Introduction

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.

git clone https://github.com/neuronsimulator/nrn nrn
cd nrn
mkdir build
cd build
cmake .. # default install to /usr/local
make -j
sudo make -j install

The -j option to make invokes a parallel make using all available cores. This is often very much faster than a single process make. One can add a number after the -j (e.g. make -j 6) to specify the maximum number of processes to use. This can be useful if there is the possibility of running out of memory.

The make targets that are made available by cmake can be listed with

make help

You can list CMake options with cmake .. -LH which runs cmake .. as above and lists the cache variables along with help strings which are not marked as INTERNAL or ADVANCED. Alternatively,

ccmake ..

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 functional components.

cmake .. -DCMAKE_INSTALL_PREFIX=/usr/local \
  -DCMAKE_BUILD_TYPE=RelWithDebInfo \
  -DNRN_ENABLE_INTERVIEWS=ON \
  -DNRN_ENABLE_MPI=ON \
  -DNRN_ENABLE_PYTHON=ON \
  -DNRN_ENABLE_CORENEURON=OFF

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.

General options

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>

CMAKE_INSTALL_PREFIX:PATH=<path-where-nrn-should-be-installed>

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.
-DCMAKE_INSTALL_PREFIX=install
so that the installation folder is .../nrn/build/install . In this case the user should prepend .../nrn/build/install/bin to PATH and it may be useful to
export PYTHONPATH=.../nrn/build/install/lib/python
where in each case ... is the full path prefix to nrn.

CMAKE_BUILD_TYPE:STRING=RelWithDebInfo

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 nrn/cmake/ReleaseDebugAutoFlags.cmake

Ninja

Use the Ninja build system (make is 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=install are:
# 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 help prints the target names that can be built individually

ninja -j 1 does a non-parallel build.

ninja -v shows each command.

InterViews options

NRN_ENABLE_INTERVIEWS:BOOL=ON

Enable GUI with INTERVIEWS

Unless you specify IV_DIR, InterViews will be automatically cloned as a subproject, built, and installed in CMAKE_INSTALL_PREFIX.

IV_DIR:PATH=<path-to-external-installation-of-interviews>

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

-DIV_DIR=$HOME/neuron/ivcmake/build/install

IV_ENABLE_SHARED:BOOL=OFF

Build libraries shared or static

I generally build InterViews static. The nrn build will then incorporate all of InterViews into libnrniv.so

IV_ENABLE_X11_DYNAMIC:BOOL=OFF

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.

IV_ENABLE_X11_DYNAMIC_MAKE_HEADERS:BOOL=OFF

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.

MPI options:

NRN_ENABLE_MPI:BOOL=ON

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.

NRN_ENABLE_MPI_DYNAMIC:BOOL=OFF

Enable dynamic MPI library support

This is mostly useful for binary distibutions where MPI may or may not exist on the target machine.

NRN_MPI_DYNAMIC:STRING=

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

-DNRN_MPI_DYNAMIC="/usr/local/include/;/home/hines/soft/mpich/include"

This option is ignored unless NRN_ENABLE_MPI_DYNAMIC=ON

NRN_ENABLE_MUSIC:BOOL=OFF

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 /path with -DMUSIC_ROOT=/path to 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.)

Python options:

NRN_ENABLE_PYTHON:BOOL=ON

Enable Python interpreter support (default python, fallback to python3, but see PYTHON_EXECUTABLE below)

NRN_ENABLE_PYTHON_DYNAMIC:BOOL=OFF

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.

NRN_PYTHON_DYNAMIC:STRING=

semicolon (;) separated list of python executables to create interfaces. (default python3)

If the string is empty use the python specified by PYTHON_EXECUTABLE or else the default python. 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. Eg. the mac package build script on my machine, nrn/bldnrnmacpkgcmake.sh uses
-DNRN_PYTHON_DYNAMIC="python3.8;python3.9;python3.10;python3.11"
This option is ignored unless NRN_ENABLE_PYTHON_DYNAMIC=ON

PYTHON_EXECUTABLE:PATH=

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`

NRN_ENABLE_MODULE_INSTALL:BOOL=ON

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.

NRN_ENABLE_RX3D:BOOL=ON

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.

NRN_RX3D_OPT_LEVEL:STRING=0

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

CoreNEURON options:

NRN_ENABLE_CORENEURON:BOOL=OFF

Enable CoreNEURON support

If ON CoreNEURON will be built and any needed NMODL submodule dependencies cloned as external submodules.

NRN_ENABLE_MOD_COMPATIBILITY:BOOL=OFF

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/BlueBrain/CoreNeuron/blob/master/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.

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.

CMAKE_C_COMPILER:FILEPATH=/usr/bin/cc

C compiler

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

CMAKE_CXX_COMPILER:FILEPATH=/usr/bin/c++

C plus plus compiler

NRN_ENABLE_DOCS:BOOL=OFF

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:

doxygen generates Doxygen documentation from the NEURON source code.

notebooks executes 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.

sphinx generates 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-clean removes 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.

docs is shorthand for building doxygen, notebooks, sphinx and notebooks-clean in that order.

Warning

Executing the notebooks requires a functional NEURON installation. There are two possibilities here:

The default, which is sensible for local development, is that the notebooks target 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 notebooks does 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 nrnivmodl work and that import neuron works in Python.

NRN_EXTRA_CXX_FLAGS:STRING=””

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.

NRN_EXTRA_MECH_CXX_FLAGS:STRING=””

Compiler flags that are used to build the C code generated by nocmodl but not source code files that are committed to the repository.

NRN_NMODL_CXX_FLAGS:STRING=””

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"

Readline_ROOT_DIR:PATH=/usr

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

NRN_ENABLE_TESTS:BOOL=OFF

Enable unit tests

Clones the submodule catch2 from https://github.com/catchorg/Catch2.git and after a build using make can run the tests with make test. May also need to pip install pytest. make test is 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 -s shows all output on the terminal.) Note: It is helpful to make test first 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 make test and ctest -VV. Otherwise the value specified by PYTHON_EXECUTABLE is used.

Example
mkdir 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

NRN_ENABLE_COVERAGE:BOOL=OFF

Enable code coverage

Requires lcov (e.g. sudo apt install lcov).

Provides two make targets to simplify the repeated “run tests, examine coverage” workflow.

– make cover_begin erases all previous coverage data (*.gcda files), and creates a baseline report. (Note all files and folders are created in the CMAKE_BINARY_DIR where you ran cmake.)

—make cover_html creates a coverage report for the sum of all the software runs since the last cover_begin and 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

NRN_COVERAGE_FILES:STRING=

Coverage limited to semicolon (;) separated list of file paths relative to PROJECT_SOURCE_DIR.

-DNRN_COVERAGE_FILES="src/nrniv/partrans.cpp;src/nmodl/parsact.cpp;src/nrnpython/nrnpy_hoc.cpp"

NRN_SANITIZERS:STRING=

Enable some combination of AddressSanitizer, LeakSanitizer and UndefinedBehaviorSanitizer. Accepts a comma-separated list of address, leak and undefined. See the “Diagnosis and Debugging” section for more information.

Miscellaneous Rarely used options specific to NEURON:

NRN_ENABLE_DISCRETE_EVENT_OBSERVER:BOOL=ON

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.

NRN_DYNAMIC_UNITS_USE_LEGACY:BOOL=OFF

Default is to use modern faraday, R, etc. from 2019 nist constants. When Off or ON, and in the absence of the NRNUNIT_USE_LEGACY=0or1 environment variable, the default dynamic value of h.nrnunit_use_legacy() will be 0 or 1 respectively.

At launch time (or import neuron), use of legacy or modern units can be specified with the NRNUNIT_USE_LEGACY=0or1 environment variable. The use of legacy or modern units can be dynamically specified after launch with the h.nrnunit_use_legacy(0or1) function (with no args, returns the current use flag).

NRN_ENABLE_MECH_DLL_STYLE:BOOL=ON

Dynamically load nrnmech shared library

NRN_ENABLE_SHARED:BOOL=ON

Build shared libraries (otherwise static library)

This must be ON if python is launched and imports neuron. If OFF and one wants to use python it will be necessary to launch
nrniv -python
Note that the top-level CMakeLists.txt file includes some custom configuration for Cray platforms. This may need to be adapted if you specify NRN_ENABLE_SHARED=OFF.

NRN_ENABLE_THREADS:BOOL=ON

Allow use of Pthreads

NRN_USE_REL_RPATH=OFF

Turned on when creating python wheels.

NRN_ENABLE_BACKTRACE:BOOL=OFF

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 nrn_feenableexcept().