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Build

Welcome to ARTS

ARTS is free software. Please see the file COPYING for details.

If you use data generated by ARTS in a scientific publication, then please mention this and cite the most appropriate of the ARTS publications that are summarized on http://www.radiativetransfer.org/docs/

CONTRIBUTING.md provides information on contributing to ARTS on GitHub.

For documentation, please see the files in the doc subdirectory.

For building and installation instructions please read below.

Building ARTS

Build Prerequisites:

  • gcc/g++ >=8 (or llvm/clang >=10) older versions might work, but are untested
  • cmake (>=3.9)
  • zlib
  • openblas
  • netcdf (optional)
  • Python3 (>=3.6)
    • required modules: docutils lark-parser matplotlib netCDF4 numpy pytest scipy setuptools

To build the documentation you also need:

  • pdflatex (optional)
  • doxygen (optional)
  • graphviz (optional)

Using cmake

Here are the steps to use cmake to build ARTS:

cd arts
mkdir build
cd build
cmake ..
make

If you only want to build the arts executable you can just run 'make arts' instead of 'make'.

If you have a multi-core processor or multiprocessor machine, don't forget to use the -j option to speed up the compilation:

make -jX

Where X is the number of parallel build processes. X=(Number of Cores)+1 gives you usually the fastest compilation time.

Developer install of the PyARTS Python package:

cd python
pip install --user -e .

You only have to do the package install once. If the ARTS source has changed, update the PyARTS package by running:

make -jX pyarts

Build configurations

To build a release version without assertions or debugging symbols use:

cmake -DCMAKE_BUILD_TYPE=Release ..
make clean
make

To switch back to the debug version use:

cmake -DCMAKE_BUILD_TYPE=RelWithDebInfo ..
make clean
make

This is also the default configuration if you run cmake without options in an empty build directory.

Native build (EXPERIMENTAL)

Finally, an experimental build type for Native infrastructures exists on GCC:

cmake -DCMAKE_BUILD_TYPE=Native ..
make clean
make

This option should make the executable slightly faster, more so on better systems, but not portable. Note that since this build-mode is meant for fast-but-accurate computations, some IEEE rules will be ignored. For now only complex computations are IEEE incompatible running this mode of build.

Installing PyARTS

To install the PyARTS Python package, you need to build it and install it with pip. Create your build directory and configure ARTS with cmake as described in the previous sections. Then, run the following commands inside your build directory:

make pyarts
cd python
pip install --user -e .

This will not mess with your system's Python installation. A link to the pyarts package is created in your home directory, usually $HOME/.local/lib/python3.X/site-packages/pyarts.egg-link.

You don't need to reinstall the package with pip after updating ARTS. You only need to run make pyarts again.

Tests

'make check' will run several test cases to ensure that ARTS is working properly. Use 'make check-all' to run all available controlfiles, including computation time-intensive ones.

Some tests depend on the arts-xml-data package. cmake automatically looks if it is available in the same location as ARTS itself. If necessary, a custom path can be specified:

cmake -DARTS_XML_DATA_PATH=/home/myname/arts-xml-data ..

If arts-xml-data cannot be found, those tests are ignored.

By default, the tests are executed serially. If you want to run them concurrently, you can use:

cmake -DTEST_JOBS=X ..

X is the number of tests that should be started in parallel.

You can also use the ctest command directly to run the tests:

ctest -j4

To run specific tests, use the -R option and specify part of the test case name you want to run. The following command will run all tests that have 'ppath' in their name, e.g. arts.ctlfile.fast.ppath1d ...:

ctest -R ppath

To see the output of ARTS, use the -V option:

ctest -V -R fast.doit

By default, ctest will not print any output from ARTS to the screen. The option --output-on-failure can be passed to ctest to see output in the case an error occurs. If you want to always enable this, you can set the environment variable CTEST_OUTPUT_ON_FAILURE:

export CTEST_OUTPUT_ON_FAILURE=1

HITRAN catalog support

By default, ARTS only supports the latest HITRAN 2012 catalog version. Because isotopologues have been renamed between different catalog versions, ARTS needs to be compiled for one specific HITRAN version. If you want to use HITRAN 2008, you have to recompile ARTS with:

cmake -DWITH_HITRAN2008=1 ..
make arts

To switch back to HITRAN 2012, run:

cmake -DWITH_HITRAN2008=0 ..
make arts

Optional features

To include features that rely on Fortran code located in the 3rdparty subdirectory use:

cmake -DENABLE_FORTRAN=1 -DCMAKE_Fortran_COMPILER=gfortran ..

This enables Disort, Fastem, Refice and Tmatrix.

If necessary, certain Fortran modules can be selectively disabled:

cmake -DENABLE_FORTRAN=1 -DNO_DISORT=1 ..
cmake -DENABLE_FORTRAN=1 -DNO_REFICE=1 ..

IMPORTANT: Only gfortran and Intel Fortran are currently supported. Also, a 64-bit system is required (size of long type must be 8 bytes).

Enable NetCDF: The basic matpack types can be read from NetCDF files, if NetCDF support is enabled:

cmake -DENABLE_NETCDF=1 ..

Disabling features

Disable assertions:

cmake -DNO_ASSERT=1 ..

Disable OpenMP:

cmake -DNO_OPENMP=1 ..

Disable the built-in documentation server:

cmake -DNO_DOCSERVER=1 ..

Treat warnings as errors:

cmake -DWERROR=1 ..

Disable FFTW autodetection:

ARTS automatically detects the availability of the FFTW3 library needed to speed up the calculation of HITRAN cross section species . If you need to disable FFTW support for any reason, you can do so with the following cmake option:

cmake -DNO_FFTW=1 ..

TMatrix precision

By default, ARTS uses double-precision for the T-matrix calculations. When using the Intel compiler, quad-precision can be enable with cmake:

cmake -DENABLE_FORTRAN=1 -DENABLE_TMATRIX_QUAD=1 ..

Note that quad-precision is software emulated. T-matrix calculations will around 10x slower.

ccache support

To utilize ccache when available use:

cmake -DENABLE_CCACHE=1 ..

For details see https://ccache.samba.org/

Intel compiler

If you want to compile with the Intel compiler[1], start with an empty build directory and run:

cmake -DCMAKE_C_COMPILER=icc -DCMAKE_CXX_COMPILER=icpc ..

[1] http://software.intel.com/c-compilers

LLVM/Clang compiler

If you want to compile with the LLVM/Clang compiler[1], start with an empty build directory and run:

cmake -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..

You might also have to explicitly pick the right Fortran compiler since clang doesn't have one:

cmake -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ \
-DENABLE_FORTRAN=1 -DCMAKE_Fortran_COMPILER=gfortran ..

Note that at this point, on OS X the default Apple Clang compiler does not support OpenMP. Other versions of Clang support it via libomp.

[1] http://clang.llvm.org [2] http://libcxx.llvm.org

macOS / Xcode

If you're on a Mac and have the Apple Xcode development environment installed, you can generate a project file and use Xcode to build ARTS:

cmake -G Xcode ..
open ARTS.xcodeproj

Experimental features (ONLY USE IF YOU KNOW WHAT YOU'RE DOING)

Enable C++20 (only for compatibility testing, do not use C++20 features in your code, you need CMake >=3.12):

cmake -DENABLE_CXX20=1 ..

Valgrind profiling

The callgrind plugin included in valgrind is the recommended profiling method for ARTS.

Due to limitations of valgrind, you need to disable the tmatrix code (-DNO_TMATRIX=1) when compiling ARTS with Fortran support.

Certain things should be taken into account when calling ARTS with valgrind. Since recursion (cycles) will lead to wrong profiling results it is important to use the following settings to obtain profile data for ARTS:

valgrind --tool=callgrind --separate-callers=10 --separate-recs=3 arts -n1 ...

For detail on these options consult the valgrind manual:

http://valgrind.org/docs/manual/cl-manual.html#cl-manual.cycles

-n1 should be passed to ARTS because parallelisation can further scew the results. Since executing a program in valgrind can lead to 50x slower execution, it is recommended to create a dedicated, minimal controlfile for profiling.

After execution with valgrind, the resulting callgrind.out.* file can be opened in kcachegrind[1] for visualization. It is available as a package for most Linux distributions.

Note that you don't have to do a full ARTS run. You can cancel the program after some time when you think you have gathered enough statistics.

[1] https://kcachegrind.github.io/

Linux perf profiling

The Performance Counters for Linux offer a convenient way to profile any program with basically no runtime overhead. Profiling works for all configurations (Debug, RelWithDebInfo and Release). To ensure that the calltree can be analyzed correctly, compile ARTS without frame pointers. This has minimal impact on performance:

cmake -DCMAKE_C_FLAGS="-fno-omit-frame-pointer" \
      -DCMAKE_CXX_FLAGS="-fno-omit-frame-pointer" ..

Prepend the perf command to your arts call to record callgraph information:

perf record -g src/arts MYCONTROLFILE.arts

This can also be applied to any test case:

perf record -g ctest -R TestDOIT$

After recording, use the report command to display an interactive view of the profiling information:

perf report -g graph,0.5,callees

This will show a reverse call tree with the percentage of time spent in each function. The function tree can be expanded to expose the calling functions.