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. This mode has the advantage that you can combine the computations
performed by Brian with arbitrary Python code specified as
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.
For detailed control over the compilation process (both for runtime and standalone code generation), you can change the Compiler settings that are used.
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 and actually executes that. 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 chose a compiled language
target if possible and fall back to Python otherwise (it will also raise a warning
in this case, set codegen.target to
'numpy' explicitly to avoid this warning).
There are two compiled language targets for Python 2.x,
'weave' (needing a
working installation of a C++ compiler) and
'cython' (needing the Cython
package in addition); for Python 3.x, only
'cython' is available. 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
'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.
You might find that running simulations in weave or Cython modes won’t work or is not as efficient as you were expecting. This is probably because you’re using Python functions which are not compatible with weave or Cython. For example, if you wrote something like this it would not be efficient:
from brian2 import * prefs.codegen.target = 'cython' def f(x): return abs(x) G = NeuronGroup(10000, 'dv/dt = -x*f(x) : 1')
The reason is that the function
f(x) is a Python function and so cannot
be called from C++ directly. To solve this problem, you need to provide an
implementation of the function in the target language. See Functions.
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
Network.run()) call, or several of them:
Single run call¶
At the beginning of the script, i.e. after the import statements, add:
CPPStandaloneDevice.build function will be automatically called with default arguments right after the
call. If you need non-standard arguments then you can specify them as part of the
set_device('cpp_standalone', directory='my_directory', debug=True)
Multiple run call¶
At the beginning of the script, i.e. after the import statements, add:
After the last
run() call, call
device.build(directory='output', compile=True, run=True, debug=False)
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.
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 follows:
device.insert_code('main', ''' cout << "Testing direct insertion of code." << endl; ''')
The generation of random numbers in the C++ standalone mode (e.g. when using probabilistic synaptic connections,
xi symbol in equations, or explicit calls to
randn()) is currently based on the
function from the C standard library which does not make any guarantees about its quality and is known to produce
low-quality random numbers (e.g. random number sequences with a relatively short period) on some platforms. This
does not concern runtime mode (which uses the random number generator from numpy) and we are planning to provide an
implementation of the same quality also in C++ standalone mode as part of the final 2.0 release.
After a simulation has been run (after the
run() call if
set_device() has been called with
build_on_run set to
True or after the
Device.build call with
run set to
True), state variables and
monitored variables can be accessed using standard syntax, with a few exceptions (e.g. string expressions for indexing).
Multi-threading with OpenMP¶
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.
If using C++ code generation (either via weave, 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).