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.futuresstandard 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
Executorsubclass that executes calls asynchronously using a pool of at most max_workers processes. If max_workers isNoneor not given, its value is determined from theMPI4PY_FUTURES_MAX_WORKERSenvironment 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 to0, then aValueErrorwill 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
BrokenExecutorexception.Other parameters:
python_exe: Path to the Python interpreter executable used to spawn worker processes, otherwise
sys.executableis used.python_args:
listor iterable with additional command line flags to pass to the Python executable. Command line flags determined from inspection ofsys.flags,sys.warnoptionsandsys._xoptionsin are passed unconditionally.mpi_info:
dictor iterable yielding(key, value)pairs. These(key, value)pairs are passed (through anMPI.Infoobject) to theMPI.Intracomm.Spawncall 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:
dictor 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 toFalseprevents worker processes from accessing definitions in the parent__main__namespace.path:
listor iterable with paths to append tosys.pathin 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 awdirinfo key passed through mpi_info, although such feature is not mandatory.env:
dictor iterable yielding(name, value)pairs with environment variables to updateos.environin 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 aFutureobject 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 aTimeoutErrorif__next__()is called and the result isn’t available after timeout seconds from the original call tomap(). timeout can be an int or a float. If timeout is not specified orNone, 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 asTrue, 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 ofmap()when argument parameters are already grouped in tuples from a single iterable (the data has been “pre-zipped”).map(func, *iterable)is equivalent tostarmap(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()andmap()made aftershutdown()will raiseRuntimeError.If wait is
Truethen 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 isFalsethen 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
withstatement, which will shutdown the executor instance (waiting as ifshutdown()were called with wait set toTrue).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, thenbootup()will not return until the executor resources are ready to process submissions. Resources are automatically allocated in the first call tosubmit(), thus callingbootup()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 toMPI.COMM_WORLDif not provided orNone) 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 thewithstatement is assigned either anMPIPoolExecutorinstance (at the master) orNone(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¶ 1from mpi4py.futures import MPIPoolExecutor
2
3x0, x1, w = -2.0, +2.0, 640*2
4y0, y1, h = -1.5, +1.5, 480*2
5dx = (x1 - x0) / w
6dy = (y1 - y0) / h
7
8c = complex(0, 0.65)
9
10def julia(x, y):
11 z = complex(x, y)
12 n = 255
13 while abs(z) < 3 and n > 1:
14 z = z**2 + c
15 n -= 1
16 return n
17
18def julia_line(k):
19 line = bytearray(w)
20 y = y1 - k * dy
21 for j in range(w):
22 x = x0 + j * dx
23 line[j] = julia(x, y)
24 return line
25
26if __name__ == '__main__':
27
28 with MPIPoolExecutor() as executor:
29 image = executor.map(julia_line, range(h))
30 with open('julia.pgm', 'wb') as f:
31 f.write(b'P5 %d %d %d\n' % (w, h, 255))
32 for line in image:
33 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, the user can set the MPI universe size via -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 -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¶