.. 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 <install guide 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
<http://manual.gromacs.org/documentation>`_.

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++) |GMX_GCC_MINIMUM_REQUIRED_VERSION|
* LLVM (clang/libc++) |GMX_CLANG_MINIMUM_REQUIRED_VERSION|
* 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. Note: there are
:ref:`known issues <gmx-users-known-issues>` with some versions of GCC 12-14.

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 |GMX_GCC_MINIMUM_REQUIRED_VERSION| 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 <https://mac.r-project.org/openmp/>`_
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| supports a variety of GPU acceleration options.
For end-users, here are the recommended options based on your hardware:

* AMD GPUs: SYCL (with AdaptiveCpp)
* Apple M-series: OpenCL
* Intel GPUs: SYCL (with Intel oneAPI DPC++)
* NVIDIA GPUs: CUDA

CUDA
""""

CUDA is the recommended backend for NVIDIA GPUs.

Supported hardware:

* NVIDIA GPUs (all supported by the CUDA toolkit)

Requirements:

* CUDA toolkit version |GMX_CUDA_MINIMUM_REQUIRED_VERSION| or newer
* GPU with compute capability |GMX_CUDA_MINIMUM_REQUIRED_COMPUTE_CAPABILITY| or higher

Best practices:

* Use the latest CUDA version and NVIDIA driver compatible with your hardware
* Match your gcc version with nvcc's host compiler (prefer the latest gcc/clang version supported by nvcc)

More information can be found in the `CUDA GPU acceleration`_ section.

OpenCL
""""""

OpenCL is deprecated, but is currently the only backend supporting Apple M-series GPUs.

Supported hardware:

* AMD GCN-based GPUs (RDNA-series GPUs, such as RX 5500 or RX 6900, are not supported)
* Apple M-series GPUs
* Intel GPUs (special compilation options required; Intel DataCenter GPU Max are not supported)
* NVIDIA GPUs (only prior to Volta architecture; newer GPUs, such as V100 or GTX 10xx-series, are not supported)

Requirements:

* The minimum OpenCL version |REQUIRED_OPENCL_MIN_VERSION|.

More information can be found in the `OpenCL GPU acceleration`_ section.

SYCL
""""

SYCL is the recommended backend for Intel and AMD GPUs.
For Intel GPUs, we recommend using the Intel oneAPI DPC++ compiler,
while for AMD GPUs we recommend using AdaptiveCpp compiler with ROCm runtime.

Supported hardware:

* AMD GPUs: GFX9 (Vega, Raven), CDNA-series, RDNA-series GPUs (using either oneAPI or AdaptiveCpp)
* Intel GPUs: All current integrated/discrete GPUs (using oneAPI)
* NVIDIA GPUs: All GPUs (using either oneAPI or AdaptiveCpp)

Requirements:

* oneAPI DPC++ compiler: 2024.0 or newer (Codeplay plugin required for NVIDIA/AMD Support), or
* AdaptiveCpp: 24.02 or newer.

Limitations:

* Intel GPUs and SSCP/generic compilation flow not supported with AdaptiveCpp.
* ROCm or CUDA toolkits are required for AMD and NVIDIA GPUs respectively.

More information can be found in the `SYCL GPU acceleration Intel`_ and `SYCL GPU acceleration AMD`_ sections.

HIP
"""

Supported hardware:

* AMD GPUs: GFX9, CDNA 1/2,3 RDNA 1/2/3 GPUs

Requirements:

* ROCm runtime 5.2 or newer

Limitations:

* Available from |Gromacs| 2025
* |Gromacs| 2025 supports only main non-bonded kernels
* |Gromacs| 2026 has full offload support

Experimental branch:
* Experimental feature branch with remaining AMD specific optimizations: `HIP feature branch <https://gitlab.com/gromacs/gromacs/-/tree/4947-hip-feature-enablement>`_
* Supported by AMD, with aim to merge all remaining code during the coming release cycle
* Updated together with 2026 releases to be based on latest fixes
* For support, `contact acmnpv here <mailto:paul.bauer.q@gmail.com>`_

More information can be found in the `AMD-HIP`_ section.

Important Notes
"""""""""""""""

* Only one GPU backend can be configured per build
* CPU code always runs alongside GPU acceleration
* Choose latest drivers while watching for performance regressions on older hardware

.. _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
3.0 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
<https://developer.nvidia.com/blog/introduction-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
<https://www.open-mpi.org/faq/?category=buildcuda>`_.

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
<https://www.intel.com/content/www/us/en/develop/documentation/mpi-developer-reference-linux/top/environment-variable-reference/gpu-support.html>`_.

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).

NVSHMEM Support for GPU kernel-initiated communication
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In simulations using multiple GPUs, `NVSHMEM <https://developer.nvidia.com/nvshmem>`_ provides
a programming interface that allows GPU-initiated direct communication between distinct GPU memory spaces.
This approach leverages NVSHMEM's global address space, often resulting in higher bandwidth and lower latency
communication.

Support for this feature in |Gromacs| is currently enabled by building with NVSHMEM support (``GMX_NVSHMEM=ON``) and
specifying the NVSHMEM root directory (``NVSHMEM_ROOT=<Path-to-NVSHMEM-Lib-Root-dir>``). This setup targets NVIDIA
GPUs and utilizes the NVSHMEM library for efficient inter-GPU data transfers. It should be noted that the NVSHMEM build
is not compatible with cuFFTMp, an issue that may be resolved in a future release.

This is an experimental feature. The current implementation performs well for small system sizes (up to 300,000 particles).
Performance improvements are planned for future releases, where we expect the NVSHMEM-based implementation to be faster across
all input sizes compared to MPI.

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=<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, 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 oneMath Interface Library
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The oneMath interface library (earlier called oneMKL interface library, not to be confused
with Intel oneMKL) enables the SYCL backend for |Gromacs| with cuFFT, rocFFT,
or closed-source oneMath using Intel DPC++ and Codeplay's plugins for NVIDIA and AMD GPUs.
To use, Intel DPC++ must be installed along with Codeplay's plugins for NVIDIA
and AMD GPUs as required, and CUDA and/or ROCm as required. The environment should be initialized
as with the MKL instructions above.

To use the oneMath interface library, download, build and install oneMath as directed in the
`oneMath documentation <https://uxlfoundation.github.io/oneMath/building_the_project_with_dpcpp.html>`_,
making sure that
`suitable DFT backends <https://github.com/uxlfoundation/oneMath#supported-configurations>`_
are enabled.
Then, when building |Gromacs|, set ``-DGMX_GPU_FFT_LIBRARY=ONEMATH``.

.. _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
<https://docs.nvidia.com/hpc-sdk/cufftmp>`_, 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
<https://docs.nvidia.com/hpc-sdk/cufftmp/usage/requirements.html>`_
are met before trying to use GPU PME decomposition feature.  In
particular, cuFFTMp internally uses `NVSHMEM
<https://developer.nvidia.com/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
<https://docs.nvidia.com/hpc-sdk/nvshmem/api/faq.html#general-faqs>`_ 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 <https://icl.utk.edu/fft/>`_. 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 <https://github.com/DTolm/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.
* For AMD-HIP_, VkFFT is the default FFT backend, as it supports both consumer and data center hardware.

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
  |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 xzf 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. ``IBM_VSX`` Power7, Power8, Power9 and later have this.
10. ``ARM_NEON_ASIMD`` 64-bit ARMv8 and later. For maximum performance on NVIDIA 
    Grace (ARMv9), we strongly suggest at least GNU >= 13, LLVM >= 16. 
11. ``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
to 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 -DCUDAToolkit_ROOT=/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 ``CMAKE_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 with the ``CMAKE_CUDA_ARCHITECTURES`` CMake variable,
which takes a semicolon delimited string with the two or three digit suffixes of CUDA
architectures names, for instance "60;75;86". For details, see the
`"CUDA_ARCHITECTURES" section of the CMake documentation
<https://cmake.org/cmake/help/v3.28/prop_tgt/CUDA_ARCHITECTURES.html>`_.

The GPU acceleration has been tested on AMD64/x86-64 platforms with
Linux and Windows operating systems, but Linux is the
best-tested and supported of these. Linux running on POWER 8/9 and ARM
CPUs also works well.

Experimental support is available for compiling CUDA code, both for host and
device, using clang.
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.
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:

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 <https://github.com/intel/compute-runtime/releases>`_
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 <https://github.com/DTolm/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 <https://developer.codeplay.com/products/oneapi/amd/home/>`_,
* NVIDIA GPUs with either AdaptiveCpp_ or oneAPI with `Codeplay NVIDIA plugin <https://developer.codeplay.com/products/oneapi/nvidia/home/>`_.

In table form:

==========  =============  =========================================================================================================
GPU vendor  AdaptiveCpp_   `Intel oneAPI DPC++`_
==========  =============  =========================================================================================================
Intel       not supported  supported
AMD         supported      experimental (requires `Codeplay plugin <https://developer.codeplay.com/products/oneapi/amd/home/>`_)
NVIDIA      experimental   experimental (requires `Codeplay plugin <https://developer.codeplay.com/products/oneapi/nvidia/home/>`__)
==========  =============  =========================================================================================================

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 <install guide exotic sycl>` 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 <https://github.com/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 Intel:

SYCL GPU acceleration for Intel GPUs
""""""""""""""""""""""""""""""""""""

You should install the recent `Intel oneAPI DPC++`_ compiler toolkit.
For |Gromacs| 2025, version 2025.0 is recommended, and 2024.0 is the earliest supported.
Using open-source `Intel LLVM <https://github.com/intel/llvm>`_ is possible,
but not extensively tested. We also recommend installing the most recent
`Neo driver <https://github.com/intel/compute-runtime/releases>`_.

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),
Intel Xe2 GPUs (Lunar Lake, Arc Battlemage) and newer, 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 <bbfft installation>`.

.. _SYCL GPU acceleration AMD:

SYCL GPU acceleration for AMD GPUs
""""""""""""""""""""""""""""""""""

Using `AdaptiveCpp 24.02.0 <https://github.com/AdaptiveCpp/AdaptiveCpp/releases/tag/v24.02.0>`_
and ROCm 5.7-6.2 is recommended. The earliest supported version is AdaptiveCpp 24.02.

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/ \
            -DACPP_COMPILER_FEATURE_PROFILE=minimal

After compiling and installing AdaptiveCpp, the following settings can be used for
building |Gromacs| itself (set ``ACPP_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 -DACPP_TARGETS='hip:gfxXYZ'

Multiple target architectures can be specified, e.g.,
``-DACPP_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 ``ACPP_TARGETS``
when compiling to avoid ROCm crashes at initialization.

By default, `VkFFT <https://github.com/DTolm/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 <install guide exotic sycl>` 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).


.. _AMD-HIP:

AMD HIP GPU acceleration
~~~~~~~~~~~~~~~~~~~~~~~~

HIP is the AMD interoperability layer for the `ROCm`_ toolkit used to target AMD devices.

In |Gromacs| 2026 there is full support for using HIP as the GPU backend on AMD devices.

Build instructions
""""""""""""""""""""""""""""""

In order to use HIP as the device backend, you need to have the `ROCm`_ toolkit installed, including
the `rocPrim`_ libraries. The minimum version required by |Gromacs| is ROCm 5.2, but we recommend a
recent version to take advantage of library improvements.

You can then configure the build like this

::

   cmake .. -DCMAKE_HIP_COMPILER=${ROCM_PATH}/bin/amdclang++ \
            -DCMAKE_PREFIX_PATH=${ROCM_PATH} \
            -DGMX_GPU=HIP

By default |Gromacs| will generate code for a range of different CDNA devices. In case you want to
narrow the scope of the code generation, or want to target RDNA or GCN devices, you can specify the
architectures using this flag

``-DGMX_HIP_TARGET_ARCH=gfxXYZ,gfxABCD``

When detecting a 64-wide execution architecture and no 32-wide versions, |Gromacs| will automatically
configure with

``-DGMX_GPU_NB_DISABLE_CLUSTER_PAIR_SPLIT=ON``

to improve performance on those devices. In case any 32-wide architectures are present, the maximum
execution width will be restricted to be 32-wide, even on devices that support 64-wide execution.

When |Gromacs| is built with explicit 64-wide execution (and conflicting support for 32-wide devices),
any 32-wide devices detected will be not be used.

It is possible to select the GPU FFT backend using the

``-DGMX_GPU_FFT_LIBRARY=rocFFT/VkFFT``

variable. Please be aware that ``rocFFT`` only works on devices that are officially supported by `ROCm`_.
If an incompatible device is detected, |Gromacs| will not use the it when linked against ``rocFFT``, even
though the rest of the code is compiled to support it. In this case, select ``VkFFT`` as the GPU FFT library.

H5MD trajectory output file format
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`H5MD`_ is a specification for output data from molecular dynamics simulations built on
the HDF5 format. In |Gromacs| 2026 this is added as an experimental feature.

Build instructions
""""""""""""""""""""""""""""""

To enable support for H5MD output |Gromacs| you need to have an installed copy of the
`HDF5`_ library. The minimum supported version is |GMX_HDF5_MINIMUM_REQUIRED_VERSION|.
If the library is installed you can build with H5MD support by

::

    cmake .. -DGMX_USE_HDF5=ON

Some distributed versions of the HDF5 library may be incorrectly configured, leading
to CMake not detecting its path or version. In such cases, adding
``-DHDF5_C_COMPILER_EXECUTABLE=''`` (ie. passing an empty string) may solve the issue.

Static linking
~~~~~~~~~~~~~~

Please refer to :ref:`a dedicated section <install guide static linking>`.


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 <https://github.com/kassonlab/gmxapi>`_ projects,
or DOI `10.1093/bioinformatics/bty484 <https://doi.org/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 <https://gitlab.com/MiMiC-projects/CommLib>`__.
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=ON -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 <https://github.com/cp2k/cp2k/releases/>`_.
   For CP2K specific instructions, please `follow <https://github.com/cp2k/cp2k/blob/master/INSTALL.md>`_.
   You can also check instructions on the `official CP2K web-page <https://www.cp2k.org/howto>`_.

2. Make :file:`libcp2k.a` library by executing the following command::

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

   The library archive (*e.g.* :file:`libcp2k.a`) should appear in the :file:`{<cp2k dir>}/lib/{<arch>}/{<version>}/` 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=<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.

.. _installing with Colvars:

Building with Colvars support
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

|Gromacs| bundles the `Colvars library <https://colvars.github.io/>`_
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 <https://colvars.github.io/gromacs-2026/colvars-refman-gromacs.html>`_.

.. _installing with PLUMED:

Building with PLUMED support
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

|Gromacs| bundles the interface from version 2.10 of the `PLUMED library <https://www.plumed.org/>`_
in its source distribution. The interface is compatible with any PLUMED version. 
The interface is enabled by default with |Gromacs|
unless |Gromacs| is built on Windows. You can explicitly enable the 
interface with ``-DGMX_USE_PLUMED=ON`` or deactivate it with ``-DGMX_USE_PLUMED=OFF``.
By default the option is set to ``AUTO``, during the configuration CMake will try
to activate PLUMED and in case it does not succeed it will output a "soft" warning.
If the user forces the option ``ON``, when PLUMED cannot be activated the configuration
will fail with an error message.
The User Guide contains the instructions on how to use PLUMED in a |Gromacs| simulation. 

.. _installing with Neural Network potential support:

Building with Neural Network potential support
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To build |Gromacs| with support for Neural Network potentials, it has to be compiled 
with a suitable machine learning library. At the moment, only models trained in
`Pytorch <https://pytorch.org/>`_ are supported. To be able to load them in |Gromacs|,
it has to be built with the Pytorch C++ API or Libtorch, which can be downloaded
from the `Pytorch website <https://pytorch.org/get-started/locally/>`_. 
The website offers versions including pre-CXX11 and CXX11 ABI versions on Linux.
You must use the same ABI version as you use when building the rest of
|Gromacs|, which does not support or test the pre-CXX11 ABI.
So get (or build) the CXX11 ABI version of Libtorch.
For the same reason, it is also not possible to use the Libtorch version that ships
with a conda installation of Pytorch, because it is built with the pre-CXX11 ABI by default.
The NNP interface is enabled by default when a Libtorch installation is found in the
``CMAKE_PREFIX_PATH``, or ``Torch_DIR`` is set to a ``TorchConfig.cmake`` or 
``torch-config.cmake`` usually found under ``share/cmake/Torch/`` in the libtorch 
installation directory. It may also be explicitly enabled with ``-DGMX_NNPOT=TORCH``
or disabled with ``-DGMX_NNPOT=OFF``.

In addition, |Gromacs| provides support to specify custom `Pytorch extensions
<https://pytorch.org/tutorials/advanced/cpp_extension.html>`_ at build time that may
be used by the NNP model. The path to the extension library may be specified via the
``TORCH_EXTENSION_PATH`` variable. Note that CMake will search for a file called
``libtorch_extension.so`` in the specified directory.

.. _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 <https://cmake.org/cmake/help/latest/manual/cmake.1.html#options>`__
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 <suffixes>`.

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 to, 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| `user discussion 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 <install guide exotic>`.

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``).


NVIDIA Grace
^^^^^^^^^^^^

For best performance on Grace, use GCC >= 13.1 or LLVM >= 17, and set the 
``-DCMAKE_CXX_FLAGS=-mcpu=neoverse-v2 -DCMAKE_C_FLAGS=-mcpu=neoverse-v2``
flags when configuring |Gromacs|.

With short-range non-bonded interactions calculations on the CPU (``-nb cpu`` or
when building without GPU support), performance can be improved by also setting
the ``-DGMX_SIMD=ARM_NEON_ASIMD`` CMake option.

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| CPU version of the short-range non
bonded interactions implemented with ``NEON SIMD`` instructions 
significantly outperforms the SVE version. This can be selected by setting 
``GMX_SIMD=ARM_NEON_ASIMD`` at compilation. There can be a small performance
benefit to using SVE for CPU work outside this kernel, therefore when the
short-range non bonded interactions run on the GPU it is recommended to stay 
with ``GMX_SIMD=ARM_SVE`` which is the default option when available.


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 11-14,
clang versions including 14, 18, and 19,
CUDA versions 12.1, 12.5.1, and 12.6,
nvcxx version 24.7
HIP version 5.7.1 and 6.2.2
AdaptiveCPP 24.02 and 24.10 with ROCm 5.7.1 and 6.2 (respectively),
and
oneAPI version 2024.0 and 2024.2 (including CUDA 12.0.1 and ROCm 6.1.3 backends).

For this testing, we use Ubuntu 22.04 and 24.04 operating systems.
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 <https://gitlab.com/gromacs/gromacs/>`_,
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 <http://manual.gromacs.org/>`_ for documentation,
downloads, and release notes for any |Gromacs| release.

Visit the `user forums <http://forums.gromacs.org/>`_ for discussions and advice.

Report bugs at https://gitlab.com/gromacs/gromacs/-/issues
