def __init__(self,
n=1,
mpirun="mpiexec",
working_directory=None,
options=None):
"""
`n` - the number of hosts to run on.
`mpirun` - the path to the mpirun or mpiexec executable. If a full path
is not given, the user's PATH will be searched.
`options` - extra options for SLURM
`working_directory` - directory in which to run on the hosts
"""
LaunchMode.__init__(self, working_directory, options)
class MPI(Executable):
name = mpirun
default_executable_name = mpirun
if os.path.exists(mpirun): # mpirun is a full path
mpi_cmd = MPI(path=mpirun)
else:
mpi_cmd = MPI(path=None)
self.mpirun = mpi_cmd.path
# should warn if mpirun not found
assert n > 0
self.n = int(n)
def __init__(self,
n=1,
mpirun="mpiexec",
hosts=[],
options=None,
pfi_path="/usr/local/bin/pfi.py",
working_directory=None):
"""
`n` - the number of hosts to run on.
`mpirun` - the path to the mpirun or mpiexec executable. If a full path
is not given, the user's PATH will be searched.
`hosts` - a list of host names to run on. **Currently not used.**
`options` - extra command line options for mpirun/mpiexec
`pfi_path` - the path to the pfi.py script provided with Sumatra, which
should be installed on every node and is used to obtain
platform information.
`working_directory` - directory in which to run on the hosts
"""
LaunchMode.__init__(self, working_directory, options)
class MPI(Executable):
name = mpirun
default_executable_name = mpirun
if os.path.exists(mpirun): # mpirun is a full path
mpi_cmd = MPI(path=mpirun)
else:
mpi_cmd = MPI(path=None)
self.mpirun = mpi_cmd.path
# should warn if mpirun not found
self.hosts = hosts
self.n = n
self.mpi_info = {}
self.pfi_path = pfi_path
import _gpaw
try:
world = _gpaw.Communicator()
except AttributeError:
pass
elif '_asap' in sys.modules:
import _asap
try:
world = _asap.Communicator()
except AttributeError:
pass
elif 'mpi4py' in sys.modules:
world = MPI4PY()
if world is None:
world = MPI()
def barrier():
world.barrier()
def broadcast(obj, root=0, comm=world):
"""Broadcast a Python object across an MPI communicator and return it."""
if comm.rank == root:
string = pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)
n = np.array([len(string)], int)
else:
string = None
n = np.empty(1, int)
comm.broadcast(n, root)
"""
This tutorial provides an introduction to MPI in python.
MPI (Message Passing Interface) is a protocol used for parallel computation.
It is designed to allow multiple CPUs with isolated memory to perform tasks in
parallel, communicating with each other through 'message passing'. These CPUs
could be a network of desktop computers or a supercomputer cluster (like Magnus).
MPI is highly portable and scalable, making it one of the most commonly used
forms of parallelisation in scientific computing.
You will not need to do much MPI programming to use QuOp_MPI, but understanding
how QuOp_MPI achieves parallelisation will assist you in defining your quality functions.
MPI has implementations in the C, Fortran, Java and Python programming languages.
Here we will be using the python implementation of MPI, 'mpi4py'.
"""
from mpi4py import MPI
"""
MPI programs work by running multiple copies of the same program simultaneously,
these programs work independently, except when they send messages to each other
using calls to MPI functions. Each copy of the program is referred to as an MPI 'node',
and an 'MPI communicator' groups these nodes. In an MPI communicator, the nodes
are individually identified by a 'rank' number.
The number of nodes is chosen when the MPI program is run, the terminal command:
mpiexec -N 1 python3 mpi_tutorial.py
