.. Note that this must be a single rst file in order for Sphinx to build into into a single plain-text file to place in the installation tarball. .. _install guide: ****************** Installation guide ****************** .. toctree:: :hidden: exotic .. highlight:: bash Introduction to building |Gromacs| ---------------------------------- These instructions pertain to building |Gromacs| |version|. You might also want to check the `up-to-date installation instructions`_. Quick and dirty installation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ 1. Get the latest version of your C and C++ compilers. 2. Check that you have CMake version |CMAKE_MINIMUM_REQUIRED_VERSION| or later. 3. Get and unpack the latest version of the |Gromacs| tarball. 4. Make a separate build directory and change to it. 5. Run ``cmake`` with the path to the source as an argument 6. Run ``make``, ``make check``, and ``make install`` 7. Source ``GMXRC`` to get access to |Gromacs| Or, as a sequence of commands to execute: .. parsed-literal:: tar xfz gromacs-|version|.tar.gz cd gromacs-|version| mkdir build cd build cmake .. -DGMX_BUILD_OWN_FFTW=ON -DREGRESSIONTEST_DOWNLOAD=ON make make check sudo make install source /usr/local/gromacs/bin/GMXRC This will download and build first the prerequisite FFT library followed by |Gromacs|. If you already have FFTW installed, you can remove that argument to ``cmake``. Overall, this build of |Gromacs| will be correct and reasonably fast on the machine upon which ``cmake`` ran. On another machine, it may not run, or may not run fast. If you want to get the maximum value for your hardware with |Gromacs|, you will have to read further. Sadly, the interactions of hardware, libraries, and compilers are only going to continue to get more complex. Quick and dirty cluster installation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ On a cluster where users are expected to be running across multiple nodes using MPI, make one installation similar to the above, and another using ``-DGMX_MPI=on``. The latter will install binaries and libraries named using a default suffix of ``_mpi`` ie ``gmx_mpi``. Hence it is safe and common practice to install this into the same location where the non-MPI build is installed. Typical installation ^^^^^^^^^^^^^^^^^^^^ As above, and with further details below, but you should consider using the following `CMake options`_ with the appropriate value instead of ``xxx`` : * ``-DCMAKE_C_COMPILER=xxx`` equal to the name of the C99 `Compiler`_ you wish to use (or the environment variable ``CC``) * ``-DCMAKE_CXX_COMPILER=xxx`` equal to the name of the C++17 `compiler`_ you wish to use (or the environment variable ``CXX``) * ``-DGMX_MPI=on`` to build using `MPI support`_ * ``-DGMX_GPU=CUDA`` to build with NVIDIA CUDA support enabled. * ``-DGMX_GPU=OpenCL`` to build with OpenCL_ support enabled. * ``-DGMX_GPU=SYCL`` to build with SYCL_ support enabled (using `Intel oneAPI DPC++`_ by default). * ``-DGMX_SYCL=ACPP`` to build with SYCL_ support using AdaptiveCpp_ (hipSYCL), requires ``-DGMX_GPU=SYCL``. * ``-DGMX_SIMD=xxx`` to specify the level of `SIMD support`_ of the node on which |Gromacs| will run * ``-DGMX_DOUBLE=on`` to build |Gromacs| in double precision (slower, and not normally useful) * ``-DCMAKE_PREFIX_PATH=xxx`` to add a non-standard location for CMake to `search for libraries, headers or programs`_ * ``-DCMAKE_INSTALL_PREFIX=xxx`` to install |Gromacs| to a `non-standard location`_ (default ``/usr/local/gromacs``) * ``-DBUILD_SHARED_LIBS=off`` to turn off the building of shared libraries to help with :ref:`static linking ` * ``-DGMX_FFT_LIBRARY=xxx`` to select whether to use ``fftw3``, ``mkl`` or ``fftpack`` libraries for `FFT support`_ * ``-DCMAKE_BUILD_TYPE=Debug`` to build |Gromacs| in debug mode Building older versions ^^^^^^^^^^^^^^^^^^^^^^^ Installation instructions for old |Gromacs| versions can be found at the |Gromacs| `documentation page `_. Prerequisites ------------- Platform ^^^^^^^^ |Gromacs| can be compiled for many operating systems and architectures. These include any distribution of Linux, macOS or Windows, and architectures including 64-bit x86 (AMD64/x86-64), several PowerPC including POWER9, ARM v8, and RISC-V. Compiler ^^^^^^^^ |Gromacs| can be compiled on any platform with ANSI C99 and C++17 compilers, and their respective standard C/C++ libraries. Good performance on an OS and architecture requires choosing a good compiler. We recommend gcc, because it is free, widely available and frequently provides the best performance. You should strive to use the most recent version of your compiler. Since we require full C++17 support the minimum compiler versions supported by the |Gromacs| team are * GNU (gcc/libstdc++) 9 * LLVM (clang/libc++) 7 * Microsoft (MSVC) 2019 Other compilers may work (Cray, Pathscale, older clang) but do not offer competitive performance. We recommend against PGI because the performance with C++ is very bad. The Intel classic compiler (icc/icpc) is no longer supported in |Gromacs|. Use Intel's newer clang-based compiler from oneAPI, or gcc. The xlc compiler is not supported and version 16.1 does not compile on POWER architectures for |Gromacs|\ -\ |version|. We recommend to use the GCC compiler, version 9.x to 11.x. Note: there are :ref:`known issues ` with GCC 12 and newer. You may also need the most recent version of other compiler toolchain components beside the compiler itself (e.g. assembler or linker); these are often shipped by your OS distribution's binutils package. C++17 support requires adequate support in both the compiler and the C++ library. The gcc and MSVC compilers include their own standard libraries and require no further configuration. If your vendor's compiler also manages the standard library library via compiler flags, these will be honored. For configuration of other compilers, read on. On Linux, the clang compilers typically use for their C++ library the libstdc++ which comes with g++. For |Gromacs|, we require the compiler to support libstc++ version 7.1 or higher. To select a particular libstdc++ library for a compiler whose default standard library does not work, provide the path to g++ with ``-DGMX_GPLUSPLUS_PATH=/path/to/g++``. Note that if you then build a further project that depends on |Gromacs| you will need to arrange to use the same compiler and libstdc++. To build with clang and llvm's libcxx standard library, use ``-DCMAKE_CXX_FLAGS=-stdlib=libc++``. If you are running on Mac OS X, Apple has unfortunately explicitly disabled OpenMP support in their Clang-based compiler, and running without OpenMP support means you would need to use thread-MPI for any parallelism - which is the reason the |Gromacs| configuration script now stops rather than just issues a warning you might miss. Instead of turning off OpenMP, you can try to download the unsupported `libomp distributed by the R project `_ or compile your own version - but this will likely have to be updated any time you upgrade the major Mac OS version. Alternatively, you can download a version of gcc; just make sure you actually use your downloaded gcc version, since Apple by default links /usr/bin/gcc to their own compiler. For all non-x86 platforms, your best option is typically to use gcc or the vendor's default or recommended compiler, and check for specialized information below. For updated versions of gcc to add to your Linux OS, see * Ubuntu: `Ubuntu toolchain ppa page`_ * RHEL/CentOS: `EPEL page`_ or the RedHat Developer Toolset Compiling with parallelization options ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For maximum performance you will need to examine how you will use |Gromacs| and what hardware you plan to run on. Often OpenMP_ parallelism is an advantage for |Gromacs|, but support for this is generally built into your compiler and detected automatically. .. _gmx-gpu-support: GPU support ~~~~~~~~~~~ |Gromacs| has excellent support for NVIDIA GPUs supported via CUDA. On Linux, NVIDIA CUDA_ toolkit with minimum version |REQUIRED_CUDA_VERSION| is required, and the latest version is strongly encouraged. NVIDIA GPUs with at least NVIDIA compute capability |REQUIRED_CUDA_COMPUTE_CAPABILITY| are required. You are strongly recommended to get the latest CUDA version and driver that supports your hardware, but beware of possible performance regressions in newer CUDA versions on older hardware. While some CUDA compilers (nvcc) might not officially support recent versions of gcc as the back-end compiler, we still recommend that you at least use a gcc version recent enough to get the best SIMD support for your CPU, since |Gromacs| always runs some code on the CPU. It is most reliable to use the same C++ compiler version for |Gromacs| code as used as the host compiler for nvcc. To make it possible to use other accelerators, |Gromacs| also includes OpenCL_ support as a portable GPU backend. The minimum OpenCL version required is |REQUIRED_OPENCL_MIN_VERSION| and only 64-bit implementations are supported. The current OpenCL implementation is recommended for use with GCN-based AMD GPUs, and on Linux we recommend the ROCm runtime. Intel integrated GPUs are supported with the Neo drivers. OpenCL is also supported with NVIDIA GPUs, but using the latest NVIDIA driver (which includes the NVIDIA OpenCL runtime) is recommended. Also note that there are performance limitations (inherent to the NVIDIA OpenCL runtime). It is not possible to support both Intel and other vendors' GPUs with OpenCL. A 64-bit implementation of OpenCL is required and therefore OpenCL is only supported on 64-bit platforms. Please note that OpenCL backend does not support the following GPUs: * NVIDIA Volta (CC 7.0, e.g., Tesla V100 or GTX 1630) or newer, * AMD RDNA1/2/3 (Navi 1/2X,3X, e.g., RX 5500 or RX6900). Since |Gromacs| 2021, SYCL_ support has been added. Since |Gromacs| 2023 the SYCL_ backend has matured to have near feature parity with the CUDA backend as well as broad platform support in both aspects more versatile than the OpenCL_ backend (notable exception is the Apple Silicon GPU which is only supported in OpenCL). The current SYCL implementation can be compiled either with `Intel oneAPI DPC++`_ compiler for Intel GPUs, or with AdaptiveCpp_ compiler and ROCm runtime for AMD GPUs (GFX9, CDNA 1/2, and RDNA1/2/3). Using other devices supported by these compilers is possible, but not recommended. Notably, SSCP/generic mode of AdaptiveCpp_ is not supported. It is not possible to configure several GPU backends in the same build of |Gromacs|. .. _mpi-support: MPI support ~~~~~~~~~~~ |Gromacs| can run in parallel on multiple cores of a single workstation using its built-in thread-MPI. No user action is required in order to enable this. If you wish to run in parallel on multiple machines across a network, you will need to have an MPI library installed that supports the MPI 2.0 standard. That's true for any MPI library version released since about 2009, but the |Gromacs| team recommends the latest version (for best performance) of either your vendor's library, OpenMPI_ or MPICH_. To compile with MPI set your compiler to the normal (non-MPI) compiler and add ``-DGMX_MPI=on`` to the cmake options. It is possible to set the compiler to the MPI compiler wrapper but it is neither necessary nor recommended. GPU-aware MPI support ~~~~~~~~~~~~~~~~~~~~~~ In simulations using multiple GPUs, an MPI implementation with GPU support allows communication to be performed directly between the distinct GPU memory spaces without staging through CPU memory, often resulting in higher bandwidth and lower latency communication. The only current support for this in |Gromacs| is with a CUDA build targeting Nvidia GPUs using "CUDA-aware" MPI libraries. For more details, see `Introduction to CUDA-aware MPI `_. To use CUDA-aware MPI for direct GPU communication we recommend using the latest OpenMPI version (>=4.1.0) with the latest UCX version (>=1.10), since most |Gromacs| internal testing on CUDA-aware support has been performed using these versions. OpenMPI with CUDA-aware support can be built following the procedure in `these OpenMPI build instructions `_. For GPU-aware MPI support of Intel GPUs, use Intel MPI no earlier than version 2018.8. Such a version is found in the oneAPI SDKs starting from version 2023.0. At runtime, the LevelZero SYCL backend must be used (setting environment variable ``ONEAPI_DEVICE_SELECTOR=level_zero:gpu`` will typically suffice) and GPU-aware support in the MPI runtime `selected `_. For GPU-aware MPI support on AMD GPUs, several MPI implementations with UCX support can work, we recommend the latest OpenMPI version (>=4.1.4) with the latest UCX (>=1.13) since most of our testing was done using these version. Other MPI flavors such as Cray MPICH are also GPU-aware and compatible with ROCm. With ``GMX_MPI=ON``, |Gromacs| attempts to automatically detect GPU support in the underlying MPI library at compile time, and enables direct GPU communication when this is detected. However, there are some cases when |Gromacs| may fail to detect existing GPU-aware MPI support, in which case it can be manually enabled by setting environment variable ``GMX_FORCE_GPU_AWARE_MPI=1`` at runtime (although such cases still lack substantial testing, so we urge the user to carefully check correctness of results against those using default build options, and report any issues). CMake ^^^^^ |Gromacs| builds with the CMake build system, requiring at least version |CMAKE_MINIMUM_REQUIRED_VERSION|. You can check whether CMake is installed, and what version it is, with ``cmake --version``. If you need to install CMake, then first check whether your platform's package management system provides a suitable version, or visit the `CMake installation page`_ for pre-compiled binaries, source code and installation instructions. The |Gromacs| team recommends you install the most recent version of CMake you can. .. _FFT support: Fast Fourier Transform library ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Many simulations in |Gromacs| make extensive use of fast Fourier transforms, and a software library to perform these is always required. We recommend FFTW_ (version 3 or higher only) or Intel MKL_. The choice of library can be set with ``cmake -DGMX_FFT_LIBRARY=``, where ```` is one of ``fftw3``, ``mkl``, or ``fftpack``. FFTPACK is bundled with |Gromacs| as a fallback, and is acceptable if simulation performance is not a priority. When choosing MKL, |Gromacs| will also use MKL for BLAS and LAPACK (see `linear algebra libraries`_). Generally, there is no advantage in using MKL with |Gromacs|, and FFTW is often faster. With PME GPU offload support using CUDA, a GPU-based FFT library is required. The CUDA-based GPU FFT library cuFFT is part of the CUDA toolkit (required for all CUDA builds) and therefore no additional software component is needed when building with CUDA GPU acceleration. Using FFTW ~~~~~~~~~~ FFTW_ is likely to be available for your platform via its package management system, but there can be compatibility and significant performance issues associated with these packages. In particular, |Gromacs| simulations are normally run in "mixed" floating-point precision, which is suited for the use of single precision in FFTW. The default FFTW package is normally in double precision, and good compiler options to use for FFTW when linked to |Gromacs| may not have been used. Accordingly, the |Gromacs| team recommends either * that you permit the |Gromacs| installation to download and build FFTW from source automatically for you (use ``cmake -DGMX_BUILD_OWN_FFTW=ON``), or * that you build FFTW from the source code. If you build FFTW from source yourself, get the most recent version and follow the `FFTW installation guide`_. Choose the precision for FFTW (i.e. single/float vs. double) to match whether you will later use mixed or double precision for |Gromacs|. There is no need to compile FFTW with threading or MPI support, but it does no harm. On x86 hardware, compile with all of ``--enable-sse2``, ``--enable-avx``, and ``--enable-avx2`` flags. On Intel processors supporting 512-wide AVX, including KNL, add ``--enable-avx512`` too. FFTW will create a fat library with codelets for all different instruction sets, and pick the fastest supported one at runtime. On ARM architectures with SIMD support use ``--enable-neon`` flag; on IBM Power8 and later, use ``--enable-vsx`` flag. If you are using a Cray, there is a special modified (commercial) version of FFTs using the FFTW interface which can be slightly faster. Relying on ``-DGMX_BUILD_OWN_FFTW=ON`` works well in typical situations, but does not work on Windows, when using ``ninja`` build system, when cross-compiling, with custom toolchain configurations, etc. In such cases, please build FFTW manually. Using MKL ~~~~~~~~~ To target either Intel CPUs or GPUs, use OneAPI MKL (>=2021.3) by setting up the environment, e.g., through ``source /opt/intel/oneapi/setvars.sh`` or ``source /opt/intel/oneapi/mkl/latest/env/vars.sh`` or manually setting environment variable ``MKLROOT=/full/path/to/mkl``. Then run CMake with setting ``-DGMX_FFT_LIBRARY=mkl`` and/or ``-DGMX_GPU_FFT_LIBRARY=mkl``. Using oneMKL Interface Library ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The oneMKL interface library enables the SYCL backend for |Gromacs| with cuFFT, rocFFT, or closed-source oneMKL using Intel DPC++ and Codeplay's plugins for NVIDIA and AMD GPUs. To use, Intel DPC++ must be installed (>= 2023.2.0), along with Codeplay's plugins for NVIDIA and AMD GPUs as required, and CUDA and/or ROCm as required. The enviroment should be initialized with as with the MKL instructions above. To use the oneMKL interface library, download, build and install oneMKL as directed in the [oneMKL documentation](https://oneapi-src.github.io/oneMKL/building_the_project.html#building-for-onemkl), making sure that [suitable DFT backends](https://github.com/oneapi-src/oneMKL/blob/develop/CMakeLists.txt#supported-configurations) are enabled. Then, when building |Gromacs|, set ``-DGMX_GPU_FFT_LIBRARY=ONEMKL``. .. _bbfft installation: Using double-batched FFT library ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Generally MKL will provide better performance on Intel GPUs, however this alternative open-source library from Intel (https://github.com/intel/double-batched-fft-library) is useful for very large FFT sizes in |Gromacs|. :: cmake -DGMX_GPU_FFT_LIBRARY=BBFFT -DCMAKE_PREFIX_PATH=$PATH_TO_BBFFT_INSTALL Note: in |Gromacs| 2023, the option was called ``DBFFT``. Using ARM Performance Libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The ARM Performance Libraries provides FFT transforms implementation for ARM architectures. Preliminary support is provided for ARMPL in |Gromacs| through its FFTW-compatible API. Assuming that the ARM HPC toolchain environment including the ARMPL paths are set up (e.g. through loading the appropriate modules like ``module load Module-Prefix/arm-hpc-compiler-X.Y/armpl/X.Y``) use the following cmake options: :: cmake -DGMX_FFT_LIBRARY=fftw3 \ -DFFTWF_LIBRARY="${ARMPL_DIR}/lib/libarmpl_lp64.so" \ -DFFTWF_INCLUDE_DIR=${ARMPL_DIR}/include .. _cufftmp installation: Using cuFFTMp ~~~~~~~~~~~~~ Decomposition of PME work to multiple GPUs is supported with NVIDIA GPUs when using a CUDA build. This requires building |Gromacs| with the NVIDIA `cuFFTMp (cuFFT Multi-process) library `_, shipped with the NVIDIA HPC SDK, which provides distributed FFTs including across multiple compute nodes. To enable cuFFTMp support use the following cmake options: :: cmake -DGMX_USE_CUFFTMP=ON \ -DcuFFTMp_ROOT= Please make sure `cuFFTMp's hardware and software requirements `_ are met before trying to use GPU PME decomposition feature. In particular, cuFFTMp internally uses `NVSHMEM `_, and it is vital that the NVSHMEM and cuFFTMp versions in use are compatible. Some versions of the NVIDIA HPC SDK include two versions of NVSHMEM, where the cuFFTMp compatible variant can be found at ``Linux_x86_64//comm_libs//nvshmem_cufftmp_compat``. If that directory does not exist in the SDK, then there only exists a single (compatible) version at ``Linux_x86_64//comm_libs//nvshmem``. The version can be selected by, prior to both compilation and running, updating the LD_LIBRARY_PATH environment variable as follows: :: export LD_LIBRARY_PATH=/lib:$LD_LIBRARY_PATH It is advisable to refer to the `NVSHMEM FAQ page `_ for any issues faced at runtime. .. _heffte installation: Using heFFTe ~~~~~~~~~~~~ Decomposition of PME work to multiple GPUs is supported with PME offloaded to any vendor's GPU when building |Gromacs| linked to the `heFFTe library `_. HeFFTe uses GPU-aware MPI to provide distributed FFTs including across multiple compute nodes. It requires a CUDA build to target NVIDIA GPUs and a SYCL build to target Intel or AMD GPUs. To enable heFFTe support, use the following cmake options: :: cmake -DGMX_USE_HEFFTE=ON \ -DHeffte_ROOT= You will need an installation of heFFTe configured to use the same GPU-aware MPI library that will be used by |Gromacs|, and with support that matches the intended |Gromacs| build. It is best to use the same C++ compiler and standard library also. When targeting Intel GPUs, add ``-DHeffte_ENABLE_ONEAPI=ON -DHeffte_ONEMKL_ROOT=``. When targeting AMD GPUs, add ``-DHeffte_ENABLE_ROCM=ON -DHeffte_ROCM_ROOT=``. Using VkFFT ~~~~~~~~~~~ `VkFFT `_ is a multi-backend GPU-accelerated multidimensional Fast Fourier Transform library which aims to provide an open-source alternative to vendor libraries. |Gromacs| includes VkFFT support with two goals: portability across GPU platforms and performance improvements. VkFFT can be used with OpenCL and SYCL backends: * For SYCL builds, VkFFT provides a portable backend which currently can be used on AMD and NVIDIA GPUs with AdaptiveCpp_ and `Intel oneAPI DPC++`_; it generally outperforms rocFFT hence it is recommended as default on AMD. Note that VkFFT is not supported with PME decomposition (which requires HeFFTe) since HeFFTe does not have a VkFFT backend. * For OpenCL builds, VkFFT provides an alternative to ClFFT. It is the default on macOS and when building with Visual Studio. On other platforms it is not extensively tested, but it likely outperforms ClFFT and can be enabled during cmake configuration. To enable VkFFT support, use the following CMake option: :: cmake -DGMX_GPU_FFT_LIBRARY=VKFFT |Gromacs| bundles VkFFT with its source code, but an external VkFFT can also be used (e.g. to benefit from improvements in VkFFT releases more recent than the bundled version) in the following manner: :: cmake -DGMX_GPU_FFT_LIBRARY=VKFFT \ -DGMX_EXTERNAL_VKFFT=ON -DVKFFT_INCLUDE_DIR= Other optional build components ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * Run-time detection of hardware capabilities can be improved by linking with hwloc. By default this is turned off since it might not be supported everywhere, but if you have hwloc installed it should work by just setting ``-DGMX_HWLOC=ON`` * Hardware-optimized BLAS and LAPACK libraries are useful for a few of the |Gromacs| utilities focused on normal modes and matrix manipulation, but they do not provide any benefits for normal simulations. Configuring these is discussed at `linear algebra libraries`_. * An external TNG library for trajectory-file handling can be used by setting ``-DGMX_EXTERNAL_TNG=yes``, but TNG |GMX_TNG_MINIMUM_REQUIRED_VERSION| is bundled in the |Gromacs| source already. * The lmfit library for Levenberg-Marquardt curve fitting is used in |Gromacs|. Only lmfit |GMX_LMFIT_REQUIRED_VERSION| is supported. A reduced version of that library is bundled in the |Gromacs| distribution, and the default build uses it. That default may be explicitly enabled with ``-DGMX_USE_LMFIT=internal``. To use an external lmfit library, set ``-DGMX_USE_LMFIT=external``, and adjust ``CMAKE_PREFIX_PATH`` as needed. lmfit support can be disabled with ``-DGMX_USE_LMFIT=none``. * zlib is used by TNG for compressing some kinds of trajectory data * Building the |Gromacs| documentation is optional, and requires and other software. Refer to https://manual.gromacs.org/current/dev-manual/documentation-generation.html or the ``docs/dev-manual/documentation-generation.rst`` file in the sources. * The |Gromacs| utility programs often write data files in formats suitable for the Grace plotting tool, but it is straightforward to use these files in other plotting programs, too. * Set ``-DGMX_PYTHON_PACKAGE=ON`` when configuring |Gromacs| with CMake to enable additional CMake targets for the gmxapi Python package and sample_restraint package from the main |Gromacs| CMake build. This supports additional testing and documentation generation. Doing a build of |Gromacs| -------------------------- This section will cover a general build of |Gromacs| with CMake_, but it is not an exhaustive discussion of how to use CMake. There are many resources available on the web, which we suggest you search for when you encounter problems not covered here. The material below applies specifically to builds on Unix-like systems, including Linux, and Mac OS X. For other platforms, see the specialist instructions below. .. _configure-cmake: Configuring with CMake ^^^^^^^^^^^^^^^^^^^^^^ CMake will run many tests on your system and do its best to work out how to build |Gromacs| for you. If your build machine is the same as your target machine, then you can be sure that the defaults and detection will be pretty good. However, if you want to control aspects of the build, or you are compiling on a cluster head node for back-end nodes with a different architecture, there are a few things you should consider specifying. The best way to use CMake to configure |Gromacs| is to do an "out-of-source" build, by making another directory from which you will run CMake. This can be outside the source directory, or a subdirectory of it. It also means you can never corrupt your source code by trying to build it! So, the only required argument on the CMake command line is the name of the directory containing the ``CMakeLists.txt`` file of the code you want to build. For example, download the source tarball and use .. parsed-literal:: tar xfz gromacs-|version|.tgz cd gromacs-|version| mkdir build-gromacs cd build-gromacs cmake .. You will see ``cmake`` report a sequence of results of tests and detections done by the |Gromacs| build system. These are written to the ``cmake`` cache, kept in ``CMakeCache.txt``. You can edit this file by hand, but this is not recommended because you could make a mistake. You should not attempt to move or copy this file to do another build, because file paths are hard-coded within it. If you mess things up, just delete this file and start again with ``cmake``. If there is a serious problem detected at this stage, then you will see a fatal error and some suggestions for how to overcome it. If you are not sure how to deal with that, please start by searching on the web (most computer problems already have known solutions!) and then consult the `user discussion forum`_. There are also informational warnings that you might like to take on board or not. Piping the output of ``cmake`` through ``less`` or ``tee`` can be useful, too. Once ``cmake`` returns, you can see all the settings that were chosen and information about them by using e.g. the curses interface :: ccmake .. You can actually use ``ccmake`` (available on most Unix platforms) directly in the first step, but then most of the status messages will merely blink in the lower part of the terminal rather than be written to standard output. Most platforms including Linux, Windows, and Mac OS X even have native graphical user interfaces for ``cmake``, and it can create project files for almost any build environment you want (including Visual Studio or Xcode). Check out `running CMake`_ for general advice on what you are seeing and how to navigate and change things. The settings you might normally want to change are already presented. You may make changes, then re-configure (using ``c``), so that it gets a chance to make changes that depend on yours and perform more checking. It may take several configuration passes to reach the desired configuration, in particular if you need to resolve errors. When you have reached the desired configuration with ``ccmake``, the build system can be generated by pressing ``g``. This requires that the previous configuration pass did not reveal any additional settings (if it did, you need to configure once more with ``c``). With ``cmake``, the build system is generated after each pass that does not produce errors. You cannot attempt to change compilers after the initial run of ``cmake``. If you need to change, clean up, and start again. .. _non-standard location: Where to install |Gromacs| ~~~~~~~~~~~~~~~~~~~~~~~~~~ |Gromacs| is installed in the directory to which ``CMAKE_INSTALL_PREFIX`` points. It may not be the source directory or the build directory. You require write permissions to this directory. Thus, without super-user privileges, ``CMAKE_INSTALL_PREFIX`` will have to be within your home directory. Even if you do have super-user privileges, you should use them only for the installation phase, and never for configuring, building, or running |Gromacs|! .. _cmake options: Using CMake command-line options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Once you become comfortable with setting and changing options, you may know in advance how you will configure |Gromacs|. If so, you can speed things up by invoking ``cmake`` and passing the various options at once on the command line. This can be done by setting cache variable at the cmake invocation using ``-DOPTION=VALUE``. Note that some environment variables are also taken into account, in particular variables like ``CC`` and ``CXX``. For example, the following command line :: cmake .. -DGMX_GPU=CUDA -DGMX_MPI=ON -DCMAKE_INSTALL_PREFIX=/home/marydoe/programs can be used to build with CUDA GPUs, MPI and install in a custom location. You can even save that in a shell script to make it even easier next time. You can also do this kind of thing with ``ccmake``, but you should avoid this, because the options set with ``-D`` will not be able to be changed interactively in that run of ``ccmake``. .. _gmx-simd-support: SIMD support ~~~~~~~~~~~~ |Gromacs| has extensive support for detecting and using the SIMD capabilities of many modern HPC CPU architectures. If you are building |Gromacs| on the same hardware you will run it on, then you don't need to read more about this, unless you are getting configuration warnings you do not understand. By default, the |Gromacs| build system will detect the SIMD instruction set supported by the CPU architecture (on which the configuring is done), and thus pick the best available SIMD parallelization supported by |Gromacs|. The build system will also check that the compiler and linker used also support the selected SIMD instruction set and issue a fatal error if they do not. Valid values are listed below, and the applicable value with the largest number in the list is generally the one you should choose. In most cases, choosing an inappropriate higher number will lead to compiling a binary that will not run. However, on a number of processor architectures choosing the highest supported value can lead to performance loss, e.g. on Intel Skylake-X/SP and AMD Zen (first generation). 1. ``None`` For use only on an architecture either lacking SIMD, or to which |Gromacs| has not yet been ported and none of the options below are applicable. 2. ``SSE2`` This SIMD instruction set was introduced in Intel processors in 2001, and AMD in 2003. Essentially all x86 machines in existence have this, so it might be a good choice if you need to support dinosaur x86 computers too. 3. ``SSE4.1`` Present in all Intel core processors since 2007, but notably not in AMD Magny-Cours. Still, almost all recent processors support this, so this can also be considered a good baseline if you are content with slow simulations and prefer portability between reasonably modern processors. 4. ``AVX_128_FMA`` AMD Bulldozer, Piledriver (and later Family 15h) processors have this but it is NOT supported on any AMD processors since Zen1. 5. ``AVX_256`` Intel processors since Sandy Bridge (2011). While this code will work on the AMD Bulldozer and Piledriver processors, it is significantly less efficient than the ``AVX_128_FMA`` choice above - do not be fooled to assume that 256 is better than 128 in this case. 6. ``AVX2_128`` AMD Zen/Zen2 and Hygon Dhyana microarchitecture processors; it will enable AVX2 with 3-way fused multiply-add instructions. While these microarchitectures do support 256-bit AVX2 instructions, hence ``AVX2_256`` is also supported, 128-bit will generally be faster, in particular when the non-bonded tasks run on the CPU -- hence the default ``AVX2_128``. With GPU offload however ``AVX2_256`` can be faster on Zen processors. 7. ``AVX2_256`` Present on Intel Haswell (and later) processors (2013) and AMD Zen3 and later (2020); it will also enable 3-way fused multiply-add instructions. 8. ``AVX_512`` Skylake-X desktop and Skylake-SP Xeon processors (2017) and AMD Zen4 (2022); on Intel it will generally be fastest on the higher-end desktop and server processors with two 512-bit fused multiply-add units (e.g. Core i9 and Xeon Gold). However, certain desktop and server models (e.g. Xeon Bronze and Silver) come with only one AVX512 FMA unit and therefore on these processors ``AVX2_256`` is faster (compile- and runtime checks try to inform about such cases). On AMD it is beneficial to use starting with Zen4. Additionally, with GPU accelerated runs ``AVX2_256`` can also be faster on high-end Skylake CPUs with both 512-bit FMA units enabled. 9. ``AVX_512_KNL`` Knights Landing Xeon Phi processors. 10. ``IBM_VSX`` Power7, Power8, Power9 and later have this. 11. ``ARM_NEON_ASIMD`` 64-bit ARMv8 and later. For maximum performance on NVIDIA Grace (ARMv9), we strongly suggest at least GNU >= 13, LLVM >= 16. 12. ``ARM_SVE`` 64-bit ARMv8 and later with the Scalable Vector Extensions (SVE). The SVE vector length is fixed at CMake configure time. The default vector length is automatically detected, and this can be changed via the ``GMX_SIMD_ARM_SVE_LENGTH`` CMake variable. If compiling for a different target architecture than the compilation machine, ``GMX_SIMD_ARM_SVE_LENGTH`` should be set to the hardware vector length implemented by the target machine. There is no expected performance benefit from setting a smaller value than the implemented vector length, and setting a larger length can lead to unexpected crashes. Minimum required compiler versions are GNU >= 10, LLVM >=13, or ARM >= 21.1. For maximum performance we strongly suggest the latest gcc compilers, or at least LLVM 14 or ARM 22.0. Lower performance has been observed with LLVM 13 and Arm compiler 21.1. The CMake configure system will check that the compiler you have chosen can target the architecture you have chosen. mdrun will check further at runtime, so if in doubt, choose the lowest number you think might work, and see what mdrun says. The configure system also works around many known issues in many versions of common HPC compilers. A further ``GMX_SIMD=Reference`` option exists, which is a special SIMD-like implementation written in plain C that developers can use when developing support in |Gromacs| for new SIMD architectures. It is not designed for use in production simulations, but if you are using an architecture with SIMD support to which |Gromacs| has not yet been ported, you may wish to try this option instead of the default ``GMX_SIMD=None``, as it can often out-perform this when the auto-vectorization in your compiler does a good job. And post on the |Gromacs| `user discussion forum`_, because |Gromacs| can probably be ported for new SIMD architectures in a few days. CMake advanced options ~~~~~~~~~~~~~~~~~~~~~~ The options that are displayed in the default view of ``ccmake`` are ones that we think a reasonable number of users might want to consider changing. There are a lot more options available, which you can see by toggling the advanced mode in ``ccmake`` on and off with ``t``. Even there, most of the variables that you might want to change have a ``CMAKE_`` or ``GMX_`` prefix. There are also some options that will be visible or not according to whether their preconditions are satisfied. .. _search for libraries, headers or programs: Helping CMake find the right libraries, headers, or programs ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If libraries are installed in non-default locations their location can be specified using the following variables: * ``CMAKE_INCLUDE_PATH`` for header files * ``CMAKE_LIBRARY_PATH`` for libraries * ``CMAKE_PREFIX_PATH`` for header, libraries and binaries (e.g. ``/usr/local``). The respective ``include``, ``lib``, or ``bin`` is appended to the path. For each of these variables, a list of paths can be specified (on Unix, separated with ":"). These can be set as environment variables like: :: CMAKE_PREFIX_PATH=/opt/fftw:/opt/cuda cmake .. (assuming ``bash`` shell). Alternatively, these variables are also ``cmake`` options, so they can be set like ``-DCMAKE_PREFIX_PATH=/opt/fftw:/opt/cuda``. The ``CC`` and ``CXX`` environment variables are also useful for indicating to ``cmake`` which compilers to use. Similarly, ``CFLAGS``/``CXXFLAGS`` can be used to pass compiler options, but note that these will be appended to those set by |Gromacs| for your build platform and build type. You can customize some of this with advanced CMake options such as ``CMAKE_C_FLAGS`` and its relatives. See also the page on `CMake environment variables`_. .. _CUDA GPU acceleration: CUDA GPU acceleration ~~~~~~~~~~~~~~~~~~~~~ If you have the CUDA_ Toolkit installed, you can use ``cmake`` with: :: cmake .. -DGMX_GPU=CUDA -DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda (or whichever path has your installation). In some cases, you might need to specify manually which of your C++ compilers should be used, e.g. with the advanced option ``CUDA_HOST_COMPILER``. By default, code will be generated for the most common CUDA architectures. However, to reduce build time and binary size we do not generate code for every single possible architecture, which in rare cases (say, Tegra systems) can result in the default build not being able to use some GPUs. If this happens, or if you want to remove some architectures to reduce binary size and build time, you can alter the target CUDA architectures. This can be done either with the ``GMX_CUDA_TARGET_SM`` or ``GMX_CUDA_TARGET_COMPUTE`` CMake variables, which take a semicolon delimited string with the two digit suffixes of CUDA (virtual) architectures names, for instance "60;75;86". For details, see the "Options for steering GPU code generation" section of the nvcc documentation / man page. The GPU acceleration has been tested on AMD64/x86-64 platforms with Linux, Mac OS X and Windows operating systems, but Linux is the best-tested and supported of these. Linux running on POWER 8/9 and ARM v8 CPUs also works well. Experimental support is available for compiling CUDA code, both for host and device, using clang (version 6.0 or later). A CUDA toolkit is still required but it is used only for GPU device code generation and to link against the CUDA runtime library. The clang CUDA support simplifies compilation and provides benefits for development (e.g. allows the use code sanitizers in CUDA host-code). Additionally, using clang for both CPU and GPU compilation can be beneficial to avoid compatibility issues between the GNU toolchain and the CUDA toolkit. clang for CUDA can be triggered using the ``GMX_CLANG_CUDA=ON`` CMake option. Target architectures can be selected with ``GMX_CUDA_TARGET_SM``, virtual architecture code is always embedded for all requested architectures (hence GMX_CUDA_TARGET_COMPUTE is ignored). Note that this is mainly a developer-oriented feature but its performance is generally close to that of code compiled with nvcc. OpenCL GPU acceleration ~~~~~~~~~~~~~~~~~~~~~~~ The primary targets of the |Gromacs| OpenCL support is accelerating simulations on AMD and Intel hardware. For AMD, we target both discrete GPUs and APUs (integrated CPU+GPU chips), and for Intel we target the integrated GPUs found on modern workstation and mobile hardware. The |Gromacs| OpenCL on NVIDIA GPUs works, but performance and other limitations make it less practical (for details see the user guide). To build |Gromacs| with OpenCL_ support enabled, two components are required: the OpenCL_ headers and the wrapper library that acts as a client driver loader (so-called ICD loader). The additional, runtime-only dependency is the vendor-specific GPU driver for the device targeted. This also contains the OpenCL_ compiler. As the GPU compute kernels are compiled on-demand at run time, this vendor-specific compiler and driver is not needed for building |Gromacs|. The former, compile-time dependencies are standard components, hence stock versions can be obtained from most Linux distribution repositories (e.g. ``opencl-headers`` and ``ocl-icd-libopencl1`` on Debian/Ubuntu). Only the compatibility with the required OpenCL_ version |REQUIRED_OPENCL_MIN_VERSION| needs to be ensured. Alternatively, the headers and library can also be obtained from vendor SDKs, which must be installed in a path found in ``CMAKE_PREFIX_PATH``. To trigger an OpenCL_ build the following CMake flags must be set :: cmake .. -DGMX_GPU=OpenCL To build with support for Intel integrated GPUs, it is required to add ``-DGMX_GPU_NB_CLUSTER_SIZE=4`` to the cmake command line, so that the GPU kernels match the characteristics of the hardware. The `Neo driver `_ is recommended. On Mac OS, an AMD GPU can be used only with OS version 10.10.4 and higher; earlier OS versions are known to run incorrectly. By default, on Linux, any clFFT library on the system will be used with |Gromacs|, but if none is found then the code will fall back on a version bundled with |Gromacs|. To require |Gromacs| to link with an external library, use :: cmake .. -DGMX_GPU=OpenCL -DclFFT_ROOT_DIR=/path/to/your/clFFT -DGMX_EXTERNAL_CLFFT=TRUE On Windows with MSVC and on macOS, `VkFFT `_ is used instead of clFFT, but this can provide performance benefits on other platforms as well. .. _SYCL GPU acceleration: SYCL GPU acceleration ~~~~~~~~~~~~~~~~~~~~~ SYCL_ is a modern portable heterogeneous acceleration API, with multiple implementations targeting different hardware platforms (similar to OpenCL_). |Gromacs| can be used with different SYCL compilers/runtimes to target the following hardware: * Intel GPUs using `Intel oneAPI DPC++`_ (both OpenCL and LevelZero backends), * AMD GPUs with AdaptiveCpp_ (previously known as hipSYCL), There is also experimental support for: * AMD GPUs with oneAPI with `Codeplay AMD plugin `_, * NVIDIA GPUs with either AdaptiveCpp_ or oneAPI with `Codeplay NVIDIA plugin `_. In table form: ========== ====================== ======================================================================================================== GPU vendor AdaptiveCpp_ (hipSYCL) `Intel oneAPI DPC++`_ ========== ====================== ======================================================================================================== Intel not supported supported AMD supported experimental (requires `Codeplay plugin `_) NVIDIA experimental experimental (requires `Codeplay plugin `__) ========== ====================== ======================================================================================================== Here, "experimental support" means that the combination has received limited testing and is expected to work (with possible limitations), but is not recommended for production use. Please refer to :ref:`a separate section in the installation guide ` to use them. The SYCL_ support in |Gromacs| is intended to replace OpenCL_ as an acceleration mechanism for AMD and Intel hardware. For NVIDIA GPUs, we strongly advise using CUDA. Apple M1/M2 GPUs are not supported with SYCL but can be used with OpenCL_. Codeplay ComputeCpp is not supported. Open-source `Intel LLVM `_ can be used in the same way as Intel oneAPI DPC++. Note: SYCL_ support in |Gromacs| and the underlying compilers and runtimes are less mature than either OpenCL or CUDA. Please, pay extra attention to simulation correctness when you are using it. SYCL GPU acceleration for Intel GPUs """""""""""""""""""""""""""""""""""" You should install the recent `Intel oneAPI DPC++`_ compiler toolkit. For |Gromacs| 2024, version 2023.2 is recommended, and 2023.0 is the earliest supported. Using open-source `Intel LLVM `_ is possible, but not extensively tested. We also recommend installing the most recent `Neo driver `_. With the toolkit installed and added to the environment (usually by running ``source /opt/intel/oneapi/setvars.sh`` or using an appropriate :command:`module load` on an HPC system), the following CMake flags must be set: :: cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGMX_GPU=SYCL -DGMX_SYCL=DPCPP When compiling for Intel Data Center GPU Max (also knows as Ponte Vecchio / PVC), we recommend passing additional flags for compatibility and improved performance: :: cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \ -DGMX_GPU=SYCL -DGMX_SYCL=DPCPP \ -DGMX_GPU_NB_NUM_CLUSTER_PER_CELL_X=1 -DGMX_GPU_NB_CLUSTER_SIZE=8 You might also consider using :ref:`double-batched FFT library `. .. _SYCL GPU acceleration AMD: SYCL GPU acceleration for AMD GPUs """""""""""""""""""""""""""""""""" Using `AdaptiveCpp 23.10.0 `_ and ROCm 5.3-5.7 is recommended. The earliest supported version is hipSYCL 0.9.4. We strongly recommend using the clang compiler bundled with ROCm for building both AdaptiveCpp and |Gromacs|. Mainline Clang releases can also work. The following CMake command can be used **when configuring AdaptiveCpp** to ensure that the proper Clang is used (assuming ``ROCM_PATH`` is set correctly, e.g. to ``/opt/rocm`` in the case of default installation): :: cmake .. -DCMAKE_C_COMPILER=${ROCM_PATH}/llvm/bin/clang \ -DCMAKE_CXX_COMPILER=${ROCM_PATH}/llvm/bin/clang++ \ -DLLVM_DIR=${ROCM_PATH}/llvm/lib/cmake/llvm/ If ROCm 5.0 or earlier is used, AdaptiveCpp might require `additional build flags `_. Using hipSYCL 0.9.4 with ROCm 5.7+ / Clang 17+ might also require `extra workarounds `_. After compiling and installing AdaptiveCpp, the following settings can be used for building |Gromacs| itself (set ``HIPSYCL_TARGETS`` to the target hardware): :: cmake .. -DCMAKE_C_COMPILER=${ROCM_PATH}/llvm/bin/clang \ -DCMAKE_CXX_COMPILER=${ROCM_PATH}/llvm/bin/clang++ \ -DGMX_GPU=SYCL -DGMX_SYCL=ACPP -DHIPSYCL_TARGETS='hip:gfxXYZ' Multiple target architectures can be specified, e.g., ``-DHIPSYCL_TARGETS='hip:gfx908,gfx90a'``. Having both RDNA (``gfx1xyz``) and GCN/CDNA (``gfx9xx``) devices in the same build is possible but will incur a minor performance penalty compared to building for GCN/CDNA devices only. If you have multiple AMD GPUs of different generations in the same system (e.g., integrated APU and a discrete GPU) the ROCm runtime requires code to be available for each device at runtime, so you need to specify every device in ``HIPSYCL_TARGETS`` when compiling to avoid ROCm crashes at initialization. By default, `VkFFT `_ is used to perform FFT on GPU. You can switch to rocFFT by passing ``-DGMX_GPU_FFT_LIBRARY=rocFFT`` CMake flag. Please note that rocFFT is not officially supported and tends not to work on most consumer GPUs. AMD GPUs can also be targeted via `Intel oneAPI DPC++`_; please refer to :ref:`a separate section ` for the build instructions. SYCL GPU compilation options """""""""""""""""""""""""""" The following flags can be passed to CMake in order to tune |Gromacs|: ``-DGMX_GPU_NB_CLUSTER_SIZE`` changes the data layout of non-bonded kernels. When compiling with `Intel oneAPI DPC++`_, the default value is 4, which is optimal for most Intel GPUs except Data Center MAX (Ponte Vecchio), for which 8 is better. When compiling with AdaptiveCpp_, the default value is 8, which is the only supported value for AMD and NVIDIA devices. ``-DGMX_GPU_NB_NUM_CLUSTER_PER_CELL_X``, ``-DGMX_GPU_NB_NUM_CLUSTER_PER_CELL_Y``, ``-DGMX_GPU_NB_NUM_CLUSTER_PER_CELL_Z`` Sets the number of clusters along X, Y, or Z in a pair-search grid cell, default 2. When targeting Intel Ponte Vecchio GPUs, set ``-DGMX_GPU_NB_NUM_CLUSTER_PER_CELL_X=1`` and leave the other values as the default. ``-DGMX_GPU_NB_DISABLE_CLUSTER_PAIR_SPLIT`` Disables cluster pair splitting in the GPU non-bonded kernels. This is only supported in SYCL, and it is compatible with and improves performance on GPUs with 64-wide execution like AMD GCN and CDNA family. This option is automatically enabled in all builds that target GCN or CDNA GPUs (but not RDNA). Static linking ~~~~~~~~~~~~~~ Please refer to :ref:`a dedicated section `. gmxapi C++ API ~~~~~~~~~~~~~~ For dynamic linking builds and on non-Windows platforms, an extra library and headers are installed by setting ``-DGMXAPI=ON`` (default). Build targets ``gmxapi-cppdocs`` and ``gmxapi-cppdocs-dev`` produce documentation in ``docs/api-user`` and ``docs/api-dev``, respectively. For more project information and use cases, refer to the tracked :issue:`2585`, associated GitHub `gmxapi `_ projects, or DOI `10.1093/bioinformatics/bty484 `_. gmxapi is not yet tested on Windows or with static linking, but these use cases are targeted for future versions. Portability of a |Gromacs| build ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ A |Gromacs| build will normally not be portable, not even across hardware with the same base instruction set, like x86. Non-portable hardware-specific optimizations are selected at configure-time, such as the SIMD instruction set used in the compute kernels. This selection will be done by the build system based on the capabilities of the build host machine or otherwise specified to ``cmake`` during configuration. Often it is possible to ensure portability by choosing the least common denominator of SIMD support, e.g. SSE2 for x86. In rare cases of very old x86 machines, ensure that you use ``cmake -DGMX_USE_RDTSCP=off`` if any of the target CPU architectures does not support the ``RDTSCP`` instruction. However, we discourage attempts to use a single |Gromacs| installation when the execution environment is heterogeneous, such as a mix of AVX and earlier hardware, because this will lead to programs (especially mdrun) that run slowly on the new hardware. Building two full installations and locally managing how to call the correct one (e.g. using a module system) is the recommended approach. Alternatively, one can use different suffixes to install several versions of |Gromacs| in the same location. To achieve this, one can first build a full installation with the least-common-denominator SIMD instruction set, e.g. ``-DGMX_SIMD=SSE2``, in order for simple commands like ``gmx grompp`` to work on all machines, then build specialized ``gmx`` binaries for each architecture present in the heterogeneous environment. By using custom binary and library suffixes (with CMake variables ``-DGMX_BINARY_SUFFIX=xxx`` and ``-DGMX_LIBS_SUFFIX=xxx``), these can be installed to the same location. Portability of binaries across GPUs is generally better, targeting multiple generations of GPUs from the same vendor is in most cases possible with a single |Gromacs| build. CUDA_ builds will by default be able to run on any NVIDIA GPU supported by the CUDA toolkit used since the |Gromacs| build system generates code for these at build-time. With SYCL_ multiple target architectures of the same GPU vendor can be selected when using AdaptiveCpp_ (i.e. only AMD or only NVIDIA). The SSCP/generic compilation mode of AdaptiveCpp_ is currently not supported. With OpenCL_, due to just-in-time compilation of GPU code for the device in use this is not a concern. Linear algebra libraries ~~~~~~~~~~~~~~~~~~~~~~~~ As mentioned above, sometimes vendor BLAS and LAPACK libraries can provide performance enhancements for |Gromacs| when doing normal-mode analysis or covariance analysis. For simplicity, the text below will refer only to BLAS, but the same options are available for LAPACK. By default, CMake will search for BLAS, use it if it is found, and otherwise fall back on a version of BLAS internal to |Gromacs|. The ``cmake`` option ``-DGMX_EXTERNAL_BLAS=on`` will be set accordingly. The internal versions are fine for normal use. If you need to specify a non-standard path to search, use ``-DCMAKE_PREFIX_PATH=/path/to/search``. If you need to specify a library with a non-standard name (e.g. ESSL on Power machines or ARMPL on ARM machines), then set ``-DGMX_BLAS_USER=/path/to/reach/lib/libwhatever.a``. If you are using Intel MKL_ for FFT, then the BLAS and LAPACK it provides are used automatically. This could be over-ridden with ``GMX_BLAS_USER``, etc. On Apple platforms where the Accelerate Framework is available, these will be automatically used for BLAS and LAPACK. This could be over-ridden with ``GMX_BLAS_USER``, etc. .. _installing with MiMiC: Building with MiMiC QM/MM support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MiMiC QM/MM interface integration will require linking against MiMiC communication library, that establishes the communication channel between |Gromacs| and CPMD. The MiMiC Communication library can be downloaded `here `__. Compile and install it. Check that the installation folder of the MiMiC library is added to CMAKE_PREFIX_PATH if it is installed in non-standard location. Building QM/MM-capable version requires double-precision version of |Gromacs| compiled with MPI support: * ``-DGMX_DOUBLE=ON -DGMX_MPI -DGMX_MIMIC=ON`` .. _installing with CP2K: Building with CP2K QM/MM support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CP2K QM/MM interface integration will require linking against libcp2k library, that incorporates CP2K functionality into |Gromacs|. 1. Download, compile and install CP2K (version 8.1 or higher is required). CP2K latest distribution can be downloaded `here `_. For CP2K specific instructions please `follow `_. You can also check instructions on the `official CP2K web-page `_. 2. Make :file:`libcp2k.a` library by executing the following command:: make ARCH= VERSION= libcp2k The library archive (*e.g.* :file:`libcp2k.a`) should appear in the :file:`{}/lib/{}/{}/` directory. 3. Configure |Gromacs| with :command:`cmake`, adding the following flags. Build should be static: ``-DBUILD_SHARED_LIBS=OFF -DGMXAPI=OFF -DGMX_INSTALL_NBLIB_API=OFF`` Double precision in general is better than single for QM/MM (however both options are viable): ``-DGMX_DOUBLE=ON`` FFT, BLAS and LAPACK libraries should be the same between CP2K and |Gromacs|. Use the following flags to do so: * ``-DGMX_FFT_LIBRARY= -DFFTWF_LIBRARY= -DFFTWF_INCLUDE_DIR=`` * ``-DGMX_BLAS_USER=`` * ``-DGMX_LAPACK_USER=`` 4. Compilation of QM/MM interface is controled by the following flags. ``-DGMX_CP2K=ON`` Activates QM/MM interface compilation ``-DCP2K_DIR="/lib/local/psmp`` Directory with libcp2k.a library ``-DCP2K_LINKER_FLAGS=""`` (optional for CP2K 9.1 or newer) Other libraries used by CP2K. Typically that should be combination of LDFLAGS and LIBS from the ARCH file used for CP2K compilation. Sometimes ARCH file could have several lines defining LDFLAGS and LIBS or even split one line into several using "\\". In that case all of them should be concatenated into one long string without any extra slashes or quotes. For CP2K versions 9.1 or newer, CP2K_LINKER_FLAGS is not required but still might be used in very specific situations. .. _installing with Colvars: Building with Colvars support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ |Gromacs| bundles the `Colvars library `_ in its source distribution. The library and its interface with |Gromacs| are enabled by default when building |Gromacs|. This behavior may also be enabled explicitly with ``-DGMX_USE_COLVARS=internal``. Alternatively, Colvars support may be disabled with ``-DGMX_USE_COLVARS=none``. How to use Colvars in a |Gromacs| simulation is described in the User Guide, as well as in the `Colvars documentation `_. .. _suffixes: Changing the names of |Gromacs| binaries and libraries ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It is sometimes convenient to have different versions of the same |Gromacs| programs installed. The most common use cases have been single and double precision, and with and without MPI. This mechanism can also be used to install side-by-side multiple versions of mdrun optimized for different CPU architectures, as mentioned previously. By default, |Gromacs| will suffix programs and libraries for such builds with ``_d`` for double precision and/or ``_mpi`` for MPI (and nothing otherwise). This can be controlled manually with ``GMX_DEFAULT_SUFFIX (ON/OFF)``, ``GMX_BINARY_SUFFIX`` (takes a string) and ``GMX_LIBS_SUFFIX`` (also takes a string). For instance, to set a custom suffix for programs and libraries, one might specify: :: cmake .. -DGMX_DEFAULT_SUFFIX=OFF -DGMX_BINARY_SUFFIX=_mod -DGMX_LIBS_SUFFIX=_mod Thus the names of all programs and libraries will be appended with ``_mod``. Changing installation tree structure ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ By default, a few different directories under ``CMAKE_INSTALL_PREFIX`` are used when when |Gromacs| is installed. Some of these can be changed, which is mainly useful for packaging |Gromacs| for various distributions. The directories are listed below, with additional notes about some of them. Unless otherwise noted, the directories can be renamed by editing the installation paths in the main CMakeLists.txt. ``bin/`` The standard location for executables and some scripts. Some of the scripts hardcode the absolute installation prefix, which needs to be changed if the scripts are relocated. The name of the directory can be changed using ``CMAKE_INSTALL_BINDIR`` CMake variable. ``include/gromacs/`` The standard location for installed headers. ``lib/`` The standard location for libraries. The default depends on the system, and is determined by CMake. The name of the directory can be changed using ``CMAKE_INSTALL_LIBDIR`` CMake variable. ``lib/pkgconfig/`` Information about the installed ``libgromacs`` library for ``pkg-config`` is installed here. The ``lib/`` part adapts to the installation location of the libraries. The installed files contain the installation prefix as absolute paths. ``share/cmake/`` CMake package configuration files are installed here. ``share/gromacs/`` Various data files and some documentation go here. The first part can be changed using ``CMAKE_INSTALL_DATADIR``, and the second by using ``GMX_INSTALL_DATASUBDIR`` Using these CMake variables is the preferred way of changing the installation path for ``share/gromacs/top/``, since the path to this directory is built into ``libgromacs`` as well as some scripts, both as a relative and as an absolute path (the latter as a fallback if everything else fails). ``share/man/`` Installed man pages go here. Compiling and linking ^^^^^^^^^^^^^^^^^^^^^ Once you have configured with ``cmake``, you can build |Gromacs| with ``make``. It is expected that this will always complete successfully, and give few or no warnings. The CMake-time tests |Gromacs| makes on the settings you choose are pretty extensive, but there are probably a few cases we have not thought of yet. Search the web first for solutions to problems, but if you need help, ask on the `user discussion forum`_, being sure to provide as much information as possible about what you did, the system you are building on, and what went wrong. This may mean scrolling back a long way through the output of ``make`` to find the first error message! If you have a multi-core or multi-CPU machine with ``N`` processors, then using :: make -j N will generally speed things up by quite a bit. Other build generator systems supported by ``cmake`` (e.g. ``ninja``) also work well. .. _building just the mdrun binary: Installing |Gromacs| ^^^^^^^^^^^^^^^^^^^^ Finally, ``make install`` will install |Gromacs| in the directory given in ``CMAKE_INSTALL_PREFIX``. If this is a system directory, then you will need permission to write there, and you should use super-user privileges only for ``make install`` and not the whole procedure. .. _getting access to |Gromacs|: Getting access to |Gromacs| after installation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ |Gromacs| installs the script ``GMXRC`` in the ``bin`` subdirectory of the installation directory (e.g. ``/usr/local/gromacs/bin/GMXRC``), which you should source from your shell: :: source /your/installation/prefix/here/bin/GMXRC It will detect what kind of shell you are running and set up your environment for using |Gromacs|. You may wish to arrange for your login scripts to do this automatically; please search the web for instructions on how to do this for your shell. Many of the |Gromacs| programs rely on data installed in the ``share/gromacs`` subdirectory of the installation directory. By default, the programs will use the environment variables set in the ``GMXRC`` script, and if this is not available they will try to guess the path based on their own location. This usually works well unless you change the names of directories inside the install tree. If you still need to do that, you might want to recompile with the new install location properly set, or edit the ``GMXRC`` script. |Gromacs| also installs a CMake cache file to help with building client software (using the `-C option `__ when configuring the client software with CMake.) For an installation at ``/your/installation/prefix/here``, hints files will be installed at ``/your/installation/prefix/share/cmake/gromacs${GMX_LIBS_SUFFIX}/gromacs-hints${GMX_LIBS_SUFFIX}.cmake`` where ``${GMX_LIBS_SUFFIX}`` is :ref:`as documented above `. Testing |Gromacs| for correctness ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Since 2011, the |Gromacs| development uses an automated system where every new code change is subject to regression testing on a number of platforms and software combinations. While this improves reliability quite a lot, not everything is tested, and since we increasingly rely on cutting edge compiler features there is non-negligible risk that the default compiler on your system could have bugs. We have tried our best to test and refuse to use known bad versions in ``cmake``, but we strongly recommend that you run through the tests yourself. It only takes a few minutes, after which you can trust your build. The simplest way to run the checks is to build |Gromacs| with ``-DREGRESSIONTEST_DOWNLOAD``, and run ``make check``. |Gromacs| will automatically download and run the tests for you. Alternatively, you can download and unpack the |Gromacs| regression test suite |gmx-regressiontests-package| tarball yourself and use the advanced ``cmake`` option ``REGRESSIONTEST_PATH`` to specify the path to the unpacked tarball, which will then be used for testing. If the above does not work, then please read on. The regression tests are also available from the download_ section. Once you have downloaded them, unpack the tarball, source ``GMXRC`` as described above, and run ``./gmxtest.pl all`` inside the regression tests folder. You can find more options (e.g. adding ``double`` when using double precision, or ``-only expanded`` to run just the tests whose names match "expanded") if you just execute the script without options. Hopefully, you will get a report that all tests have passed. If there are individual failed tests it could be a sign of a compiler bug, or that a tolerance is just a tiny bit too tight. Check the output files the script directs you too, and try a different or newer compiler if the errors appear to be real. If you cannot get it to pass the regression tests, you might try dropping a line to the |Gromacs| `users forum `__, but then you should include a detailed description of your hardware, and the output of ``gmx mdrun -version`` (which contains valuable diagnostic information in the header). Non-standard suffix ~~~~~~~~~~~~~~~~~~~ If your ``gmx`` program has been suffixed in a non-standard way, then the ``./gmxtest.pl -suffix`` option will let you specify that suffix to the test machinery. You can use ``./gmxtest.pl -double`` to test the double-precision version. You can use ``./gmxtest.pl -crosscompiling`` to stop the test harness attempting to check that the programs can be run. You can use ``./gmxtest.pl -mpirun srun`` if your command to run an MPI program is called ``srun``. Running MPI-enabled tests ~~~~~~~~~~~~~~~~~~~~~~~~~ The ``make check`` target also runs integration-style tests that may run with MPI if ``GMX_MPI=ON`` was set. To make these work with various possible MPI libraries, you may need to set the CMake variables ``MPIEXEC``, ``MPIEXEC_NUMPROC_FLAG``, ``MPIEXEC_PREFLAGS`` and ``MPIEXEC_POSTFLAGS`` so that ``mdrun-mpi-test_mpi`` would run on multiple ranks via the shell command :: ${MPIEXEC} ${MPIEXEC_NUMPROC_FLAG} ${NUMPROC} ${MPIEXEC_PREFLAGS} \ mdrun-mpi-test_mpi ${MPIEXEC_POSTFLAGS} -otherflags A typical example for SLURM is :: cmake .. -DGMX_MPI=on -DMPIEXEC=srun -DMPIEXEC_NUMPROC_FLAG=-n \ -DMPIEXEC_PREFLAGS= -DMPIEXEC_POSTFLAGS= Testing |Gromacs| for performance ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ We are still working on a set of benchmark systems for testing the performance of |Gromacs|. Until that is ready, we recommend that you try a few different parallelization options, and experiment with tools such as ``gmx tune_pme``. Having difficulty? ^^^^^^^^^^^^^^^^^^ You are not alone - this can be a complex task! If you encounter a problem with installing |Gromacs|, then there are a number of locations where you can find assistance. It is recommended that you follow these steps to find the solution: 1. Read the installation instructions again, taking note that you have followed each and every step correctly. 2. Search the |Gromacs| webpage_ and `user discussion forum`_ for information on the error. Adding ``site:https://gromacs.bioexcel.eu/c/gromacs-user-forum/5`` to a Google search may help filter better results. It is also a good idea to check the `gmx-users mailing list archive`_ at ``https://mailman-1.sys.kth.se/pipermail/gromacs.org_gmx-users`` 3. Search the internet using a search engine such as Google. 4. Ask for assistance on the |Gromacs| `user discussion forum`_. Be sure to give a full description of what you have done and why you think it did not work. Give details about the system on which you are installing. Copy and paste your command line and as much of the output as you think might be relevant - certainly from the first indication of a problem. In particular, please try to include at least the header from the mdrun logfile, and preferably the entire file. People who might volunteer to help you do not have time to ask you interactive detailed follow-up questions, so you will get an answer faster if you provide as much information as you think could possibly help. High quality bug reports tend to receive rapid high quality answers. .. _gmx-special-build: Special instructions for some platforms --------------------------------------- Some less common configurations are described in a :ref:`separate manual section `. Building on Windows ^^^^^^^^^^^^^^^^^^^ Building on Windows using native compilers is rather similar to building on Unix, so please start by reading the above. Then, download and unpack the |Gromacs| source archive. Make a folder in which to do the out-of-source build of |Gromacs|. For example, make it within the folder unpacked from the source archive, and call it ``build-gromacs``. For CMake, you can either use the graphical user interface provided on Windows, or you can use a command line shell with instructions similar to the UNIX ones above. If you open a shell from within your IDE (e.g. Microsoft Visual Studio), it will configure the environment for you, but you might need to tweak this in order to get either a 32-bit or 64-bit build environment. The latter provides the fastest executable. If you use a normal Windows command shell, then you will need to either set up the environment to find your compilers and libraries yourself, or run the ``vcvarsall.bat`` batch script provided by MSVC (just like sourcing a bash script under Unix). With the graphical user interface, you will be asked about what compilers to use at the initial configuration stage, and if you use the command line they can be set in a similar way as under UNIX. Unfortunately ``-DGMX_BUILD_OWN_FFTW=ON`` (see `Using FFTW`_) does not work on Windows, because there is no supported way to build FFTW on Windows. You can either build FFTW some other way (e.g. MinGW), or use the built-in fftpack (which may be slow), or `using MKL`_. For the build, you can either load the generated solutions file into e.g. Visual Studio, or use the command line with ``cmake --build`` so the right tools get used. Building on Cray ^^^^^^^^^^^^^^^^ |Gromacs| builds mostly out of the box on modern Cray machines, but you may need to specify the use of static binaries with ``-DGMX_BUILD_SHARED_EXE=off``, and you may need to set the F77 environmental variable to ``ftn`` when compiling FFTW. The ARM ThunderX2 Cray XC50 machines differ only in that the recommended compiler is the ARM HPC Compiler (``armclang``). Intel Xeon Phi ^^^^^^^^^^^^^^ Xeon Phi processors, hosted or self-hosted, are supported. The Knights Landing-based Xeon Phi processors behave like standard x86 nodes, but support a special SIMD instruction set. When cross-compiling for such nodes, use the ``AVX_512_KNL`` SIMD flavor. Knights Landing processors support so-called "clustering modes" which allow reconfiguring the memory subsystem for lower latency. |Gromacs| can benefit from the quadrant or SNC clustering modes. Care needs to be taken to correctly pin threads. In particular, threads of an MPI rank should not cross cluster and NUMA boundaries. In addition to the main DRAM memory, Knights Landing has a high-bandwidth stacked memory called MCDRAM. Using it offers performance benefits if it is ensured that ``mdrun`` runs entirely from this memory; to do so it is recommended that MCDRAM is configured in "Flat mode" and ``mdrun`` is bound to the appropriate NUMA node (use e.g. ``numactl --membind 1`` with quadrant clustering mode). NVIDIA Grace ^^^^^^^^^^^^ Summary: For best performance on Grace, run with GNU >= 13.1 and choose the ``-DCMAKE_CXX_FLAGS=-mcpu=neoverse-v2 -DCMAKE_C_FLAGS=-mcpu=neoverse-v2 -DGMX_SIMD=ARM_NEON_ASIMD`` CMake options. At minimum any compiler being used for Grace should implement neoverse-v2, such as GNU >= 12.3 and LLVM >= 16. There is a significant improvement in Arm performance between gcc-13 and gcc-12 so GNU >= 13.1 is strongly recommended. The ``-mcpu=neoverse-v2`` flag ensures that the compiler is not defaulting to the older Armv8-A target. On both GNU and LLVM, the |Gromacs| version implemented with ``NEON SIMD`` instructions significantly outperforms the SVE version. This can be selected by setting ``GMX_SIMD=ARM_NEON_ASIMD`` at compilation. These Grace specific config optimisations are most important when running in CPU only mode, where much of the run time is spent in code which is sensitive to SIMD performance. Tested platforms ---------------- While it is our best belief that |Gromacs| will build and run pretty much everywhere, it is important that we tell you where we really know it works because we have tested it. Every commit in our git source code repository is currently tested with a range of configuration options on x86 with gcc versions including 9 and 12, clang versions including 9 and 15, CUDA versions 11.0 and 11.7, hipSYCL 0.9.4 with ROCm 5.3, and a version of oneAPI containing Intel's clang-based compiler. For this testing, we use Ubuntu 20.04 operating system. Other compiler, library, and OS versions are tested less frequently. For details, you can have a look at the `continuous integration server used by the GitLab project `_, which uses GitLab runner on a local k8s x86 cluster with NVIDIA, AMD, and Intel GPU support. We test irregularly on ARM v8, Fujitsu A64FX, Cray, Power9, and other environments, and with other compilers and compiler versions, too. Support ------- Please refer to the `manual `_ for documentation, downloads, and release notes for any |Gromacs| release. Visit the `user forums `_ for discussions and advice. Report bugs at https://gitlab.com/gromacs/gromacs/-/issues