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. The Brian2GeNN package provides such a ‘device’ to run Brian 2 code on the GeNN (GPU enhanced Neuronal Networks) backend. GeNN is in itself a code-generation based framework to generate and execute code for NVIDIA CUDA. Through Brian2GeNN one can hence generate and run CUDA code on NVIDIA GPUs based solely in Brian 2 input.
Installing the Brian2GeNN interface¶
In order to use the Brian2GeNN interface, all three Brian 2, GeNN and
Brian2GeNN need to be fully installed. The easiest way to do this is by using
the conda package provided in the
brian-team channel on https://anaconda.org.
This will install Brian 2 and its dependencies, and Brian2GeNN with an internal
version of GeNN (you can always switch to using an existing GeNN installation
by setting the devices.genn.path preference). Note that this will not
install the CUDA toolkit and driver necessary to run simulations on a NVIDIA
graphics card. These will have to be installed manually, e.g. from NVIDIA’s
web site (you can always run
simulations in the “CPU-only” mode, but that of course defeats the main
purpose of Brian2GeNN…). Depending on the installation method, you might
also have to manually set the
CUDA_PATH environment variable (or
alternatively the devices.genn.cuda_path preference) to point to
CUDA’s installation directory.
To install Brian2GeNN via conda use:
conda install -c brian-team brian2genn
If you are not using the conda package manager or if there is no conda package for your architecture, you can always install brian2genn from its source package on http://pypi.python.org/
pip install brian2genn
(might require administrator privileges depending on the configuration of your
--user to force an installation with user privileges only).
Note that in this case, GeNN needs to be installed manually (see its
and either the
GENN_PATH environment variable of the devices.genn.path
preference have to point to its directory. In addition, the CUDA libraries have
to be installed (see above).
The above commands install the necessary packages to run simulations with
Brian2/GeNN, but most users would install additional packages, e.g.
matplotlib for plotting. This can be done with
the same package management tools mentioned above, e.g. use
conda install matplotlib or
pip install matplotlib.
Using the Brian2GeNN interface¶
To use the interface one then needs to import the brian2genn interface:
The you need to choose the ‘genn’ device at the beginning of the Brian 2 script, i.e. after the import statements, add:
At the encounter of the first
run statement (Brian2GeNN does currently
only support a single
run statement per script), code for GeNN will be
generated, compiled and executed.
set_device function can also take additional arguments, e.g. to run
GeNN in its “CPU-only” mode and to get additional debugging output, use:
set_device('genn', useGPU=False, debug=True)
Not all features of Brian work with Brian2GeNN. The current list of excluded features is detailed in Unsupported features in Brian2GeNN.