The NEURON build system now uses CMake as of version 7.8 circa Nov 2019. The previous autotools (./configure) build system has been removed after 8.0 release.
The NEURON simulator as well as Interviews, CoreNEURON and NMODL can be installed together using the following instructions:
Clone the latest version or specific release:
git clone https://github.com/neuronsimulator/nrn # latest development branch
git clone https://github.com/neuronsimulator/nrn -b 8.2.3 # specific release version 8.2.3
To build NEURON from source you either need to clone the
NEURON Git repository or download a source code archive that includes Git submodules, such as the
nrn-full-src-package-X.Y.Z.tar.gzfile in the NEURON releases on GitHub. The tarballs like
Source code (tar.gz)or
Source code (zip)created by GitHub are incomplete.
Create a build directory:
cmakewith the appropriate options (see below for a list of common options). A full list of options can be found in
nrn/CMakeLists.txtand defaults are shown in
nrn/cmake/BuildOptionDefaults.cmake. e.g. a bare-bones installation:
cmake .. \
-DPYTHON_EXECUTABLE=$(which python3) \
Build the code:
cmake --build . --parallel 8 --target install
Feel free to set the number of parallel jobs (i.e. 8) according to your
system using the
NEURONis installed with
CoreNEURONoption enabled then
NMODLis also installed with the
NMODLPython bindings which increase a lot the compilation complexity and memory requirements. For that purpose it’s recommended to either disable this option if the Python bindings are not needed using the
-DNMODL_ENABLE_PYTHON_BINDINGS=OFFor restrict the number of parallel jobs running in parallel in the
cmake --parallel <number_of_parallel_jobs>. i.e. in a machine with 8 threads do
cmake -parallel 4.
Set PATH and PYTHONPATH environmental variables to use the installation:
The make targets that are made available by CMake can be listed with
You can list CMake options with
cmake .. -LH
cmake .. as above and lists the cache variables along with help
strings which are not marked as INTERNAL or ADVANCED. Alternatively,
allows one to interactively inspect cached variables.
In the build folder,
cmake -LH (missing <path-to-source>) will not
run cmake, but if there is a
CMakeCache.txt file, the cache variables
will be listed.
The above default
cmake .. specifies a default installation location
and build type, and includes (or leaves out) the following major
cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local \
Cmake option values persist with subsequent invocations of cmake unless explicitly changed by specifying arguments to cmake (or by modifying them with ccmake). It is intended that all build dependencies are taken into account so that it is not necessary to start fresh with an empty build folder when modifying cmake arguments. However, there may be unknown exceptions to this (bugs) so in case of problems it is generally sufficient to delete all contents of the build folder and start again with the desired cmake arguments.
First arg is always
<path-to-source> which is the path (absolute or relative)
to the top level nrn folder (e.g. cloned from github). It is very common
to create a folder named
build in the top level nrn folder and run cmake
in that. e.g.
cd nrn mkdir build cd build cmake .. <more args>
Install path prefix, prepended onto install directories. This can be a full path or relative. Default is /usr/local . A common install folder is ‘install’ in the build folder. e.g.
so that the installation folder is
.../nrn/build/install. In this case the user should prepend
.../nrn/build/install/binto PATH and it may be useful toexport PYTHONPATH=.../nrn/build/install/lib/python
where in each case
...is the full path prefix to nrn.
Empty or one of Custom;Debug;Release;RelWithDebInfo;Fast.
RelWithDebInfo means to compile using -O2 -g options.
Debug means to compile with just -g (and optimization level -O0) This is very useful for debugging with gdb as, otherwise, local variables may be optimized away that are useful to inspect.
Release means to compile with -O2 -DNDEBUG. The latter eliminates assert statements.
Custom requires that you specify flags with CMAKE_C_FLAGS and CMAKE_CXX_FLAGS
Fast requires that you specify flags as indicated in nrn/cmake/ReleaseDebugAutoFlags.cmake
Custom and Fast depend on specific compilers and (super)computers and are tailored to those machines. See
Use the Ninja build system (
makeis the default CMake build system).cmake .. -G Ninja ... ninja install
Ninja can be faster than make during development when compiling just a few files. Some rough timings on a mac powerbook arm64 with and without -G Ninja for
cmake .. -G Ninja -DCMAKE_INSTALL_PREFIX=installare:# Note: make executed in build-make folder, ninja executed in build-ninja folder. time make -j install) # 39s time ninja install # 35s touch ../src/nrnoc/section.h time make -j # 8.3s time ninja # 7.4s
On mac, install ninja with
brew install ninja
ninja helpprints the target names that can be built individually
ninja -j 1does a non-parallel build.
ninja -vshows each command.
Enable GUI with INTERVIEWS
Unless you specify IV_DIR, InterViews will be automatically cloned as a subproject, built, and installed in CMAKE_INSTALL_PREFIX.
The directory containing a CMake configuration file for iv.
IV_DIR is the install location of iv and the directory actually containing the cmake configuration files is
IV_DIR/lib/cmake. This is useful when you have many clones of nrn for different development purposes and wish to use a single independent InterViews installation for many/all of them. E.g. I generally invoke
dlopen X11 after launch
This is most useful for building Mac distributions where XQuartz (X11) may not be installed on the user’s machine and the user does not require InterViews graphics. If XQuartz is subsequently installed, InterViews graphics will suddenly be available.
Remake the X11 dynamic .h files.
Don’t use this. The scripts are very brittle and X11 is very stable. If it is ever necessary to remake the X11 dynamic .h files, I will do so and push them to the https://github.com/neuronsimulator/iv respository.
Enable MPI support
Requires an MPI installation, e.g. openmpi or mpich. Note that the Python mpi4py module generally uses openmpi which cannot be mixed with mpich.
Enable dynamic MPI library support
This is mostly useful for binary distibutions where MPI may or may not exist on the target machine.
semicolon (;) separated list of MPI include directories to build against. Default to first found mpi)
Cmake knows about openmpi, mpich, mpt, and msmpi. The dynamic loader for linux tries to load libmpi.so and if that fails, libmpich.so (the latter is good for cray mpi). The system then checks to see if a specific symbol exists in the libmpi… and determines whether to load the libnrnmp_xxx.so for openmpi, mpich, or mpt. To make binary installers good for openmpi and mpich, I use
This option is ignored unless NRN_ENABLE_MPI_DYNAMIC=ON
Enable MUSIC. MUlti SImulation Coordinator.
MUSIC must already be installed. See https://github.com/INCF/MUSIC. Hints for MUSIC installation: use the switch-to-MPI-C-interface branch. Python3 must have mpi4py and cython modules. I needed a PYTHON_PREFIX, so on my Apple M1 used:
./configure --prefix=`pwd`/musicinstall PYTHON_PREFIX=/Library/Frameworks/Python.framework/Versions/3.11 --disable-anysource
MPI and Python must be enabled.
If MUSIC is installed but CMake cannot find its
/path, augment the semicolon separated list of paths
-DCMAKE_PREFIX_PATH=...;/path;...or pass the
-DMUSIC_ROOT=/pathto cmake. CMake needs to find/path/include/music.hh /path/lib/libmusic.so
With the music installed above, cmake configuration example is
build % cmake .. -G Ninja -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_INSTALL_PREFIX=install -DPYTHON_EXECUTABLE=`which python3.11` -DNRN_ENABLE_RX3D=OFF -DCMAKE_BUILD_TYPE=Debug -DNRN_ENABLE_TESTS=ON -DNRN_ENABLE_MUSIC=ON -DCMAKE_PREFIX_PATH=$HOME/neuron/MUSIC/musicinstall
If -DNRN_ENABLE_MPI_DYNAMIC=ON then the nrnmusic interface to NEURON will also be dynamically loaded at runtime. (Generally useful only for binary distributions of NEURON (e.g. wheels) where NEURON may be installed and used prior to installing music.)
Enable Python interpreter support (default python, fallback to python3, but see PYTHON_EXECUTABLE below)
Enable dynamic Python version support
This is mostly useful for binary distributions where it is unknown which version, if any, of python exists on the target machine.
Semicolon (;) separated list of Python executables to build support for.
If the string is empty use the python specified by
PYTHON_EXECUTABLE. or else the default python (
$PATH). Binary distributions often specify a list of python versions so that if any one of them is available on the target machine, NEURON + Python will be fully functional. You must specify exactly one executable for each minor version of Python that you would like to support. For example:-DNRN_PYTHON_DYNAMIC="python3.8;python3.9;python3.10;python3.11"
The first entry in the list is considered to be the default version, followed by alternatives in decreasing order of preference. The default version is used to execute build scripts, and many tests are only executed using this version.
This option is ignored unless
NRN_ENABLE_PYTHON_DYNAMIC=ON, in which case
Use provided python binary instead of the one found by CMake. This must be a full path. We generally use-DPYTHON_EXECUTABLE=`which python3.8`
Enable installation of the NEURON Python module. By default, the NEURON module is installed in CMAKE_INSTALL_PREFIX/lib/python.
Note: When building wheels, this must be set to OFF since the top-level setup.py is already building the extensions.
Enable rx3d support
No longer any reason to turn this off as build time is not significantly increased due to compiling cython generated files with -O0 by default.
Optimization level for Cython generated files (non-zero may compile slowly)
It is not clear to me if -O0 has significantly less performance than -O2. Binary distributions are (or should be) built with-DNRN_RX3D_OPT_LEVEL=2
Enable CoreNEURON support
If ON CoreNEURON will be built and any needed NMODL submodule dependencies cloned as external submodules.
Enable CoreNEURON compatibility for MOD files
CoreNEURON does not allow the common NEURON THREADSAFE promotion of GLOBAL variables that appear on the right hand side of assignment statements to become thread specific variables. This option is automatically turned on if NRN_ENABLE_CORENEURON=ON.
Other CoreNEURON options:
There are 20 or so cmake arguments specific to a CoreNEURON build that are listed in https://github.com/neuronsimulator/nrn/blob/master/src/coreneuron/CMakeLists.txt. The ones of particular interest that can be used on the NEURON CMake configure line are CORENRN_ENABLE_NMODL and CORENRN_ENABLE_GPU.
To see all the NMODL CMake options you can look in https://github.com/BlueBrain/nmodl/blob/master/CMakeLists.txt.
Enable pybind11 based python bindings
Using this option the user can use the NMODL python package to use NMODL via python. For more information look at the NMODL documentation in https://bluebrain.github.io/nmodl/html/notebooks/nmodl-python-tutorial.html.
Occasionally useful advanced options:
See all the options with
ccmake ..in the build folder. They are also in the CMakeCache.txt file. Following is a definitely incomplete list.
On the mac, prior to knowing about
export SDK_ROOT=$(xcrun -sdk macosx --show-sdk-path)I got into the habit of-DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
to avoid the problem of gcc not being able to find stdio.h when python was compiling inithoc.cpp
C plus plus compiler
Enable documentation targets in the build. This also makes all documentation dependencies into hard requirements, so CMake will report an error if anything is missing. There are five documentation targets:
doxygengenerates Doxygen documentation from the NEURON source code.
notebooksexecutes the various Jupyter notebooks that are included in the documentation, so they contain both code and results, instead of just code. These are run in situ in the source tree, so if you run this target manually then make sure not to accidentally commit the results to git.
sphinxgenerates Sphinx documentation. This logically depends on
notebooks, as it generates HTML from the executed notebooks, but this dependency is not declared in the build system.
notebooks-cleanremoves the execution results from the Jupyter notebooks, leaving them in a clean state. This logically depends on
sphinx, as the execution results need to be converted to HTML before they are discarded, but this dependency is not declared in the build system.
docsis shorthand for building
notebooks-cleanin that order.
Executing the notebooks requires a functional NEURON installation. There are two possibilities here:
The default, which is sensible for local development, is that the
notebookstarget uses NEURON from the current CMake build directory. This implies that building the documentation builds NEURON too.
The alternative, which is enabled by setting
-DNRN_ENABLE_DOCS_WITH_EXTERNAL_INSTALLATION=ON, is that
notebooksdoes not depend on any other NEURON build targets. In this case you must provide an installation of NEURON by some other means. It will be assumed that commands like
nrnivmodlwork and that
import neuronworks in Python.
Compiler flags that are used to build NEURON code but not (unlike
CMAKE_CXX_FLAGS) code of dependencies built as submodules. This can be useful for tuning things like compiler warning flags.
Compiler flags that are used to build the C code generated by
nocmodlbut not source code files that are committed to the repository.
Compiler flag to build tools like nocmodl, modlunit.
In cluster environment with different architecture of login node and compute node, we need to compile tools like nocmodl and modlunit with different compiler options to run them on login/build nodes. This option appends provided flags to CMAKE_CXX_FLAGS.
For example, with intel compiler compiling NEURON for KNL but building on a Skylake node: .. code-block:-DCMAKE_BUILD_TYPE=Custom -DCMAKE_CXX_FLAGS="-xMIC-AVX512" -DNRN_NMODL_CXX_FLAGS="-XHost"
Install directory prefix where readline is installed.
If cmake can’t find readline, you can give this hint with the directory path under which readline is installed. Note that on some platforms with multi-arch support (e.g. Debian/Ubuntu), CMake versions < 3.20 are not able to find readline library when NVHPC/PGI compiler is used (for GPU support). In this case you can install newer CMake (>= 3.20) or explicitly specify readline library using -DReadline_LIBRARY= option: .. code-block:-DReadline_LIBRARY=/usr/lib/x86_64-linux-gnu/libreadline.so
Enable unit tests
Clones the submodule catch2 from https://github.com/catchorg/Catch2.git and after a build using
makecan run the tests with
make test. May also need to
pip install pytest.
make testis quite terse. To get the same verbose output that is seen with the CI tests, use
ctest -VV(executed in the build folder) or an individual test with
ctest -VV -R name_of_the_test. One can also run individual test files with
python3 -m pytest -s <testfile.py>or all the test files in that folder with
python3 -m pytest -s. (The
-sshows all output on the terminal.) Note: It is helpful to
make testfirst to ensure any mod files needed are available to the tests. If running a test outside the folder where the test is located, it may be necessary to add the folder to PYTHONPATH. Note: The last python mentioned in the
-DNRN_PYTHON_DYNAMIC=...(if the semicolon separated list is non-empty and
-DNRN_ENABLE_PYTHON_DYNAMIC=ON) is the one used for
ctest -VV. Otherwise the value specified by
Examplemkdir build cmake .. -DNRN_ENABLE_TESTS=ON ... make -j make test ctest -VV -R parallel_tests cd ../test/pynrn python3 -m pytest python3 -m pytest test_currents.py
Enable code coverage
sudo apt install lcov).
Provides two make targets to simplify the repeated “run tests, examine coverage” workflow.
make cover_beginerases all previous coverage data (
*.gcdafiles), and creates a baseline report. (Note all files and folders are created in the
CMAKE_BINARY_DIRwhere you ran cmake.)
make cover_htmlcreates a coverage report for the sum of all the software runs since the last
cover_beginand prints a file url that you can paste into your browser to review the coverage.
When using an iterative workflow to examine test coverage of a single or a few files, the above targets run much faster when this option is combined with NRN_COVERAGE_FILES:STRING=
Code coverage without the use of this option is explained in Developer Builds: Code Coverage
Coverage limited to semicolon (;) separated list of file paths relative to
For a list of all the cpp files changed in a pull request, consider copy/pasting the
;separated list obtained witha=`git diff --name-only master | grep '\.cpp'` echo $a | sed 's/ /;/g'
Enable some combination of AddressSanitizer, LeakSanitizer, ThreadSanitizer and UndefinedBehaviorSanitizer. Accepts a comma-separated list of
undefined. See the “Diagnosis and Debugging” section for more information. Note that on macOS it can be a little intricate to combine
-DNRN_SANITIZERS=addresswith the use of Python virtual environments; if you attempt this then the CMake code should recommend a solution.
addresssanitizer also prints leak infornation when a launch exits. That can be avoided with
Miscellaneous Rarely used options specific to NEURON:
Enable Observer to be a subclass of DiscreteEvent Can save space but a lot of component destruction may not notify other components that are watching it to no longer use that component. Useful only if one builds a model without needing to eliminate pieces of the model.
Dynamically load nrnmech shared library
Allow use of Pthreads
Turned on when creating python wheels.
Generate a backtrace on floating, segfault, and bus exceptions.
Avoids the need to use gdb to view the backtrace.
Does not work with python.
Note: floating exceptions are turned on with
Semicolon (;) separated list of Python executables that NEURON is not built with support for, for use in tests of error messages and reporting. For these purposes, minor versions (3.X and 3.Y) are considered different and patch versions (3.8.X and 3.8.Y) are considered to be the same.
Enable extra math optimisations.
When using compilers like GCC and Clang, one needs to explicitly use compiler flags like -funsafe-math-optimizations in order to generate SIMD/vectorised code using vector math library. This flag adds these extra compiler flags to enable SIMD code.
Note: Compilers like Intel, NVHPC, Cray etc enable such optimisations by default.