ParallelNetManager
create_cell · gatherspikes · gid_exists · nc_append · pcontinue · pinit · prstat · prun · psolve · register_cell · round_robin · set_gid2node · spike_record · splitcell · want_all_spikes

# ParallelNetManager¶↑

class ParallelNetManager
Syntax:
pnm = h.ParallelNetManager(ncell)
Description:

Manages the setup and running of a network simulation on a cluster of workstations or any parallel computer that has mpi installed. A version for PVM is also available. This class, implemented in nrn/share/lib/hoc/netparmpi.hoc, presents an interface compatible with the NetGUI style of network specification, and is implemented using the Parallel Network methods. Those methods are available only if NEURON has been built with the configuration option, –with-mpi. The netparmpi.hoc file at last count was only 430 lines long so if you have questions about how it works that are not answered here, take a look.

Experience with published network models where there is an order of magnitude more segments or cells than machines, suggests that superlinear speedup occurs up to about 20 to 50 machines due to the fact that the parallel machine has much more effective high speed cache memory than a single cpu. Basically, good performance will occur if there is a lot for each machine to do and the amount of effort to simulate each machine’s subnet is about equal. If cell granularity causes load balance to be a signficant problem see ParallelNetManager.splitcell(). The “lot for each machine to do” is relative to the number of spikes that must be exchanged between machines and how often these exchanges take place. The latter is determined by the minimum delay between a spike event generated on one machine that must be delivered to another machine since that defines the interval that each machine is allowed to integrate before having to share all spikes it generated which are destined for other machines.

The fundamental requirement for the use of this class is for the programmer to be able to associate a unique global id (gid) for each cell and define the connectivity by means of the source_cell_gid and the target_cell_gid. If the target cell happens to have synapses, we assume they can be found via a local synapse index into the target cell’s synapse list. We absolutely must use global indices because it will be the case that when a connection is requested on a machine that either the source or the target cell or both may not actually exist on the machine – the last case is a no-op.

The following describes the author’s intention as to how this class can be used to construct and simulate a parallel network. It is assumed that every machine executes exactly the same code (though with different data).

1. So that the concatenation of all the following fragments will end up being a valid network simulation for a ring of 128 artificial cells where cell i sends a spike to cell i+1, let’s start out with

from neuron import h
tstop = 1000


Yes, I know that this example is foolish since there is no computation going on except when a cell receives a spike. I don’t expect any benefit from parallelization but it is simple enough to allow me to focus on the process of setup and run instead of cluttering the example with a large cell class.

2. load the netparmpi.hoc file and create a ParallelNetManager

h.load_file("netparmpi.hoc")
ncell = 128
pnm = h.ParallelNetManager(ncell)


If you know the global number of cells put it in. For the non-MPI implementation of ParallelNetManager, ncell is absolutely necessary since that implementation constructs many mapping vectors that allow it to figure out what cell is being talked about when the gid is known. The MPI implementation uses dynamically constructed maps and it is not necessary to know the global number of cells at this time. Note that ncell refers to the global number of cells and NOT the number of cells to be created on this machine.

3. Tell the system which gid’s are on which machines. The simplest distribution mechanism is round_robin()

pnm.round_robin()


which will certainly give good load balance if the number of each cell type to be constructed is an integer multiple of the number of machines. Otherwise specify which gid’s are on which machines through the use of ParallelNetManager.set_gid2node() . Note that you only HAVE to call pnm.set_gid2node(gid, myid) for the subset of gid’s that are supposed to be associated with this machines particular myid = pnm.pc.id but it is usually simpler just to call it for all gid’s since the set_gid2node call is a no-op when the second argument does not match the pc.id. Also, the PVM version REQUIRES that you call the function for all the gid values.

There are three performance considerations with regard to sprinkling gid values on machines.

1. By far the most important is load balance. That is simple if all your cells take the same time to integrate over the same interval. If cells have very different sizes or cpu’s end up with very different amounts of work to do so that load balance is a serious problem then ParallelNetManager.splitcell() can be used to solve it.
2. Of lesser importance but still quite important is to maximize the delay of NetCon’s that span machines. This isn’t an issue if all your NetCon delays are the same. The minimum delay across machines defines the maximum step size that each machine can integrate before having to share spikes. In principle, Metis can help with this and C) but don’t waste your time unless you have established that communication overhead is your rate limiting step. See ParallelNetManager.prstat() and ParallelContext.wait_time() .
3. I am only guessing that this is less important than B, it is certainly related, but obviously things will be better if you minimize the number of spanning NetCon’s. For our ring example it obviously would be best to keep neighboring cells together but the improvement may be too small to measure.
4. Now create only the cells that are supposed to be on this machine using ParallelNetManager.register_cell().

for i in range(ncell):
if pnm.gid_exists(i):
pnm.register_cell(i, h.IntFire1())


Notice how we don’t construct a cell if the gid does not exist. You only HAVE to call register_cell for those gid’s which are actually owned by this machine and need to send spikes to other machines. If the gid does not exist, then register_cell will call gid_exists for you. Note that 2) and 3) can be combined but it is a serious bug if a gid exists on more than one machine. You can even start connecting as discussed in item 4) but of course a NetCon presupposes the existence of whatever cells it needs on this machine.

Of course this presupposes that you have already read the files that define your cell classes. We assume your cell classes for “real” cells follow the NetworkReadyCell policy required by the NetGUI tool. That is, each “real” cell type has a synapse list, eg. the first synapse is cell.synlist.object(0) (the programmer will have to make use of those synapse indices when such cells are the target of a NetCon) and each “real” cell type has a connect2target method that constructs a netcon (returns it in the second argument) with that cell as the source and its first argument as the synapse or artificial cell object.

Artificial cells can either be unwrapped or follow the NetGUI tool policy where they are wrapped in a cell class in which the actual artificial cell is given by the cell.pp field and the cell class also has a connect2target method.

If you don’t know what I’ve been talking about in the last two paragraphs, use the NetGUI tool on a single machine to construct a toy network consisting of a few real and artificial cells and save it to a hoc file for examination.

5. Connect the cells using ParallelNetManager.nc_append()

for i in range(ncell):
pnm.nc_append(i, (i + 1) % ncell, -1, 1.1, 2)


Again, it only has to be called if i, or i + 1, or both, are on this machine. It is a no-op if neither are on this machine and usually a no-op if only the source is on this machine since it will only mark the source cell as output cell, once.

The -1 just refers to the synapse index which should be -1 for artificial cells. The delay is 2 ms and the weight is 1.1 which guarantees that the IntFire1 cell will fire when it receives a spike.

Our example requires a stimulus and this is not an unreasonable time to stimulate the net. Let’s get the ring going by forcing the gid==4 cell to fire.

# stimulate
if pnm.gid_exists(4):
stim = h.NetStim(0.5)
ncstim = h.NetCon(stim, pnm.pc.gid2obj(4))
ncstim.weight[0] = 1.1
ncstim.delay = 0
stim.number=1
stim.start=1


Note the stimulator does not require a gid even though it is an artificial cell because its connections do not span machines. But it does have to be on the machine that has the cell it is connecting to.

6. Have the system figure out the minimum spanning NetCon delay so it knows the maximum step size.

pnm.set_maxstep(100) # will end up being 2

7. Decide what output to collect

pnm.want_all_spikes()


If you want to record spikes from only a few cells you can use ParallelNetManager.spike_record() explicitly. If you want to record range variable trajectories, check that the cell exists with ParallelNetManager.gid_exists() and then use Vector.record().

8. Initialize and run.

import time
h.stdinit()
runtime = time.time()
pnm.psolve(tstop)
runtime = time.time() - runtime

9. Print the results.

for spike, i in zip(pnm.spikevec, pnm.idvec):
print('%g %g' % (spike, i))


If you save the stdout to a file you can sort the results. A nice idiom is sort -k 1n,1n -k 2n,2n temp1 > temp

A perhaps more flexible alternative is to separate the master from all the workers somewhere after item 4) and before item 8) using ParallelContext.runworker() and then making use of the ParallelNetManager.prun() and ParallelNetManager.gatherspikes() with the normal ParallelContext control in a master worker framework.

At any rate, before we quit we have to call it so that the master can tell all the workers to quit.

ParallelNetManager.set_gid2node()
Syntax:
pnm.set_gid2node(gid, machine_id)
Description:
When MPI is being used, this is just a wrapper for the ParallelContext version of ParallelContext.set_gid2node() .

ParallelNetManager.round_robin()
Syntax:
pnm.round_robin()
Description:
The gid ranging from 0 to ncell-1 is assigned to machine (gid + 1) % nhost. There is no good reason anymore for the “+1”. ParallelContext.nhost() is the number of machines available.

ParallelNetManager.gid_exists()
Syntax:
result = pnm.gid_exists(gid)
Description:
Returns 1 if the gid exists on this machine, 2 if it exists and has been declared to be an output cell. 0 otherwise. Just a wrapper for ParallelContext.gid_exists() when MPI is being used.

ParallelNetManager.create_cell()
Syntax:
cellobject = pnm.create_cell(gid, "obexpr")
Description:

This is deprecated. Use ParallelNetManager.register_cell() .

If the gid exists on this machine the obexpr is executed in HOC in a statement equivalent to pnm.cells.append(obexpr). Obexpr should be something like "new Pyramid()" or any function that returns a cell object. Valid “real” cell objects should have a connect2target method and a synlist synapse list field just as the types used by the NetGUI builder. Artificial cell objects can be bare or enclosed in a wrapper class using the pp field.

Note: the following has been changed so that the source is always an outputcell.

At the end of this call, pnm.gid_exists(gid) will return either 0 or 1 because the cell has not yet been declared to be an outputcell. That will be done when the first connection is requested for which this cell is a source but the target is on another machine.

ParallelNetManager.register_cell()
Syntax:
pnm.register_cell(gid, cellobject)
Description:

Associate gid and cellobject. If ParallelContext.gid_exists() is zero then this procedure calls ParallelContext.set_gid2node() If the cell is “real” or encapsulates a point process artificial cell, then the cellobject.connect2target is called. The cellobject is declared to be an ParallelContext.outputcell() .

This method supersedes the create_cell method since it more easily handles cell creation arguments.

ParallelNetManager.nc_append()
Syntax:
netcon = pnm.nc_append(src_gid, target_gid, synapse_id, weight, delay)
Description:

If the source and target exist on this machine a NetCon is created and added to the pnm.nclist.

If the target exists and is a real cell the synapse object is pnm.gid2obj(target_gid).synlist(synapse_id).

If the target exists and is a wrapped artificial cell then the synapse_id should be -1 and the target artificial cell is pnm.gid2obj(target_gid).pp. If the target exists and is an ArtificialCell the synapse_id should be -1 and the target artificial cell is pnm.gid2obj(target_gid). Note that the target is an unwrapped artificial cell if StringFunctions.is_point_process() returns a non-zero value.

If the target exists but not the source, the netcon is created via ParallelContext.gid_connect() and added to the pnm.nclist.

If the source exists but not the target, and ParallelContext.gid_exists() returns 1 (instead of 2) then the cell is marked to be an ParallelContext.outputcell() .

If the source exists and is a real cell or wrapped artificial cell pnm.gid2obj(src_id).connect2target(synapse_target_object, nc) is used to create the NetCon.

If the source exists and is a artificial cell then the NetCon is created directly.

If neither the source or target exists, there is nothing to do.

ParallelNetManager.want_all_spikes()
Syntax:
pnm.want_all_spikes()
Description:
Records all spikes of all cells on this machine into the pnm.spikevec and pnm.idvec Vector objects. The spikevec holds spike times and the idvec holds the corresponding gid values.

ParallelNetManager.spike_record()
Syntax:
pnm.spike_record(gid)
Description:
Wraps ParallelContext.spike_record() but calls it only if ParallelContext.gid_exists() is nonzero and records the spikes into the pnm.spikevec and pnm.gidvec Vector objects.

ParallelNetManager.prun()
Syntax:
pnm.prun()
Description:
All the workers and the master are asked to ParallelNetManager.pinit() and ParallelNetManager.pcontinue() up to tstop.

ParallelNetManager.psolve()
Syntax:
pnm.psolve(tstop)
Description:
Wraps ParallelContext.psolve() .

ParallelNetManager.pinit()
Syntax:
pnm.pinit()
Description:
All the workers and the master execute a call to ParallelContext.set_maxstep() to determine the maximum possible step size and all the workers and the master execute a call to the stdinit() of the standard run system.

ParallelNetManager.pcontinue()
Syntax:
pnm.pcontinue(tstop)
Description:
All the workers and the master execute a call to ParallelContext.psolve() to integrate from the current value of t to the argument value.

ParallelNetManager.prstat()
Syntax:

pnm.prstat(0)

pnm.prstat(1)

Description:

Prints a high resolution amount of time all the machines have waited for spike exchange. If some are much higher than others then there is likely a load balance problem. If they are all high relative to the simulation time then spike exchange may be the rate limiting step.

If the argument is 1, then, in addition to wait time, spike_statistics are printed. The format is

pc.id wait_time(s) nsendmax nsend nrecv nrecv_useful
%d\t  %g\t %d\t %d\t %d\t %d\n


ParallelNetManager.gatherspikes()
Syntax:
pnm.gatherspikes
Description:
All the workers are asked to post their spikevec and idvec Vectors for taking by the master and concatenated to the master’s spikevec and idvec Vectors.

ParallelNetManager.splitcell()
Syntax:
pnm.splitcell(hostcas, hostparent, sec=split_at)
Description:

The cell is split at the section split_at and that section’s parent into two subtrees rooted at the old connection end of split_at and the old split_at connecting point of the parent (latter must be 0 or 1). The split_at subtree will be preserved on the host specified by hostcas and the parent subtree will be destroyed. The parent subtree will be preserved on the host specified by hostparent and the split_at subtree destroyed. Hostparent must be either host_split_at+1 or host_split_at-1.

Splitcell works only if NEURON has been configured with the –with-paranrn option. A split cell has exactly the same stability and accuracy properties as if it were on a single machine. Splitcell cannot be used with variable step methods at this time. A cell can be split into only two pieces.

Splitcell is implemented using the ParallelContext.splitcell() method of ParallelContext.