Computational methods and efficiency

Brian has several different methods for running the computations in a simulation. The default mode is Runtime code generation, which runs the simulation loop in Python but compiles and executes the modules doing the actual simulation work (numerical integration, synaptic propagation, etc.) in a defined target language. Brian will select the best available target language automatically. On Windows, to ensure that you get the advantages of compiled code, read the instructions on installing a suitable compiler in Requirements for C++ code generation. Runtime mode has the advantage that you can combine the computations performed by Brian with arbitrary Python code specified as NetworkOperation.

The fact that the simulation is run in Python means that there is a (potentially big) overhead for each simulated time step. An alternative is to run Brian in with Standalone code generation – this is in general faster (for certain types of simulations much faster) but cannot be used for all kinds of simulations. To enable this mode, add the following line after your Brian import, but before your simulation code:

set_device('cpp_standalone')

For detailed control over the compilation process (both for runtime and standalone code generation), you can change the Cleaning up after a run that are used.

The following topics are not essential for beginners.

Runtime code generation 

Code generation means that Brian takes the Python code and strings in your model and generates code in one of several possible different languages which is then executed. The target language for this code generation process is set in the codegen.target preference. By default, this preference is set to 'auto', meaning that it will choose the compiled language target if possible and fall back to Python otherwise (also raising a warning). The compiled language target is 'cython' which needs the Cython package in addition to a working C++ compiler. If you want to chose a code generation target explicitly (e.g. because you want to get rid of the warning that only the Python fallback is available), set the preference to 'numpy' or 'cython' at the beginning of your script:

from brian2 import *
prefs.codegen.target = 'numpy'  # use the Python fallback

See Preferences for different ways of setting preferences.

Caching 

When you run code with cython for the first time, it will take some time to compile the code. For short simulations, this can make these targets to appear slow compared to the numpy target where such compilation is not necessary. However, the compiled code is stored on disk and will be re-used for later runs, making these simulations start faster. If you run many simulations with different code (e.g. Brian’s test suite), this code can take quite a bit of space on the disk. During the import of the brian2 package, we check whether the size of the disk cache exceeds the value set by the codegen.max_cache_dir_size preference (by default, 1GB) and display a message if this is the case. You can clear the disk cache manually, or use the clear_cache function, e.g. clear_cache('cython').

Note

If you run simulations on parallel on a machine using the Network File System, see this known issue.

Standalone code generation 

Brian supports generating standalone code for multiple devices. In this mode, running a Brian script generates source code in a project tree for the target device/language. This code can then be compiled and run on the device, and modified if needed. At the moment, the only “device” supported is standalone C++ code. In some cases, the speed gains can be impressive, in particular for smaller networks with complicated spike propagation rules (such as STDP).

To use the C++ standalone mode, you only have to make very small changes to your script. The exact change depends on whether your script has only a single run() (or Network.run) call, or several of them:

Single run call 

At the beginning of the script, i.e. after the import statements, add:

set_device('cpp_standalone')

The Device.build function will be automatically called with default arguments right after the run() call. If you need non-standard arguments then you can specify them as part of the set_device() call:

set_device('cpp_standalone', directory='my_directory', debug=True)

Multiple run calls 

At the beginning of the script, i.e. after the import statements, add:

set_device('cpp_standalone', build_on_run=False)

After the last run() call, call CPPStandaloneDevice.build explicitly:

device.build()

The build function has several arguments to specify the output directory, whether or not to compile and run the project after creating it and whether or not to compile it with debugging support or not.

Multiple full simulation runs 

To run multiple full, independent, simulations (i.e. not just multiple run() calls as discussed above), you can use the device’s run function after an initial build. This will use the previously generated and compiled code, and will therefore run immediately. Note that you cannot change the model or its parameters in the usual way between the build and run calls. If you want to change some of its parameters, you will have to use the run_args argument as described below.

Running multiple simulations with same parameters

By default, a device’s run will run the simulation again, using the same model parameters and initializations. This can be useful, when the model is itself stochastic (e.g. using the xi noise term in the equations, using a stochastic group such as PoissonGroup or PoissonInput, etc.), when it uses random synaptic connections, or when it uses random variable initialization:

set_device('cpp_standalone')
group = NeuronGroup(1, 'dv/dt = -v / (10*ms) : 1')  # a simple IF neuron without threshold
group.v = 'rand()'  # v is randomly initialized between 0 and 1
mon = StateMonitor(group, 'v', record=0)
run(100*ms)  # calls device.build and device.run
results = [mon.v[0]]
# Do 9 more runs without recompiling, each time initializing v to a new value
for _ in range(9):
    device.run()
    results.append(mon.v[0])

For more consistent code, you might consider to disable the automatic device.build/device.run call, so that the initial run of the simulation is not different to subsequent runs:

set_device('cpp_standalone', build_on_run=False)
# ... Set up model as before
run(100*ms)  # will not call device.build/device.run
device.build(run=False)  # Compile the code
results = []
# Do 10 runs without recompiling, each time initializing v to a new value
for _ in range(10):
    device.run()
    results.append(mon.v[0])

Running multiple simulations with different parameters

When launching new simulation runs as described above, you can also change parameters of the model. Note that this only concerns parameters that are included in equations, you cannot change externally defined constants. You can easily work around this limitation, however, by declaring such constants in the equations, using the (shared, constant) flags. Here’s a similar example to the one shown before, now exploring the effect of the time constant tau, while assuring via a seed() call that the random initializations are identical across runs:

set_device('cpp_standalone', build_on_run=False)
seed(111)  # same random numbers for each run
group = NeuronGroup(10, '''dv/dt = -v / tau : 1
                           tau : second (shared, constant)''')  # 10 simple IF neuron without threshold
group.v = 'rand()'
mon = StateMonitor(group, 'v', record=0)
run(100*ms)
device.build(run=False)  # Compile the code
results = []
# Do 10 runs without recompiling, each time setting group.tau to a new value
for tau_value in (np.arange(10)+1)*5*ms:
    device.run(run_args={group.tau: tau_value})
    results.append(mon.v[:])

You can use the same mechanism to provide an array of initial values for a group. E.g., to systematically try out different initializations of v, you could use:

set_device('cpp_standalone', build_on_run=False)
group = NeuronGroup(10, 'dv/dt = -v / (10*ms) : 1')  # ten simple IF neurons without threshold
mon = StateMonitor(group, 'v', record=True)
run(100*ms)  # will not call device.build/device.run
device.build(run=False)  # Compile the code
results = []
# Do 10 runs without recompiling, each time initializing v differently
for idx in range(10):
    device.run(run_args={group.v: np.arange(10)*0.01 + 0.1*idx})
    results.append(mon.v[0])

You can also overwrite the values in a TimedArray using this mechanism, by using the TimedArray as a key in the run_args dictionary:

set_device('cpp_standalone', build_on_run=False)
stim = TimedArray(np.zeros(10), dt=10*ms)
group = NeuronGroup(10, 'dv/dt = (stim(t) - v)/ (10*ms) : 1')  # time-dependent stimulus
mon = StateMonitor(group, 'v', record=True)
run(100 * ms)
device.build(run=False)
results = []
# Do 10 runs with a 10ms at a random time
for idx in range(10):
    values = np.zeros(10)
    values[np.random.randint(0, 10)] = 1
    device.run(run_args={stim: values})
    results.append(mon.v[0])

By default, the initialization provided via run_args overwrites any initializations done in the usual way. This might not exactly do what you want if you use string-based variable initializations that refer to each other. For example, if your equations contain two synaptic time constants tau_exc and tau_inh, and you always want the latter to be twice the value of the former, you can write:

group.tau_exc = 5*ms
group.tau_inh = 'tau_exc * 2'

If you now use the run_args argument to set tau_exc to a different value, this will not be taken into account for setting tau_inh, since the value change for tau_exc happens after the initialization of tau_inh. Of course you can simply set the value for tau_inh manually using run_args as well, but a more general solution is to move the point where the run_args are applied. You can do this by calling the device’s apply_run_args function:

group.tau_exc = 5*ms
device.apply_run_args()
group.tau_inh = 'tau_exc * 2'

With this change, setting tau_exc via run_args will affect the value of tau_inh.

Running multiple simulations in parallel

The techniques mentioned above cannot be directly used to run simulations in parallel (e.g. with Python’s multiprocessing module), since all of them will try to write the results to the same place. You can circumvent this problem by specifying the results_directory argument, and setting it to a different value for each run. Note that using the standalone device with multiprocessing can be a bit tricky, since the currently selected device is stored globally in the device module. Use the approach presented below to make sure the device is selected correctly. Here’s a variant of the previously shown example running a simulation with random initialization repeatedly, this time running everything in parallel using Python’s multiprocessing module:

class SimWrapper:
    def __init__(self):
        # Runs once to set up the simulation
        group = NeuronGroup(1, 'dv/dt = -v / (10*ms) : 1', name='group')
        group.v = 'rand()'  # v is randomly initialized between 0 and 1
        mon = StateMonitor(group, 'v', record=0, name='monitor')
        # Store everything in a network
        self.network = Network([group, mon])
        self.network.run(100*ms)
        device.build(run=False)
        self.device = get_device()  # store device object

    def do_run(self, result_dir):
        # Runs in every process
        # Workaround to set the device globally in this context
        from brian2.devices import device_module
        device_module.active_device = self.device
        self.device.run(results_directory=result_dir)
        # Return the results
        return self.network['monitor'].v[0]

if __name__ == '__main__':  # Important for running on Windows and OS X
    set_device('cpp_standalone', build_on_run=False)
    sim = SimWrapper()
    import multiprocessing
    with multiprocessing.Pool() as p:
        # Run 10 simulations in parallel
        results = p.map(sim.do_run, [f'result_{idx}' for idx in range(10)])

You can also use parallel runs with the run_args argument. For example, to do 10 simulations with different (deterministic) initial values for v:

class SimWrapper:
    # ... model definition without random initialization

    def do_run(self, v_init):
        # Set result directory based on variable
        result_dir = f'result_{v_init}'
        self.device.run(run_args={self.network['group'].v: v_init},
                        results_directory=result_dir)
        # Return the results
        return self.network['monitor'].v[0]

if __name__ == '__main__':  # Important for running on Windows and OS X
    set_device('cpp_standalone', build_on_run=False)
    sim = SimWrapper()
    import multiprocessing
    with multiprocessing.Pool() as p:
        # Run 10 simulations in parallel
        results = p.map(sim.do_run, np.linspace(0, 1, 10))

Note

Python’s multiprocessing module cannot deal with user-defined functions (including TimedArray) and other complex code structures. If you run into PicklingError or AttributeError exceptions, you might have to use the pathos (https://pypi.org/project/pathos) package instead, which can handle more complex code structures.

Limitations 

Not all features of Brian will work with C++ standalone, in particular Python based network operations and some array based syntax such as S.w[0, :] = ... will not work. If possible, rewrite these using string based syntax and they should work. Also note that since the Python code actually runs as normal, code that does something like this may not behave as you would like:

results = []
for val in vals:
    # set up a network
    run()
    results.append(result)

The current C++ standalone code generation only works for a fixed number of run statements, not with loops. If you need to do loops or other features not supported automatically, you can do so by inspecting the generated C++ source code and modifying it, or by inserting code directly into the main loop as described below.

Variables 

In standalone mode, code will only be executed when the simulation is run (after the run() call by default, or after a call to build, if set_device() has been called with build_on_run set to False). This means that it is not possible to access state variables and synaptic connection indices in the Python script doing the set up of the model. For example, the following code would work fine in runtime mode, but raise a NotImplementedError in standalone mode:

neuron = NeuronGroup(10, 'v : volt')
neuron.v = '-70*mV + rand()*10*mV'
print(np.mean(neuron.v))

Sometimes, access is needed to make one variable depend on another variable for initialization. In such cases, it is often possible to circumvent the issue by using initialization with string expressions for both variables. For example, to set the initial membrane potential relative to a random leak reversal potential, the following code would work in runtime mode but fail in standalone mode:

neuron = NeuronGroup(10, 'dv/dt = -g_L*(v - E_L)/tau : volt')
neuron.E_L = '-70*mV + rand()*10*mV'  # E_L between -70mV and -60mV
neuron.v = neuron.E_L  # initial membrane potential equal to E_L

Instead, you can initialize the variable v with a string expression, which means that standalone will execute it during the run when the value of E_L is available:

neuron = NeuronGroup(10, 'dv/dt = -g_L*(v - E_L)/tau : volt')
neuron.E_L = '-70*mV + rand()*10*mV'  # E_L between -70mV and -60mV
neuron.v = 'E_L'  # works both in runtime and standalone mode

The same applies to synaptic indices. For example, if we want to set weights differently depending on the target index of a synapse, the following would work in runtime mode but fail in standalone mode, since the synaptic indices have not been determined yet:

neurons = NeuronGroup(10, '')
synapses = Synapses(neurons, neurons, 'w : 1')
synapses.connect(p=0.25)
# Set weights to low values when targetting first five neurons and to high values otherwise
synapses.w[:, :5] = 0.1
synapses.w[:, 5:] = 0.9

Again, this initialization can be replaced by string expressions, so that standalone mode can evaluate them in the generated code after synapse creation:

neurons = NeuronGroup(10, '')
synapses = Synapses(neurons, neurons, 'w : 1')
synapses.connect(p=0.25)
# Set weights to low values when targetting first five neurons and to high values otherwise
synapses.w['j < 5'] = 0.1
synapses.w['j >= 5'] = 0.9

Note that this limitation only applies if the variables or synapses have been initialized in ways that require the execution of code. If instead they are initialized with concrete values, they can be accessed in Python code even in standalone mode:

neurons = NeuronGroup(10, 'v : volt')
neurons.v = -70*mV
print(np.mean(neurons.v))  # works in standalone
synapses = Synapses(neurons, neurons, 'w : 1')
synapses.connect(i=[0, 2, 4, 6, 8], j=[1, 3, 5, 7, 9])
# works as well, since synaptic indices are known
synapses.w[:, :5] = 0.1
synapses.w[:, 5:] = 0.9

In any case, state variables, synaptic indices, and monitored variables can be accessed using standard syntax after a run (with a few exceptions, e.g. string expressions for indexing).

Multi-threading with OpenMP 

Warning

OpenMP code has not yet been well tested and so may be inaccurate.

When using the C++ standalone mode, you have the opportunity to turn on multi-threading, if your C++ compiler is compatible with OpenMP. By default, this option is turned off and only one thread is used. However, by changing the preferences of the codegen.cpp_standalone object, you can turn it on. To do so, just add the following line in your python script:

prefs.devices.cpp_standalone.openmp_threads = XX

XX should be a positive value representing the number of threads that will be used during the simulation. Note that the speedup will strongly depend on the network, so there is no guarantee that the speedup will be linear as a function of the number of threads. However, this is working fine for networks with not too small timestep (dt > 0.1ms), and results do not depend on the number of threads used in the simulation.

Custom code injection 

It is possible to insert custom code directly into the generated code of a standalone simulation using a Device’s insert_code method:

device.insert_code(slot, code)

slot can be one of main, before_start, after_start, before_network_run, after_network_run, before_end and after_end, which determines where the code is inserted. code is the code in the Device’s language. Here is an example for the C++ Standalone Device:

device.insert_code('main', '''
cout << "Testing direct insertion of code." << endl;
''')

For the C++ Standalone Device, all code is inserted into the main.cpp file, here into the main slot, referring to the main simulation function. This is a simplified version of this function in main.cpp:

int main(int argc, char **argv)
{
    // before_start
    brian_start();
    // after_start

    {{main_lines}}

    // before_end
    brian_end();
    // after_end

    return 0;
}

{{main_lines}} is replaced in the generated code with the actual simulation. Code inserted into the main slot will be placed within the {{main_lines}}. brian_start allocates and initializes all arrays needed during the simulation and brian_end writes the results to disc and deallocates memory. Within the {{main_lines}}, all Network objects defined in Python are created and run. Code inserted in the before/after_network_run slot will be inserted around the Network.run call, which starts the time loop. Note that if your Python script has multiple Network objects or multiple run calls, code in the before/after_network_run slot will be inserted around each Network.run call in the generated code.

The code injection mechanism has been used for benchmarking experiments, see e.g. here for Brian2CUDA benchmarks or here for Brian2GeNN benchmarks.

Customizing the build process 

In standalone mode, a standard “make file” is used to orchestrate the compilation and linking. To provide additional arguments to the make command (respectively nmake on Windows), you can use the devices.cpp_standalone.extra_make_args_unix or devices.cpp_standalone.extra_make_args_windows preference. On Linux, this preference is by default set to ['-j'] to enable parallel compilation. Note that you can also use these arguments to overwrite variables in the make file, e.g. to use clang instead of the default gcc compiler:

prefs.devices.cpp_standalone.extra_make_args_unix += ['CC=clang++']

Cleaning up after a run 

Standalone simulations store all results of a simulation (final state variable values and values stored in monitors) to disk. These results can take up quite significant amount of space, and you might therefore want to delete these results when you do not need them anymore. You can do this by using the device’s delete method:

device.delete()

Be aware that deleting the data will make all access to state variables fail, including the access to values in monitors. You should therefore only delete the data after doing all analysis/plotting that you are interested in.

By default, this function will delete both the generated code and the data, i.e. the full project directory. If you want to keep the code (which typically takes up little space compared to the results), exclude it from the deletion:

device.delete(code=False)

If you added any additional files to the project directory manually, these will not be deleted by default. To delete the full directory regardless of its content, use the force option:

device.delete(force=True)

Note

When you initialize state variables with concrete values (and not with a string expression), they will be stored to disk from your Python script and loaded from disk at the beginning of the standalone run. Since these values are necessary for the compiled binary file to run, they are considered “code” from the point of view of the delete function.

Compiler settings 

If using C++ code generation (either via cython or standalone), the compiler settings can make a big difference for the speed of the simulation. By default, Brian uses a set of compiler settings that switches on various optimizations and compiles for running on the same architecture where the code is compiled. This allows the compiler to make use of as many advanced instructions as possible, but reduces portability of the generated executable (which is not usually an issue).

If there are any issues with these compiler settings, for example because you are using an older version of the C++ compiler or because you want to run the generated code on a different architecture, you can change the settings by manually specifying the codegen.cpp.extra_compile_args preference (or by using codegen.cpp.extra_compile_args_gcc or codegen.cpp.extra_compile_args_msvc if you want to specify the settings for either compiler only).