New in version 2.1.0.

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


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


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.


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.


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,:mod: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 picklig and unpickling.


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, **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_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.

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)
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, [2]*32, range(32)):
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):

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.

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()

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.


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.


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.


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(, [-42, 42]))
       assert answer == {42}


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 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 16 workers and connect 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.


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 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.


The following 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.
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 =, range(h))
        with open('julia.pgm', 'wb') as f:
            f.write(b'P5 %d %d %d\n' % (w, h, 255))
            for line in image:

The recommended way to execute the script is using the mpiexec command specifying one MPI process and (optional but recommended) the desired MPI universe size [1].

$ mpiexec -n 1 -usize 17 python

The mpiexec command launches a single MPI process (the master) running the Python interpreter and executing the main script. When required, mpi4py.futures spawns 16 additional MPI processes (the children) to dynamically allocate the pool of workers. The master submits tasks to the children and waits for the results. The children receive incoming tasks, execute them, and send back the results to the master.

Alternatively, users may decide to execute the script in a more traditional way, that is, all the MPI process 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

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 from the master.

[1]This mpiexec invocation example using the -usize flag (alternatively, setting the MPIEXEC_UNIVERSE_SIZE environment variable) assumes the backend MPI implementation is an MPICH derivative using the Hydra process manager. In the Open MPI implementation, the MPI universe size can be specified by setting the OMPI_UNIVERSE_SIZE environment variable to a positive integer. Check the documentation of your actual MPI implementation and/or batch system for the ways to specify the desired MPI universe size.