# 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).