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For information on how to build older versions of SU2, have a look here.

Manual installation of GoZ files for ZBrush 4 on Mac OS X When prompted, don’t forget to enter the login and password of a user account which has administrator privileges. This is necessary even if you are logged onto your computer under an account which has such privileges.

Note that the following guide works only on Linux/MacOS and on Windows using Cygwin or the Linux Subsystem.

• WARNING: Before you install. Release5 Radial Suite Software (Mac OS 10.4.11 or 10.5.8): Download or Go To: File Type: Disk image of Radial SeaSonde10R5 RadialApps.
• 2.1 How to get R.app. R.APP is part of the binary distribution of R for Mac OS X available from CRAN.That distribution consists of one package containing the R framework and R.APP. Development versions of R.APP are made available on daily basis in the form of a disk image containing the R.APP itself. See the Mac OS X pages on CRAN for detail how to obtain such snapshots (currently at http.

• Requirements
• Automatically installed dependencies
• Configuration and Compilation
  • Advanced Configuration
• Troubleshooting

Quick Compilation Guide

This is a quick guide to compile and install a basic version of SU2. For more information on the requirements and a more detailed description of the build system continue reading the rest of this page.

Short summary of the minimal requirements:

• C/C++ compiler
• Python 3

Note: all other necessary build tools and dependencies are shipped with the source code or are downloaded automatically.

If you have these tools installed, you can create a configuration using the meson.py found in the root source code folder:

Use ninja to compile and install the code

Requirements

Compilers

Installing SU2 from source requires a C++ compiler. The GNU compilers (gcc/g++) are open-source, widely used, and reliable for building SU2. The Intel compiler set has been optimized to run on Intel hardware and has also been used successfully by the development team to build the source code, though it is commercially licensed. The Apple LLVM compiler (Clang) is also commonly used by the developers.

• GNU gcc / g++
• Intel icc / icpc
• Apple LLVM (clang)

Note: SU2 uses some C++11 features, that means at least GCC >= v4.7, Clang >= v3.0 or Intel C++ >= v12.0 is necessary.

MPI

In order to build SU2 with parallel support, you need a suitable MPI installation on your machine. During the configuration the build tool does a check (using pkg-config) and enables MPI support. If no installation is found, a serial version of SU2 will be compiled.It is possible to force the MPI mode with the meson option -Dcustom-mpi=true, it is then assumed that the compilers and/or the environment variables have the right flags, include directories, and linker arguments.

Note: Problems have been reported with MPICH where the build system does not detect the MPI installation, this can be solved using the meson options -Dcustom-mpi=true -Dextra-deps=mpich. If MPI was installed in a user directory, ensure also that the environment variable PKG_CONFIG_PATH is correctly setup.

Python

SU2 requires Python 3 for compilation and for running the python scripts. Make sure that you have properly set up the python3 executables in your environment.

Optional: swig and mpi4py

If you want to use the python wrapper capabilities, also swig and mpi4py are required. On Linuxswig should be available in the package manager of your distribution and mpi4py can be installed using pip.

On Mac OS X, you can use the Homebrew package manager. Once it is installed on your system, you can install Swig by running:

Install mpi4py with Python pip using easy install:

Automatically installed dependencies

The following dependencies are automatically downloaded (or initialized if source code was cloned using git) during the configuration.

Meson and Ninja

The build system of SU2 is based on a combination of meson (as the front-end) and ninja (as the back-end). Meson is an open source build system meant to be both extremely fast, and, even more importantly, as user friendly as possible. Ninja is a small low-level build system with a focus on speed.

CoDiPack and MeDiPack

In order to use the discrete adjoint solver the compilation requires two additional (header-only) libraries. CoDi provides the AD datatype and MeDi provides the infrastructure for the MPI communication when the reverse mode of AD is used.

Configuration and Compilation

Like mentioned above, SU2 uses meson and ninja for configuration and compilation, respectively. A configuration using meson is generated first and then an invocation of ninja is used to compile SU2 with this configuration.

Basic Configuration

In the root folder of the sources you will find a python script called meson.py. This script generates a configuration. It will also check whether all dependencies are found and downloads some of them if necessary see previous section.

Note: For the following steps you can also use preinstalled versions of meson and ninja available on your machine. Just replace the ./meson.py and ./ninja calls with the binaries of the respective installations. However, this way you have to manually make sure that the correct versions of CoDiPack and MeDiPack are placed in the externals/ folders.

The only required argument for meson.py is a name of a directory where it should store the configuration. You can have multiple configurations in different folders next to each other. To generate a basic configuration that will be stored in the folder build use

Options can be passed to the script to enable or disable different features of SU2. Below you find a list of project options and their default values:

For example to enable AD support pass the option to the meson.py script along with a value:

To set a installation directory for the binaries and python scripts, use the --prefix option, e.g.:

If you are not interested in setting custom compiler flags and other options you can now go directly to the Compilation section, otherwise continue reading the next section.

Advanced Configuration

In general meson appends flags set with the environment variable CXXFLAGS. It is however recommended to use mesons built-in options to set debug mode, warning levels and optimizations. All options can be found here or by using ./meson.py configure. An already created configuration can be modified by using the --reconfigure flag, e.g.:

Note that it is only possible to change one option at once.

Build Type

The debug mode can be enabled by using the --buildtype=debug option. This adds -g flag and disables all compiler optimizations. If you still want to have optimizations, use --buildtype=debugoptimized. The default build type is release.

Compiler optimizations

The optimization level can be set with --optimization=level, where level corresponds to a number between 0 (no optimization) and 3 (highest level of optimizations) which is the default.However, that may not result in optimum performance, for example with the GNU compilers level 2 and the extra flag -funroll-loops results in better performance for most problems.

Some numerical schemes support vectorization (see which ones in the Convective Schemes page), to make the most out of it the compiler needs to be informed of the target CPU architecture, so it knows what “kind of vectorization” it can generate (256 or 512bit, 128bit being the default).With gcc, clang, and icc this can be done via the -march=?? and -mtune=?? options, where ?? needs to be set appropriately e.g. skylake, ryzen, etc., these flags can be passed to the compiler by setting CXXFLAGS before first running meson (which will print some messages acknowledging the flags).

Warning level

The warning level can be set with --warnlevel=level, where level corresponds to a number between 0 (no warnings) and 3 (highest level of warning output). Level 1 corresponds to -Wall, level 2 to -Wall -Wextra and level 3 to -Wall -Wextra -Wpedantic. The default level is 0.

Note: The warning flags -Wno-unused-parameter, -Wno-empty-body and -Wno-format-security are always added by default.

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Linear algebra options

Compiling with support for a BLAS library (-Denable-mkl or -Denable-openblas) is highly recommended if you use the high order finite element solver, or radial basis function (RBF) interpolation in fluid structure interaction problems.Linear solvers and preconditioners can be accelerated with option -Denable-mixedprec=true, which will switch those computations to single precision while all other aspects of SU2 remain in double precision, for fluid simulations this does not reduce accuracy since the solution is iterative. However, large structural FEA problems may be adversely affected.To a lesser extent MKL 2019 is also used to accelerate (~5%) sparse linear algebra operations.-Denable-mkl takes precedence over -Denable-openblas, the system tries to find MKL via pkg-config, if that fails it will then look for MKL in /opt/intel/mkl, this can be changed via option -Dmkl_root.When OpenBLAS support is requested the build system uses pkg-config to search the system for package openblas, option -Dblas-name, if the library was built from source it may be necessary to set the environment variable PKG_CONFIG_PATH.

For large structural FEA problems on highly anisotropic grids iterative linear solvers might fail. Version 7 introduces experimental support for the direct sparse solver PaStiX (-Denable-pastix) see detailed instructions in TestCases/pastix_support/readme.txt.

If the use of BLAS is restricted to RBF interpolation, parallel versions of OpenBLAS can be used, the number of threads will then have to be controlled via the appropriate environment variable (consult the OpenBLAS documentation). Otherwise sequential BLAS should be used.

Note: The BLAS library needs to provide support for LAPACK functions. If this is not the case, the linker will fail with “undefined reference” errors, this problem can be solved by installing LAPACK and specifying it as an extra dependency when running meson.py using -Dextra-deps=lapack (this uses pkg-config, use commas to separate the names of multiple extra dependencies).

Compilation

Finally to compile and install SU2 use

where build is again a folder with a configuration created using a call to meson.py described in the previous section. By default ninja uses all available cores in your system for the compilation. You can set the number of cores manually by using the -jN flag, where N is the number of cores you want to use.

Setting environment variables

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Set the environment variables to use the executables from any directory without explicity specifying the path as described in the installation section.

Troubleshooting

MPI installation is not found

Meson looks for an MPI installation using pkg-config. But if your MPI implementation does not provide them, it will search for the standard wrapper executables, mpic, mpicxx, mpic++. If these are not in your path, they can be specified by setting the standard environment variables MPICC, MPICXX during configuration.

mpi4py library is not found

Meson imports the mpi4py module and searches for the include path. If it is installed in a custom location, make sure to add this path to the PYTHONPATH environment variable prior calling meson.py.

Ninja compiles but fails to install

If building on a cluster that uses a NFS filesystem, ninja may finish the compilation but fail to install with an error such as:

This is a known bug in earlier versions of Python 3. Try upgrading to Python >= 3.7 then rerun ninja.

CCTBX¶

At this point the CCTBX package is only needed tosimulate powder diffraction patterns with the powderx utility.

For powderx, it will be necessary to have the full CCTBX package installedand the cctbx.python executable in your path. Downloads are availablefor numerous operating systems, including Mac OS X, Windows 7 & XP, andseveral flavors of Linux. Additionally, it is possible to buildCCTBX from a source bundle or, for the more ambitious,the SVN repository at sourceforge.

Because of the unusual python interpreter behavior forced bythe cctbx.python executable,it is necessary to have all dependencies (except CCTBX itself)installed both to the cctbx.python interpreter and to a system pythoninterpreter (e.g. at /usr/local/bin/python).

The difficulty here might be in using pip with CCTBX ifyou are using one of the pre-built CCTBX distributionscalled “cctbx+Python” or “cctbx plus”,wherein the CCTBX distribution pythonwill be different from your system python.

One way to overcome this difficulty is simply to install all of thepackages twice, once to the CCTBX pythonand once to the system python.

Installing to the system python interpreter is easy with pip.For example, to install the pyfscache package:

Note that “%” is the prompt and is not actually typed.

For the CCTBX python, things are slightly more complicated. First, whenfollowing the pip installation instructions, use cctbx.pythonwith ez_setup.py and get-pip.py. For example:

Once this latter command completes, you’ll see among the final lines of outputsomething similar to:

The directory path in this output points to the location of CCTBX’s pip,which can be used directly. Using pyfscache as an example: