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mpi4py - MPI for Python
This document describes the MPI for Python package. MPI for Python provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors.
This package is constructed on top of the MPI-1/2/3 specifications and provides an object oriented interface which resembles the MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object, as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects)
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.
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].
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.
As this work started and evolved, some ideas were borrowed from well known MPI and Python related open source projects from the Internet.
Additionally, we would like to mention some available tools for scientific computing and software development with Python.
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.
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.
In MPI for Python, MPI.Comm is the base class of communicators. The MPI.Intracomm and MPI.Intercomm classes are sublcasses of the MPI.Comm class. The MPI.Comm.Is_inter() method (and MPI.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: MPI.COMM_SELF and MPI.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 MPI.Comm.Get_size() and MPI.Comm.Get_rank(). The associated process group can be retrieved from a communicator by calling the MPI.Comm.Get_group() method, which returns an instance of the MPI.Group class. Set operations with MPI.Group objects like like MPI.Group.Union(), MPI.Group.Intersect() and MPI.Group.Difference() are fully supported, as well as the creation of new communicators from these groups using MPI.Comm.Create() and MPI.Comm.Create_group().
New communicator instances can be obtained with the MPI.Comm.Clone(), MPI.Comm.Dup() and MPI.Comm.Split() methods, as well methods MPI.Intracomm.Create_intercomm() and MPI.Intercomm.Merge().
Virtual topologies (MPI.Cartcomm, MPI.Graphcomm and MPI.Distgraphcomm classes, which are specializations of the MPI.Intracomm class) are fully supported. New instances can be obtained from intracommunicator instances with factory methods MPI.Intracomm.Create_cart() and MPI.Intracomm.Create_graph().
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.
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 MPI.Comm.Send(), MPI.Comm.Recv() and MPI.Comm.Sendrecv() methods of communicator objects provide support for blocking point-to-point communications within MPI.Intracomm and MPI.Intercomm instances. These methods can communicate memory buffers. The variants MPI.Comm.send(), MPI.Comm.recv() and MPI.Comm.sendrecv() can communicate general Python objects.
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 MPI.Comm.Isend() and MPI.Comm.Irecv() methods initiate send and receive operations, respectively. These methods return a MPI.Request instance, uniquely identifying the started operation. Its completion can be managed using the MPI.Request.Test(), MPI.Request.Wait() and MPI.Request.Cancel() methods. The management of MPI.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.
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 MPI.Comm.Send_init() and MPI.Comm.Recv_init() methods create persistent requests for a send and receive operation, respectively. These methods return an instance of the MPI.Prequest class, a subclass of the MPI.Request class. The actual communication can be effectively started using the MPI.Prequest.Start() method, and its completion can be managed as previously described.
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.
In MPI for Python, the MPI.Comm.Bcast(), MPI.Comm.Scatter(), MPI.Comm.Gather(), MPI.Comm.Allgather(), and MPI.Comm.Alltoall() MPI.Comm.Alltoallw() methods provide support for collective communications of memory buffers. The lower-case variants MPI.Comm.bcast(), MPI.Comm.scatter(), MPI.Comm.gather(), MPI.Comm.allgather() and MPI.Comm.alltoall() can communicate general Python objects. The vector variants (which can communicate different amounts of data to each process) MPI.Comm.Scatterv(), MPI.Comm.Gatherv(), MPI.Comm.Allgatherv(), MPI.Comm.Alltoallv() and MPI.Comm.Alltoallw() are also supported, they can only communicate objects exposing memory buffers.
Global reduction operations on memory buffers are accessible through the MPI.Comm.Reduce(), MPI.Comm.Reduce_scatter, MPI.Comm.Allreduce(), MPI.Intracomm.Scan() and MPI.Intracomm.Exscan() methods. The lower-case variants MPI.Comm.reduce(), MPI.Comm.allreduce(), MPI.Intracomm.scan() and MPI.Intracomm.exscan() can communicate general Python objects; however, the actual required reduction computations are performed sequentially at some process. All the predefined (i.e., MPI.SUM, MPI.PROD, MPI.MAX, etc.) reduction operations can be applied.
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 MPI.Intracomm.Spawn() method within an intracommunicator. This call returns a new intercommunicator (i.e., an MPI.Intercomm instance) at the parent process group. The child process group can retrieve the matching intercommunicator by calling the MPI.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 MPI.Open_port() function to open a port and the MPI.Publish_name() function to publish a provided service, and next call the MPI.Intracomm.Accept() method. Any client applications can first find a published service by calling the MPI.Lookup_name() function, which returns the port where a server can be contacted; and next call the MPI.Intracomm.Connect() method. Both MPI.Intracomm.Accept() and MPI.Intracomm.Connect() methods return an MPI.Intercomm instance. When connection between client/server processes is no longer needed, all of them must cooperatively call the MPI.Comm.Disconnect() method. Additionally, server applications should release resources by calling the MPI.Unpublish_name() and MPI.Close_port() functions.
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 MPI.Win class. New window objects are created by calling the MPI.Win.Create() method at all processes within a communicator and specifying a memory buffer . When a window instance is no longer needed, the MPI.Win.Free() method should be called.
The three one-sided MPI operations for remote write, read and reduction are available through calling the methods MPI.Win.Put(), MPI.Win.Get(), and MPI.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 MPI.Win.Start() and MPI.Win.Complete() methods at the origin process, and target process cooperates by calling the MPI.Win.Post() and MPI.Win.Wait() methods. There is also a collective variant provided by the MPI.Win.Fence() method. Passive target synchronization is more lenient, only the origin process calls the MPI.Win.Lock() and MPI.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.
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 MPI.File class. File handles are obtained by calling the MPI.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 MPI.File.Close() method. Files even can be deleted by calling method MPI.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 MPI.File.Set_view() and MPI.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.
Module functions MPI.Init() or MPI.Init_thread() and MPI.Finalize() provide MPI initialization and finalization respectively. Module functions MPI.Is_initialized() and MPI.Is_finalized() provide the respective tests for initialization and finalization.
NOTE:
NOTE:
MPI timer functionalities are available through the MPI.Wtime() and MPI.Wtick() functions.
In order facilitate handle sharing with other Python modules interfacing MPI-based parallel libraries, the predefined MPI error handlers MPI.ERRORS_RETURN and MPI.ERRORS_ARE_FATAL can be assigned to and retrieved from communicators, windows and files using methods MPI.{Comm|Win|File}.Set_errhandler() and MPI.{Comm|Win|File}.Get_errhandler().
When the predefined error handler MPI.ERRORS_RETURN is set, errors returned from MPI calls within Python code will raise an instance of the exception class MPI.Exception, which is a subclass of the standard Python exception RuntimeError.
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WARNING:
WARNING:
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).
You have to use all-lowercase methods (of the Comm class), like send(), recv(), bcast(). An object to be sent is passed as a paramenter to the communication call, and the received object is simply the return value.
The isend() and irecv() methods return Request instances; completion of these methods can be managed using the test() and wait() methods of the Request class.
The recv() and 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 recv() or irecv() must be at least as long as the pickled data transmitted to the receiver.
Collective calls like scatter(), gather(), allgather(), 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.
You have to use method names starting with an upper-case letter (of the Comm class), like Send(), Recv(), Bcast(), Scatter(), 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 Scatterv() and 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 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.
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.
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)
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()
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)
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)
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
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
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
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)
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)
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
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()
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()
#!/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()
#!/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()
/* 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);
}
// 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);
>>> from mpi4py import MPI >>> import helloworld >>> helloworld.sayhello(MPI.COMM_WORLD) Hello, World! I am process 0 of 1.
! 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
$ f2py -c --f90exec=mpif90 helloworld.f90 -m helloworld
>>> from mpi4py import MPI >>> import helloworld >>> fcomm = MPI.COMM_WORLD.py2f() >>> helloworld.sayhello(fcomm) Hello, World! I am process 0 of 1.
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.
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:
SEE ALSO:
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:
WARNING:
Other parameters:
executor = MPIPoolExecutor(max_workers=1) future = executor.submit(pow, 321, 1234) print(future.result())
executor = MPIPoolExecutor(max_workers=3) for result in executor.map(pow, [2]*32, range(32)):
print(result)
executor = MPIPoolExecutor(max_workers=3) iterable = ((2, n) for n in range(32)) for result in executor.starmap(pow, iterable):
print(result)
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()
NOTE:
WARNING:
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.
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:
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:
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:
SEE ALSO:
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 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.
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 MPI.ERRORS_ARE_FATAL error handler in favor of MPI.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:
The use of -m mpi4py to execute Python code on the command line resembles that of the Python interpreter.
SEE ALSO:
If MPI for Python been significant to a project that leads to an academic publication, please acknowledge that fact by citing the project.
You need to have the following software properly installed in order to build MPI for Python:
NOTE:
NOTE:
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:
$ [sudo] pip install mpi4py
or alternatively setuptools easy_install (deprecated):
$ [sudo] easy_install mpi4py
NOTE:
$ [sudo] env MPICC=/path/to/mpicc pip install mpi4py $ [sudo] env MPICC=/path/to/mpicc easy_install mpi4py
The MPI for Python package is available for download at the project website generously hosted by Bitbucket. You can use curl or wget to get a release tarball.
$ curl -O https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-X.Y.tar.gz
$ wget https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-X.Y.tar.gz
After unpacking the release tarball:
$ tar -zxf mpi4py-X.Y.tar.gz $ cd mpi4py-X.Y
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:
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
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/
WARNING:
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' >>>
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.
$ tar -zxf mpich-X.X.X.tar.gz $ cd mpich-X.X.X $ ./configure --enable-shared --prefix=/usr/local/mpich $ make $ make install
$ tar -zxf openmpi-X.X.X tar.gz $ cd openmpi-X.X.X $ ./configure --prefix=/usr/local/openmpi $ make all $ make install
$ 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.).
MPI_DIR=/usr/local/mpich export LD_LIBRARY_PATH=$MPI_DIR/lib:$LD_LIBRARY_PATH
MPI_DIR=/usr/local/openmpi export LD_LIBRARY_PATH=$MPI_DIR/lib:$LD_LIBRARY_PATH
MPI_DIR=/usr/local/mpich1 export LD_LIBRARY_PATH=$MPI_DIR/lib/shared:$LD_LIBRARY_PATH: export MPICH_USE_SHLIB=yes
WARNING:
Lisandro Dalcin
2021, Lisandro Dalcin
| January 21, 2021 | 3.0 |