MPICommunicator: MPICommunicator

__init__(self, root=0, comm=None, contract=None, logger=None): 

get_nprocs(self): 

get_id(self): 

initialize(self): 

finalize(self, exc_type, exc_val, exc_tb): 

@property
comm(self): 

Returns the communicator used by the paralellizer

• :returns: MPIParallelizer.MPICommunicator

@property
on_main(self): 

broadcast(self, data, **kwargs): 

Sends the same data to all processes

• data: Any

• kwargs: Any

• :returns: _

scatter(self, data, shape=None, **kwargs): 

Performs a scatter of data to the different available parallelizer processes. NOTE: unlike in the MPI case, data does not need to be evenly divisible by the number of available processes

• data: Any

• kwargs: Any

• :returns: _

gather(self, data, shape=None, **kwargs): 

Performs a gather of data from the different available parallelizer processes

• data: Any

• kwargs: Any

• :returns: _

map(self, func, data, input_shape=None, output_shape=None, **kwargs): 

Performs a parallel map of function over the held data on different processes

• function: Any

• data: Any

• kwargs: Any

• :returns: _

apply(self, func, *args, **kwargs): 

Applies func to args in parallel on all of the processes. For MPI, since jobs are always started with mpirun, this is just a regular apply

• func: Any

• args: Any

• kwargs: Any

• :returns: _

from_config(**kw):