int, int]

Sub(remain_dims)
    Return cartesian communicators that form lower-dimensional subgrids

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(comm=None)
    Bases: object

    Communicator

Parameters
    comm (Optional[Comm]) –

Return type
    Comm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Comm]) –

Return type
    Comm

    Methods Summary
    Abort([errorcode]) | Terminate MPI execution environment
    Allgather(sendbuf, recvbuf) | Gather to All, gather data from all processes and distribute it to all other processes in a group
    Allgatherv(sendbuf, recvbuf) | Gather to All Vector, gather data from all processes and distribute it to all other processes in a group providing different amount of data and displacements
    Allreduce(sendbuf, recvbuf[, op]) | Reduce to All
    Alltoall(sendbuf, recvbuf) | All to All Scatter/Gather, send data from all to all processes in a group
    Alltoallv(sendbuf, recvbuf) | All to All Scatter/Gather Vector, send data from all to all processes in a group providing different amount of data and displacements
    Alltoallw(sendbuf, recvbuf) | Generalized All-to-All communication allowing different counts, displacements and datatypes for each partner
    Barrier() | Barrier synchronization
    Bcast(buf[, root]) | Broadcast a message from one process to all other processes in a group
    Bsend(buf, dest[, tag]) | Blocking send in buffered mode
    Bsend_init(buf, dest[, tag]) | Persistent request for a send in buffered mode
    Call_errhandler(errorcode) | Call the error handler installed on a communicator
    Clone() | Clone an existing communicator
    Compare(comm1, comm2) | Compare two communicators
    Create(group) | Create communicator from group
    Create_group(group[, tag]) | Create communicator from group
    Create_keyval([copy_fn, delete_fn, nopython]) | Create a new attribute key for communicators
    Delete_attr(keyval) | Delete attribute value associated with a key
    Disconnect() | Disconnect from a communicator
    Dup([info]) | Duplicate an existing communicator
    Dup_with_info(info) | Duplicate an existing communicator
    Free() | Free a communicator
    Free_keyval(keyval) | Free an attribute key for communicators
    Gather(sendbuf, recvbuf[, root]) | Gather together values from a group of processes
    Gatherv(sendbuf, recvbuf[, root]) | Gather Vector, gather data to one process from all other processes in a group providing different amount of data and displacements at the receiving sides
    Get_attr(keyval) | Retrieve attribute value by key
    Get_errhandler() | Get the error handler for a communicator
    Get_group() | Access the group associated with a communicator
    Get_info() | Return the hints for a communicator that are currently in use
    Get_name() | Get the print name for this communicator
    Get_parent() | Return the parent intercommunicator for this process
    Get_rank() | Return the rank of this process in a communicator
    Get_size() | Return the number of processes in a communicator
    Get_topology() | Determine the type of topology (if any) associated with a communicator
    Iallgather(sendbuf, recvbuf) | Nonblocking Gather to All
    Iallgatherv(sendbuf, recvbuf) | Nonblocking Gather to All Vector
    Iallreduce(sendbuf, recvbuf[, op]) | Nonblocking Reduce to All
    Ialltoall(sendbuf, recvbuf) | Nonblocking All to All Scatter/Gather
    Ialltoallv(sendbuf, recvbuf) | Nonblocking All to All Scatter/Gather Vector
    Ialltoallw(sendbuf, recvbuf) | Nonblocking Generalized All-to-All
    Ibarrier() | Nonblocking Barrier
    Ibcast(buf[, root]) | Nonblocking Broadcast
    Ibsend(buf, dest[, tag]) | Nonblocking send in buffered mode
    Idup() | Nonblocking duplicate an existing communicator
    Igather(sendbuf, recvbuf[, root]) | Nonblocking Gather
    Igatherv(sendbuf, recvbuf[, root]) | Nonblocking Gather Vector
    Improbe([source, tag, status]) | Nonblocking test for a matched message
    Iprobe([source, tag, status]) | Nonblocking test for a message
    Irecv(buf[, source, tag]) | Nonblocking receive
    Ireduce(sendbuf, recvbuf[, op, root]) | Nonblocking Reduce to Root
    Ireduce_scatter(sendbuf, recvbuf[, ...]) | Nonblocking Reduce-Scatter (vector version)
    Ireduce_scatter_block(sendbuf, recvbuf[, op]) | Nonblocking Reduce-Scatter Block (regular, non-vector version)
    Irsend(buf, dest[, tag]) | Nonblocking send in ready mode
    Is_inter() | Test to see if a comm is an intercommunicator
    Is_intra() | Test to see if a comm is an intracommunicator
    Iscatter(sendbuf, recvbuf[, root]) | Nonblocking Scatter
    Iscatterv(sendbuf, recvbuf[, root]) | Nonblocking Scatter Vector
    Isend(buf, dest[, tag]) | Nonblocking send
    Issend(buf, dest[, tag]) | Nonblocking send in synchronous mode
    Join(fd) | Create a intercommunicator by joining two processes connected by a socket
    Mprobe([source, tag, status]) | Blocking test for a matched message
    Probe([source, tag, status]) | Blocking test for a message
    Recv(buf[, source, tag, status]) | Blocking receive
    Recv_init(buf[, source, tag]) | Create a persistent request for a receive
    Reduce(sendbuf, recvbuf[, op, root]) | Reduce to Root
    Reduce_scatter(sendbuf, recvbuf[, ...]) | Reduce-Scatter (vector version)
    Reduce_scatter_block(sendbuf, recvbuf[, op]) | Reduce-Scatter Block (regular, non-vector version)
    Rsend(buf, dest[, tag]) | Blocking send in ready mode
    Rsend_init(buf, dest[, tag]) | Persistent request for a send in ready mode
    Scatter(sendbuf, recvbuf[, root]) | Scatter data from one process to all other processes in a group
    Scatterv(sendbuf, recvbuf[, root]) | Scatter Vector, scatter data from one process to all other processes in a group providing different amount of data and displacements at the sending side
    Send(buf, dest[, tag]) | Blocking send
    Send_init(buf, dest[, tag]) | Create a persistent request for a standard send
    Sendrecv(sendbuf, dest[, sendtag, recvbuf, ...]) | Send and receive a message
    Sendrecv_replace(buf, dest[, sendtag, ...]) | Send and receive a message
    Set_attr(keyval, attrval) | Store attribute value associated with a key
    Set_errhandler(errhandler) | Set the error handler for a communicator
    Set_info(info) | Set new values for the hints associated with a communicator
    Set_name(name) | Set the print name for this communicator
    Split([color, key]) | Split communicator by color and key
    Split_type(split_type[, key, info]) | Split communicator by split type
    Ssend(buf, dest[, tag]) | Blocking send in synchronous mode
    Ssend_init(buf, dest[, tag]) | Persistent request for a send in synchronous mode
    allgather(sendobj) | Gather to All
    allreduce(sendobj[, op]) | Reduce to All
    alltoall(sendobj) | All to All Scatter/Gather
    barrier() | Barrier
    bcast(obj[, root]) | Broadcast
    bsend(obj, dest[, tag]) | Send in buffered mode
    f2py(arg) |
    gather(sendobj[, root]) | Gather
    ibsend(obj, dest[, tag]) | Nonblocking send in buffered mode
    improbe([source, tag, status]) | Nonblocking test for a matched message
    iprobe([source, tag, status]) | Nonblocking test for a message
    irecv([buf, source, tag]) | Nonblocking receive
    isend(obj, dest[, tag]) | Nonblocking send
    issend(obj, dest[, tag]) | Nonblocking send in synchronous mode
    mprobe([source, tag, status]) | Blocking test for a matched message
    probe([source, tag, status]) | Blocking test for a message
    py2f() |
    recv([buf, source, tag, status]) | Receive
    reduce(sendobj[, op, root]) | Reduce to Root
    scatter(sendobj[, root]) | Scatter
    send(obj, dest[, tag]) | Send
    sendrecv(sendobj, dest[, sendtag, recvbuf, ...]) | Send and Receive
    ssend(obj, dest[, tag]) | Send in synchronous mode

    Attributes Summary
    group | communicator group
    info | communicator info
    is_inter | is intercommunicator
    is_intra | is intracommunicator
    is_topo | is a topology communicator
    name | communicator name
    rank | rank of this process in communicator
    size | number of processes in communicator
    topology | communicator topology type

    Methods Documentation

Abort(errorcode=0)
    Terminate MPI execution environment

    WARNING: This is a direct call, use it with care!!!.

Parameters
    errorcode (int) –

Return type
    NoReturn

Allgather(sendbuf, recvbuf)
    Gather to All, gather data from all processes and distribute it to all other processes in a group

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpecB) –

Return type
    None

Allgatherv(sendbuf, recvbuf)
    Gather to All Vector, gather data from all processes and distribute it to all other processes in a group providing different amount of data and displacements

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpecV) –

Return type
    None

Allreduce(sendbuf, recvbuf, op=SUM)
    Reduce to All

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   op (Op) –

Return type
    None

Alltoall(sendbuf, recvbuf)
    All to All Scatter/Gather, send data from all to all processes in a group

Parameters

•   sendbuf (Union[BufSpecB, InPlace]) –

•   recvbuf (BufSpecB) –

Return type
    None

Alltoallv(sendbuf, recvbuf)
    All to All Scatter/Gather Vector, send data from all to all processes in a group providing different amount of data and displacements

Parameters

•   sendbuf (Union[BufSpecV, InPlace]) –

•   recvbuf (BufSpecV) –

Return type
    None

Alltoallw(sendbuf, recvbuf)
    Generalized All-to-All communication allowing different counts, displacements and datatypes for each partner

Parameters

•   sendbuf (Union[BufSpecW, InPlace]) –

•   recvbuf (BufSpecW) –

Return type
    None

Barrier()
    Barrier synchronization

Return type
    None

Bcast(buf, root=0)
    Broadcast a message from one process to all other processes in a group

Parameters

•   buf (BufSpec) –

•   root (int) –

Return type
    None

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
    Comm

classmethod Compare(comm1, comm2)
    Compare two communicators

Parameters

•   comm1 (Comm) –

•   comm2 (Comm) –

Return type
    int

Create(group)
    Create communicator from group

Parameters
    group (Group) –

Return type
    Comm

Create_group(group, tag=0)
    Create communicator from group

Parameters

•   group (Group) –

•   tag (int) –

Return type
    Comm

classmethod Create_keyval(copy_fn=None, delete_fn=None, nopython=False)
    Create a new attribute key for communicators

Parameters

•   copy_fn (Optional[Callable[[Comm, int, Any], Any]]) –

•   delete_fn (Optional[Callable[[Comm, int, Any], None]]) –

•   nopython (bool) –

Return type
    int

Delete_attr(keyval)
    Delete attribute value associated with a key

Parameters
    keyval (int) –

Return type
    None

Disconnect()
    Disconnect from a communicator

Return type
    None

Dup(info=None)
    Duplicate an existing communicator

Parameters
    info (Optional[Info]) –

Return type
    Comm

Dup_with_info(info)
    Duplicate an existing communicator

Parameters
    info (Info) –

Return type
    Comm

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 together values from a group of processes

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (Optional[BufSpecB]) –

•   root (int) –

Return type
    None

Gatherv(sendbuf, recvbuf, root=0)
    Gather Vector, gather data to one process from all other processes in a group providing different amount of data and displacements at the receiving sides

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (Optional[BufSpecV]) –

•   root (int) –

Return type
    None

Get_attr(keyval)
    Retrieve attribute value by key

Parameters
    keyval (int) –

Return type
    Optional[Union[int, Any]]

Get_errhandler()
    Get the error handler for a communicator

Return type
    Errhandler

Get_group()
    Access the group associated with a communicator

Return type
    Group

Get_info()
    Return the hints for a communicator that are currently in use

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

Return type
    int

Iallgather(sendbuf, recvbuf)
    Nonblocking Gather to All

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpecB) –

Return type
    Request

Iallgatherv(sendbuf, recvbuf)
    Nonblocking Gather to All Vector

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpecV) –

Return type
    Request

Iallreduce(sendbuf, recvbuf, op=SUM)
    Nonblocking Reduce to All

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   op (Op) –

Return type
    Request

Ialltoall(sendbuf, recvbuf)
    Nonblocking All to All Scatter/Gather

Parameters

•   sendbuf (Union[BufSpecB, InPlace]) –

•   recvbuf (BufSpecB) –

Return type
    Request

Ialltoallv(sendbuf, recvbuf)
    Nonblocking All to All Scatter/Gather Vector

Parameters

•   sendbuf (Union[BufSpecV, InPlace]) –

•   recvbuf (BufSpecV) –

Return type
    Request

Ialltoallw(sendbuf, recvbuf)
    Nonblocking Generalized All-to-All

Parameters

•   sendbuf (Union[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()
    Nonblocking duplicate an existing communicator

Return type
    Tuple[Comm, Request]

Igather(sendbuf, recvbuf, root=0)
    Nonblocking Gather

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (Optional[BufSpecB]) –

•   root (int) –

Return type
    Request

Igatherv(sendbuf, recvbuf, root=0)
    Nonblocking Gather Vector

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (Optional[BufSpecV]) –

•   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 (Optional[Status]) –

Return type
    Optional[Message]

Iprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)
    Nonblocking test for a message

Parameters

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

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 (Union[BufSpec, InPlace]) –

•   recvbuf (Optional[BufSpec]) –

•   op (Op) –

•   root (int) –

Return type
    Request

Ireduce_scatter(sendbuf, recvbuf, recvcounts=None, op=SUM)
    Nonblocking Reduce-Scatter (vector version)

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   recvcounts (Optional[Sequence[int]]) –

•   op (Op) –

Return type
    Request

Ireduce_scatter_block(sendbuf, recvbuf, op=SUM)
    Nonblocking Reduce-Scatter Block (regular, non-vector version)

Parameters

•   sendbuf (Union[BufSpecB, InPlace]) –

•   recvbuf (Union[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()
    Test to see if a comm is an intercommunicator

Return type
    bool

Is_intra()
    Test to see if a comm is an intracommunicator

Return type
    bool

Iscatter(sendbuf, recvbuf, root=0)
    Nonblocking Scatter

Parameters

•   sendbuf (Optional[BufSpecB]) –

•   recvbuf (Union[BufSpec, InPlace]) –

•   root (int) –

Return type
    Request

Iscatterv(sendbuf, recvbuf, root=0)
    Nonblocking Scatter Vector

Parameters

•   sendbuf (Optional[BufSpecV]) –

•   recvbuf (Union[BufSpec, InPlace]) –

•   root (int) –

Return type
    Request

Isend(buf, dest, tag=0)
    Nonblocking send

Parameters

•   buf (BufSpec) –

•   dest (int) –

•   tag (int) –

Return type
    Request

Issend(buf, dest, tag=0)
    Nonblocking send in synchronous mode

Parameters

•   buf (BufSpec) –

•   dest (int) –

•   tag (int) –

Return type
    Request

classmethod Join(fd)
    Create a intercommunicator by joining 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 (Optional[Status]) –

Return type
    Message

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 (Optional[Status]) –

Return type
    Literal[True]

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 (Optional[Status]) –

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 (Union[BufSpec, InPlace]) –

•   recvbuf (Optional[BufSpec]) –

•   op (Op) –

•   root (int) –

Return type
    None

Reduce_scatter(sendbuf, recvbuf, recvcounts=None, op=SUM)
    Reduce-Scatter (vector version)

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   recvcounts (Optional[Sequence[int]]) –

•   op (Op) –

Return type
    None

Reduce_scatter_block(sendbuf, recvbuf, op=SUM)
    Reduce-Scatter Block (regular, non-vector version)

Parameters

•   sendbuf (Union[BufSpecB, InPlace]) –

•   recvbuf (Union[BufSpec, BufSpecB]) –

•   op (Op) –

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 in a group

Parameters

•   sendbuf (Optional[BufSpecB]) –

•   recvbuf (Union[BufSpec, InPlace]) –

•   root (int) –

Return type
    None

Scatterv(sendbuf, recvbuf, root=0)
    Scatter Vector, scatter data from one process to all other processes in a group providing different amount of data and displacements at the sending side

Parameters

•   sendbuf (Optional[BufSpecV]) –

•   recvbuf (Union[BufSpec, InPlace]) –

•   root (int) –

Return type
    None

Send(buf, dest, tag=0)
    Blocking send

    NOTE: This function may block until the message is received. Whether or not Send blocks 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) –

•   source (int) –

•   recvtag (int) –

•   status (Optional[Status]) –

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 (Optional[Status]) –

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

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 (Union[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

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

gather(sendobj, root=0)
    Gather

Parameters

•   sendobj (Any) –

•   root (int) –

Return type
    Optional[List[Any]]

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 (Optional[Status]) –

Return type
    Optional[Message]

iprobe(source=ANY_SOURCE, tag=ANY_TAG, status=None)
    Nonblocking test for a message

Parameters

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

Return type
    bool

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

Parameters

•   buf (Optional[Buffer]) –

•   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 (Optional[Status]) –

Return type
    Message

probe(source=ANY_SOURCE, tag=ANY_TAG, status=None)
    Blocking test for a message

Parameters

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

Return type
    Literal[True]

py2f()

Return type
    int

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

Parameters

•   buf (Optional[Buffer]) –

•   source (int) –

•   tag (int) –

•   status (Optional[Status]) –

Return type
    Any

reduce(sendobj, op=SUM, root=0)
    Reduce to Root

Parameters

•   sendobj (Any) –

•   op (Union[Op, Callable[[Any, Any], Any]]) –

•   root (int) –

Return type
    Optional[Any]

scatter(sendobj, root=0)
    Scatter

Parameters

•   sendobj (Sequence[Any]) –

•   root (int) –

Return type
    Any

send(obj, dest, tag=0)
    Send

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 (Optional[Buffer]) –

•   source (int) –

•   recvtag (int) –

•   status (Optional[Status]) –

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
    communicator group

info
    communicator info

is_inter
    is intercommunicator

is_intra
    is intracommunicator

is_topo
    is a topology communicator

name
    communicator name

rank
    rank of this process in communicator

size
    number of processes in communicator

topology
    communicator topology type

  mpi4py.MPI.Datatype

class mpi4py.MPI.Datatype(datatype=None)
    Bases: object

    Datatype object

Parameters
    datatype (Optional[Datatype]) –

Return type
    Datatype

static __new__(cls, datatype=None)

Parameters
    datatype (Optional[Datatype]) –

Return type
    Datatype

    Methods Summary
    Commit() | Commit the datatype
    Create_contiguous(count) | Create a contiguous datatype
    Create_darray(size, rank, gsizes, distribs, ...) | Create a datatype representing an HPF-like distributed array on Cartesian process grids
    Create_f90_complex(p, r) | Return a bounded complex datatype
    Create_f90_integer(r) | Return a bounded integer datatype
    Create_f90_real(p, r) | Return a bounded real datatype
    Create_hindexed(blocklengths, displacements) | Create an indexed datatype with displacements in bytes
    Create_hindexed_block(blocklength, displacements) | Create an indexed datatype with constant-sized blocks and displacements in bytes
    Create_hvector(count, blocklength, stride) | Create a vector (strided) datatype
    Create_indexed(blocklengths, displacements) | Create an indexed datatype
    Create_indexed_block(blocklength, displacements) | Create an indexed datatype with constant-sized blocks
    Create_keyval([copy_fn, delete_fn, nopython]) | Create a new attribute key for datatypes
    Create_resized(lb, extent) | Create a datatype with a new lower bound and extent
    Create_struct(blocklengths, displacements, ...) | Create an datatype from a general set of block sizes, displacements and datatypes
    Create_subarray(sizes, subsizes, starts[, order]) | Create a datatype for a subarray of a regular, multidimensional array
    Create_vector(count, blocklength, stride) | Create a vector (strided) datatype
    Delete_attr(keyval) | Delete attribute value associated with a key
    Dup() | Duplicate a datatype
    Free() | Free the datatype
    Free_keyval(keyval) | Free an attribute key for datatypes
    Get_attr(keyval) | Retrieve attribute value by key
    Get_contents() | Retrieve the actual arguments used in the call that created a datatype
    Get_envelope() | Return information on the number and type of input arguments used in the call that created a datatype
    Get_extent() | Return lower bound and extent of datatype
    Get_name() | Get the print name for this datatype
    Get_size() | Return the number of bytes occupied by entries in the datatype
    Get_true_extent() | Return the true lower bound and extent of a datatype
    Match_size(typeclass, size) | Find a datatype matching a specified size in bytes
    Pack(inbuf, outbuf, position, comm) | Pack into contiguous memory according to datatype.
    Pack_external(datarep, inbuf, outbuf, position) | Pack into contiguous memory according to datatype, using a portable data representation (external32).
    Pack_external_size(datarep, count) | Return the upper bound on the amount of space (in bytes) needed to pack a message according to datatype, using a portable data representation (external32).
    Pack_size(count, comm) | Return the upper bound on the amount of space (in bytes) needed to pack a message according to datatype.
    Set_attr(keyval, attrval) | Store attribute value associated with a key
    Set_name(name) | Set the print name for this datatype
    Unpack(inbuf, position, outbuf, comm) | Unpack from contiguous memory according to datatype.
    Unpack_external(datarep, inbuf, position, outbuf) | Unpack from contiguous memory according to datatype, using a portable data representation (external32).
    decode() | Convenience method for decoding a datatype
    f2py(arg) |
    py2f() |

    Attributes Summary
    combiner | datatype combiner
    contents | datatype contents
    envelope | datatype envelope
    extent |
    is_named | is a named datatype
    is_predefined | is a predefined datatype
    lb | lower bound
    name | datatype name
    size |
    true_extent | true extent
    true_lb | true lower bound
    true_ub | true upper bound
    ub | upper bound

    Methods Documentation

Commit()
    Commit the datatype

Return type
    Datatype

Create_contiguous(count)
    Create a contiguous datatype

Parameters
    count (int) –

Return type
    Datatype

Create_darray(size, rank, gsizes, distribs, dargs, psizes, order=ORDER_C)
    Create a datatype representing an HPF-like 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
    Datatype

classmethod Create_f90_complex(p, r)
    Return a bounded complex datatype

Parameters

•   p (int) –

•   r (int) –

Return type
    Datatype

classmethod Create_f90_integer(r)
    Return a bounded integer datatype

Parameters
    r (int) –

Return type
    Datatype

classmethod Create_f90_real(p, r)
    Return a bounded real datatype

Parameters

•   p (int) –

•   r (int) –

Return type
    Datatype

Create_hindexed(blocklengths, displacements)
    Create an indexed datatype with displacements in bytes

Parameters

•   blocklengths (Sequence[int]) –

•   displacements (Sequence[int]) –

Return type
    Datatype

Create_hindexed_block(blocklength, displacements)
    Create an indexed datatype with constant-sized blocks and displacements in bytes

Parameters

•   blocklength (int) –

•   displacements (Sequence[int]) –

Return type
    Datatype

Create_hvector(count, blocklength, stride)
    Create a vector (strided) datatype

Parameters

•   count (int) –

•   blocklength (int) –

•   stride (int) –

Return type
    Datatype

Create_indexed(blocklengths, displacements)
    Create an indexed datatype

Parameters

•   blocklengths (Sequence[int]) –

•   displacements (Sequence[int]) –

Return type
    Datatype

Create_indexed_block(blocklength, displacements)
    Create an indexed datatype with constant-sized blocks

Parameters

•   blocklength (int) –

•   displacements (Sequence[int]) –

Return type
    Datatype

classmethod Create_keyval(copy_fn=None, delete_fn=None, nopython=False)
    Create a new attribute key for datatypes

Parameters

•   copy_fn (Optional[Callable[[Datatype, int, Any], Any]]) –

•   delete_fn (Optional[Callable[[Datatype, int, Any], 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
    Datatype

classmethod Create_struct(blocklengths, displacements, datatypes)
    Create an datatype from a general set of block sizes, displacements and datatypes

Parameters

•   blocklengths (Sequence[int]) –

•   displacements (Sequence[int]) –

•   datatypes (Sequence[Datatype]) –

Return type
    Datatype

Create_subarray(sizes, subsizes, starts, order=ORDER_C)
    Create a datatype for a subarray of a regular, multidimensional array

Parameters

•   sizes (Sequence[int]) –

•   subsizes (Sequence[int]) –

•   starts (Sequence[int]) –

•   order (int) –

Return type
    Datatype

Create_vector(count, blocklength, stride)
    Create a vector (strided) datatype

Parameters

•   count (int) –

•   blocklength (int) –

•   stride (int) –

Return type
    Datatype

Delete_attr(keyval)
    Delete attribute value associated with a key

Parameters
    keyval (int) –

Return type
    None

Dup()
    Duplicate a datatype

Return type
    Datatype

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
    Optional[Union[int, Any]]

Get_contents()
    Retrieve the actual arguments used in the call that created a datatype

Return type
    Tuple[List[int], List[int], List[Datatype]]

Get_envelope()
    Return information on the number and type of input arguments used in the call that created a datatype

Return type
    Tuple[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 Match_size(typeclass, size)
    Find a datatype matching a specified size in bytes

Parameters

•   typeclass (int) –

•   size (int) –

Return type
    Datatype

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, using a portable data representation (external32).

Parameters

•   datarep (str) –

•   inbuf (BufSpec) –

•   outbuf (BufSpec) –

•   position (int) –

Return type
    int

Pack_external_size(datarep, count)
    Return the upper bound on the amount of space (in bytes) needed to pack a message according to datatype, using a portable data representation (external32).

Parameters

•   datarep (str) –

•   count (int) –

Return type
    int

Pack_size(count, comm)
    Return the upper bound on the amount of space (in bytes) needed to pack a message according to datatype.

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, using a 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

py2f()

Return type
    int

    Attributes Documentation

combiner
    datatype combiner

contents
    datatype contents

envelope
    datatype envelope

extent

is_named
    is a named datatype

is_predefined
    is a predefined datatype

lb  lower bound

name
    datatype name

size

true_extent
    true extent

true_lb
    true lower bound

true_ub
    true upper bound

ub  upper bound

  mpi4py.MPI.Distgraphcomm

class mpi4py.MPI.Distgraphcomm(comm=None)
    Bases: Topocomm

    Distributed graph topology intracommunicator

Parameters
    comm (Optional[Distgraphcomm]) –

Return type
    Distgraphcomm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Distgraphcomm]) –

Return type
    Distgraphcomm

    Methods Summary
    Get_dist_neighbors() | Return adjacency information for a distributed graph topology
    Get_dist_neighbors_count() | Return adjacency information for a distributed graph topology

    Methods Documentation

Get_dist_neighbors()
    Return adjacency information for a distributed graph topology

Return type
    Tuple[List[int], List[int], Optional[Tuple[List[int], List[int]]]]

Get_dist_neighbors_count()
    Return adjacency information for a distributed graph topology

Return type
    int

  mpi4py.MPI.Errhandler

class mpi4py.MPI.Errhandler(errhandler=None)
    Bases: object

    Error handler

Parameters
    errhandler (Optional[Errhandler]) –

Return type
    Errhandler

static __new__(cls, errhandler=None)

Parameters
    errhandler (Optional[Errhandler]) –

Return type
    Errhandler

    Methods Summary
    Free() | Free an error handler
    f2py(arg) |
    py2f() |

    Methods Documentation

Free()
    Free an error handler

Return type
    None

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    Errhandler

py2f()

Return type
    int

  mpi4py.MPI.File

class mpi4py.MPI.File(file=None)
    Bases: object

    File handle

Parameters
    file (Optional[File]) –

Return type
    File

static __new__(cls, file=None)

Parameters
    file (Optional[File]) –

Return type
    File

    Methods Summary
    Call_errhandler(errorcode) | Call the error handler installed on a file
    Close() | Close a file
    Delete(filename[, info]) | Delete a file
    Get_amode() | Return the file access mode
    Get_atomicity() | Return the atomicity mode
    Get_byte_offset(offset) | Return the absolute byte position in the file corresponding to 'offset' etypes relative to the current view
    Get_errhandler() | Get the error handler for a file
    Get_group() | Return the group of processes that opened the file
    Get_info() | Return the hints for a file that that are currently in use
    Get_position() | Return the current position of the individual file pointer in etype units relative to the current view
    Get_position_shared() | Return the current position of the shared file pointer in etype units relative to the current view
    Get_size() | Return the file size
    Get_type_extent(datatype) | Return the extent of datatype in the file
    Get_view() | Return the file view
    Iread(buf) | Nonblocking read using individual file pointer
    Iread_all(buf) | Nonblocking collective read using individual file pointer
    Iread_at(offset, buf) | Nonblocking read using explicit offset
    Iread_at_all(offset, buf) | Nonblocking collective read using explicit offset
    Iread_shared(buf) | Nonblocking read using shared file pointer
    Iwrite(buf) | Nonblocking write using individual file pointer
    Iwrite_all(buf) | Nonblocking collective write using individual file pointer
    Iwrite_at(offset, buf) | Nonblocking write using explicit offset
    Iwrite_at_all(offset, buf) | Nonblocking collective write using explicit offset
    Iwrite_shared(buf) | Nonblocking write using shared file pointer
    Open(comm, filename[, amode, info]) | Open a file
    Preallocate(size) | Preallocate storage space for a file
    Read(buf[, status]) | Read using individual file pointer
    Read_all(buf[, status]) | Collective read using individual file pointer
    Read_all_begin(buf) | Start a split collective read using individual file pointer
    Read_all_end(buf[, status]) | Complete a split collective read using individual file pointer
    Read_at(offset, buf[, status]) | Read using explicit offset
    Read_at_all(offset, buf[, status]) | Collective read using explicit offset
    Read_at_all_begin(offset, buf) | Start a split collective read using explict offset
    Read_at_all_end(buf[, status]) | Complete a split collective read using explict offset
    Read_ordered(buf[, status]) | Collective read using shared file pointer
    Read_ordered_begin(buf) | Start a split collective read using shared file pointer
    Read_ordered_end(buf[, status]) | Complete a split collective read using shared file pointer
    Read_shared(buf[, status]) | Read using shared file pointer
    Seek(offset[, whence]) | Update the individual file pointer
    Seek_shared(offset[, whence]) | Update the shared file pointer
    Set_atomicity(flag) | Set the atomicity mode
    Set_errhandler(errhandler) | Set the error handler for a file
    Set_info(info) | Set new values for the hints associated with a file
    Set_size(size) | Sets the file size
    Set_view([disp, etype, filetype, datarep, info]) | Set the file view
    Sync() | Causes all previous writes to be transferred to the storage device
    Write(buf[, status]) | Write using individual file pointer
    Write_all(buf[, status]) | Collective write using individual file pointer
    Write_all_begin(buf) | Start a split collective write using individual file pointer
    Write_all_end(buf[, status]) | Complete a split collective write using individual file pointer
    Write_at(offset, buf[, status]) | Write using explicit offset
    Write_at_all(offset, buf[, status]) | Collective write using explicit offset
    Write_at_all_begin(offset, buf) | Start a split collective write using explict offset
    Write_at_all_end(buf[, status]) | Complete a split collective write using explict offset
    Write_ordered(buf[, status]) | Collective write using shared file pointer
    Write_ordered_begin(buf) | Start a split collective write using shared file pointer
    Write_ordered_end(buf[, status]) | Complete a split collective write using shared file pointer
    Write_shared(buf[, status]) | Write using shared file pointer
    f2py(arg) |
    py2f() |

    Attributes Summary
    amode | file access mode
    atomicity |
    group | file group
    info | file info
    size | file size

    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 Delete(filename, info=INFO_NULL)
    Delete a file

Parameters

•   filename (str) –

•   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 corresponding to ‘offset’ etypes relative to the current view

Parameters
    offset (int) –

Return type
    int

Get_errhandler()
    Get the error handler for a file

Return type
    Errhandler

Get_group()
    Return the group of processes that opened the file

Return type
    Group

Get_info()
    Return the hints for a file that that are currently in use

Return type
    Info

Get_position()
    Return the current position of the individual file pointer in etype units relative to the current view

Return type
    int

Get_position_shared()
    Return the current position of the shared file pointer 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 (str) –

•   amode (int) –

•   info (Info) –

Return type
    File

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 (Optional[Status]) –

Return type
    None

Read_all(buf, status=None)
    Collective read using individual file pointer

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

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 (Optional[Status]) –

Return type
    None

Read_at(offset, buf, status=None)
    Read using explicit offset

Parameters

•   offset (int) –

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

Read_at_all(offset, buf, status=None)
    Collective read using explicit offset

Parameters

•   offset (int) –

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

Read_at_all_begin(offset, buf)
    Start a split collective read using explict offset

Parameters

•   offset (int) –

•   buf (BufSpec) –

Return type
    None

Read_at_all_end(buf, status=None)
    Complete a split collective read using explict offset

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

Read_ordered(buf, status=None)
    Collective read using shared file pointer

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

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 (Optional[Status]) –

Return type
    None

Read_shared(buf, status=None)
    Read using shared file pointer

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

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)
    Sets 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 (Optional[Datatype]) –

•   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 (Optional[Status]) –

Return type
    None

Write_all(buf, status=None)
    Collective write using individual file pointer

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

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 (Optional[Status]) –

Return type
    None

Write_at(offset, buf, status=None)
    Write using explicit offset

Parameters

•   offset (int) –

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

Write_at_all(offset, buf, status=None)
    Collective write using explicit offset

Parameters

•   offset (int) –

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

Write_at_all_begin(offset, buf)
    Start a split collective write using explict offset

Parameters

•   offset (int) –

•   buf (BufSpec) –

Return type
    None

Write_at_all_end(buf, status=None)
    Complete a split collective write using explict offset

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

Write_ordered(buf, status=None)
    Collective write using shared file pointer

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

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 (Optional[Status]) –

Return type
    None

Write_shared(buf, status=None)
    Write using shared file pointer

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    File

py2f()

Return type
    int

    Attributes Documentation

amode
    file access mode

atomicity

group
    file group

info
    file info

size
    file size

  mpi4py.MPI.Graphcomm

class mpi4py.MPI.Graphcomm(comm=None)
    Bases: Topocomm

    General graph topology intracommunicator

Parameters
    comm (Optional[Graphcomm]) –

Return type
    Graphcomm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Graphcomm]) –

Return type
    Graphcomm

    Methods Summary
    Get_dims() | Return the number of nodes and edges
    Get_neighbors(rank) | Return list of neighbors of a process
    Get_neighbors_count(rank) | Return number of neighbors of a process
    Get_topo() | Return index and edges

    Attributes Summary
    dims | number of nodes and edges
    edges |
    index |
    nedges | number of edges
    neighbors |
    nneighbors | number of neighbors
    nnodes | number of nodes
    topo | topology information

    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

index

nedges
    number of edges

neighbors

nneighbors
    number of neighbors

nnodes
    number of nodes

topo
    topology information

  mpi4py.MPI.Grequest

class mpi4py.MPI.Grequest(request=None)
    Bases: Request

    Generalized request handle

Parameters
    request (Optional[Grequest]) –

Return type
    Grequest

static __new__(cls, request=None)

Parameters
    request (Optional[Grequest]) –

Return type
    Grequest

    Methods Summary
    Complete() | Notify that a user-defined request is complete
    Start(query_fn, free_fn, cancel_fn[, args, ...]) | Create and return a user-defined request

    Methods Documentation

Complete()
    Notify that a user-defined request is complete

Return type
    None

classmethod Start(query_fn, free_fn, cancel_fn, args=None, kargs=None)
    Create and return a user-defined request

Parameters

•   query_fn (Callable[..., None]) –

•   free_fn (Callable[..., None]) –

•   cancel_fn (Callable[..., None]) –

•   args (Optional[Tuple[Any]]) –

•   kargs (Optional[Dict[str, Any]]) –

Return type
    Grequest

  mpi4py.MPI.Group

class mpi4py.MPI.Group(group=None)
    Bases: object

    Group of processes

Parameters
    group (Optional[Group]) –

Return type
    Group

static __new__(cls, group=None)

Parameters
    group (Optional[Group]) –

Return type
    Group

    Methods Summary
    Compare(group1, group2) | Compare two groups
    Difference(group1, group2) | Produce a group from the difference of two existing groups
    Dup() | Duplicate a group
    Excl(ranks) | Produce a group by reordering an existing group and taking only unlisted members
    Free() | Free a group
    Get_rank() | Return the rank of this process in a group
    Get_size() | Return the size of a group
    Incl(ranks) | Produce a group by reordering an existing group and taking only listed members
    Intersection(group1, group2) | Produce a group as the intersection of two existing groups
    Range_excl(ranks) | Create a new group by excluding ranges of processes from an existing group
    Range_incl(ranks) | Create a new group from ranges of of ranks in an existing group
    Translate_ranks(group1, ranks1[, group2]) | Translate the ranks of processes in one group to those in another group
    Union(group1, group2) | Produce a group by combining two existing groups
    f2py(arg) |
    py2f() |

    Attributes Summary
    rank | rank of this process in group
    size | number of processes in group

    Methods Documentation

classmethod Compare(group1, group2)
    Compare two groups

Parameters

•   group1 (Group) –

•   group2 (Group) –

Return type
    int

classmethod Difference(group1, group2)
    Produce a group from the difference of two existing groups

Parameters

•   group1 (Group) –

•   group2 (Group) –

Return type
    Group

Dup()
    Duplicate a group

Return type
    Group

Excl(ranks)
    Produce a group by reordering an existing group and taking only unlisted members

Parameters
    ranks (Sequence[int]) –

Return type
    Group

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 size of a group

Return type
    int

Incl(ranks)
    Produce a group by reordering an existing group and taking only listed members

Parameters
    ranks (Sequence[int]) –

Return type
    Group

classmethod Intersection(group1, group2)
    Produce a group as the intersection of two existing groups

Parameters

•   group1 (Group) –

•   group2 (Group) –

Return type
    Group

Range_excl(ranks)
    Create a new group by excluding ranges of processes from an existing group

Parameters
    ranks (Sequence[Tuple[int, int, int]]) –

Return type
    Group

Range_incl(ranks)
    Create a new group from ranges of of ranks in an existing group

Parameters
    ranks (Sequence[Tuple[int, int, int]]) –

Return type
    Group

classmethod Translate_ranks(group1, ranks1, group2=None)
    Translate the ranks of processes in one group to those in another group

Parameters

•   group1 (Group) –

•   ranks1 (Sequence[int]) –

•   group2 (Optional[Group]) –

Return type
    List[int]

classmethod Union(group1, group2)
    Produce a group by combining two existing groups

Parameters

•   group1 (Group) –

•   group2 (Group) –

Return type
    Group

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    Group

py2f()

Return type
    int

    Attributes Documentation

rank
    rank of this process in group

size
    number of processes in group

  mpi4py.MPI.Info

class mpi4py.MPI.Info(info=None)
    Bases: object

    Info object

Parameters
    info (Optional[Info]) –

Return type
    Info

static __new__(cls, info=None)

Parameters
    info (Optional[Info]) –

Return type
    Info

    Methods Summary
    Create() | Create a new, empty info object
    Delete(key) | Remove a (key, value) pair from info
    Dup() | Duplicate an existing info object, creating a new object, with the same (key, value) pairs and the same ordering of keys
    Free() | Free a info object
    Get(key[, maxlen]) | Retrieve the value associated with a key
    Get_nkeys() | Return the number of currently defined keys in info
    Get_nthkey(n) | Return the nth defined key in info.
    Set(key, value) | Add the (key, value) pair to info, and overrides the value if a value for the same key was previously set
    clear() | info clear
    copy() | info copy
    f2py(arg) |
    get(key[, default]) | info get
    items() | info items
    keys() | info keys
    pop(key, *default) | info pop
    popitem() | info popitem
    py2f() |
    update([other]) | info update
    values() | info values

    Methods Documentation

classmethod Create()
    Create a new, empty info object

Return type
    Info

Delete(key)
    Remove a (key, value) pair from info

Parameters
    key (str) –

Return type
    None

Dup()
    Duplicate an existing info object, creating a new object, with the same (key, value) pairs and the same ordering of keys

Return type
    Info

Free()
    Free a info object

Return type
    None

Get(key, maxlen=-1)
    Retrieve the value associated with a key

Parameters

•   key (str) –

•   maxlen (int) –

Return type
    Optional[str]

Get_nkeys()
    Return the number of currently defined keys in info

Return type
    int

Get_nthkey(n)
    Return the nth defined key in info. Keys are numbered in the range [0, N) where N is the value returned by Info.Get_nkeys()

Parameters
    n (int) –

Return type
    str

Set(key, value)
    Add the (key, value) pair to info, and overrides the value if a value for the same key was previously set

Parameters

•   key (str) –

•   value (str) –

Return type
    None

clear()
    info clear

Return type
    None

copy()
    info copy

Return type
    Info

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    Info

get(key, default=None)
    info get

Parameters

•   key (str) –

•   default (Optional[str]) –

Return type
    Optional[str]

items()
    info items

Return type
    List[Tuple[str, str]]

keys()
    info keys

Return type
    List[str]

pop(key, *default)
    info pop

Parameters

•   key (str) –

•   default (str) –

Return type
    str

popitem()
    info popitem

Return type
    Tuple[str, str]

py2f()

Return type
    int

update(other=(), **kwds)
    info update

Parameters

•   other (Union[Info, Mapping[str, str], Iterable[Tuple[str, str]]]) –

•   kwds (str) –

Return type
    None

values()
    info values

Return type
    List[str]

  mpi4py.MPI.Intercomm

class mpi4py.MPI.Intercomm(comm=None)
    Bases: Comm

    Intercommunicator

Parameters
    comm (Optional[Intercomm]) –

Return type
    Intercomm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Intercomm]) –

Return type
    Intercomm

    Methods Summary
    Get_remote_group() | Access the remote group associated with the inter-communicator
    Get_remote_size() | Intercommunicator remote size
    Merge([high]) | Merge intercommunicator

    Attributes Summary
    remote_group | remote group
    remote_size | number of remote processes

    Methods Documentation

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

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(comm=None)
    Bases: Comm

    Intracommunicator

Parameters
    comm (Optional[Intracomm]) –

Return type
    Intracomm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Intracomm]) –

Return type
    Intracomm

    Methods Summary
    Accept(port_name[, info, root]) | Accept a request to form a new intercommunicator
    Cart_map(dims[, periods]) | Return an optimal placement for the calling process on the physical machine
    Connect(port_name[, info, root]) | Make a request to form a new intercommunicator
    Create_cart(dims[, periods, reorder]) | Create cartesian communicator
    Create_dist_graph(sources, degrees, destinations) | Create distributed graph communicator
    Create_dist_graph_adjacent(sources, destinations) | Create distributed graph communicator
    Create_graph(index, edges[, reorder]) | Create graph communicator
    Create_intercomm(local_leader, peer_comm, ...) | Create intercommunicator
    Exscan(sendbuf, recvbuf[, op]) | Exclusive Scan
    Graph_map(index, edges) | Return an optimal placement for the calling process on the physical machine
    Iexscan(sendbuf, recvbuf[, op]) | Inclusive Scan
    Iscan(sendbuf, recvbuf[, op]) | Inclusive Scan
    Scan(sendbuf, recvbuf[, op]) | Inclusive Scan
    Spawn(command[, args, maxprocs, info, root, ...]) | Spawn instances of a single MPI application
    Spawn_multiple(command[, args, maxprocs, ...]) | Spawn instances of multiple MPI applications
    exscan(sendobj[, op]) | Exclusive Scan
    scan(sendobj[, op]) | Inclusive Scan

    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)
    Return an optimal placement for the calling process on the physical machine

Parameters

•   dims (Sequence[int]) –

•   periods (Optional[Sequence[bool]]) –

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 (Optional[Sequence[bool]]) –

•   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 (Optional[Sequence[int]]) –

•   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 (Optional[Sequence[int]]) –

•   destweights (Optional[Sequence[int]]) –

•   info (Info) –

•   reorder (bool) –

Return type
    Distgraphcomm

Create_graph(index, edges, reorder=False)
    Create graph communicator

Parameters

•   index (Sequence[int]) –

•   edges (Sequence[int]) –

•   reorder (bool) –

Return type
    Graphcomm

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 (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   op (Op) –

Return type
    None

Graph_map(index, edges)
    Return an optimal placement for the calling process on the physical machine

Parameters

•   index (Sequence[int]) –

•   edges (Sequence[int]) –

Return type
    int

Iexscan(sendbuf, recvbuf, op=SUM)
    Inclusive Scan

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   op (Op) –

Return type
    Request

Iscan(sendbuf, recvbuf, op=SUM)
    Inclusive Scan

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   op (Op) –

Return type
    Request

Scan(sendbuf, recvbuf, op=SUM)
    Inclusive Scan

Parameters

•   sendbuf (Union[BufSpec, InPlace]) –

•   recvbuf (BufSpec) –

•   op (Op) –

Return type
    None

Spawn(command, args=None, maxprocs=1, info=INFO_NULL, root=0, errcodes=None)
    Spawn instances of a single MPI application

Parameters

•   command (str) –

•   args (Optional[Sequence[str]]) –

•   maxprocs (int) –

•   info (Info) –

•   root (int) –

•   errcodes (Optional[list]) –

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 (Optional[Sequence[Sequence[str]]]) –

•   maxprocs (Optional[Sequence[int]]) –

•   info (Union[Info, Sequence[Info]]) –

•   root (int) –

•   errcodes (Optional[list]) –

Return type
    Intercomm

exscan(sendobj, op=SUM)
    Exclusive Scan

Parameters

•   sendobj (Any) –

•   op (Union[Op, Callable[[Any, Any], Any]]) –

Return type
    Any

scan(sendobj, op=SUM)
    Inclusive Scan

Parameters

•   sendobj (Any) –

•   op (Union[Op, Callable[[Any, Any], Any]]) –

Return type
    Any

  mpi4py.MPI.Message

class mpi4py.MPI.Message(message=None)
    Bases: object

    Matched message handle

Parameters
    message (Optional[Message]) –

Return type
    Message

static __new__(cls, message=None)

Parameters
    message (Optional[Message]) –

Return type
    Message

    Methods Summary
    Iprobe(comm[, source, tag, status]) | Nonblocking test for a matched message
    Irecv(buf) | Nonblocking receive of matched message
    Probe(comm[, source, tag, status]) | Blocking test for a matched message
    Recv(buf[, status]) | Blocking receive of matched message
    f2py(arg) |
    iprobe(comm[, source, tag, status]) | Nonblocking test for a matched message
    irecv() | Nonblocking receive of matched message
    probe(comm[, source, tag, status]) | Blocking test for a matched message
    py2f() |
    recv([status]) | Blocking receive of matched message

    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 (Optional[Status]) –

Return type
    Optional[Message]

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 (Optional[Status]) –

Return type
    Message

Recv(buf, status=None)
    Blocking receive of matched message

Parameters

•   buf (BufSpec) –

•   status (Optional[Status]) –

Return type
    None

classmethod f2py(arg)

Parameters
    arg (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 (Optional[Status]) –

Return type
    Optional[Message]

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 (Optional[Status]) –

Return type
    Message

py2f()

Return type
    int

recv(status=None)
    Blocking receive of matched message

Parameters
    status (Optional[Status]) –

Return type
    Any

  mpi4py.MPI.Op

class mpi4py.MPI.Op(op=None)
    Bases: object

    Operation object

Parameters
    op (Optional[Op]) –

Return type
    Op

static __new__(cls, op=None)

Parameters
    op (Optional[Op]) –

Return type
    Op

    Methods Summary
    Create(function[, commute]) | Create a user-defined operation
    Free() | Free the operation
    Is_commutative() | Query reduction operations for their commutativity
    Reduce_local(inbuf, inoutbuf) | Apply a reduction operator to local data
    f2py(arg) |
    py2f() |

    Attributes Summary
    is_commutative | is commutative
    is_predefined | is a predefined operation

    Methods Documentation

classmethod Create(function, commute=False)
    Create a user-defined operation

Parameters

•   function (Callable[[Buffer, Buffer, Datatype], None]) –

•   commute (bool) –

Return type
    Op

Free()
    Free the operation

Return type
    None

Is_commutative()
    Query reduction operations for their commutativity

Return type
    bool

Reduce_local(inbuf, inoutbuf)
    Apply a reduction operator to local data

Parameters

•   inbuf (BufSpec) –

•   inoutbuf (BufSpec) –

Return type
    None

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    Op

py2f()

Return type
    int

    Attributes Documentation

is_commutative
    is commutative

is_predefined
    is a predefined operation

  mpi4py.MPI.Pickle

class mpi4py.MPI.Pickle(dumps=None, loads=None, protocol=None)
    Bases: object

    Pickle/unpickle Python objects

Parameters

•   dumps (Optional[Callable[[Any, int], bytes]]) –

•   loads (Optional[Callable[[Buffer], Any]]) –

•   protocol (Optional[int]) –

__init__(dumps=None, loads=None, protocol=None)

Parameters

•   dumps (Optional[Callable[[Any, int], bytes]]) –

•   loads (Optional[Callable[[Buffer], Any]]) –

•   protocol (Optional[int]) –

Return type
    None

    Methods Summary
    dumps(obj[, buffer_callback]) | Serialize object to pickle data stream.
    loads(data[, buffers]) | Deserialize object from pickle data stream.

    Attributes Summary
    PROTOCOL | pickle protocol

    Methods Documentation

dumps(obj, buffer_callback=None)
    Serialize object to pickle data stream.

Parameters

•   obj (Any) –

•   buffer_callback (Optional[Callable[[Buffer], Any]]) –

Return type
    bytes

loads(data, buffers=None)
    Deserialize object from pickle data stream.

Parameters

•   data (Buffer) –

•   buffers (Optional[Iterable[Buffer]]) –

Return type
    Any

    Attributes Documentation

PROTOCOL
    pickle protocol

  mpi4py.MPI.Prequest

class mpi4py.MPI.Prequest(request=None)
    Bases: Request

    Persistent request handle

Parameters
    request (Optional[Prequest]) –

Return type
    Prequest

static __new__(cls, request=None)

Parameters
    request (Optional[Prequest]) –

Return type
    Prequest

    Methods Summary
    Start() | Initiate a communication with a persistent request
    Startall(requests) | Start a collection of persistent requests

    Methods Documentation

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(request=None)
    Bases: object

    Request handle

Parameters
    request (Optional[Request]) –

Return type
    Request

static __new__(cls, request=None)

Parameters
    request (Optional[Request]) –

Return type
    Request

    Methods Summary
    Cancel() | Cancel a communication request
    Free() | Free a communication request
    Get_status([status]) | Non-destructive test for the completion of a request
    Test([status]) | Test for the completion of a send or receive
    Testall(requests[, statuses]) | Test for completion of all previously initiated requests
    Testany(requests[, status]) | Test for completion of any previously initiated request
    Testsome(requests[, statuses]) | Test for completion of some previously initiated requests
    Wait([status]) | Wait for a send or receive to complete
    Waitall(requests[, statuses]) | Wait for all previously initiated requests to complete
    Waitany(requests[, status]) | Wait for any previously initiated request to complete
    Waitsome(requests[, statuses]) | Wait for some previously initiated requests to complete
    cancel() | Cancel a communication request
    f2py(arg) |
    get_status([status]) | Non-destructive test for the completion of a request
    py2f() |
    test([status]) | Test for the completion of a send or receive
    testall(requests[, statuses]) | Test for completion of all previously initiated requests
    testany(requests[, status]) | Test for completion of any previously initiated request
    testsome(requests[, statuses]) | Test for completion of some previously initiated requests
    wait([status]) | Wait for a send or receive to complete
    waitall(requests[, statuses]) | Wait for all previously initiated requests to complete
    waitany(requests[, status]) | Wait for any previously initiated request to complete
    waitsome(requests[, statuses]) | Wait for some previously initiated requests to complete

    Methods Documentation

Cancel()
    Cancel a communication 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 (Optional[Status]) –

Return type
    bool

Test(status=None)
    Test for the completion of a send or receive

Parameters
    status (Optional[Status]) –

Return type
    bool

classmethod Testall(requests, statuses=None)
    Test for completion of all previously initiated requests

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    bool

classmethod Testany(requests, status=None)
    Test for completion of any previously initiated request

Parameters

•   requests (Sequence[Request]) –

•   status (Optional[Status]) –

Return type
    Tuple[int, bool]

classmethod Testsome(requests, statuses=None)
    Test for completion of some previously initiated requests

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    Optional[List[int]]

Wait(status=None)
    Wait for a send or receive to complete

Parameters
    status (Optional[Status]) –

Return type
    Literal[True]

classmethod Waitall(requests, statuses=None)
    Wait for all previously initiated requests to complete

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    Literal[True]

classmethod Waitany(requests, status=None)
    Wait for any previously initiated request to complete

Parameters

•   requests (Sequence[Request]) –

•   status (Optional[Status]) –

Return type
    int

classmethod Waitsome(requests, statuses=None)
    Wait for some previously initiated requests to complete

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    Optional[List[int]]

cancel()
    Cancel a communication request

Return type
    None

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    Request

get_status(status=None)
    Non-destructive test for the completion of a request

Parameters
    status (Optional[Status]) –

Return type
    bool

py2f()

Return type
    int

test(status=None)
    Test for the completion of a send or receive

Parameters
    status (Optional[Status]) –

Return type
    Tuple[bool, Optional[Any]]

classmethod testall(requests, statuses=None)
    Test for completion of all previously initiated requests

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    Tuple[bool, Optional[List[Any]]]

classmethod testany(requests, status=None)
    Test for completion of any previously initiated request

Parameters

•   requests (Sequence[Request]) –

•   status (Optional[Status]) –

Return type
    Tuple[int, bool, Optional[Any]]

classmethod testsome(requests, statuses=None)
    Test for completion of some previously initiated requests

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    Tuple[Optional[List[int]], Optional[List[Any]]]

wait(status=None)
    Wait for a send or receive to complete

Parameters
    status (Optional[Status]) –

Return type
    Any

classmethod waitall(requests, statuses=None)
    Wait for all previously initiated requests to complete

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    List[Any]

classmethod waitany(requests, status=None)
    Wait for any previously initiated request to complete

Parameters

•   requests (Sequence[Request]) –

•   status (Optional[Status]) –

Return type
    Tuple[int, Any]

classmethod waitsome(requests, statuses=None)
    Wait for some previously initiated requests to complete

Parameters

•   requests (Sequence[Request]) –

•   statuses (Optional[List[Status]]) –

Return type
    Tuple[Optional[List[int]], Optional[List[Any]]]

  mpi4py.MPI.Status

class mpi4py.MPI.Status(status=None)
    Bases: object

    Status object

Parameters
    status (Optional[Status]) –

Return type
    Status

static __new__(cls, status=None)

Parameters
    status (Optional[Status]) –

Return type
    Status

    Methods Summary
    Get_count([datatype]) | Get the number of top level elements
    Get_elements(datatype) | Get the number of basic elements in a datatype
    Get_error() | Get message error
    Get_source() | Get message source
    Get_tag() | Get message tag
    Is_cancelled() | Test to see if a request was cancelled
    Set_cancelled(flag) | Set the cancelled state associated with a status
    Set_elements(datatype, count) | Set the number of elements in a status
    Set_error(error) | Set message error
    Set_source(source) | Set message source
    Set_tag(tag) | Set message tag
    f2py(arg) |
    py2f() |

    Attributes Summary
    cancelled | cancelled state
    count | byte count
    error |
    source |
    tag |

    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 should be only used 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 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
    Status

py2f()

Return type
    List[int]

    Attributes Documentation

cancelled
    cancelled state

count
    byte count

error

source

tag

  mpi4py.MPI.Topocomm

class mpi4py.MPI.Topocomm(comm=None)
    Bases: Intracomm

    Topology intracommunicator

Parameters
    comm (Optional[Topocomm]) –

Return type
    Topocomm

static __new__(cls, comm=None)

Parameters
    comm (Optional[Topocomm]) –

Return type
    Topocomm

    Methods Summary
    Ineighbor_allgather(sendbuf, recvbuf) | Nonblocking Neighbor Gather to All
    Ineighbor_allgatherv(sendbuf, recvbuf) | Nonblocking Neighbor Gather to All Vector
    Ineighbor_alltoall(sendbuf, recvbuf) | Nonblocking Neighbor All-to-All
    Ineighbor_alltoallv(sendbuf, recvbuf) | Nonblocking Neighbor All-to-All Vector
    Ineighbor_alltoallw(sendbuf, recvbuf) | Nonblocking Neighbor All-to-All Generalized
    Neighbor_allgather(sendbuf, recvbuf) | Neighbor Gather to All
    Neighbor_allgatherv(sendbuf, recvbuf) | Neighbor Gather to All Vector
    Neighbor_alltoall(sendbuf, recvbuf) | Neighbor All-to-All
    Neighbor_alltoallv(sendbuf, recvbuf) | Neighbor All-to-All Vector
    Neighbor_alltoallw(sendbuf, recvbuf) | Neighbor All-to-All Generalized
    neighbor_allgather(sendobj) | Neighbor Gather to All
    neighbor_alltoall(sendobj) | Neighbor All to All Scatter/Gather

    Attributes Summary
    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

    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 Generalized

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_allgatherv(sendbuf, recvbuf)
    Neighbor Gather to All Vector

Parameters

•   sendbuf (BufSpec) –

•   recvbuf (BufSpecV) –

Return type
    None

Neighbor_alltoall(sendbuf, recvbuf)
    Neighbor All-to-All

Parameters

•   sendbuf (BufSpecB) –

•   recvbuf (BufSpecB) –

Return type
    None

Neighbor_alltoallv(sendbuf, recvbuf)
    Neighbor All-to-All Vector

Parameters

•   sendbuf (BufSpecV) –

•   recvbuf (BufSpecV) –

Return type
    None

Neighbor_alltoallw(sendbuf, recvbuf)
    Neighbor All-to-All Generalized

Parameters

•   sendbuf (BufSpecW) –

•   recvbuf (BufSpecW) –

Return type
    None

neighbor_allgather(sendobj)
    Neighbor Gather to All

Parameters
    sendobj (Any) –

Return type
    List[Any]

neighbor_alltoall(sendobj)
    Neighbor All to All Scatter/Gather

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(win=None)
    Bases: object

    Window handle

Parameters
    win (Optional[Win]) –

Return type
    Win

static __new__(cls, win=None)

Parameters
    win (Optional[Win]) –

Return type
    Win

    Methods Summary
    Accumulate(origin, target_rank[, target, op]) | Accumulate data into the target process
    Allocate(size[, disp_unit, info, comm]) | Create an window object for one-sided communication
    Allocate_shared(size[, disp_unit, info, comm]) | Create an window object for one-sided communication
    Attach(memory) | Attach a local memory region
    Call_errhandler(errorcode) | Call the error handler installed on a window
    Compare_and_swap(origin, compare, result, ...) | Perform one-sided atomic compare-and-swap
    Complete() | Completes an RMA operations begun after an Win.Start()
    Create(memory[, disp_unit, info, comm]) | Create an window object for one-sided communication
    Create_dynamic([info, comm]) | Create an window object for one-sided communication
    Create_keyval([copy_fn, delete_fn, nopython]) | Create a new attribute key for windows
    Delete_attr(keyval) | Delete attribute value associated with a key
    Detach(memory) | Detach a local memory region
    Fence([assertion]) | Perform an MPI fence synchronization on a window
    Fetch_and_op(origin, result, target_rank[, ...]) | Perform one-sided read-modify-write
    Flush(rank) | Complete all outstanding RMA operations at the given target
    Flush_all() | Complete all outstanding RMA operations at all targets
    Flush_local(rank) | Complete locally all outstanding RMA operations at the given target
    Flush_local_all() | Complete locally all outstanding RMA opera- tions at all targets
    Free() | Free a window
    Free_keyval(keyval) | Free an attribute key for windows
    Get(origin, target_rank[, target]) | Get data from a memory window on a remote process.
    Get_accumulate(origin, result, target_rank) | Fetch-and-accumulate data into the target process
    Get_attr(keyval) | Retrieve attribute value by key
    Get_errhandler() | Get the error handler for a window
    Get_group() | Return a duplicate of the group of the communicator used to create the window
    Get_info() | Return the hints for a windows that are currently in use
    Get_name() | Get the print name associated with the window
    Lock(rank[, lock_type, assertion]) | Begin an RMA access epoch at the target process
    Lock_all([assertion]) | Begin an RMA access epoch at all processes
    Post(group[, assertion]) | Start an RMA exposure epoch
    Put(origin, target_rank[, target]) | Put data into a memory window on a remote process.
    Raccumulate(origin, target_rank[, target, op]) | Fetch-and-accumulate data into the target process
    Rget(origin, target_rank[, target]) | Get data from a memory window on a remote process.
    Rget_accumulate(origin, result, target_rank) | Accumulate data into the target process using remote memory access.
    Rput(origin, target_rank[, target]) | Put data into a memory window on a remote process.
    Set_attr(keyval, attrval) | Store attribute value associated with a key
    Set_errhandler(errhandler) | Set the error handler for a window
    Set_info(info) | Set new values for the hints associated with a window
    Set_name(name) | Set the print name associated with the window
    Shared_query(rank) | Query the process-local address for remote memory segments created with Win.Allocate_shared()
    Start(group[, assertion]) | Start an RMA access epoch for MPI
    Sync() | Synchronize public and private copies of the given window
    Test() | Test whether an RMA exposure epoch has completed
    Unlock(rank) | Complete an RMA access epoch at the target process
    Unlock_all() | Complete an RMA access epoch at all processes
    Wait() | Complete an RMA exposure epoch begun with Win.Post()
    f2py(arg) |
    py2f() |
    tomemory() | Return window memory buffer

    Attributes Summary
    attrs | window attributes
    flavor | window create flavor
    group | window group
    info | window info
    model | window memory model
    name | window name

    Methods Documentation

Accumulate(origin, target_rank, target=None, op=SUM)
    Accumulate data into the target process

Parameters

•   origin (BufSpec) –

•   target_rank (int) –

•   target (Optional[TargetSpec]) –

•   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
    Win

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
    Win

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()
    Completes an RMA operations begun after an Win.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 (Union[Buffer, Bottom, None]) –

•   disp_unit (int) –

•   info (Info) –

•   comm (Intracomm) –

Return type
    Win

classmethod Create_dynamic(info=INFO_NULL, comm=COMM_SELF)
    Create an window object for one-sided communication

Parameters

•   info (Info) –

•   comm (Intracomm) –

Return type
    Win

classmethod Create_keyval(copy_fn=None, delete_fn=None, nopython=False)
    Create a new attribute key for windows

Parameters

•   copy_fn (Optional[Callable[[Win, int, Any], Any]]) –

•   delete_fn (Optional[Callable[[Win, int, Any], 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 the given 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 the given target

Parameters
    rank (int) –

Return type
    None

Flush_local_all()
    Complete locally all outstanding RMA opera- tions 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 (Optional[TargetSpec]) –

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 (Optional[TargetSpec]) –

•   op (Op) –

Return type
    None

Get_attr(keyval)
    Retrieve attribute value by key

Parameters
    keyval (int) –

Return type
    Optional[Union[int, Any]]

Get_errhandler()
    Get the error handler for a window

Return type
    Errhandler

Get_group()
    Return a duplicate of the group of the communicator used to create the window

Return type
    Group

Get_info()
    Return the hints for a windows that are currently in use

Return type
    Info

Get_name()
    Get the print name associated with the 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 (Optional[TargetSpec]) –

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 (Optional[TargetSpec]) –

•   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 (Optional[TargetSpec]) –

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 (Optional[TargetSpec]) –

•   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 (Optional[TargetSpec]) –

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 associated with the window

Parameters
    name (str) –

Return type
    None

Shared_query(rank)
    Query the process-local address for remote memory segments created with Win.Allocate_shared()

Parameters
    rank (int) –

Return type
    Tuple[memory, 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 given 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 Win.Post()

Return type
    Literal[True]

classmethod f2py(arg)

Parameters
    arg (int) –

Return type
    Win

py2f()

Return type
    int

tomemory()
    Return window memory buffer

Return type
    memory

    Attributes Documentation

attrs
    window attributes

flavor
    window create flavor

group
    window group

info
    window info

model
    window memory model

name
    window name

  mpi4py.MPI.memory

class mpi4py.MPI.memory(buf)
    Bases: object

    Memory buffer

Parameters
    buf (Buffer) –

Return type
    memory

static __new__(cls, buf)

Parameters
    buf (Buffer) –

Return type
    memory

    Methods Summary
    allocate(nbytes[, clear]) | Memory allocation
    fromaddress(address, nbytes[, readonly]) | Memory from address and size in bytes
    frombuffer(obj[, readonly]) | Memory from buffer-like object
    release() | Release the underlying buffer exposed by the memory object
    tobytes([order]) | Return the data in the buffer as a byte string
    toreadonly() | Return a readonly version of the memory object

    Attributes Summary
    address | Memory address
    format | A string with the format of each element
    itemsize | The size in bytes of each element
    nbytes | Memory size (in bytes)
    obj | The underlying object of the memory
    readonly | Boolean indicating whether the memory is read-only

    Methods Documentation

static allocate(nbytes, clear=False)
    Memory allocation

Parameters

•   nbytes (int) –

•   clear (bool) –

Return type
    memory

static fromaddress(address, nbytes, readonly=False)
    Memory from address and size in bytes

Parameters

•   address (int) –

•   nbytes (int) –

•   readonly (bool) –

Return type
    memory

static frombuffer(obj, readonly=False)
    Memory from buffer-like object

Parameters

•   obj (Buffer) –

•   readonly (bool) –

Return type
    memory

release()
    Release the underlying buffer exposed by the memory object

Return type
    None

tobytes(order=None)
    Return the data in the buffer as a byte string

Parameters
    order (Optional[str]) –

Return type
    bytes

toreadonly()
    Return a readonly version of the memory object

Return type
    memory

    Attributes Documentation

address
    Memory address

format
    A string with the format of each element

itemsize
    The size in bytes of each element

nbytes
    Memory size (in bytes)

obj The underlying object of the memory

readonly
    Boolean indicating whether the memory is read-only

    Exceptions
    Exception([ierr]) | Exception class

  mpi4py.MPI.Exception

exception mpi4py.MPI.Exception(ierr=SUCCESS)
    Bases: RuntimeError

    Exception class

Parameters
    ierr (int) –

Return type
    Exception

static __new__(cls, ierr=SUCCESS)

Parameters
    ierr (int) –

Return type
    Exception

    Methods Summary
    Get_error_class() | Error class
    Get_error_code() | Error code
    Get_error_string() | Error string

    Attributes Summary
    error_class | error class
    error_code | error code
    error_string | error string

    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
    Add_error_class() | Add an error class to the known error classes
    Add_error_code(errorclass) | Add an error code to an error class
    Add_error_string(errorcode, string) | Associate an error string with an error class or errorcode
    Aint_add(base, disp) | Return the sum of base address and displacement
    Aint_diff(addr1, addr2) | Return the difference between absolute addresses
    Alloc_mem(size[, info]) | Allocate memory for message passing and RMA
    Attach_buffer(buf) | Attach a user-provided buffer for sending in buffered mode
    Close_port(port_name) | Close a port
    Compute_dims(nnodes, dims) | Return a balanced distribution of processes per coordinate direction
    Detach_buffer() | Remove an existing attached buffer
    Finalize() | Terminate the MPI execution environment
    Free_mem(mem) | Free memory allocated with Alloc_mem()
    Get_address(location) | Get the address of a location in memory
    Get_error_class(errorcode) | Convert an error code into an error class
    Get_error_string(errorcode) | Return the error string for a given error class or error code
    Get_library_version() | Obtain the version string of the MPI library
    Get_processor_name() | Obtain the name of the calling processor
    Get_version() | Obtain the version number of the MPI standard supported by the implementation as a tuple (version, subversion)
    Init() | Initialize the MPI execution environment
    Init_thread([required]) | Initialize the MPI execution environment
    Is_finalized() | Indicates whether Finalize has completed
    Is_initialized() | Indicates whether Init has been called
    Is_thread_main() | Indicate whether this thread called Init or Init_thread
    Lookup_name(service_name[, info]) | Lookup a port name given a service name
    Open_port([info]) | Return an address that can be used to establish connections between groups of MPI processes
    Pcontrol(level) | Control profiling
    Publish_name(service_name, port_name[, info]) | Publish a service name
    Query_thread() | Return the level of thread support provided by the MPI library
    Register_datarep(datarep, read_fn, write_fn, ...) | Register user-defined data representations
    Unpublish_name(service_name, port_name[, info]) | Unpublish a service name
    Wtick() | Return the resolution of Wtime
    Wtime() | Return an elapsed time on the calling processor
    get_vendor() | Infomation about the underlying MPI implementation

  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 errorcode

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 RMA

Parameters

•   size (int) –

•   info (Info) –

Return type
    memory

  mpi4py.MPI.Attach_buffer

mpi4py.MPI.Attach_buffer(buf)
    Attach a user-provided buffer for sending in buffered mode

Parameters
    buf (Buffer) –

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 (Union[int, Sequence[int]]) –

Return type
    List[int]

  mpi4py.MPI.Detach_buffer

mpi4py.MPI.Detach_buffer()
    Remove an existing attached buffer

Return type
    Buffer

  mpi4py.MPI.Finalize

mpi4py.MPI.Finalize()
    Terminate the MPI execution environment

Return type
    None

  mpi4py.MPI.Free_mem

mpi4py.MPI.Free_mem(mem)
    Free memory allocated with Alloc_mem()

Parameters
    mem (memory) –

Return type
    None

  mpi4py.MPI.Get_address

mpi4py.MPI.Get_address(location)
    Get the address of a location in memory

Parameters
    location (Union[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_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 supported by the implementation as a tuple (version, subversion)

Return type
    Tuple[int, int]

  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()
    Indicates whether Finalize has completed

Return type
    bool

  mpi4py.MPI.Is_initialized

mpi4py.MPI.Is_initialized()
    Indicates 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 that can be used to establish connections between groups of MPI 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)
    Register user-defined data representations

Parameters

•   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.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()
    Infomation about the underlying MPI implementation

Returns

•   a string with the name of the MPI implementation

•   an integer 3-tuple version (major, minor, micro)

Return type
    Tuple[str, Tuple[int, int, int]]

    Attributes
    UNDEFINED | int UNDEFINED
    ANY_SOURCE | int ANY_SOURCE
    ANY_TAG | int ANY_TAG
    PROC_NULL | int PROC_NULL
    ROOT | int ROOT
    BOTTOM | Bottom BOTTOM
    IN_PLACE | InPlace IN_PLACE
    KEYVAL_INVALID | int KEYVAL_INVALID
    TAG_UB | int TAG_UB
    HOST | int HOST
    IO | int IO
    WTIME_IS_GLOBAL | int WTIME_IS_GLOBAL
    UNIVERSE_SIZE | int UNIVERSE_SIZE
    APPNUM | int APPNUM
    LASTUSEDCODE | int LASTUSEDCODE
    WIN_BASE | int WIN_BASE
    WIN_SIZE | int WIN_SIZE
    WIN_DISP_UNIT | int WIN_DISP_UNIT
    WIN_CREATE_FLAVOR | int WIN_CREATE_FLAVOR
    WIN_FLAVOR | int WIN_FLAVOR
    WIN_MODEL | int WIN_MODEL
    SUCCESS | int SUCCESS
    ERR_LASTCODE | int ERR_LASTCODE
    ERR_COMM | int ERR_COMM
    ERR_GROUP | int ERR_GROUP
    ERR_TYPE | int ERR_TYPE
    ERR_REQUEST | int ERR_REQUEST
    ERR_OP | int ERR_OP
    ERR_BUFFER | int ERR_BUFFER
    ERR_COUNT | int ERR_COUNT
    ERR_TAG | int ERR_TAG
    ERR_RANK | int ERR_RANK
    ERR_ROOT | int ERR_ROOT
    ERR_TRUNCATE | int ERR_TRUNCATE
    ERR_IN_STATUS | int ERR_IN_STATUS
    ERR_PENDING | int ERR_PENDING
    ERR_TOPOLOGY | int ERR_TOPOLOGY
    ERR_DIMS | int ERR_DIMS
    ERR_ARG | int ERR_ARG
    ERR_OTHER | int ERR_OTHER
    ERR_UNKNOWN | int ERR_UNKNOWN
    ERR_INTERN | int ERR_INTERN
    ERR_INFO | int ERR_INFO
    ERR_FILE | int ERR_FILE
    ERR_WIN | int ERR_WIN
    ERR_KEYVAL | int ERR_KEYVAL
    ERR_INFO_KEY | int ERR_INFO_KEY
    ERR_INFO_VALUE | int ERR_INFO_VALUE
    ERR_INFO_NOKEY | int ERR_INFO_NOKEY
    ERR_ACCESS | int ERR_ACCESS
    ERR_AMODE | int ERR_AMODE
    ERR_BAD_FILE | int ERR_BAD_FILE
    ERR_FILE_EXISTS | int ERR_FILE_EXISTS
    ERR_FILE_IN_USE | int ERR_FILE_IN_USE
    ERR_NO_SPACE | int ERR_NO_SPACE
    ERR_NO_SUCH_FILE | int ERR_NO_SUCH_FILE
    ERR_IO | int ERR_IO
    ERR_READ_ONLY | int ERR_READ_ONLY
    ERR_CONVERSION | int ERR_CONVERSION
    ERR_DUP_DATAREP | int ERR_DUP_DATAREP
    ERR_UNSUPPORTED_DATAREP | int ERR_UNSUPPORTED_DATAREP
    ERR_UNSUPPORTED_OPERATION | int ERR_UNSUPPORTED_OPERATION
    ERR_NAME | int ERR_NAME
    ERR_NO_MEM | int ERR_NO_MEM
    ERR_NOT_SAME | int ERR_NOT_SAME
    ERR_PORT | int ERR_PORT
    ERR_QUOTA | int ERR_QUOTA
    ERR_SERVICE | int ERR_SERVICE
    ERR_SPAWN | int ERR_SPAWN
    ERR_BASE | int ERR_BASE
    ERR_SIZE | int ERR_SIZE
    ERR_DISP | int ERR_DISP
    ERR_ASSERT | int ERR_ASSERT
    ERR_LOCKTYPE | int ERR_LOCKTYPE
    ERR_RMA_CONFLICT | int ERR_RMA_CONFLICT
    ERR_RMA_SYNC | int ERR_RMA_SYNC
    ERR_RMA_RANGE | int ERR_RMA_RANGE
    ERR_RMA_ATTACH | int ERR_RMA_ATTACH
    ERR_RMA_SHARED | int ERR_RMA_SHARED
    ERR_RMA_FLAVOR | int ERR_RMA_FLAVOR
    ORDER_C | int ORDER_C
    ORDER_FORTRAN | int ORDER_FORTRAN
    ORDER_F | int ORDER_F
    TYPECLASS_INTEGER | int TYPECLASS_INTEGER
    TYPECLASS_REAL | int TYPECLASS_REAL
    TYPECLASS_COMPLEX | int TYPECLASS_COMPLEX
    DISTRIBUTE_NONE | int DISTRIBUTE_NONE
    DISTRIBUTE_BLOCK | int DISTRIBUTE_BLOCK
    DISTRIBUTE_CYCLIC | int DISTRIBUTE_CYCLIC
    DISTRIBUTE_DFLT_DARG | int DISTRIBUTE_DFLT_DARG
    COMBINER_NAMED | int COMBINER_NAMED
    COMBINER_DUP | int COMBINER_DUP
    COMBINER_CONTIGUOUS | int COMBINER_CONTIGUOUS
    COMBINER_VECTOR | int COMBINER_VECTOR
    COMBINER_HVECTOR | int COMBINER_HVECTOR
    COMBINER_INDEXED | int COMBINER_INDEXED
    COMBINER_HINDEXED | int COMBINER_HINDEXED
    COMBINER_INDEXED_BLOCK | int COMBINER_INDEXED_BLOCK
    COMBINER_HINDEXED_BLOCK | int COMBINER_HINDEXED_BLOCK
    COMBINER_STRUCT | int COMBINER_STRUCT
    COMBINER_SUBARRAY | int COMBINER_SUBARRAY
    COMBINER_DARRAY | int COMBINER_DARRAY
    COMBINER_RESIZED | int COMBINER_RESIZED
    COMBINER_F90_REAL | int COMBINER_F90_REAL
    COMBINER_F90_COMPLEX | int COMBINER_F90_COMPLEX
    COMBINER_F90_INTEGER | int COMBINER_F90_INTEGER
    IDENT | int IDENT
    CONGRUENT | int CONGRUENT
    SIMILAR | int SIMILAR
    UNEQUAL | int UNEQUAL
    CART | int CART
    GRAPH | int GRAPH
    DIST_GRAPH | int DIST_GRAPH
    UNWEIGHTED | int UNWEIGHTED
    WEIGHTS_EMPTY | int WEIGHTS_EMPTY
    COMM_TYPE_SHARED | int COMM_TYPE_SHARED
    BSEND_OVERHEAD | int BSEND_OVERHEAD
    WIN_FLAVOR_CREATE | int WIN_FLAVOR_CREATE
    WIN_FLAVOR_ALLOCATE | int WIN_FLAVOR_ALLOCATE
    WIN_FLAVOR_DYNAMIC | int WIN_FLAVOR_DYNAMIC
    WIN_FLAVOR_SHARED | int WIN_FLAVOR_SHARED
    WIN_SEPARATE | int WIN_SEPARATE
    WIN_UNIFIED | int WIN_UNIFIED
    MODE_NOCHECK | int MODE_NOCHECK
    MODE_NOSTORE | int MODE_NOSTORE
    MODE_NOPUT | int MODE_NOPUT
    MODE_NOPRECEDE | int MODE_NOPRECEDE
    MODE_NOSUCCEED | int MODE_NOSUCCEED
    LOCK_EXCLUSIVE | int LOCK_EXCLUSIVE
    LOCK_SHARED | int LOCK_SHARED
    MODE_RDONLY | int MODE_RDONLY
    MODE_WRONLY | int MODE_WRONLY
    MODE_RDWR | int MODE_RDWR
    MODE_CREATE | int MODE_CREATE
    MODE_EXCL | int MODE_EXCL
    MODE_DELETE_ON_CLOSE | int MODE_DELETE_ON_CLOSE
    MODE_UNIQUE_OPEN | int MODE_UNIQUE_OPEN
    MODE_SEQUENTIAL | int MODE_SEQUENTIAL
    MODE_APPEND | int MODE_APPEND
    SEEK_SET | int SEEK_SET
    SEEK_CUR | int SEEK_CUR
    SEEK_END | int SEEK_END
    DISPLACEMENT_CURRENT | int DISPLACEMENT_CURRENT
    DISP_CUR | int DISP_CUR
    THREAD_SINGLE | int THREAD_SINGLE
    THREAD_FUNNELED | int THREAD_FUNNELED
    THREAD_SERIALIZED | int THREAD_SERIALIZED
    THREAD_MULTIPLE | int THREAD_MULTIPLE
    VERSION | int VERSION
    SUBVERSION | int SUBVERSION
    MAX_PROCESSOR_NAME | int MAX_PROCESSOR_NAME
    MAX_ERROR_STRING | int MAX_ERROR_STRING
    MAX_PORT_NAME | int MAX_PORT_NAME
    MAX_INFO_KEY | int MAX_INFO_KEY
    MAX_INFO_VAL | int MAX_INFO_VAL
    MAX_OBJECT_NAME | int MAX_OBJECT_NAME
    MAX_DATAREP_STRING | int MAX_DATAREP_STRING
    MAX_LIBRARY_VERSION_STRING | int MAX_LIBRARY_VERSION_STRING
    DATATYPE_NULL | Datatype DATATYPE_NULL
    UB | Datatype UB
    LB | Datatype LB
    PACKED | Datatype PACKED
    BYTE | Datatype BYTE
    AINT | Datatype AINT
    OFFSET | Datatype OFFSET
    COUNT | Datatype COUNT
    CHAR | Datatype CHAR
    WCHAR | Datatype WCHAR
    SIGNED_CHAR | Datatype SIGNED_CHAR
    SHORT | Datatype SHORT
    INT | Datatype INT
    LONG | Datatype LONG
    LONG_LONG | Datatype LONG_LONG
    UNSIGNED_CHAR | Datatype UNSIGNED_CHAR
    UNSIGNED_SHORT | Datatype UNSIGNED_SHORT
    UNSIGNED | Datatype UNSIGNED
    UNSIGNED_LONG | Datatype UNSIGNED_LONG
    UNSIGNED_LONG_LONG | Datatype UNSIGNED_LONG_LONG
    FLOAT | Datatype FLOAT
    DOUBLE | Datatype DOUBLE
    LONG_DOUBLE | Datatype LONG_DOUBLE
    C_BOOL | Datatype C_BOOL
    INT8_T | Datatype INT8_T
    INT16_T | Datatype INT16_T
    INT32_T | Datatype INT32_T
    INT64_T | Datatype INT64_T
    UINT8_T | Datatype UINT8_T
    UINT16_T | Datatype UINT16_T
    UINT32_T | Datatype UINT32_T
    UINT64_T | Datatype UINT64_T
    C_COMPLEX | Datatype C_COMPLEX
    C_FLOAT_COMPLEX | Datatype C_FLOAT_COMPLEX
    C_DOUBLE_COMPLEX | Datatype C_DOUBLE_COMPLEX
    C_LONG_DOUBLE_COMPLEX | Datatype C_LONG_DOUBLE_COMPLEX
    CXX_BOOL | Datatype CXX_BOOL
    CXX_FLOAT_COMPLEX | Datatype CXX_FLOAT_COMPLEX
    CXX_DOUBLE_COMPLEX | Datatype CXX_DOUBLE_COMPLEX
    CXX_LONG_DOUBLE_COMPLEX | Datatype CXX_LONG_DOUBLE_COMPLEX
    SHORT_INT | Datatype SHORT_INT
    INT_INT | Datatype INT_INT
    TWOINT | Datatype TWOINT
    LONG_INT | Datatype LONG_INT
    FLOAT_INT | Datatype FLOAT_INT
    DOUBLE_INT | Datatype DOUBLE_INT
    LONG_DOUBLE_INT | Datatype LONG_DOUBLE_INT
    CHARACTER | Datatype CHARACTER
    LOGICAL | Datatype LOGICAL
    INTEGER | Datatype INTEGER
    REAL | Datatype REAL
    DOUBLE_PRECISION | Datatype DOUBLE_PRECISION
    COMPLEX | Datatype COMPLEX
    DOUBLE_COMPLEX | Datatype DOUBLE_COMPLEX
    LOGICAL1 | Datatype LOGICAL1
    LOGICAL2 | Datatype LOGICAL2
    LOGICAL4 | Datatype LOGICAL4
    LOGICAL8 | Datatype LOGICAL8
    INTEGER1 | Datatype INTEGER1
    INTEGER2 | Datatype INTEGER2
    INTEGER4 | Datatype INTEGER4
    INTEGER8 | Datatype INTEGER8
    INTEGER16 | Datatype INTEGER16
    REAL2 | Datatype REAL2
    REAL4 | Datatype REAL4
    REAL8 | Datatype REAL8
    REAL16 | Datatype REAL16
    COMPLEX4 | Datatype COMPLEX4
    COMPLEX8 | Datatype COMPLEX8
    COMPLEX16 | Datatype COMPLEX16
    COMPLEX32 | Datatype COMPLEX32
    UNSIGNED_INT | Datatype UNSIGNED_INT
    SIGNED_SHORT | Datatype SIGNED_SHORT
    SIGNED_INT | Datatype SIGNED_INT
    SIGNED_LONG | Datatype SIGNED_LONG
    SIGNED_LONG_LONG | Datatype SIGNED_LONG_LONG
    BOOL | Datatype BOOL
    SINT8_T | Datatype SINT8_T
    SINT16_T | Datatype SINT16_T
    SINT32_T | Datatype SINT32_T
    SINT64_T | Datatype SINT64_T
    F_BOOL | Datatype F_BOOL
    F_INT | Datatype F_INT
    F_FLOAT | Datatype F_FLOAT
    F_DOUBLE | Datatype F_DOUBLE
    F_COMPLEX | Datatype F_COMPLEX
    F_FLOAT_COMPLEX | Datatype F_FLOAT_COMPLEX
    F_DOUBLE_COMPLEX | Datatype F_DOUBLE_COMPLEX
    REQUEST_NULL | Request REQUEST_NULL
    MESSAGE_NULL | Message MESSAGE_NULL
    MESSAGE_NO_PROC | Message MESSAGE_NO_PROC
    OP_NULL | Op OP_NULL
    MAX | Op MAX
    MIN | Op MIN
    SUM | Op SUM
    PROD | Op PROD
    LAND | Op LAND
    BAND | Op BAND
    LOR | Op LOR
    BOR | Op BOR
    LXOR | Op LXOR
    BXOR | Op BXOR
    MAXLOC | Op MAXLOC
    MINLOC | Op MINLOC
    REPLACE | Op REPLACE
    NO_OP | Op NO_OP
    GROUP_NULL | Group GROUP_NULL
    GROUP_EMPTY | Group GROUP_EMPTY
    INFO_NULL | Info INFO_NULL
    INFO_ENV | Info INFO_ENV
    ERRHANDLER_NULL | Errhandler ERRHANDLER_NULL
    ERRORS_RETURN | Errhandler ERRORS_RETURN
    ERRORS_ARE_FATAL | Errhandler ERRORS_ARE_FATAL
    COMM_NULL | Comm COMM_NULL
    COMM_SELF | Intracomm COMM_SELF
    COMM_WORLD | Intracomm COMM_WORLD
    WIN_NULL | Win WIN_NULL
    FILE_NULL | File FILE_NULL
    pickle | Pickle pickle

  mpi4py.MPI.UNDEFINED

mpi4py.MPI.UNDEFINED: int = UNDEFINED
    int UNDEFINED

  mpi4py.MPI.ANY_SOURCE

mpi4py.MPI.ANY_SOURCE: int = ANY_SOURCE
    int ANY_SOURCE

  mpi4py.MPI.ANY_TAG

mpi4py.MPI.ANY_TAG: int = ANY_TAG
    int ANY_TAG

  mpi4py.MPI.PROC_NULL

mpi4py.MPI.PROC_NULL: int = PROC_NULL
    int PROC_NULL

  mpi4py.MPI.ROOT

mpi4py.MPI.ROOT: int = ROOT
    int ROOT

  mpi4py.MPI.BOTTOM

mpi4py.MPI.BOTTOM: Bottom = BOTTOM
    Bottom BOTTOM

  mpi4py.MPI.IN_PLACE

mpi4py.MPI.IN_PLACE: InPlace = IN_PLACE
    InPlace IN_PLACE

  mpi4py.MPI.KEYVAL_INVALID

mpi4py.MPI.KEYVAL_INVALID: int = KEYVAL_INVALID
    int KEYVAL_INVALID

  mpi4py.MPI.TAG_UB

mpi4py.MPI.TAG_UB: int = TAG_UB
    int TAG_UB

  mpi4py.MPI.HOST

mpi4py.MPI.HOST: int = HOST
    int HOST

  mpi4py.MPI.IO

mpi4py.MPI.IO: int = IO
    int IO

  mpi4py.MPI.WTIME_IS_GLOBAL

mpi4py.MPI.WTIME_IS_GLOBAL: int = WTIME_IS_GLOBAL
    int WTIME_IS_GLOBAL

  mpi4py.MPI.UNIVERSE_SIZE

mpi4py.MPI.UNIVERSE_SIZE: int = UNIVERSE_SIZE
    int UNIVERSE_SIZE

  mpi4py.MPI.APPNUM

mpi4py.MPI.APPNUM: int = APPNUM
    int APPNUM

  mpi4py.MPI.LASTUSEDCODE

mpi4py.MPI.LASTUSEDCODE: int = LASTUSEDCODE
    int LASTUSEDCODE

  mpi4py.MPI.WIN_BASE

mpi4py.MPI.WIN_BASE: int = WIN_BASE
    int WIN_BASE

  mpi4py.MPI.WIN_SIZE

mpi4py.MPI.WIN_SIZE: int = WIN_SIZE
    int WIN_SIZE

  mpi4py.MPI.WIN_DISP_UNIT

mpi4py.MPI.WIN_DISP_UNIT: int = WIN_DISP_UNIT
    int WIN_DISP_UNIT

  mpi4py.MPI.WIN_CREATE_FLAVOR

mpi4py.MPI.WIN_CREATE_FLAVOR: int = WIN_CREATE_FLAVOR
    int WIN_CREATE_FLAVOR

  mpi4py.MPI.WIN_FLAVOR

mpi4py.MPI.WIN_FLAVOR: int = WIN_FLAVOR
    int WIN_FLAVOR

  mpi4py.MPI.WIN_MODEL

mpi4py.MPI.WIN_MODEL: int = WIN_MODEL
    int WIN_MODEL

  mpi4py.MPI.SUCCESS

mpi4py.MPI.SUCCESS: int = SUCCESS
    int SUCCESS

  mpi4py.MPI.ERR_LASTCODE

mpi4py.MPI.ERR_LASTCODE: int = ERR_LASTCODE
    int ERR_LASTCODE

  mpi4py.MPI.ERR_COMM

mpi4py.MPI.ERR_COMM: int = ERR_COMM
    int ERR_COMM

  mpi4py.MPI.ERR_GROUP

mpi4py.MPI.ERR_GROUP: int = ERR_GROUP
    int ERR_GROUP

  mpi4py.MPI.ERR_TYPE

mpi4py.MPI.ERR_TYPE: int = ERR_TYPE
    int ERR_TYPE

  mpi4py.MPI.ERR_REQUEST

mpi4py.MPI.ERR_REQUEST: int = ERR_REQUEST
    int ERR_REQUEST

  mpi4py.MPI.ERR_OP

mpi4py.MPI.ERR_OP: int = ERR_OP
    int ERR_OP

  mpi4py.MPI.ERR_BUFFER

mpi4py.MPI.ERR_BUFFER: int = ERR_BUFFER
    int ERR_BUFFER

  mpi4py.MPI.ERR_COUNT

mpi4py.MPI.ERR_COUNT: int = ERR_COUNT
    int ERR_COUNT

  mpi4py.MPI.ERR_TAG

mpi4py.MPI.ERR_TAG: int = ERR_TAG
    int ERR_TAG

  mpi4py.MPI.ERR_RANK

mpi4py.MPI.ERR_RANK: int = ERR_RANK
    int ERR_RANK

  mpi4py.MPI.ERR_ROOT

mpi4py.MPI.ERR_ROOT: int = ERR_ROOT
    int ERR_ROOT

  mpi4py.MPI.ERR_TRUNCATE

mpi4py.MPI.ERR_TRUNCATE: int = ERR_TRUNCATE
    int ERR_TRUNCATE

  mpi4py.MPI.ERR_IN_STATUS

mpi4py.MPI.ERR_IN_STATUS: int = ERR_IN_STATUS
    int ERR_IN_STATUS

  mpi4py.MPI.ERR_PENDING

mpi4py.MPI.ERR_PENDING: int = ERR_PENDING
    int ERR_PENDING

  mpi4py.MPI.ERR_TOPOLOGY

mpi4py.MPI.ERR_TOPOLOGY: int = ERR_TOPOLOGY
    int ERR_TOPOLOGY

  mpi4py.MPI.ERR_DIMS

mpi4py.MPI.ERR_DIMS: int = ERR_DIMS
    int ERR_DIMS

  mpi4py.MPI.ERR_ARG

mpi4py.MPI.ERR_ARG: int = ERR_ARG
    int ERR_ARG

  mpi4py.MPI.ERR_OTHER

mpi4py.MPI.ERR_OTHER: int = ERR_OTHER
    int ERR_OTHER

  mpi4py.MPI.ERR_UNKNOWN

mpi4py.MPI.ERR_UNKNOWN: int = ERR_UNKNOWN
    int ERR_UNKNOWN

  mpi4py.MPI.ERR_INTERN

mpi4py.MPI.ERR_INTERN: int = ERR_INTERN
    int ERR_INTERN

  mpi4py.MPI.ERR_INFO

mpi4py.MPI.ERR_INFO: int = ERR_INFO
    int ERR_INFO

  mpi4py.MPI.ERR_FILE

mpi4py.MPI.ERR_FILE: int = ERR_FILE
    int ERR_FILE

  mpi4py.MPI.ERR_WIN

mpi4py.MPI.ERR_WIN: int = ERR_WIN
    int ERR_WIN

  mpi4py.MPI.ERR_KEYVAL

mpi4py.MPI.ERR_KEYVAL: int = ERR_KEYVAL
    int ERR_KEYVAL

  mpi4py.MPI.ERR_INFO_KEY

mpi4py.MPI.ERR_INFO_KEY: int = ERR_INFO_KEY
    int ERR_INFO_KEY

  mpi4py.MPI.ERR_INFO_VALUE

mpi4py.MPI.ERR_INFO_VALUE: int = ERR_INFO_VALUE
    int ERR_INFO_VALUE

  mpi4py.MPI.ERR_INFO_NOKEY

mpi4py.MPI.ERR_INFO_NOKEY: int = ERR_INFO_NOKEY
    int ERR_INFO_NOKEY

  mpi4py.MPI.ERR_ACCESS

mpi4py.MPI.ERR_ACCESS: int = ERR_ACCESS
    int ERR_ACCESS

  mpi4py.MPI.ERR_AMODE

mpi4py.MPI.ERR_AMODE: int = ERR_AMODE
    int ERR_AMODE

  mpi4py.MPI.ERR_BAD_FILE

mpi4py.MPI.ERR_BAD_FILE: int = ERR_BAD_FILE
    int ERR_BAD_FILE

  mpi4py.MPI.ERR_FILE_EXISTS

mpi4py.MPI.ERR_FILE_EXISTS: int = ERR_FILE_EXISTS
    int ERR_FILE_EXISTS

  mpi4py.MPI.ERR_FILE_IN_USE

mpi4py.MPI.ERR_FILE_IN_USE: int = ERR_FILE_IN_USE
    int ERR_FILE_IN_USE

  mpi4py.MPI.ERR_NO_SPACE

mpi4py.MPI.ERR_NO_SPACE: int = ERR_NO_SPACE
    int ERR_NO_SPACE

  mpi4py.MPI.ERR_NO_SUCH_FILE

mpi4py.MPI.ERR_NO_SUCH_FILE: int = ERR_NO_SUCH_FILE
    int ERR_NO_SUCH_FILE

  mpi4py.MPI.ERR_IO

mpi4py.MPI.ERR_IO: int = ERR_IO
    int ERR_IO

  mpi4py.MPI.ERR_READ_ONLY

mpi4py.MPI.ERR_READ_ONLY: int = ERR_READ_ONLY
    int ERR_READ_ONLY

  mpi4py.MPI.ERR_CONVERSION

mpi4py.MPI.ERR_CONVERSION: int = ERR_CONVERSION
    int ERR_CONVERSION

  mpi4py.MPI.ERR_DUP_DATAREP

mpi4py.MPI.ERR_DUP_DATAREP: int = ERR_DUP_DATAREP
    int ERR_DUP_DATAREP

  mpi4py.MPI.ERR_UNSUPPORTED_DATAREP

mpi4py.MPI.ERR_UNSUPPORTED_DATAREP: int = ERR_UNSUPPORTED_DATAREP
    int ERR_UNSUPPORTED_DATAREP

  mpi4py.MPI.ERR_UNSUPPORTED_OPERATION

mpi4py.MPI.ERR_UNSUPPORTED_OPERATION: int = ERR_UNSUPPORTED_OPERATION
    int ERR_UNSUPPORTED_OPERATION

  mpi4py.MPI.ERR_NAME

mpi4py.MPI.ERR_NAME: int = ERR_NAME
    int ERR_NAME

  mpi4py.MPI.ERR_NO_MEM

mpi4py.MPI.ERR_NO_MEM: int = ERR_NO_MEM
    int ERR_NO_MEM

  mpi4py.MPI.ERR_NOT_SAME

mpi4py.MPI.ERR_NOT_SAME: int = ERR_NOT_SAME
    int ERR_NOT_SAME

  mpi4py.MPI.ERR_PORT

mpi4py.MPI.ERR_PORT: int = ERR_PORT
    int ERR_PORT

  mpi4py.MPI.ERR_QUOTA

mpi4py.MPI.ERR_QUOTA: int = ERR_QUOTA
    int ERR_QUOTA

  mpi4py.MPI.ERR_SERVICE

mpi4py.MPI.ERR_SERVICE: int = ERR_SERVICE
    int ERR_SERVICE

  mpi4py.MPI.ERR_SPAWN

mpi4py.MPI.ERR_SPAWN: int = ERR_SPAWN
    int ERR_SPAWN

  mpi4py.MPI.ERR_BASE

mpi4py.MPI.ERR_BASE: int = ERR_BASE
    int ERR_BASE

  mpi4py.MPI.ERR_SIZE

mpi4py.MPI.ERR_SIZE: int = ERR_SIZE
    int ERR_SIZE

  mpi4py.MPI.ERR_DISP

mpi4py.MPI.ERR_DISP: int = ERR_DISP
    int ERR_DISP

  mpi4py.MPI.ERR_ASSERT

mpi4py.MPI.ERR_ASSERT: int = ERR_ASSERT
    int ERR_ASSERT

  mpi4py.MPI.ERR_LOCKTYPE

mpi4py.MPI.ERR_LOCKTYPE: int = ERR_LOCKTYPE
    int ERR_LOCKTYPE

  mpi4py.MPI.ERR_RMA_CONFLICT

mpi4py.MPI.ERR_RMA_CONFLICT: int = ERR_RMA_CONFLICT
    int ERR_RMA_CONFLICT

  mpi4py.MPI.ERR_RMA_SYNC

mpi4py.MPI.ERR_RMA_SYNC: int = ERR_RMA_SYNC
    int ERR_RMA_SYNC

  mpi4py.MPI.ERR_RMA_RANGE

mpi4py.MPI.ERR_RMA_RANGE: int = ERR_RMA_RANGE
    int ERR_RMA_RANGE

  mpi4py.MPI.ERR_RMA_ATTACH

mpi4py.MPI.ERR_RMA_ATTACH: int = ERR_RMA_ATTACH
    int ERR_RMA_ATTACH

  mpi4py.MPI.ERR_RMA_SHARED

mpi4py.MPI.ERR_RMA_SHARED: int = ERR_RMA_SHARED
    int ERR_RMA_SHARED

  mpi4py.MPI.ERR_RMA_FLAVOR

mpi4py.MPI.ERR_RMA_FLAVOR: int = ERR_RMA_FLAVOR
    int ERR_RMA_FLAVOR

  mpi4py.MPI.ORDER_C

mpi4py.MPI.ORDER_C: int = ORDER_C
    int ORDER_C

  mpi4py.MPI.ORDER_FORTRAN

mpi4py.MPI.ORDER_FORTRAN: int = ORDER_FORTRAN
    int ORDER_FORTRAN

  mpi4py.MPI.ORDER_F

mpi4py.MPI.ORDER_F: int = ORDER_F
    int ORDER_F

  mpi4py.MPI.TYPECLASS_INTEGER

mpi4py.MPI.TYPECLASS_INTEGER: int = TYPECLASS_INTEGER
    int TYPECLASS_INTEGER

  mpi4py.MPI.TYPECLASS_REAL

mpi4py.MPI.TYPECLASS_REAL: int = TYPECLASS_REAL
    int TYPECLASS_REAL

  mpi4py.MPI.TYPECLASS_COMPLEX

mpi4py.MPI.TYPECLASS_COMPLEX: int = TYPECLASS_COMPLEX
    int TYPECLASS_COMPLEX

  mpi4py.MPI.DISTRIBUTE_NONE

mpi4py.MPI.DISTRIBUTE_NONE: int = DISTRIBUTE_NONE
    int DISTRIBUTE_NONE

  mpi4py.MPI.DISTRIBUTE_BLOCK

mpi4py.MPI.DISTRIBUTE_BLOCK: int = DISTRIBUTE_BLOCK
    int DISTRIBUTE_BLOCK

  mpi4py.MPI.DISTRIBUTE_CYCLIC

mpi4py.MPI.DISTRIBUTE_CYCLIC: int = DISTRIBUTE_CYCLIC
    int DISTRIBUTE_CYCLIC

  mpi4py.MPI.DISTRIBUTE_DFLT_DARG

mpi4py.MPI.DISTRIBUTE_DFLT_DARG: int = DISTRIBUTE_DFLT_DARG
    int DISTRIBUTE_DFLT_DARG

  mpi4py.MPI.COMBINER_NAMED

mpi4py.MPI.COMBINER_NAMED: int = COMBINER_NAMED
    int COMBINER_NAMED

  mpi4py.MPI.COMBINER_DUP

mpi4py.MPI.COMBINER_DUP: int = COMBINER_DUP
    int COMBINER_DUP

  mpi4py.MPI.COMBINER_CONTIGUOUS

mpi4py.MPI.COMBINER_CONTIGUOUS: int = COMBINER_CONTIGUOUS
    int COMBINER_CONTIGUOUS

  mpi4py.MPI.COMBINER_VECTOR

mpi4py.MPI.COMBINER_VECTOR: int = COMBINER_VECTOR
    int COMBINER_VECTOR

  mpi4py.MPI.COMBINER_HVECTOR

mpi4py.MPI.COMBINER_HVECTOR: int = COMBINER_HVECTOR
    int COMBINER_HVECTOR

  mpi4py.MPI.COMBINER_INDEXED

mpi4py.MPI.COMBINER_INDEXED: int = COMBINER_INDEXED
    int COMBINER_INDEXED

  mpi4py.MPI.COMBINER_HINDEXED

mpi4py.MPI.COMBINER_HINDEXED: int = COMBINER_HINDEXED
    int COMBINER_HINDEXED

  mpi4py.MPI.COMBINER_INDEXED_BLOCK

mpi4py.MPI.COMBINER_INDEXED_BLOCK: int = COMBINER_INDEXED_BLOCK
    int COMBINER_INDEXED_BLOCK

  mpi4py.MPI.COMBINER_HINDEXED_BLOCK

mpi4py.MPI.COMBINER_HINDEXED_BLOCK: int = COMBINER_HINDEXED_BLOCK
    int COMBINER_HINDEXED_BLOCK

  mpi4py.MPI.COMBINER_STRUCT

mpi4py.MPI.COMBINER_STRUCT: int = COMBINER_STRUCT
    int COMBINER_STRUCT

  mpi4py.MPI.COMBINER_SUBARRAY

mpi4py.MPI.COMBINER_SUBARRAY: int = COMBINER_SUBARRAY
    int COMBINER_SUBARRAY

  mpi4py.MPI.COMBINER_DARRAY

mpi4py.MPI.COMBINER_DARRAY: int = COMBINER_DARRAY
    int COMBINER_DARRAY

  mpi4py.MPI.COMBINER_RESIZED

mpi4py.MPI.COMBINER_RESIZED: int = COMBINER_RESIZED
    int COMBINER_RESIZED

  mpi4py.MPI.COMBINER_F90_REAL

mpi4py.MPI.COMBINER_F90_REAL: int = COMBINER_F90_REAL
    int COMBINER_F90_REAL

  mpi4py.MPI.COMBINER_F90_COMPLEX

mpi4py.MPI.COMBINER_F90_COMPLEX: int = COMBINER_F90_COMPLEX
    int COMBINER_F90_COMPLEX

  mpi4py.MPI.COMBINER_F90_INTEGER

mpi4py.MPI.COMBINER_F90_INTEGER: int = COMBINER_F90_INTEGER
    int COMBINER_F90_INTEGER

  mpi4py.MPI.IDENT

mpi4py.MPI.IDENT: int = IDENT
    int IDENT

  mpi4py.MPI.CONGRUENT

mpi4py.MPI.CONGRUENT: int = CONGRUENT
    int CONGRUENT

  mpi4py.MPI.SIMILAR

mpi4py.MPI.SIMILAR: int = SIMILAR
    int SIMILAR

  mpi4py.MPI.UNEQUAL

mpi4py.MPI.UNEQUAL: int = UNEQUAL
    int UNEQUAL

  mpi4py.MPI.CART

mpi4py.MPI.CART: int = CART
    int CART

  mpi4py.MPI.GRAPH

mpi4py.MPI.GRAPH: int = GRAPH
    int GRAPH

  mpi4py.MPI.DIST_GRAPH

mpi4py.MPI.DIST_GRAPH: int = DIST_GRAPH
    int DIST_GRAPH

  mpi4py.MPI.UNWEIGHTED

mpi4py.MPI.UNWEIGHTED: int = UNWEIGHTED
    int UNWEIGHTED

  mpi4py.MPI.WEIGHTS_EMPTY

mpi4py.MPI.WEIGHTS_EMPTY: int = WEIGHTS_EMPTY
    int WEIGHTS_EMPTY

  mpi4py.MPI.COMM_TYPE_SHARED

mpi4py.MPI.COMM_TYPE_SHARED: int = COMM_TYPE_SHARED
    int COMM_TYPE_SHARED

  mpi4py.MPI.BSEND_OVERHEAD

mpi4py.MPI.BSEND_OVERHEAD: int = BSEND_OVERHEAD
    int BSEND_OVERHEAD

  mpi4py.MPI.WIN_FLAVOR_CREATE

mpi4py.MPI.WIN_FLAVOR_CREATE: int = WIN_FLAVOR_CREATE
    int WIN_FLAVOR_CREATE

  mpi4py.MPI.WIN_FLAVOR_ALLOCATE

mpi4py.MPI.WIN_FLAVOR_ALLOCATE: int = WIN_FLAVOR_ALLOCATE
    int WIN_FLAVOR_ALLOCATE

  mpi4py.MPI.WIN_FLAVOR_DYNAMIC

mpi4py.MPI.WIN_FLAVOR_DYNAMIC: int = WIN_FLAVOR_DYNAMIC
    int WIN_FLAVOR_DYNAMIC

  mpi4py.MPI.WIN_FLAVOR_SHARED

mpi4py.MPI.WIN_FLAVOR_SHARED: int = WIN_FLAVOR_SHARED
    int WIN_FLAVOR_SHARED

  mpi4py.MPI.WIN_SEPARATE

mpi4py.MPI.WIN_SEPARATE: int = WIN_SEPARATE
    int WIN_SEPARATE

  mpi4py.MPI.WIN_UNIFIED

mpi4py.MPI.WIN_UNIFIED: int = WIN_UNIFIED
    int WIN_UNIFIED

  mpi4py.MPI.MODE_NOCHECK

mpi4py.MPI.MODE_NOCHECK: int = MODE_NOCHECK
    int MODE_NOCHECK

  mpi4py.MPI.MODE_NOSTORE

mpi4py.MPI.MODE_NOSTORE: int = MODE_NOSTORE
    int MODE_NOSTORE

  mpi4py.MPI.MODE_NOPUT

mpi4py.MPI.MODE_NOPUT: int = MODE_NOPUT
    int MODE_NOPUT

  mpi4py.MPI.MODE_NOPRECEDE

mpi4py.MPI.MODE_NOPRECEDE: int = MODE_NOPRECEDE
    int MODE_NOPRECEDE

  mpi4py.MPI.MODE_NOSUCCEED

mpi4py.MPI.MODE_NOSUCCEED: int = MODE_NOSUCCEED
    int MODE_NOSUCCEED

  mpi4py.MPI.LOCK_EXCLUSIVE

mpi4py.MPI.LOCK_EXCLUSIVE: int = LOCK_EXCLUSIVE
    int LOCK_EXCLUSIVE

  mpi4py.MPI.LOCK_SHARED

mpi4py.MPI.LOCK_SHARED: int = LOCK_SHARED
    int LOCK_SHARED

  mpi4py.MPI.MODE_RDONLY

mpi4py.MPI.MODE_RDONLY: int = MODE_RDONLY
    int MODE_RDONLY

  mpi4py.MPI.MODE_WRONLY

mpi4py.MPI.MODE_WRONLY: int = MODE_WRONLY
    int MODE_WRONLY

  mpi4py.MPI.MODE_RDWR

mpi4py.MPI.MODE_RDWR: int = MODE_RDWR
    int MODE_RDWR

  mpi4py.MPI.MODE_CREATE

mpi4py.MPI.MODE_CREATE: int = MODE_CREATE
    int MODE_CREATE

  mpi4py.MPI.MODE_EXCL

mpi4py.MPI.MODE_EXCL: int = MODE_EXCL
    int MODE_EXCL

  mpi4py.MPI.MODE_DELETE_ON_CLOSE

mpi4py.MPI.MODE_DELETE_ON_CLOSE: int = MODE_DELETE_ON_CLOSE
    int MODE_DELETE_ON_CLOSE

  mpi4py.MPI.MODE_UNIQUE_OPEN

mpi4py.MPI.MODE_UNIQUE_OPEN: int = MODE_UNIQUE_OPEN
    int MODE_UNIQUE_OPEN

  mpi4py.MPI.MODE_SEQUENTIAL

mpi4py.MPI.MODE_SEQUENTIAL: int = MODE_SEQUENTIAL
    int MODE_SEQUENTIAL

  mpi4py.MPI.MODE_APPEND

mpi4py.MPI.MODE_APPEND: int = MODE_APPEND
    int MODE_APPEND

  mpi4py.MPI.SEEK_SET

mpi4py.MPI.SEEK_SET: int = SEEK_SET
    int SEEK_SET

  mpi4py.MPI.SEEK_CUR

mpi4py.MPI.SEEK_CUR: int = SEEK_CUR
    int SEEK_CUR

  mpi4py.MPI.SEEK_END

mpi4py.MPI.SEEK_END: int = SEEK_END
    int SEEK_END

  mpi4py.MPI.DISPLACEMENT_CURRENT

mpi4py.MPI.DISPLACEMENT_CURRENT: int = DISPLACEMENT_CURRENT
    int DISPLACEMENT_CURRENT

  mpi4py.MPI.DISP_CUR

mpi4py.MPI.DISP_CUR: int = DISP_CUR
    int DISP_CUR

  mpi4py.MPI.THREAD_SINGLE

mpi4py.MPI.THREAD_SINGLE: int = THREAD_SINGLE
    int THREAD_SINGLE

  mpi4py.MPI.THREAD_FUNNELED

mpi4py.MPI.THREAD_FUNNELED: int = THREAD_FUNNELED
    int THREAD_FUNNELED

  mpi4py.MPI.THREAD_SERIALIZED

mpi4py.MPI.THREAD_SERIALIZED: int = THREAD_SERIALIZED
    int THREAD_SERIALIZED

  mpi4py.MPI.THREAD_MULTIPLE

mpi4py.MPI.THREAD_MULTIPLE: int = THREAD_MULTIPLE
    int THREAD_MULTIPLE

  mpi4py.MPI.VERSION

mpi4py.MPI.VERSION: int = VERSION
    int VERSION

  mpi4py.MPI.SUBVERSION

mpi4py.MPI.SUBVERSION: int = SUBVERSION
    int SUBVERSION

  mpi4py.MPI.MAX_PROCESSOR_NAME

mpi4py.MPI.MAX_PROCESSOR_NAME: int = MAX_PROCESSOR_NAME
    int MAX_PROCESSOR_NAME

  mpi4py.MPI.MAX_ERROR_STRING

mpi4py.MPI.MAX_ERROR_STRING: int = MAX_ERROR_STRING
    int MAX_ERROR_STRING

  mpi4py.MPI.MAX_PORT_NAME

mpi4py.MPI.MAX_PORT_NAME: int = MAX_PORT_NAME
    int MAX_PORT_NAME

  mpi4py.MPI.MAX_INFO_KEY

mpi4py.MPI.MAX_INFO_KEY: int = MAX_INFO_KEY
    int MAX_INFO_KEY

  mpi4py.MPI.MAX_INFO_VAL

mpi4py.MPI.MAX_INFO_VAL: int = MAX_INFO_VAL
    int MAX_INFO_VAL

  mpi4py.MPI.MAX_OBJECT_NAME

mpi4py.MPI.MAX_OBJECT_NAME: int = MAX_OBJECT_NAME
    int MAX_OBJECT_NAME

  mpi4py.MPI.MAX_DATAREP_STRING

mpi4py.MPI.MAX_DATAREP_STRING: int = MAX_DATAREP_STRING
    int MAX_DATAREP_STRING

  mpi4py.MPI.MAX_LIBRARY_VERSION_STRING

mpi4py.MPI.MAX_LIBRARY_VERSION_STRING: int = MAX_LIBRARY_VERSION_STRING
    int MAX_LIBRARY_VERSION_STRING

  mpi4py.MPI.DATATYPE_NULL

mpi4py.MPI.DATATYPE_NULL: Datatype = DATATYPE_NULL
    Datatype DATATYPE_NULL

  mpi4py.MPI.UB

mpi4py.MPI.UB: Datatype = UB
    Datatype UB

  mpi4py.MPI.LB

mpi4py.MPI.LB: Datatype = LB
    Datatype LB

  mpi4py.MPI.PACKED

mpi4py.MPI.PACKED: Datatype = PACKED
    Datatype PACKED

  mpi4py.MPI.BYTE

mpi4py.MPI.BYTE: Datatype = BYTE
    Datatype BYTE

  mpi4py.MPI.AINT

mpi4py.MPI.AINT: Datatype = AINT
    Datatype AINT

  mpi4py.MPI.OFFSET

mpi4py.MPI.OFFSET: Datatype = OFFSET
    Datatype OFFSET

  mpi4py.MPI.COUNT

mpi4py.MPI.COUNT: Datatype = COUNT
    Datatype COUNT

  mpi4py.MPI.CHAR

mpi4py.MPI.CHAR: Datatype = CHAR
    Datatype CHAR

  mpi4py.MPI.WCHAR

mpi4py.MPI.WCHAR: Datatype = WCHAR
    Datatype WCHAR

  mpi4py.MPI.SIGNED_CHAR

mpi4py.MPI.SIGNED_CHAR: Datatype = SIGNED_CHAR
    Datatype SIGNED_CHAR

  mpi4py.MPI.SHORT

mpi4py.MPI.SHORT: Datatype = SHORT
    Datatype SHORT

  mpi4py.MPI.INT

mpi4py.MPI.INT: Datatype = INT
    Datatype INT

  mpi4py.MPI.LONG

mpi4py.MPI.LONG: Datatype = LONG
    Datatype LONG

  mpi4py.MPI.LONG_LONG

mpi4py.MPI.LONG_LONG: Datatype = LONG_LONG
    Datatype LONG_LONG

  mpi4py.MPI.UNSIGNED_CHAR

mpi4py.MPI.UNSIGNED_CHAR: Datatype = UNSIGNED_CHAR
    Datatype UNSIGNED_CHAR

  mpi4py.MPI.UNSIGNED_SHORT

mpi4py.MPI.UNSIGNED_SHORT: Datatype = UNSIGNED_SHORT
    Datatype UNSIGNED_SHORT

  mpi4py.MPI.UNSIGNED

mpi4py.MPI.UNSIGNED: Datatype = UNSIGNED
    Datatype UNSIGNED

  mpi4py.MPI.UNSIGNED_LONG

mpi4py.MPI.UNSIGNED_LONG: Datatype = UNSIGNED_LONG
    Datatype UNSIGNED_LONG

  mpi4py.MPI.UNSIGNED_LONG_LONG

mpi4py.MPI.UNSIGNED_LONG_LONG: Datatype = UNSIGNED_LONG_LONG
    Datatype UNSIGNED_LONG_LONG

  mpi4py.MPI.FLOAT

mpi4py.MPI.FLOAT: Datatype = FLOAT
    Datatype FLOAT

  mpi4py.MPI.DOUBLE

mpi4py.MPI.DOUBLE: Datatype = DOUBLE
    Datatype DOUBLE

  mpi4py.MPI.LONG_DOUBLE

mpi4py.MPI.LONG_DOUBLE: Datatype = LONG_DOUBLE
    Datatype LONG_DOUBLE

  mpi4py.MPI.C_BOOL

mpi4py.MPI.C_BOOL: Datatype = C_BOOL
    Datatype C_BOOL

  mpi4py.MPI.INT8_T

mpi4py.MPI.INT8_T: Datatype = INT8_T
    Datatype INT8_T

  mpi4py.MPI.INT16_T

mpi4py.MPI.INT16_T: Datatype = INT16_T
    Datatype INT16_T

  mpi4py.MPI.INT32_T

mpi4py.MPI.INT32_T: Datatype = INT32_T
    Datatype INT32_T

  mpi4py.MPI.INT64_T

mpi4py.MPI.INT64_T: Datatype = INT64_T
    Datatype INT64_T

  mpi4py.MPI.UINT8_T

mpi4py.MPI.UINT8_T: Datatype = UINT8_T
    Datatype UINT8_T

  mpi4py.MPI.UINT16_T

mpi4py.MPI.UINT16_T: Datatype = UINT16_T
    Datatype UINT16_T

  mpi4py.MPI.UINT32_T

mpi4py.MPI.UINT32_T: Datatype = UINT32_T
    Datatype UINT32_T

  mpi4py.MPI.UINT64_T

mpi4py.MPI.UINT64_T: Datatype = UINT64_T
    Datatype UINT64_T

  mpi4py.MPI.C_COMPLEX

mpi4py.MPI.C_COMPLEX: Datatype = C_COMPLEX
    Datatype C_COMPLEX

  mpi4py.MPI.C_FLOAT_COMPLEX

mpi4py.MPI.C_FLOAT_COMPLEX: Datatype = C_FLOAT_COMPLEX
    Datatype C_FLOAT_COMPLEX

  mpi4py.MPI.C_DOUBLE_COMPLEX

mpi4py.MPI.C_DOUBLE_COMPLEX: Datatype = C_DOUBLE_COMPLEX
    Datatype C_DOUBLE_COMPLEX

  mpi4py.MPI.C_LONG_DOUBLE_COMPLEX

mpi4py.MPI.C_LONG_DOUBLE_COMPLEX: Datatype = C_LONG_DOUBLE_COMPLEX
    Datatype C_LONG_DOUBLE_COMPLEX

  mpi4py.MPI.CXX_BOOL

mpi4py.MPI.CXX_BOOL: Datatype = CXX_BOOL
    Datatype CXX_BOOL

  mpi4py.MPI.CXX_FLOAT_COMPLEX

mpi4py.MPI.CXX_FLOAT_COMPLEX: Datatype = CXX_FLOAT_COMPLEX
    Datatype CXX_FLOAT_COMPLEX

  mpi4py.MPI.CXX_DOUBLE_COMPLEX

mpi4py.MPI.CXX_DOUBLE_COMPLEX: Datatype = CXX_DOUBLE_COMPLEX
    Datatype CXX_DOUBLE_COMPLEX

  mpi4py.MPI.CXX_LONG_DOUBLE_COMPLEX

mpi4py.MPI.CXX_LONG_DOUBLE_COMPLEX: Datatype = CXX_LONG_DOUBLE_COMPLEX
    Datatype CXX_LONG_DOUBLE_COMPLEX

  mpi4py.MPI.SHORT_INT

mpi4py.MPI.SHORT_INT: Datatype = SHORT_INT
    Datatype SHORT_INT

  mpi4py.MPI.INT_INT

mpi4py.MPI.INT_INT: Datatype = INT_INT
    Datatype INT_INT

  mpi4py.MPI.TWOINT

mpi4py.MPI.TWOINT: Datatype = TWOINT
    Datatype TWOINT

  mpi4py.MPI.LONG_INT

mpi4py.MPI.LONG_INT: Datatype = LONG_INT
    Datatype LONG_INT

  mpi4py.MPI.FLOAT_INT

mpi4py.MPI.FLOAT_INT: Datatype = FLOAT_INT
    Datatype FLOAT_INT

  mpi4py.MPI.DOUBLE_INT

mpi4py.MPI.DOUBLE_INT: Datatype = DOUBLE_INT
    Datatype DOUBLE_INT

  mpi4py.MPI.LONG_DOUBLE_INT

mpi4py.MPI.LONG_DOUBLE_INT: Datatype = LONG_DOUBLE_INT
    Datatype LONG_DOUBLE_INT

  mpi4py.MPI.CHARACTER

mpi4py.MPI.CHARACTER: Datatype = CHARACTER
    Datatype CHARACTER

  mpi4py.MPI.LOGICAL

mpi4py.MPI.LOGICAL: Datatype = LOGICAL
    Datatype LOGICAL

  mpi4py.MPI.INTEGER

mpi4py.MPI.INTEGER: Datatype = INTEGER
    Datatype INTEGER

  mpi4py.MPI.REAL

mpi4py.MPI.REAL: Datatype = REAL
    Datatype REAL

  mpi4py.MPI.DOUBLE_PRECISION

mpi4py.MPI.DOUBLE_PRECISION: Datatype = DOUBLE_PRECISION
    Datatype DOUBLE_PRECISION

  mpi4py.MPI.COMPLEX

mpi4py.MPI.COMPLEX: Datatype = COMPLEX
    Datatype COMPLEX

  mpi4py.MPI.DOUBLE_COMPLEX

mpi4py.MPI.DOUBLE_COMPLEX: Datatype = DOUBLE_COMPLEX
    Datatype DOUBLE_COMPLEX

  mpi4py.MPI.LOGICAL1

mpi4py.MPI.LOGICAL1: Datatype = LOGICAL1
    Datatype LOGICAL1

  mpi4py.MPI.LOGICAL2

mpi4py.MPI.LOGICAL2: Datatype = LOGICAL2
    Datatype LOGICAL2

  mpi4py.MPI.LOGICAL4

mpi4py.MPI.LOGICAL4: Datatype = LOGICAL4
    Datatype LOGICAL4

  mpi4py.MPI.LOGICAL8

mpi4py.MPI.LOGICAL8: Datatype = LOGICAL8
    Datatype LOGICAL8

  mpi4py.MPI.INTEGER1

mpi4py.MPI.INTEGER1: Datatype = INTEGER1
    Datatype INTEGER1

  mpi4py.MPI.INTEGER2

mpi4py.MPI.INTEGER2: Datatype = INTEGER2
    Datatype INTEGER2

  mpi4py.MPI.INTEGER4

mpi4py.MPI.INTEGER4: Datatype = INTEGER4
    Datatype INTEGER4

  mpi4py.MPI.INTEGER8

mpi4py.MPI.INTEGER8: Datatype = INTEGER8
    Datatype INTEGER8

  mpi4py.MPI.INTEGER16

mpi4py.MPI.INTEGER16: Datatype = INTEGER16
    Datatype INTEGER16

  mpi4py.MPI.REAL2

mpi4py.MPI.REAL2: Datatype = REAL2
    Datatype REAL2

  mpi4py.MPI.REAL4

mpi4py.MPI.REAL4: Datatype = REAL4
    Datatype REAL4

  mpi4py.MPI.REAL8

mpi4py.MPI.REAL8: Datatype = REAL8
    Datatype REAL8

  mpi4py.MPI.REAL16

mpi4py.MPI.REAL16: Datatype = REAL16
    Datatype REAL16

  mpi4py.MPI.COMPLEX4

mpi4py.MPI.COMPLEX4: Datatype = COMPLEX4
    Datatype COMPLEX4

  mpi4py.MPI.COMPLEX8

mpi4py.MPI.COMPLEX8: Datatype = COMPLEX8
    Datatype COMPLEX8

  mpi4py.MPI.COMPLEX16

mpi4py.MPI.COMPLEX16: Datatype = COMPLEX16
    Datatype COMPLEX16

  mpi4py.MPI.COMPLEX32

mpi4py.MPI.COMPLEX32: Datatype = COMPLEX32
    Datatype COMPLEX32

  mpi4py.MPI.UNSIGNED_INT

mpi4py.MPI.UNSIGNED_INT: Datatype = UNSIGNED_INT
    Datatype UNSIGNED_INT

  mpi4py.MPI.SIGNED_SHORT

mpi4py.MPI.SIGNED_SHORT: Datatype = SIGNED_SHORT
    Datatype SIGNED_SHORT

  mpi4py.MPI.SIGNED_INT

mpi4py.MPI.SIGNED_INT: Datatype = SIGNED_INT
    Datatype SIGNED_INT

  mpi4py.MPI.SIGNED_LONG

mpi4py.MPI.SIGNED_LONG: Datatype = SIGNED_LONG
    Datatype SIGNED_LONG

  mpi4py.MPI.SIGNED_LONG_LONG

mpi4py.MPI.SIGNED_LONG_LONG: Datatype = SIGNED_LONG_LONG
    Datatype SIGNED_LONG_LONG

  mpi4py.MPI.BOOL

mpi4py.MPI.BOOL: Datatype = BOOL
    Datatype BOOL

  mpi4py.MPI.SINT8_T

mpi4py.MPI.SINT8_T: Datatype = SINT8_T
    Datatype SINT8_T

  mpi4py.MPI.SINT16_T

mpi4py.MPI.SINT16_T: Datatype = SINT16_T
    Datatype SINT16_T

  mpi4py.MPI.SINT32_T

mpi4py.MPI.SINT32_T: Datatype = SINT32_T
    Datatype SINT32_T

  mpi4py.MPI.SINT64_T

mpi4py.MPI.SINT64_T: Datatype = SINT64_T
    Datatype SINT64_T

  mpi4py.MPI.F_BOOL

mpi4py.MPI.F_BOOL: Datatype = F_BOOL
    Datatype F_BOOL

  mpi4py.MPI.F_INT

mpi4py.MPI.F_INT: Datatype = F_INT
    Datatype F_INT

  mpi4py.MPI.F_FLOAT

mpi4py.MPI.F_FLOAT: Datatype = F_FLOAT
    Datatype F_FLOAT

  mpi4py.MPI.F_DOUBLE

mpi4py.MPI.F_DOUBLE: Datatype = F_DOUBLE
    Datatype F_DOUBLE

  mpi4py.MPI.F_COMPLEX

mpi4py.MPI.F_COMPLEX: Datatype = F_COMPLEX
    Datatype F_COMPLEX

  mpi4py.MPI.F_FLOAT_COMPLEX

mpi4py.MPI.F_FLOAT_COMPLEX: Datatype = F_FLOAT_COMPLEX
    Datatype F_FLOAT_COMPLEX

  mpi4py.MPI.F_DOUBLE_COMPLEX

mpi4py.MPI.F_DOUBLE_COMPLEX: Datatype = F_DOUBLE_COMPLEX
    Datatype F_DOUBLE_COMPLEX

  mpi4py.MPI.REQUEST_NULL

mpi4py.MPI.REQUEST_NULL: Request = REQUEST_NULL
    Request REQUEST_NULL

  mpi4py.MPI.MESSAGE_NULL

mpi4py.MPI.MESSAGE_NULL: Message = MESSAGE_NULL
    Message MESSAGE_NULL

  mpi4py.MPI.MESSAGE_NO_PROC

mpi4py.MPI.MESSAGE_NO_PROC: Message = MESSAGE_NO_PROC
    Message MESSAGE_NO_PROC

  mpi4py.MPI.OP_NULL

mpi4py.MPI.OP_NULL: Op = OP_NULL
    Op OP_NULL

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.MAX

mpi4py.MPI.MAX: Op = MAX
    Op MAX

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.MIN

mpi4py.MPI.MIN: Op = MIN
    Op MIN

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.SUM

mpi4py.MPI.SUM: Op = SUM
    Op SUM

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.PROD

mpi4py.MPI.PROD: Op = PROD
    Op PROD

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.LAND

mpi4py.MPI.LAND: Op = LAND
    Op LAND

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.BAND

mpi4py.MPI.BAND: Op = BAND
    Op BAND

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.LOR

mpi4py.MPI.LOR: Op = LOR
    Op LOR

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.BOR

mpi4py.MPI.BOR: Op = BOR
    Op BOR

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.LXOR

mpi4py.MPI.LXOR: Op = LXOR
    Op LXOR

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.BXOR

mpi4py.MPI.BXOR: Op = BXOR
    Op BXOR

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.MAXLOC

mpi4py.MPI.MAXLOC: Op = MAXLOC
    Op MAXLOC

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.MINLOC

mpi4py.MPI.MINLOC: Op = MINLOC
    Op MINLOC

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.REPLACE

mpi4py.MPI.REPLACE: Op = REPLACE
    Op REPLACE

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.NO_OP

mpi4py.MPI.NO_OP: Op = NO_OP
    Op NO_OP

Parameters

•   x (Any) –

•   y (Any) –

Return type
    Any

  mpi4py.MPI.GROUP_NULL

mpi4py.MPI.GROUP_NULL: Group = GROUP_NULL
    Group GROUP_NULL

  mpi4py.MPI.GROUP_EMPTY

mpi4py.MPI.GROUP_EMPTY: Group = GROUP_EMPTY
    Group GROUP_EMPTY

  mpi4py.MPI.INFO_NULL

mpi4py.MPI.INFO_NULL: Info = INFO_NULL
    Info INFO_NULL

  mpi4py.MPI.INFO_ENV

mpi4py.MPI.INFO_ENV: Info = INFO_ENV
    Info INFO_ENV

  mpi4py.MPI.ERRHANDLER_NULL

mpi4py.MPI.ERRHANDLER_NULL: Errhandler = ERRHANDLER_NULL
    Errhandler ERRHANDLER_NULL

  mpi4py.MPI.ERRORS_RETURN

mpi4py.MPI.ERRORS_RETURN: Errhandler = ERRORS_RETURN
    Errhandler ERRORS_RETURN

  mpi4py.MPI.ERRORS_ARE_FATAL

mpi4py.MPI.ERRORS_ARE_FATAL: Errhandler = ERRORS_ARE_FATAL
    Errhandler ERRORS_ARE_FATAL

  mpi4py.MPI.COMM_NULL

mpi4py.MPI.COMM_NULL: Comm = COMM_NULL
    Comm COMM_NULL

  mpi4py.MPI.COMM_SELF

mpi4py.MPI.COMM_SELF: Intracomm = COMM_SELF
    Intracomm COMM_SELF

  mpi4py.MPI.COMM_WORLD

mpi4py.MPI.COMM_WORLD: Intracomm = COMM_WORLD
    Intracomm COMM_WORLD

  mpi4py.MPI.WIN_NULL

mpi4py.MPI.WIN_NULL: Win = WIN_NULL
    Win WIN_NULL

  mpi4py.MPI.FILE_NULL

mpi4py.MPI.FILE_NULL: File = FILE_NULL
    File FILE_NULL

  mpi4py.MPI.pickle

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

•   L. Dalcin and Y.-L. L. Fang, mpi4py: Status Update After 12 Years of Development, Computing in Science & Engineering, 23(4):47-54, 2021. https://doi.org/10.1109/MCSE.2021.3083216

•   L. Dalcin, P. Kler, R. Paz, and A. Cosimo, Parallel Distributed Computing using Python, Advances in Water Resources, 34(9):1124-1139, 2011. https://doi.org/10.1016/j.advwatres.2011.04.013

•   L. Dalcin, R. Paz, M. Storti, and J. D’Elia, MPI for Python: performance improvements and MPI-2 extensions, Journal of Parallel and Distributed Computing, 68(5):655-662, 2008. https://doi.org/10.1016/j.jpdc.2007.09.005

•   L. Dalcin, R. Paz, and M. Storti, MPI for Python, Journal of Parallel and Distributed Computing, 65(9):1108-1115, 2005. https://doi.org/10.1016/j.jpdc.2005.03.010

INSTALLATION

  Requirements

You need to have the following software properly installed in order to build MPI for Python:

•   A working MPI implementation, preferably supporting MPI-3 and built with shared/dynamic libraries.

    NOTE: If you want to build some MPI implementation from sources, check the instructions at Building MPI from sources in the appendix.

•   Python 2.7, 3.5 or above.

    NOTE: Some MPI-1 implementations do require the actual command line arguments to be passed in MPI_Init(). In this case, you will need to use a rebuilt, MPI-enabled, Python interpreter executable. MPI for Python has some support for alleviating you from this task. Check the instructions at MPI-enabled Python interpreter in the appendix.

  Using pip

If you already have a working MPI (either if you installed it from sources or by using a pre-built package from your favourite GNU/Linux distribution) and the mpicc compiler wrapper is on your search path, you can use pip:

$ python -m pip install mpi4py

NOTE: If the mpicc compiler wrapper is not on your search path (or if it has a different name) you can use env to pass the environment variable MPICC providing the full path to the MPI compiler wrapper executable:

$ env MPICC=/path/to/mpicc python -m pip install mpi4py

WARNING: pip keeps previouly built wheel files on its cache for future reuse. If you want to reinstall the mpi4py package using a different or updated MPI implementation, you have to either first remove the cached wheel file with:

$ python -m pip cache remove mpi4py

or ask pip to disable the cache:

$ python -m pip install --no-cache-dir mpi4py

  Using distutils

The MPI for Python package is available for download at the project website generously hosted by GitHub. You can use curl or wget to get a release tarball.

•   Using curl:

    $ curl -O https://github.com/mpi4py/mpi4py/releases/download/X.Y.Z/mpi4py-X.Y.Z.tar.gz

•   Using wget:

    $ wget https://github.com/mpi4py/mpi4py/releases/download/X.Y.Z/mpi4py-X.Y.Z.tar.gz

    After unpacking the release tarball:

    $ tar -zxf mpi4py-X.Y.Z.tar.gz
    $ cd mpi4py-X.Y.Z

    the package is ready for building.

    MPI for Python uses a standard distutils-based build system. However, some distutils commands (like build) have additional options:

--mpicc=
    Lets you specify a special location or name for the mpicc compiler wrapper.

--mpi=
    Lets you pass a section with MPI configuration within a special configuration file.

--configure
    Runs exhaustive tests for checking about missing MPI types, constants, and functions. This option should be passed in order to build MPI for Python against old MPI-1 or MPI-2 implementations, possibly providing a subset of MPI-3.

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

    If mpicc is located somewhere in your search path, simply run the build command:

    $ python setup.py build

    If mpicc is not in your search path or the compiler wrapper has a different name, you can run the build command specifying its location:

    $ python setup.py build --mpicc=/where/you/have/mpicc

    Alternatively, you can provide all the relevant information about your MPI implementation by editing the file called mpi.cfg. You can use the default section [mpi] or add a new, custom section, for example [other_mpi] (see the examples provided in the mpi.cfg file as a starting point to write your own section):

    [mpi]
    include_dirs         = /usr/local/mpi/include
    libraries            = mpi
    library_dirs         = /usr/local/mpi/lib
    runtime_library_dirs = /usr/local/mpi/lib
    [other_mpi]
    include_dirs         = /opt/mpi/include ...
    libraries            = mpi ...
    library_dirs         = /opt/mpi/lib ...
    runtime_library_dirs = /op/mpi/lib ...
    ...

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

    $ python setup.py build --mpi=other_mpi

    After building, the package is ready for install.

    If you have root privileges (either by log-in as the root user of by using sudo) and you want to install MPI for Python in your system for all users, just do:

    $ python setup.py install

    The previous steps will install the mpi4py package at standard location prefix/lib/pythonX.X/site-packages.

    If you do not have root privileges or you want to install MPI for Python for your private use, just do:

    $ python setup.py install --user

  Testing

To quickly test the installation:

$ mpiexec -n 5 python -m mpi4py.bench helloworld
Hello, World! I am process 0 of 5 on localhost.
Hello, World! I am process 1 of 5 on localhost.
Hello, World! I am process 2 of 5 on localhost.
Hello, World! I am process 3 of 5 on localhost.
Hello, World! I am process 4 of 5 on localhost.

If you installed from source, issuing at the command line:

$ mpiexec -n 5 python demo/helloworld.py

or (in the case of ancient MPI-1 implementations):

$ mpirun -np 5 python `pwd`/demo/helloworld.py

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

You can also run all the unittest scripts:

$ mpiexec -n 5 python test/runtests.py

or, if you have nose unit testing framework installed:

$ mpiexec -n 5 nosetests -w test

or, if you have py.test unit testing framework installed:

$ mpiexec -n 5 py.test test/

APPENDIX

  MPI-enabled Python interpreter

WARNING: These days it is no longer required to use the MPI-enabled Python interpreter in most cases, and, therefore, it is not built by default anymore because it is too difficult to reliably build a Python interpreter across different distributions. If you know that you still really need it, see below on how to use the build_exe and install_exe commands.

Some MPI-1 implementations (notably, MPICH 1) do require the actual command line arguments to be passed at the time MPI_Init() is called. In this case, you will need to use a re-built, MPI-enabled, Python interpreter binary executable. A basic implementation (targeting Python 2.X) of what is required is shown below:

#include <Python.h>
#include <mpi.h>
int main(int argc, char *argv[])
{
   int status, flag;
   MPI_Init(&argc, &argv);
   status = Py_Main(argc, argv);
   MPI_Finalized(&flag);
   if (!flag) MPI_Finalize();
   return status;
}

The source code above is straightforward; compiling it should also be. However, the linking step is more tricky: special flags have to be passed to the linker depending on your platform. In order to alleviate you for such low-level details, MPI for Python provides some pure-distutils based support to build and install an MPI-enabled Python interpreter executable:

$ cd mpi4py-X.X.X
$ python setup.py build_exe [--mpi=<name>|--mpicc=/path/to/mpicc]
$ [sudo] python setup.py install_exe [--install-dir=$HOME/bin]

After the above steps you should have the MPI-enabled interpreter installed as prefix/bin/pythonX.X-mpi (or $HOME/bin/pythonX.X-mpi). Assuming that prefix/bin (or $HOME/bin) is listed on your PATH, you should be able to enter your MPI-enabled Python interactively, for example:

$ python2.7-mpi
Python 2.7.8 (default, Nov 10 2014, 08:19:18)
[GCC 4.9.2 20141101 (Red Hat 4.9.2-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> sys.executable
'/usr/bin/python2.7-mpi'
>>>

  Building MPI from sources

In the list below you have some executive instructions for building some of the open-source MPI implementations out there with support for shared/dynamic libraries on POSIX environments.

•   MPICH

    $ tar -zxf mpich-X.X.X.tar.gz
    $ cd mpich-X.X.X
    $ ./configure --enable-shared --prefix=/usr/local/mpich
    $ make
    $ make install

•   Open MPI

    $ tar -zxf openmpi-X.X.X tar.gz
    $ cd openmpi-X.X.X
    $ ./configure --prefix=/usr/local/openmpi
    $ make all
    $ make install

•   MPICH 1

    $ tar -zxf mpich-X.X.X.tar.gz
    $ cd mpich-X.X.X
    $ ./configure --enable-sharedlib --prefix=/usr/local/mpich1
    $ make
    $ make install

    Perhaps you will need to set the LD_LIBRARY_PATH environment variable (using export, setenv or what applies to your system) pointing to the directory containing the MPI libraries . In case of getting runtime linking errors when running MPI programs, the following lines can be added to the user login shell script (.profile, .bashrc, etc.).

•   MPICH

    MPI_DIR=/usr/local/mpich
    export LD_LIBRARY_PATH=$MPI_DIR/lib:$LD_LIBRARY_PATH

•   Open MPI

    MPI_DIR=/usr/local/openmpi
    export LD_LIBRARY_PATH=$MPI_DIR/lib:$LD_LIBRARY_PATH

•   MPICH 1

    MPI_DIR=/usr/local/mpich1
    export LD_LIBRARY_PATH=$MPI_DIR/lib/shared:$LD_LIBRARY_PATH:
    export MPICH_USE_SHLIB=yes

    WARNING: MPICH 1 support for dynamic libraries is not completely transparent. Users should set the environment variable MPICH_USE_SHLIB to yes in order to avoid link problems when using the mpicc compiler wrapper.

AUTHOR
Lisandro Dalcin

COPYRIGHT
2022, Lisandro Dalcin