Source code for hbp_nrp_distributed_nest.cle.DistributedPyNNControlAdapter

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# The Human Brain Project is a European Commission funded project
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"""
This module defined a CLE control adapter that notifies all remote brain processes
when they should step the simulation.
"""

from builtins import range
from hbp_nrp_cle.brainsim.pynn.PyNNControlAdapter import PyNNControlAdapter
from hbp_nrp_distributed_nest.launch.NestBrainProcess import NestBrainProcess

from hbp_nrp_cle.brainsim import COMM_NRP


[docs]class DistributedPyNNControlAdapter(PyNNControlAdapter): """ This class is required as multi-threading the brain process to receive MPI messages while blocking to step the brain causes segmentation faults in the CLE. The overhead is minimal here and allows us to have dynamic behavior in the brain processes between simulation steps. """
[docs] def load_brain(self, network_file, populations): """ Notify all remote brain processes to load the brain with population definitions specified. TODO: the network_file will have to be accessed from common storage between processes for any configuration other than local installataions :param network_file: The path to the python file containing the network :param populations: A named list of populations to create """ # notify all other processes, blocking send calls for them to receive for rank in range(COMM_NRP.Get_size()): if rank == COMM_NRP.Get_rank(): continue COMM_NRP.send({'command': 'LoadBrain', 'file': network_file, 'populations': populations}, dest=rank, tag=NestBrainProcess.MPI_MSG_TAG) # run the actual brain load on this process super(DistributedPyNNControlAdapter, self).load_brain(network_file) super(DistributedPyNNControlAdapter, self).load_populations(populations)
[docs] def run_step(self, dt): """ Notify all remote brain processes to run a simulation step, then run the step in this process. """ # notify all other processes, blocking send calls for them to receive for rank in range(COMM_NRP.Get_size()): if rank == COMM_NRP.Get_rank(): continue COMM_NRP.send('step', dest=rank, tag=NestBrainProcess.MPI_MSG_TAG) # run the actual simulation step super(DistributedPyNNControlAdapter, self).run_step(dt)