Installation guide

Introduction to building GROMACS

These instructions pertain to building GROMACS 2024.4. 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 3.18.4 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:

tar xfz gromacs-2024.4.tar.gz
cd gromacs-2024.4
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 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 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-2024.4. We recommend to use the GCC compiler, version 9.x to 11.x. Note: there are 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.

GPU support

GROMACS has excellent support for NVIDIA GPUs supported via CUDA. On Linux, NVIDIA CUDA toolkit with minimum version 11.0 is required, and the latest version is strongly encouraged. NVIDIA GPUs with at least NVIDIA compute capability 3.5 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 unknown 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

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 3.18.4. 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.

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=<name>, where <name> 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 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

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=<path to NVIDIA HPC SDK math_libs folder>

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/<SDK_version>/comm_libs/<CUDA_version>/nvshmem_cufftmp_compat. If that directory does not exist in the SDK, then there only exists a single (compatible) version at Linux_x86_64/<SDK_version>/comm_libs/<CUDA_version>/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=<path to compatible NVSHMEM folder>/lib:$LD_LIBRARY_PATH

It is advisable to refer to the NVSHMEM FAQ page for any issues faced at runtime.

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=<path to heFFTe folder>

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=<path to oneMKL folder>. When targeting AMD GPUs, add -DHeffte_ENABLE_ROCM=ON -DHeffte_ROCM_ROOT=<path to ROCm folder>.

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=<path to VkFFT directory>

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 1.7.10 is bundled in the GROMACS source already.

• The lmfit library for Levenberg-Marquardt curve fitting is used in GROMACS. Only lmfit 7.0 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.

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

tar xfz gromacs-2024.4.tgz
cd gromacs-2024.4
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.

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!

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.

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.

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

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 unknown 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 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 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, oneAPI version 2023.2 or 2024.0 are tested regularly and are recommended, although later versions might work and can offer better performance. The earliest supported version is oneAPI 2023.0. 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 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 double-batched FFT library.

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.

With AdaptiveCpp 23.10 or newer, the performance can be improved by passing -DSYCL_CXX_FLAGS_EXTRA=-DHIPSYCL_ALLOW_INSTANT_SUBMISSION=1 CMake flag when building GROMACS, especially for small systems (under 20k atoms), for runs with CPU tasks, or when running on multiple GPUs. There are no known downsides to using this flag.

AMD GPUs can also be targeted via Intel oneAPI DPC++; please refer to 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 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.

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

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 libcp2k.a library by executing the following command::

   make ARCH=<your arch file> VERSION=<your version like psmp> libcp2k

The library archive (e.g. libcp2k.a) should appear in the <cp2k dir>/lib/<arch>/<version>/ directory.

3. Configure GROMACS with 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=<your library like fftw3> -DFFTWF_LIBRARY=<path to library> -DFFTWF_INCLUDE_DIR=<path to directory with headers>

• -DGMX_BLAS_USER=<path to your BLAS>

• -DGMX_LAPACK_USER=<path to your LAPACK>

4. Compilation of QM/MM interface is controled by the following flags.

-DGMX_CP2K=ON

Activates QM/MM interface compilation

-DCP2K_DIR="<path to cp2k>/lib/local/psmp

Directory with libcp2k.a library

-DCP2K_LINKER_FLAGS="<combination of LDFLAGS and LIBS>" (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.

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.

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.

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 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 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 https://ftp.gromacs.org/regressiontests/regressiontests-2024.4.tar.gz 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.

Special instructions for some platforms

Some less common configurations are described in a 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