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"""
This module contains routines related to parallel operation via
the Message Passing Interface (MPI).
Attributes
----------
comm :: mpi4py.MPI.Intracomm
The MPI communicator.
rank :: int
The rank of the current process.
size :: int
The size of the pool of processes.
"""
from mpi4py import MPI as _MPI
comm = _MPI.COMM_WORLD
size = comm.size
rank = comm.rank
[docs]def barrier():
"""
Creates an MPI barrier. All processes wait here for others to catch up.
"""
comm.Barrier()
[docs]class call_pattern():
"""
This context manager calls the code within its block using the
specified calling pattern.
Parameters
----------
pattern: str
'collective', each process calls the block of code simultaneously.
'sequential', processes call block of code in order of rank.
Example
-------
This example is redundant as it will only run with a single process.
However, where run in parallel, you should expect the outputs to be
ordered according to process rank. Note also that for deterministic
printing in parallel, and you may need to run Python unbuffered
(`mpirun -np 4 python -u yourscript.py`, for example).
>>> import underworld as uw
>>> with uw.mpi.call_pattern(pattern="sequential"):
... print("My rank is {}".format(uw.mpi.rank))
My rank is 0
"""
def __init__(self, pattern="collective", returnobj=None):
if not isinstance(pattern,str):
raise TypeError("`pattern` parameter must be of type `str`")
pattern = pattern.lower()
if pattern not in ("collective", "sequential"):
raise ValueError("`pattern` must take values `collective` or `sequential`.")
self.pattern = pattern
self.returnobj = returnobj
def __enter__(self):
if self.pattern == "sequential":
if rank!=0:
comm.recv(source=rank-1, tag=333)
return self.returnobj
def __exit__(self, *args):
if self.pattern == "sequential":
dest = rank+1
if dest<comm.size:
comm.send(None, dest=rank+1, tag=333)