3.7. Packages with extra build options

When building with some packages, additional steps may be required, in addition to

CMake build	Traditional make
$ cmake -D PKG_NAME=yes	$ make yes-name

as described on the Build_package doc page.

For a CMake build there may be additional optional or required variables to set. For a build with make, a provided library under the lammps/lib directory may need to be built first. Or an external library may need to exist on your system or be downloaded and built. You may need to tell LAMMPS where it is found on your system.

This is the list of packages that may require additional steps.

3.7.1. COMPRESS package

To build with this package you must have the zlib compression library available on your system to build dump styles with a ‘/gz’ suffix. There are also styles using the Zstandard library which have a ‘/zstd’ suffix.

If CMake cannot find the zlib library or include files, you can set these variables:

-D ZLIB_INCLUDE_DIR=path    # path to zlib.h header file
-D ZLIB_LIBRARY=path        # path to libz.a (.so) file

Support for Zstandard compression is auto-detected and for that CMake depends on the pkg-config tool to identify the necessary flags to compile with this library, so the corresponding libzstandard.pc file must be in a folder where pkg-config can find it, which may require adding it to the PKG_CONFIG_PATH environment variable.

To include support for Zstandard compression, -DLAMMPS_ZSTD must be added to the compiler flags. If make cannot find the libraries, you can edit the file lib/compress/Makefile.lammps to specify the paths and library names. This must be done before the package is installed.

3.7.2. GPU package

To build with this package, you must choose options for precision and which GPU hardware to build for. The GPU package currently supports three different types of backends: OpenCL, CUDA and HIP.

CMake build

-D GPU_API=value             # value = opencl (default) or cuda or hip
-D GPU_PREC=value            # precision setting
                             # value = double or mixed (default) or single
-D OCL_TUNE=value            # hardware choice for GPU_API=opencl
                             # generic (default) or intel (Intel CPU) or fermi, kepler, cypress (NVIDIA)
-D GPU_ARCH=value            # primary GPU hardware choice for GPU_API=cuda
                             # value = sm_XX, see below
                             # default is sm_50
-D HIP_ARCH=value            # primary GPU hardware choice for GPU_API=hip
                             # value depends on selected HIP_PLATFORM
                             # default is 'gfx906' for HIP_PLATFORM=hcc and 'sm_50' for HIP_PLATFORM=nvcc
-D HIP_USE_DEVICE_SORT=value # enables GPU sorting
                             # value = yes (default) or no
-D CUDPP_OPT=value           # optimization setting for GPU_API=cuda
                             # enables CUDA Performance Primitives Optimizations
                             # value = yes (default) or no
-D CUDA_MPS_SUPPORT=value    # enables some tweaks required to run with active nvidia-cuda-mps daemon
                             # value = yes or no (default)

GPU_ARCH settings for different GPU hardware is as follows:

sm_12 or sm_13 for GT200 (supported by CUDA 3.2 until CUDA 6.5)
sm_20 or sm_21 for Fermi (supported by CUDA 3.2 until CUDA 7.5)
sm_30 for Kepler (supported since CUDA 5 and until CUDA 10.x)
sm_35 or sm_37 for Kepler (supported since CUDA 5 and until CUDA 11.x)
sm_50 or sm_52 for Maxwell (supported since CUDA 6)
sm_60 or sm_61 for Pascal (supported since CUDA 8)
sm_70 for Volta (supported since CUDA 9)
sm_75 for Turing (supported since CUDA 10)
sm_80 for Ampere (supported since CUDA 11)

A more detailed list can be found, for example, at Wikipedia’s CUDA article

CMake can detect which version of the CUDA toolkit is used and thus will try to include support for all major GPU architectures supported by this toolkit. Thus the GPU_ARCH setting is merely an optimization, to have code for the preferred GPU architecture directly included rather than having to wait for the JIT compiler of the CUDA driver to translate it.

When building with CMake, you must NOT build the GPU library in lib/gpu using the traditional build procedure. CMake will detect files generated by that process and will terminate with an error and a suggestion for how to remove them.

If you are compiling with HIP, note that before running CMake you will have to set appropriate environment variables. Some variables such as HCC_AMDGPU_TARGET or CUDA_PATH are necessary for hipcc and the linker to work correctly.

# AMDGPU target
export HIP_PLATFORM=hcc
export HCC_AMDGPU_TARGET=gfx906
cmake -D PKG_GPU=on -D GPU_API=HIP -D HIP_ARCH=gfx906 -D CMAKE_CXX_COMPILER=hipcc ..
make -j 4

# CUDA target (not recommended, use GPU_ARCH=cuda)
# !!! DO NOT set CMAKE_CXX_COMPILER !!!
export HIP_PLATFORM=nvcc
export CUDA_PATH=/usr/local/cuda
cmake -D PKG_GPU=on -D GPU_API=HIP -D HIP_ARCH=sm_70 ..
make -j 4

Traditional make

Before building LAMMPS, you must build the GPU library in lib/gpu. You can do this manually if you prefer; follow the instructions in lib/gpu/README. Note that the GPU library uses MPI calls, so you must use the same MPI library (or the STUBS library) settings as the main LAMMPS code. This also applies to the -DLAMMPS_BIGBIG, -DLAMMPS_SMALLBIG, or -DLAMMPS_SMALLSMALL settings in whichever Makefile you use.

You can also build the library in one step from the lammps/src dir, using a command like these, which simply invoke the lib/gpu/Install.py script with the specified args:

$ make lib-gpu               # print help message
$ make lib-gpu args="-b"     # build GPU library with default Makefile.linux
$ make lib-gpu args="-m xk7 -p single -o xk7.single"  # create new Makefile.xk7.single, altered for single-precision
$ make lib-gpu args="-m mpi -a sm_60 -p mixed -b" # build GPU library with mixed precision and P100 using other settings in Makefile.mpi

Note that this procedure starts with a Makefile.machine in lib/gpu, as specified by the “-m” switch. For your convenience, machine makefiles for “mpi” and “serial” are provided, which have the same settings as the corresponding machine makefiles in the main LAMMPS source folder. In addition you can alter 4 important settings in the Makefile.machine you start from via the corresponding -c, -a, -p, -e switches (as in the examples above), and also save a copy of the new Makefile if desired:

CUDA_HOME = where NVIDIA CUDA software is installed on your system
CUDA_ARCH = sm_XX, what GPU hardware you have, same as CMake GPU_ARCH above
CUDA_PRECISION = precision (double, mixed, single)
EXTRAMAKE = which Makefile.lammps.* file to copy to Makefile.lammps

The file Makefile.linux_multi is set up to include support for multiple GPU architectures as supported by the CUDA toolkit in use. This is done through using the “–gencode ” flag, which can be used multiple times and thus support all GPU architectures supported by your CUDA compiler.

If the library build is successful, 3 files should be created: lib/gpu/libgpu.a, lib/gpu/nvc_get_devices, and lib/gpu/Makefile.lammps. The latter has settings that enable LAMMPS to link with CUDA libraries. If the settings in Makefile.lammps for your machine are not correct, the LAMMPS build will fail, and lib/gpu/Makefile.lammps may need to be edited.

Note

If you re-build the GPU library in lib/gpu, you should always un-install the GPU package in lammps/src, then re-install it and re-build LAMMPS. This is because the compilation of files in the GPU package uses the library settings from the lib/gpu/Makefile.machine used to build the GPU library.

3.7.3. KIM package

To build with this package, the KIM library with API v2 must be downloaded and built on your system. It must include the KIM models that you want to use with LAMMPS.

If you would like to use the kim_query command, you also need to have libcurl installed with the matching development headers and the curl-config tool.

If you would like to use the kim_property command, you need to build LAMMPS with the PYTHON package installed and linked to Python 3.6 or later. See the PYTHON package build info for more details on this. After successfully building LAMMPS with Python, you also need to install the kim-property Python package, which can be easily done using pip as pip install kim-property, or from the conda-forge channel as conda install kim-property if LAMMPS is built in Conda. More detailed information is available at: kim-property installation.

In addition to installing the KIM API, it is also necessary to install the library of KIM models (interatomic potentials). See Obtaining KIM Models to learn how to install a pre-build binary of the OpenKIM Repository of Models. See the list of all KIM models here: https://openkim.org/browse/models

(Also note that when downloading and installing from source the KIM API library with all its models, may take a long time (tens of minutes to hours) to build. Of course you only need to do that once.)

-D DOWNLOAD_KIM=value           # download OpenKIM API v2 for build, value = no (default) or yes
-D LMP_DEBUG_CURL=value         # set libcurl verbose mode on/off, value = off (default) or on
-D LMP_NO_SSL_CHECK=value       # tell libcurl to not verify the peer, value = no (default) or yes

If DOWNLOAD_KIM is set to yes (or on), the KIM API library will be downloaded and built inside the CMake build directory. If the KIM library is already installed on your system (in a location where CMake cannot find it), you may need to set the PKG_CONFIG_PATH environment variable so that libkim-api can be found, or run the command source kim-api-activate.

You can download and build the KIM library manually if you prefer; follow the instructions in lib/kim/README. You can also do this in one step from the lammps/src dir, using a command like these, which simply invoke the lib/kim/Install.py script with the specified args.

$ make lib-kim              # print help message
$ make lib-kim args="-b "   # (re-)install KIM API lib with only example models
$ make lib-kim args="-b -a Glue_Ercolessi_Adams_Al__MO_324507536345_001"  # ditto plus one model
$ make lib-kim args="-b -a everything"     # install KIM API lib with all models
$ make lib-kim args="-n -a EAM_Dynamo_Ackland_W__MO_141627196590_002"       # add one model or model driver
$ make lib-kim args="-p /usr/local" # use an existing KIM API installation at the provided location
$ make lib-kim args="-p /usr/local -a EAM_Dynamo_Ackland_W__MO_141627196590_002" # ditto but add one model or driver

Settings for debugging OpenKIM web queries discussed below need to be applied by adding them to the LMP_INC variable through editing the Makefile.machine you are using. For example:

LMP_INC =       -DLMP_NO_SSL_CHECK

Debugging OpenKIM web queries in LAMMPS

If LMP_DEBUG_CURL is set, the libcurl verbose mode will be turned on, and any libcurl calls within the KIM web query display a lot of information about libcurl operations. You hardly ever want this set in production use, you will almost always want this when you debug or report problems.

The libcurl library performs peer SSL certificate verification by default. This verification is done using a CA certificate store that the SSL library can use to make sure the peer’s server certificate is valid. If SSL reports an error (“certificate verify failed”) during the handshake and thus refuses further communicate with that server, you can set LMP_NO_SSL_CHECK to override that behavior. When LAMMPS is compiled with LMP_NO_SSL_CHECK set, libcurl does not verify the peer and connection attempts will succeed regardless of the names in the certificate. This option is insecure. As an alternative, you can specify your own CA cert path by setting the environment variable CURL_CA_BUNDLE to the path of your choice. A call to the KIM web query would get this value from the environment variable.

3.7.4. KOKKOS package

Using the KOKKOS package requires choosing several settings. You have to select whether you want to compile with parallelization on the host and whether you want to include offloading of calculations to a device (e.g. a GPU). The default setting is to have no host parallelization and no device offloading. In addition, you can select the hardware architecture to select the instruction set. Since most hardware is backward compatible, you may choose settings for an older architecture to have an executable that will run on this and newer architectures.

Note

If you run Kokkos on a different GPU architecture than what LAMMPS was compiled with, there will be a delay during device initialization while the just-in-time compiler is recompiling all GPU kernels for the new hardware. This is, however, only supported for GPUs of the same major hardware version and different minor hardware versions, e.g. 5.0 and 5.2 but not 5.2 and 6.0. LAMMPS will abort with an error message indicating a mismatch, if that happens.

The settings discussed below have been tested with LAMMPS and are confirmed to work. Kokkos is an active project with ongoing improvements and projects working on including support for additional architectures. More information on Kokkos can be found on the Kokkos GitHub project.

Available Architecture settings

These are the possible choices for the Kokkos architecture ID. They must be specified in uppercase.

Arch-ID	HOST or GPU	Description
AMDAVX	HOST	AMD 64-bit x86 CPU (AVX 1)
ZEN	HOST	AMD Zen class CPU (AVX 2)
ZEN2	HOST	AMD Zen2 class CPU (AVX 2)
ARMV80	HOST	ARMv8.0 Compatible CPU
ARMV81	HOST	ARMv8.1 Compatible CPU
ARMV8_THUNDERX	HOST	ARMv8 Cavium ThunderX CPU
ARMV8_THUNDERX2	HOST	ARMv8 Cavium ThunderX2 CPU
WSM	HOST	Intel Westmere CPU (SSE 4.2)
SNB	HOST	Intel Sandy/Ivy Bridge CPU (AVX 1)
HSW	HOST	Intel Haswell CPU (AVX 2)
BDW	HOST	Intel Broadwell Xeon E-class CPU (AVX 2 + transactional mem)
SKX	HOST	Intel Sky Lake Xeon E-class HPC CPU (AVX512 + transactional mem)
KNC	HOST	Intel Knights Corner Xeon Phi
KNL	HOST	Intel Knights Landing Xeon Phi
BGQ	HOST	IBM Blue Gene/Q CPU
POWER7	HOST	IBM POWER7 CPU
POWER8	HOST	IBM POWER8 CPU
POWER9	HOST	IBM POWER9 CPU
KEPLER30	GPU	NVIDIA Kepler generation CC 3.0 GPU
KEPLER32	GPU	NVIDIA Kepler generation CC 3.2 GPU
KEPLER35	GPU	NVIDIA Kepler generation CC 3.5 GPU
KEPLER37	GPU	NVIDIA Kepler generation CC 3.7 GPU
MAXWELL50	GPU	NVIDIA Maxwell generation CC 5.0 GPU
MAXWELL52	GPU	NVIDIA Maxwell generation CC 5.2 GPU
MAXWELL53	GPU	NVIDIA Maxwell generation CC 5.3 GPU
PASCAL60	GPU	NVIDIA Pascal generation CC 6.0 GPU
PASCAL61	GPU	NVIDIA Pascal generation CC 6.1 GPU
VOLTA70	GPU	NVIDIA Volta generation CC 7.0 GPU
VOLTA72	GPU	NVIDIA Volta generation CC 7.2 GPU
TURING75	GPU	NVIDIA Turing generation CC 7.5 GPU
AMPERE80	GPU	NVIDIA Ampere generation CC 8.0 GPU
VEGA900	GPU	AMD GPU MI25 GFX900
VEGA906	GPU	AMD GPU MI50/MI60 GFX906
INTEL_GEN	GPU	Intel GPUs Gen9+

This list was last updated for version 3.2 of the Kokkos library.

For multicore CPUs using OpenMP, set these 2 variables.

-D Kokkos_ARCH_HOSTARCH=yes  # HOSTARCH = HOST from list above
-D Kokkos_ENABLE_OPENMP=yes
-D BUILD_OMP=yes

Please note that enabling OpenMP for KOKKOS requires that OpenMP is also enabled for the rest of LAMMPS.

For Intel KNLs using OpenMP, set these variables:

-D Kokkos_ARCH_KNL=yes
-D Kokkos_ENABLE_OPENMP=yes

For NVIDIA GPUs using CUDA, set these variables:

-D Kokkos_ARCH_HOSTARCH=yes   # HOSTARCH = HOST from list above
-D Kokkos_ARCH_GPUARCH=yes    # GPUARCH = GPU from list above
-D Kokkos_ENABLE_CUDA=yes
-D Kokkos_ENABLE_OPENMP=yes
-D CMAKE_CXX_COMPILER=wrapper # wrapper = full path to Cuda nvcc wrapper

This will also enable executing FFTs on the GPU, either via the internal KISSFFT library, or - by preference - with the cuFFT library bundled with the CUDA toolkit, depending on whether CMake can identify its location. The wrapper value for CMAKE_CXX_COMPILER variable is the path to the CUDA nvcc compiler wrapper provided in the Kokkos library: lib/kokkos/bin/nvcc_wrapper. The setting should include the full path name to the wrapper, e.g.

-D CMAKE_CXX_COMPILER=${HOME}/lammps/lib/kokkos/bin/nvcc_wrapper

To simplify compilation, three preset files are included in the cmake/presets folder, kokkos-serial.cmake, kokkos-openmp.cmake, and kokkos-cuda.cmake. They will enable the KOKKOS package and enable some hardware choice. So to compile with OpenMP host parallelization, CUDA device parallelization (for GPUs with CC 5.0 and up) with some common packages enabled, you can do the following:

mkdir build-kokkos-cuda
cd build-kokkos-cuda
cmake -C ../cmake/presets/minimal.cmake -C ../cmake/presets/kokkos-cuda.cmake ../cmake
cmake --build .

Choose which hardware to support in Makefile.machine via KOKKOS_DEVICES and KOKKOS_ARCH settings. See the src/MAKE/OPTIONS/Makefile.kokkos* files for examples.

For multicore CPUs using OpenMP:

KOKKOS_DEVICES = OpenMP
KOKKOS_ARCH = HOSTARCH          # HOSTARCH = HOST from list above

For Intel KNLs using OpenMP:

KOKKOS_DEVICES = OpenMP
KOKKOS_ARCH = KNL

For NVIDIA GPUs using CUDA:

KOKKOS_DEVICES = Cuda
KOKKOS_ARCH = HOSTARCH,GPUARCH  # HOSTARCH = HOST from list above that is hosting the GPU
KOKKOS_CUDA_OPTIONS = "enable_lambda"
                                # GPUARCH = GPU from list above
FFT_INC = -DFFT_CUFFT           # enable use of cuFFT (optional)
FFT_LIB = -lcufft               # link to cuFFT library

For GPUs, you also need the following lines in your Makefile.machine before the CC line is defined. They tell mpicxx to use an nvcc compiler wrapper, which will use nvcc for compiling CUDA files and a C++ compiler for non-Kokkos, non-CUDA files.

# For OpenMPI
KOKKOS_ABSOLUTE_PATH = $(shell cd $(KOKKOS_PATH); pwd)
export OMPI_CXX = $(KOKKOS_ABSOLUTE_PATH)/config/nvcc_wrapper
CC = mpicxx

# For MPICH and derivatives
KOKKOS_ABSOLUTE_PATH = $(shell cd $(KOKKOS_PATH); pwd)
CC = mpicxx -cxx=$(KOKKOS_ABSOLUTE_PATH)/config/nvcc_wrapper

Advanced KOKKOS compilation settings

There are other allowed options when building with the KOKKOS package that can improve performance or assist in debugging or profiling. Below are some examples that may be useful in combination with LAMMPS. For the full list (which keeps changing as the Kokkos package itself evolves), please consult the Kokkos library documentation.

As alternative to using multi-threading via OpenMP (-DKokkos_ENABLE_OPENMP=on or KOKKOS_DEVICES=OpenMP) it is also possible to use Posix threads directly (-DKokkos_ENABLE_PTHREAD=on or KOKKOS_DEVICES=Pthread). While binding of threads to individual or groups of CPU cores is managed in OpenMP with environment variables, you need assistance from either the “hwloc” or “libnuma” library for the Pthread thread parallelization option. To enable use with CMake: -DKokkos_ENABLE_HWLOC=on or -DKokkos_ENABLE_LIBNUMA=on; and with conventional make: KOKKOS_USE_TPLS=hwloc or KOKKOS_USE_TPLS=libnuma.

The CMake option -DKokkos_ENABLE_LIBRT=on or the makefile setting KOKKOS_USE_TPLS=librt enables the use of a more accurate timer mechanism on many Unix-like platforms for internal profiling.

The CMake option -DKokkos_ENABLE_DEBUG=on or the makefile setting KOKKOS_DEBUG=yes enables printing of run-time debugging information that can be useful. It also enables runtime bounds checking on Kokkos data structures. As to be expected, enabling this option will negatively impact the performance and thus is only recommended when developing a Kokkos-enabled style in LAMMPS.

The CMake option -DKokkos_ENABLE_CUDA_UVM=on or the makefile setting KOKKOS_CUDA_OPTIONS=enable_lambda,force_uvm enables the use of CUDA “Unified Virtual Memory” (UVM) in Kokkos. UVM allows to transparently use RAM on the host to supplement the memory used on the GPU (with some performance penalty) and thus enables running larger problems that would otherwise not fit into the RAM on the GPU.

Please note, that the LAMMPS KOKKOS package must always be compiled with the enable_lambda option when using GPUs. The CMake configuration will thus always enable it.

3.7.5. LATTE package

To build with this package, you must download and build the LATTE library.

-D DOWNLOAD_LATTE=value    # download LATTE for build, value = no (default) or yes
-D LATTE_LIBRARY=path      # LATTE library file (only needed if a custom location)

If DOWNLOAD_LATTE is set, the LATTE library will be downloaded and built inside the CMake build directory. If the LATTE library is already on your system (in a location CMake cannot find it), LATTE_LIBRARY is the filename (plus path) of the LATTE library file, not the directory the library file is in.

You can download and build the LATTE library manually if you prefer; follow the instructions in lib/latte/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invokes the lib/latte/Install.py script with the specified args:

$ make lib-latte                          # print help message
$ make lib-latte args="-b"                # download and build in lib/latte/LATTE-master
$ make lib-latte args="-p $HOME/latte"    # use existing LATTE installation in $HOME/latte
$ make lib-latte args="-b -m gfortran"    # download and build in lib/latte and
                                          #   copy Makefile.lammps.gfortran to Makefile.lammps

Note that 3 symbolic (soft) links, includelink and liblink and filelink.o, are created in lib/latte to point to required folders and files in the LATTE home directory. When LAMMPS itself is built it will use these links. You should also check that the Makefile.lammps file you create is appropriate for the compiler you use on your system to build LATTE.

3.7.6. MESSAGE package

This package can optionally include support for messaging via sockets, using the open-source ZeroMQ library, which must be installed on your system.

-D MESSAGE_ZMQ=value    # build with ZeroMQ support, value = no (default) or yes
-D ZMQ_LIBRARY=path     # ZMQ library file (only needed if a custom location)
-D ZMQ_INCLUDE_DIR=path # ZMQ include directory (only needed if a custom location)

Before building LAMMPS, you must build the CSlib library in lib/message. You can build the CSlib library manually if you prefer; follow the instructions in lib/message/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/message/Install.py script with the specified args:

$ make lib-message               # print help message
$ make lib-message args="-m -z"  # build with MPI and socket (ZMQ) support
$ make lib-message args="-s"     # build as serial lib with no ZMQ support

The build should produce two files: lib/message/cslib/src/libmessage.a and lib/message/Makefile.lammps. The latter is copied from an existing Makefile.lammps.* and has settings to link with the ZeroMQ library if requested in the build.

3.7.7. MSCG package

To build with this package, you must download and build the MS-CG library. Building the MS-CG library requires that the GSL (GNU Scientific Library) headers and libraries are installed on your machine. See the lib/mscg/README and MSCG/Install files for more details.

-D DOWNLOAD_MSCG=value    # download MSCG for build, value = no (default) or yes
-D MSCG_LIBRARY=path      # MSCG library file (only needed if a custom location)
-D MSCG_INCLUDE_DIR=path  # MSCG include directory (only needed if a custom location)

If DOWNLOAD_MSCG is set, the MSCG library will be downloaded and built inside the CMake build directory. If the MSCG library is already on your system (in a location CMake cannot find it), MSCG_LIBRARY is the filename (plus path) of the MSCG library file, not the directory the library file is in. MSCG_INCLUDE_DIR is the directory the MSCG include file is in.

You can download and build the MS-CG library manually if you prefer; follow the instructions in lib/mscg/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/mscg/Install.py script with the specified args:

$ make lib-mscg             # print help message
$ make lib-mscg args="-b -m serial"   # download and build in lib/mscg/MSCG-release-master
                                      # with the settings compatible with "make serial"
$ make lib-mscg args="-b -m mpi"      # download and build in lib/mscg/MSCG-release-master
                                      # with the settings compatible with "make mpi"
$ make lib-mscg args="-p /usr/local/mscg-release" # use the existing MS-CG installation in /usr/local/mscg-release

Note that 2 symbolic (soft) links, includelink and liblink, will be created in lib/mscg to point to the MS-CG src/installation dir. When LAMMPS is built in src it will use these links. You should not need to edit the lib/mscg/Makefile.lammps file.

3.7.8. OPT package

No additional settings are needed besides -D PKG_OPT=yes

The compiler flag -restrict must be used to build LAMMPS with the OPT package when using Intel compilers. It should be added to the CCFLAGS line of your Makefile.machine. See src/MAKE/OPTIONS/Makefile.opt for an example.

3.7.9. POEMS package

No additional settings are needed besides -D PKG_OPT=yes

Before building LAMMPS, you must build the POEMS library in lib/poems. You can do this manually if you prefer; follow the instructions in lib/poems/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/poems/Install.py script with the specified args:

$ make lib-poems                   # print help message
$ make lib-poems args="-m serial"  # build with GNU g++ compiler (settings as with "make serial")
$ make lib-poems args="-m mpi"     # build with default MPI C++ compiler (settings as with "make mpi")
$ make lib-poems args="-m icc"     # build with Intel icc compiler

The build should produce two files: lib/poems/libpoems.a and lib/poems/Makefile.lammps. The latter is copied from an existing Makefile.lammps.* and has settings needed to build LAMMPS with the POEMS library (though typically the settings are just blank). If necessary, you can edit/create a new lib/poems/Makefile.machine file for your system, which should define an EXTRAMAKE variable to specify a corresponding Makefile.lammps.machine file.

3.7.10. PYTHON package

Building with the PYTHON package requires you have a the Python development headers and library available on your system, which needs to be a Python 2.7 version or a Python 3.x version. Since support for Python 2.x has ended, using Python 3.x is strongly recommended. See lib/python/README for additional details.

-D PYTHON_EXECUTABLE=path   # path to Python executable to use

Without this setting, CMake will guess the default Python version on your system. To use a different Python version, you can either create a virtualenv, activate it and then run cmake. Or you can set the PYTHON_EXECUTABLE variable to specify which Python interpreter should be used. Note note that you will also need to have the development headers installed for this version, e.g. python2-devel.

The build uses the lib/python/Makefile.lammps file in the compile/link process to find Python. You should only need to create a new Makefile.lammps.* file (and copy it to Makefile.lammps) if the LAMMPS build fails.

3.7.11. VORONOI package

To build with this package, you must download and build the Voro++ library or install a binary package provided by your operating system.

-D DOWNLOAD_VORO=value    # download Voro++ for build, value = no (default) or yes
-D VORO_LIBRARY=path      # Voro++ library file (only needed if at custom location)
-D VORO_INCLUDE_DIR=path  # Voro++ include directory (only needed if at custom location)

If DOWNLOAD_VORO is set, the Voro++ library will be downloaded and built inside the CMake build directory. If the Voro++ library is already on your system (in a location CMake cannot find it), VORO_LIBRARY is the filename (plus path) of the Voro++ library file, not the directory the library file is in. VORO_INCLUDE_DIR is the directory the Voro++ include file is in.

You can download and build the Voro++ library manually if you prefer; follow the instructions in lib/voronoi/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/voronoi/Install.py script with the specified args:

$ make lib-voronoi                          # print help message
$ make lib-voronoi args="-b"                # download and build the default version in lib/voronoi/voro++-<version>
$ make lib-voronoi args="-p $HOME/voro++"   # use existing Voro++ installation in $HOME/voro++
$ make lib-voronoi args="-b -v voro++0.4.6" # download and build the 0.4.6 version in lib/voronoi/voro++-0.4.6

Note that 2 symbolic (soft) links, includelink and liblink, are created in lib/voronoi to point to the Voro++ source dir. When LAMMPS builds in src it will use these links. You should not need to edit the lib/voronoi/Makefile.lammps file.

3.7.12. USER-ADIOS package

The USER-ADIOS package requires the ADIOS I/O library, version 2.3.1 or newer. Make sure that you have ADIOS built either with or without MPI to match if you build LAMMPS with or without MPI. ADIOS compilation settings for LAMMPS are automatically detected, if the PATH and LD_LIBRARY_PATH environment variables have been updated for the local ADIOS installation and the instructions below are followed for the respective build systems.

-D ADIOS2_DIR=path        # path is where ADIOS 2.x is installed
-D PKG_USER-ADIOS=yes

Turn on the USER-ADIOS package before building LAMMPS. If the ADIOS 2.x software is installed in PATH, there is nothing else to do:

$ make yes-user-adios

otherwise, set ADIOS2_DIR environment variable when turning on the package:

$ ADIOS2_DIR=path make yes-user-adios   # path is where ADIOS 2.x is installed

3.7.13. USER-ATC package

The USER-ATC package requires the MANYBODY package also be installed.

No additional settings are needed besides -D PKG_USER-ATC=yes and -D PKG_MANYBODY=yes.

Before building LAMMPS, you must build the ATC library in lib/atc. You can do this manually if you prefer; follow the instructions in lib/atc/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/atc/Install.py script with the specified args:

$ make lib-atc                      # print help message
$ make lib-atc args="-m serial"     # build with GNU g++ compiler and MPI STUBS (settings as with "make serial")
$ make lib-atc args="-m mpi"        # build with default MPI compiler (settings as with "make mpi")
$ make lib-atc args="-m icc"        # build with Intel icc compiler

The build should produce two files: lib/atc/libatc.a and lib/atc/Makefile.lammps. The latter is copied from an existing Makefile.lammps.* and has settings needed to build LAMMPS with the ATC library. If necessary, you can edit/create a new lib/atc/Makefile.machine file for your system, which should define an EXTRAMAKE variable to specify a corresponding Makefile.lammps.<machine> file.

Note that the Makefile.lammps file has settings for the BLAS and LAPACK linear algebra libraries. As explained in lib/atc/README these can either exist on your system, or you can use the files provided in lib/linalg. In the latter case you also need to build the library in lib/linalg with a command like these:

$ make lib-linalg                     # print help message
$ make lib-linalg args="-m serial"    # build with GNU Fortran compiler (settings as with "make serial")
$ make lib-linalg args="-m mpi"       # build with default MPI Fortran compiler (settings as with "make mpi")
$ make lib-linalg args="-m gfortran"  # build with GNU Fortran compiler

3.7.14. USER-AWPMD package

No additional settings are needed besides -D PKG_USER-AQPMD=yes.

Before building LAMMPS, you must build the AWPMD library in lib/awpmd. You can do this manually if you prefer; follow the instructions in lib/awpmd/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/awpmd/Install.py script with the specified args:

$ make lib-awpmd                   # print help message
$ make lib-awpmd args="-m serial"  # build with GNU g++ compiler and MPI STUBS (settings as with "make serial")
$ make lib-awpmd args="-m mpi"     # build with default MPI compiler (settings as with "make mpi")
$ make lib-awpmd args="-m icc"     # build with Intel icc compiler

The build should produce two files: lib/awpmd/libawpmd.a and lib/awpmd/Makefile.lammps. The latter is copied from an existing Makefile.lammps.* and has settings needed to build LAMMPS with the AWPMD library. If necessary, you can edit/create a new lib/awpmd/Makefile.machine file for your system, which should define an EXTRAMAKE variable to specify a corresponding Makefile.lammps.<machine> file.

Note that the Makefile.lammps file has settings for the BLAS and LAPACK linear algebra libraries. As explained in lib/awpmd/README these can either exist on your system, or you can use the files provided in lib/linalg. In the latter case you also need to build the library in lib/linalg with a command like these:

$ make lib-linalg                     # print help message
$ make lib-linalg args="-m serial"    # build with GNU Fortran compiler (settings as with "make serial")
$ make lib-linalg args="-m mpi"       # build with default MPI Fortran compiler (settings as with "make mpi")
$ make lib-linalg args="-m gfortran"  # build with GNU Fortran compiler

3.7.15. USER-COLVARS package

This package includes the Colvars library into the LAMMPS distribution, which can be built for the most part with all major versions of the C++ language.

This is the recommended build procedure for using Colvars in LAMMPS. No additional settings are normally needed besides -D PKG_USER-COLVARS=yes.

Before building LAMMPS, one must build the Colvars library in lib/colvars.

This can be done manually in the same folder by using or adapting one of the provided Makefiles: for example, Makefile.g++ for the GNU C++ compiler. C++11 compatibility may need to be enabled for some older compilers (as is done in the example makefile).

In general, it is safer to use build setting consistent with the rest of LAMMPS. This is best carried out from the LAMMPS src directory using a command like these, which simply invoke the lib/colvars/Install.py script with the specified args:

$ make lib-colvars                      # print help message
$ make lib-colvars args="-m serial"     # build with GNU g++ compiler (settings as with "make serial")
$ make lib-colvars args="-m mpi"        # build with default MPI compiler (settings as with "make mpi")
$ make lib-colvars args="-m g++-debug"  # build with GNU g++ compiler and colvars debugging enabled

The “machine” argument of the “-m” flag is used to find a Makefile.machine to use as build recipe. If it does not already exist in lib/colvars, it will be auto-generated by using compiler flags consistent with those parsed from the core LAMMPS makefiles.

Optional flags may be specified as environment variables:

$ COLVARS_DEBUG=yes make lib-colvars args="-m machine"  # Build with debug code (much slower)
$ COLVARS_LEPTON=no make lib-colvars args="-m machine"  # Build without Lepton (included otherwise)

The build should produce two files: the library lib/colvars/libcolvars.a (which also includes Lepton objects if enabled) and the specification file lib/colvars/Makefile.lammps. The latter is auto-generated, and normally does not need to be edited.

3.7.16. USER-PLUMED package

Before building LAMMPS with this package, you must first build PLUMED. PLUMED can be built as part of the LAMMPS build or installed separately from LAMMPS using the generic PLUMED installation instructions. The USER-PLUMED package has been tested to work with Plumed versions 2.4.x, 2.5.x, and 2.6.x and will error out, when trying to run calculations with a different version of the Plumed kernel.

PLUMED can be linked into MD codes in three different modes: static, shared, and runtime. With the “static” mode, all the code that PLUMED requires is linked statically into LAMMPS. LAMMPS is then fully independent from the PLUMED installation, but you have to rebuild/relink it in order to update the PLUMED code inside it. With the “shared” linkage mode, LAMMPS is linked to a shared library that contains the PLUMED code. This library should preferably be installed in a globally accessible location. When PLUMED is linked in this way the same library can be used by multiple MD packages. Furthermore, the PLUMED library LAMMPS uses can be updated without the need for a recompile of LAMMPS for as long as the shared PLUMED library is ABI-compatible.

The third linkage mode is “runtime” which allows the user to specify which PLUMED kernel should be used at runtime by using the PLUMED_KERNEL environment variable. This variable should point to the location of the libplumedKernel.so dynamical shared object, which is then loaded at runtime. This mode of linking is particularly convenient for doing PLUMED development and comparing multiple PLUMED versions as these sorts of comparisons can be done without recompiling the hosting MD code. All three linkage modes are supported by LAMMPS on selected operating systems (e.g. Linux) and using either CMake or traditional make build. The “static” mode should be the most portable, while the “runtime” mode support in LAMMPS makes the most assumptions about operating system and compiler environment. If one mode does not work, try a different one, switch to a different build system, consider a global PLUMED installation or consider downloading PLUMED during the LAMMPS build.

When the -D PKG_USER-PLUMED=yes flag is included in the cmake command you must ensure that GSL is installed in locations that are specified in your environment. There are then two additional variables that control the manner in which PLUMED is obtained and linked into LAMMPS.

-D DOWNLOAD_PLUMED=value   # download PLUMED for build, value = no (default) or yes
-D PLUMED_MODE=value       # Linkage mode for PLUMED, value = static (default), shared, or runtime

If DOWNLOAD_PLUMED is set to “yes”, the PLUMED library will be downloaded (the version of PLUMED that will be downloaded is hard-coded to a vetted version of PLUMED, usually a recent stable release version) and built inside the CMake build directory. If DOWNLOAD_PLUMED is set to “no” (the default), CMake will try to detect and link to an installed version of PLUMED. For this to work, the PLUMED library has to be installed into a location where the pkg-config tool can find it or the PKG_CONFIG_PATH environment variable has to be set up accordingly. PLUMED should be installed in such a location if you compile it using the default make; make install commands.

The PLUMED_MODE setting determines the linkage mode for the PLUMED library. The allowed values for this flag are “static” (default), “shared”, or “runtime”. If you want to switch the linkage mode, just re-run CMake with a different setting. For a discussion of PLUMED linkage modes, please see above. When DOWNLOAD_PLUMED is enabled the static linkage mode is recommended.

PLUMED needs to be installed before the USER-PLUMED package is installed so that LAMMPS can find the right settings when compiling and linking the LAMMPS executable. You can either download and build PLUMED inside the LAMMPS plumed library folder or use a previously installed PLUMED library and point LAMMPS to its location. You also have to choose the linkage mode: “static” (default), “shared” or “runtime”. For a discussion of PLUMED linkage modes, please see above.

Download/compilation/configuration of the plumed library can be done from the src folder through the following make args:

$ make lib-plumed                         # print help message
$ make lib-plumed args="-b"               # download and build PLUMED in lib/plumed/plumed2
$ make lib-plumed args="-p $HOME/.local"  # use existing PLUMED installation in $HOME/.local
$ make lib-plumed args="-p /usr/local -m shared"  # use existing PLUMED installation in
                                                  # /usr/local and use shared linkage mode

Note that 2 symbolic (soft) links, includelink and liblink are created in lib/plumed that point to the location of the PLUMED build to use. A new file lib/plumed/Makefile.lammps is also created with settings suitable for LAMMPS to compile and link PLUMED using the desired linkage mode. After this step is completed, you can install the USER-PLUMED package and compile LAMMPS in the usual manner:

$ make yes-user-plumed
$ make machine

Once this compilation completes you should be able to run LAMMPS in the usual way. For shared linkage mode, libplumed.so must be found by the LAMMPS executable, which on many operating systems means, you have to set the LD_LIBRARY_PATH environment variable accordingly.

Support for the different linkage modes in LAMMPS varies for different operating systems, using the static linkage is expected to be the most portable, and thus set to be the default.

If you want to change the linkage mode, you have to re-run “make lib-plumed” with the desired settings and do a re-install if the USER-PLUMED package with “make yes-user-plumed” to update the required makefile settings with the changes in the lib/plumed folder.

3.7.17. USER-H5MD package

To build with this package you must have the HDF5 software package installed on your system, which should include the h5cc compiler and the HDF5 library.

No additional settings are needed besides -D PKG_USER-H5MD=yes.

This should auto-detect the H5MD library on your system. Several advanced CMake H5MD options exist if you need to specify where it is installed. Use the ccmake (terminal window) or cmake-gui (graphical) tools to see these options and set them interactively from their user interfaces.

Before building LAMMPS, you must build the CH5MD library in lib/h5md. You can do this manually if you prefer; follow the instructions in lib/h5md/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/h5md/Install.py script with the specified args:

$ make lib-h5md                     # print help message
$ make lib-h5md args="-m h5cc"      # build with h5cc compiler

The build should produce two files: lib/h5md/libch5md.a and lib/h5md/Makefile.lammps. The latter is copied from an existing Makefile.lammps.* and has settings needed to build LAMMPS with the system HDF5 library. If necessary, you can edit/create a new lib/h5md/Makefile.machine file for your system, which should define an EXTRAMAKE variable to specify a corresponding Makefile.lammps.<machine> file.

3.7.18. USER-INTEL package

To build with this package, you must choose which hardware you want to build for, either x86 CPUs or Intel KNLs in offload mode. You should also typically install the USER-OMP package, as it can be used in tandem with the USER-INTEL package to good effect, as explained on the USER-INTEL package page.

When using Intel compilers version 16.0 or later is required. You can also use the GNU or Clang compilers and they will provide performance improvements over regular styles and USER-OMP styles, but less so than with the Intel compilers. Please also note, that some compilers have been found to apply memory alignment constraints incompletely or incorrectly and thus can cause segmentation faults in otherwise correct code when using features from the USER-INTEL package.

-D INTEL_ARCH=value     # value = cpu (default) or knl
-D INTEL_LRT_MODE=value # value = threads, none, or c++11

Choose which hardware to compile for in Makefile.machine via the following settings. See src/MAKE/OPTIONS/Makefile.intel_cpu* and Makefile.knl files for examples. and src/USER-INTEL/README for additional information.

For CPUs:

OPTFLAGS =      -xHost -O2 -fp-model fast=2 -no-prec-div -qoverride-limits -qopt-zmm-usage=high
CCFLAGS =       -g -qopenmp -DLAMMPS_MEMALIGN=64 -no-offload -fno-alias -ansi-alias -restrict $(OPTFLAGS)
LINKFLAGS =     -g -qopenmp $(OPTFLAGS)
LIB =           -ltbbmalloc

For KNLs:

OPTFLAGS =      -xMIC-AVX512 -O2 -fp-model fast=2 -no-prec-div -qoverride-limits
CCFLAGS =       -g -qopenmp -DLAMMPS_MEMALIGN=64 -no-offload -fno-alias -ansi-alias -restrict $(OPTFLAGS)
LINKFLAGS =     -g -qopenmp $(OPTFLAGS)
LIB =           -ltbbmalloc

In Long-range thread mode (LRT) a modified verlet style is used, that operates the Kspace calculation in a separate thread concurrently to other calculations. This has to be enabled in the package intel command at runtime. With the setting “threads” it used the pthreads library, while “c++11” will use the built-in thread support of C++11 compilers. The option “none” skips compilation of this feature. The default is to use “threads” if pthreads is available and otherwise “none”.

Best performance is achieved with Intel hardware, Intel compilers, as well as the Intel TBB and MKL libraries. However, the code also compiles, links, and runs with other compilers / hardware and without TBB and MKL.

3.7.19. USER-MESONT package

This package includes a library written in Fortran 90 in the lib/mesont folder, so a working Fortran 90 compiler is required to compile it. Also, the files with the force field data for running the bundled examples are not included in the source distribution. Instead they will be downloaded the first time this package is installed.

No additional settings are needed besides -D PKG_USER-MESONT=yes

Before building LAMMPS, you must build the mesont library in lib/mesont. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/mesont/Install.py script with the specified args:

$ make lib-mesont                    # print help message
$ make lib-mesont args="-m gfortran" # build with GNU g++ compiler (settings as with "make serial")
$ make lib-mesont args="-m ifort"    # build with Intel icc compiler

The build should produce two files: lib/mesont/libmesont.a and lib/mesont/Makefile.lammps. The latter is copied from an existing Makefile.lammps.\* and has settings needed to build LAMMPS with the mesont library (though typically the settings contain only the Fortran runtime library). If necessary, you can edit/create a new lib/mesont/Makefile.machine file for your system, which should define an EXTRAMAKE variable to specify a corresponding Makefile.lammps.machine file.

3.7.20. USER-MOLFILE package

-D MOLFILE_INCLUDE_DIR=path   # (optional) path where VMD molfile plugin headers are installed
-D PKG_USER-MOLFILE=yes

Using -D PKG_USER-MOLFILE=yes enables the package, and setting -D MOLFILE_INCLUDE_DIR allows to provide a custom location for the molfile plugin header files. These should match the ABI of the plugin files used, and thus one typically sets them to include folder of the local VMD installation in use. LAMMPS ships with a couple of default header files that correspond to a popular VMD version, usually the latest release.

The lib/molfile/Makefile.lammps file has a setting for a dynamic loading library libdl.a that is typically present on all systems. It is required for LAMMPS to link with this package. If the setting is not valid for your system, you will need to edit the Makefile.lammps file. See lib/molfile/README and lib/molfile/Makefile.lammps for details. It is also possible to configure a different folder with the VMD molfile plugin header files. LAMMPS ships with a couple of default headers, but these are not compatible with all VMD versions, so it is often best to change this setting to the location of the same include files of the local VMD installation in use.

3.7.21. USER-NETCDF package

To build with this package you must have the NetCDF library installed on your system.

No additional settings are needed besides -D PKG_USER-NETCDF=yes.

This should auto-detect the NETCDF library if it is installed on your system at standard locations. Several advanced CMake NETCDF options exist if you need to specify where it was installed. Use the ccmake (terminal window) or cmake-gui (graphical) tools to see these options and set them interactively from their user interfaces.

The lib/netcdf/Makefile.lammps file has settings for NetCDF include and library files which LAMMPS needs to build with this package. If the settings are not valid for your system, you will need to edit the Makefile.lammps file. See lib/netcdf/README for details.

3.7.22. USER-OMP package

No additional settings are required besides -D PKG_USER-OMP=yes. If CMake detects OpenMP compiler support, the USER-OMP code will be compiled with multi-threading support enabled, otherwise as optimized serial code.

To enable multi-threading support in the USER-OMP package (and other styles supporting OpenMP) the following compile and link flags must be added to your Makefile.machine file. See src/MAKE/OPTIONS/Makefile.omp for an example.

CCFLAGS: -fopenmp               # for GNU and Clang Compilers
CCFLAGS: -qopenmp -restrict     # for Intel compilers on Linux
LINKFLAGS: -fopenmp             # for GNU and Clang Compilers
LINKFLAGS: -qopenmp             # for Intel compilers on Linux

For other platforms and compilers, please consult the documentation about OpenMP support for your compiler.

3.7.23. USER-QMMM package

For using LAMMPS to do QM/MM simulations via the USER-QMMM package you need to build LAMMPS as a library. A LAMMPS executable with fix qmmm included can be built, but will not be able to do a QM/MM simulation on as such. You must also build a QM code - currently only Quantum ESPRESSO (QE) is supported - and create a new executable which links LAMMPS and the QM code together. Details are given in the lib/qmmm/README file. It is also recommended to read the instructions for linking with LAMMPS as a library for background information. This requires compatible Quantum Espresso and LAMMPS versions. The current interface and makefiles have last been verified to work in February 2020 with Quantum Espresso versions 6.3 to 6.5.

When using CMake, building a LAMMPS library is required and it is recommended to build a shared library, since any libraries built from the sources in the lib folder (including the essential libqmmm.a) are not included in the static LAMMPS library and (currently) not installed, while their code is included in the shared LAMMPS library. Thus a typical command line to configure building LAMMPS for USER-QMMM would be:

cmake -C ../cmake/presets/minimal.cmake -D PKG_USER-QMMM=yes \
    -D BUILD_LIB=yes -DBUILD_SHARED_LIBS=yes ../cmake

After completing the LAMMPS build and also configuring and compiling Quantum ESPRESSO with external library support (via “make couple”), go back to the lib/qmmm folder and follow the instructions on the README file to build the combined LAMMPS/QE QM/MM executable (pwqmmm.x) in the lib/qmmm folder.

Before building LAMMPS, you must build the QMMM library in lib/qmmm. You can do this manually if you prefer; follow the first two steps explained in lib/qmmm/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/qmmm/Install.py script with the specified args:

$ make lib-qmmm                      # print help message
$ make lib-qmmm args="-m serial"     # build with GNU Fortran compiler (settings as in "make serial")
$ make lib-qmmm args="-m mpi"        # build with default MPI compiler (settings as in "make mpi")
$ make lib-qmmm args="-m gfortran"   # build with GNU Fortran compiler

The build should produce two files: lib/qmmm/libqmmm.a and lib/qmmm/Makefile.lammps. The latter is copied from an existing Makefile.lammps.* and has settings needed to build LAMMPS with the QMMM library (though typically the settings are just blank). If necessary, you can edit/create a new lib/qmmm/Makefile.<machine> file for your system, which should define an EXTRAMAKE variable to specify a corresponding Makefile.lammps.<machine> file.

You can then install QMMM package and build LAMMPS in the usual manner. After completing the LAMMPS build and compiling Quantum ESPRESSO with external library support (via “make couple”), go back to the lib/qmmm folder and follow the instructions in the README file to build the combined LAMMPS/QE QM/MM executable (pwqmmm.x) in the lib/qmmm folder.

3.7.24. USER-QUIP package

To build with this package, you must download and build the QUIP library. It can be obtained from GitHub. For support of GAP potentials, additional files with specific licensing conditions need to be downloaded and configured. See step 1 and step 1.1 in the lib/quip/README file for details on how to do this.

-D QUIP_LIBRARY=path     # path to libquip.a (only needed if a custom location)

CMake will not download and build the QUIP library. But once you have done that, a CMake build of LAMMPS with -D PKG_USER-QUIP=yes should work. Set the QUIP_LIBRARY variable if CMake cannot find the QUIP library.

The download/build procedure for the QUIP library, described in lib/quip/README file requires setting two environment variables, QUIP_ROOT and QUIP_ARCH. These are accessed by the lib/quip/Makefile.lammps file which is used when you compile and link LAMMPS with this package. You should only need to edit Makefile.lammps if the LAMMPS build can not use its settings to successfully build on your system.

3.7.25. USER-SCAFACOS package

To build with this package, you must download and build the ScaFaCoS Coulomb solver library

-D DOWNLOAD_SCAFACOS=value    # download ScaFaCoS for build, value = no (default) or yes
-D SCAFACOS_LIBRARY=path      # ScaFaCos library file (only needed if at custom location)
-D SCAFACOS_INCLUDE_DIR=path  # ScaFaCoS include directory (only needed if at custom location)

If DOWNLOAD_SCAFACOS is set, the ScaFaCoS library will be downloaded and built inside the CMake build directory. If the ScaFaCoS library is already on your system (in a location CMake cannot find it), SCAFACOS_LIBRARY is the filename (plus path) of the ScaFaCoS library file, not the directory the library file is in. SCAFACOS_INCLUDE_DIR is the directory the ScaFaCoS include file is in.

You can download and build the ScaFaCoS library manually if you prefer; follow the instructions in lib/scafacos/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/scafacos/Install.py script with the specified args:

make lib-scafacos                         # print help message
make lib-scafacos args="-b"               # download and build in lib/scafacos/scafacos-<version>
make lib-scafacos args="-p $HOME/scafacos  # use existing ScaFaCoS installation in $HOME/scafacos

Note that 2 symbolic (soft) links, includelink and liblink, are created in lib/scafacos to point to the ScaFaCoS src dir. When LAMMPS builds in src it will use these links. You should not need to edit the lib/scafacos/Makefile.lammps file.

3.7.26. USER-SMD package

To build with this package, you must download the Eigen3 library. Eigen3 is a template library, so you do not need to build it.

-D DOWNLOAD_EIGEN3            # download Eigen3, value = no (default) or yes
-D EIGEN3_INCLUDE_DIR=path    # path to Eigen library (only needed if a custom location)

If DOWNLOAD_EIGEN3 is set, the Eigen3 library will be downloaded and inside the CMake build directory. If the Eigen3 library is already on your system (in a location where CMake cannot find it), set EIGEN3_INCLUDE_DIR to the directory the Eigen3 include file is in.

You can download the Eigen3 library manually if you prefer; follow the instructions in lib/smd/README. You can also do it in one step from the lammps/src dir, using a command like these, which simply invoke the lib/smd/Install.py script with the specified args:

$ make lib-smd                         # print help message
$ make lib-smd args="-b"               # download to lib/smd/eigen3
$ make lib-smd args="-p /usr/include/eigen3"    # use existing Eigen installation in /usr/include/eigen3

Note that a symbolic (soft) link named includelink is created in lib/smd to point to the Eigen dir. When LAMMPS builds it will use this link. You should not need to edit the lib/smd/Makefile.lammps file.

3.7.27. USER-VTK package

To build with this package you must have the VTK library installed on your system.

No additional settings are needed besides -D PKG_USER-VTK=yes.

This should auto-detect the VTK library if it is installed on your system at standard locations. Several advanced VTK options exist if you need to specify where it was installed. Use the ccmake (terminal window) or cmake-gui (graphical) tools to see these options and set them interactively from their user interfaces.

The lib/vtk/Makefile.lammps file has settings for accessing VTK files and its library, which LAMMPS needs to build with this package. If the settings are not valid for your system, check if one of the other lib/vtk/Makefile.lammps.* files is compatible and copy it to Makefile.lammps. If none of the provided files work, you will need to edit the Makefile.lammps file. See lib/vtk/README for details.