# Metering¶

Arbor’s python module arbor has a meter_manager for measuring time (and if applicable memory) consumptions of regions of interest in the python code.

Users manually instrument the regions to measure. This allows the user to only measure the parts of the python code that are of interest. Once a region of code is marked for the meter_manager, the application will track the total time (and memory) spent in this region.

## Marking Metering Regions¶

First the meter_manager needs to be initiated, then the metering started and checkpoints set, wherever the meter_manager should report the meters. The measurement starts from the meter_manager.start() to the first meter_manager.checkpoint() and then in between checkpoints. Checkpoints are defined by a string describing the process to be measured.

class arbor.meter_manager
meter_manager()

Construct the meter manager.

start(context)

Start the metering using the chosen execution arbor.context. Records a time stamp, that marks the start of the first checkpoint timing region.

checkpoint(name, context)

Create a new checkpoint name using the chosen execution arbor.context. Records the time since the last checkpoint (or the call to start if no previous checkpoints exist), and restarts the timer for the next checkpoint.

checkpoint_names()

Returns a list of all metering checkpoint names.

times()

Returns a list of all metering times.

For instance, the following python code will record and summarize the total time (and memory) spent:

import arbor

meter_manager = arbor.meter_manager()
meter_manager.start(context)

n_cells = 100
recipe = my_recipe(n_cells)

meter_manager.checkpoint('recipe-create', context)

sim = arbor.simulation(recipe, decomp, context)

meter_manager.checkpoint('simulation-init', context)

tSim = 2000
dt = 0.025
sim.run(tSim, dt)

meter_manager.checkpoint('simulation-run', context)


## Metering Output¶

At any point a summary of the timing regions can be obtained by the meter_report.

class arbor.meter_report
meter_report(meter_manager, context)

Summarises the performance meter results, used to print a report to screen or file. If a distributed context is used, the report will contain a summary of results from all MPI ranks.

Take the example output from above:

print(arbor.meter_report(meter_manager, context))

>>> ---- meters -------------------------------------------------------------------------------
>>> meter                         time(s)      memory(MB)
>>> -------------------------------------------------------------------------------------------
>>> recipe-create                   0.000           0.001