mpi4py.futures — MPI for Python 3.0.3 documentation

# 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,: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 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, **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)
        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)

    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()

  `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.

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 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.

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`

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    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 using the mpiexec command specifying one MPI process and (optional but recommended) the desired MPI universe size [1].

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

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 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 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.