Table of contents

• Installation
• Detailed installation instructions
  • Cluster-specific modules
  • Installing dependencies manually
    • Other options and further information
  • Advanced solver options (compile time)
  • Compiling on Mac
  • Troubleshooting
• Configuring and running simulations
  • Quick introduction
  • Detailed description
• Visualization using Paraview
  • Visualizing shapes
  • Visualizing flow

Installation

This repository contains submodules, clone with:

git clone --recursive https://github.com/cselab/CubismUP_3D.git

To install Python dependencies, run:

python3 -m pip install --user -r requirements.txt

To compile, run:

mkdir -p build
cd build
cmake ..
make

This process should take few minutes. If that doesn't work, read the Detailed installation instructions below.

Tests

To run the tests, starting from the repository root folder, execute the following:

cd build
ctest
pytest ../tests/python/

Detailed installation instructions

CubismUP uses CMake to automatically detect required dependencies and to compile the code. If the dependencies are missing, they can be easily downloaded, compiled and locally installed using the provided script install_dependencies.sh (details below). If the dependencies are already available, but CMake does not detect them, appropriate environment variables specifying their path have to be defined.

CubismUP requires the following 3rd party libraries:

Dependency	Environment variable pointing to the existing installation
HDF5 (1.10.1) (*)	$HDF5_ROOT or $HDF5ROOT
GSL (2.1) (*)	$GSL_ROOT_DIR
MPI	See instructions (**)

(*) Tested with the listed versions, higher versions probably work too.
(**) Especially if installing the dependencies, make sure that mpicc points to a MPI-compatible C compiler, and mpic++ to a MPI-compatible C++ compiler.

We suggest first trying to compile the code with the libraries already installed on the target machine or cluster. If available, dependencies may be loaded with module load ... or module load new .... If module load is not available, but libraries are installed, set the above mentioned environment variables. Installing all dependencies may require up to half an hour.

Cluster-specific modules

Piz Daint:

module swap PrgEnv-cray PrgEnv-gnu
module load daint-gpu cray-python/3.6.1.1 cray-hdf5-parallel cray-petsc/3.8.4.0 cudatoolkit/9.2.148_3.19-6.0.7.1_2.1__g3d9acc8 CrayGNU/.18.08 GSL/2.5-CrayGNU-18.08 CMake/3.12.0

and follow the instructions in README.md

Euler:

First, load the following modules:

module load new modules gcc/6.3.0 open_mpi/2.1.1 binutils/2.25 gsl/1.16 hwloc/1.11.0

Then, manually install HDF5 using the provided script and add the required paths to your .bashrc:

./install_dependencies.sh --hdf5
echo "export HDF5_ROOT=$PWD/dependencies/build/hdf5-1.10.1-parallel/" >> ~/.bashrc
echo "export LD_LIBRARY_PATH=\$HDF5_ROOT/lib:\$LD_LIBRARY_PATH" >> ~/.bashrc

Installing dependencies manually

To install the missing dependencies, run the following code (from the repository root folder):

# Step 1: Install dependencies
./install_dependencies.sh --all

# Step 2: Append the export commands to ~/.bashrc or ~/.bash_profile (only for CMake)
./install_dependencies.sh --export >> ~/.bashrc
# or
./install_dependencies.sh --export >> ~/.bash_profile  # (Mac)

# Step 3:
source ~/.bashrc
# or
source ~/.bash_profile  # (Mac)

# Step 4: Try again
cd build
cmake ..
make

Other options and further information

The --all flag installs all dependencies known to the script (HDF5, GSL, as well as CMake itself). If only some dependencies are missing, pass instead flags like --cmake and others. Run ./install_dependencies.sh to get the full list of available flags.

All dependencies are installed in the folder dependencies/. Full installation takes 5-15 minutes, depending on the machine. To specify the number of parallel jobs in the internal make, write

JOBS=10 ./install_dependencies.sh [flags]

The default number of jobs is equal to the number of physical cores.

Advanced solver options (compile time)

The default options are sufficient for the average user. However, advanced users can customize the generated executable with the following options:

Option	Default	Description
CUP_ASYNC_DUMP	ON	This option enables asynchronous data dumps. If you run on a system with limited memory, this option can be disabled to reduce the memory footprint. Available only if MPI implementation is multithreaded (detected automatically).
CUP_DUMP_SURFACE_BINARY	OFF	Enabling this option dumps additional surface data for each obstacle in binary format.
CUP_SINGLE_PRECISION	OFF	Run simulation in single precison.
CUP_HDF5_DOUBLE_PRECISION	OFF	Dump simulation snapshots in double precision.
CUP_RK2	OFF	Enables a second order Runge-Kutta time integrator.

These options can be enabled either on the command line with, e.g., cmake -DCUP_ASYNC_DUMP=ON or with TUIs such as ccmake.

Compiling on Mac

The default compiler clang on Mac does not support OpenMP. It is therefore necessary either to install clang OpenMP extension or to install e.g. g++ compiler. The following snippet shows how to compile with g++-8:

mkdir -p build
cd build
CC=gcc-8 CXX=g++-8 cmake ..
OMPI_CC=gcc-8 MPICH_CC=gcc-8 OMPI_CXX=g++-8 MPICH_CXX=g++-8 make

Troubleshooting

If cmake .. keeps failing, delete the file CMakeCache.txt and try again.

Configuring and running simulations

Quick introduction

Perform the following steps:

1. Open launch/config_form.html in a web browser.

2. Configure the simulation.

3. Download the runscript (Save) or copy-paste it into a file.

4. Put the script into the launch/ folder (recommended, otherwise see below).

5. chmod +x scriptname.sh

6. Run the script.

Detailed description

For convenience, we have prepared a web form for customizing simulation settings, such as grid resolution, list of objects with their properties et cetera. The form can be found in launch/config_form.html and can be opened with any modern browser. Note that internet connection is required, as the web page uses external libraries for rendering and handling the form.

After setting up the system, or loading one of the presets, the generated runscript has to be run.

By default, the form assumes that the runscript will be located in the launch/ folder. If not, adjust the Repository root path to the full path of the repository. If necessary, adjust also the Results path. For debugging and reproducibility purposes, the runscript makes a copy of the executable and the current source code to the results path. Additionally, the script copies itself, together with a compact representation of all form data, that can be later imported back (the last line of the generated runscript).

Visualization using Paraview

This is a very quick overview on how to visualize results using ParaView (5.5.0). For more complex visualization, please refer to the ParaView documentation.

Visualizing shapes

1. Open restart_*-chi.xmf. Select Xdmf Reader option.

2. Press Apply in the Properties window (left).

3. If the snapshot is relatively small (< 100 MB), you can use the expensive Volume option. Change Outline to Volume.

4. If the snapshots are large, use Slice option. Go to Filters -> Common -> Slice. Change Normal to (0, 0, 1). Unselect Show Plane. Click Apply.

5. If the objects are not static, press the Play button to visualize the movement in time.

Visualizing flow

1. Open restart_*-vel.xmf. Select Xdmf Reader option.

2. Press Apply.

3. Go to Filters -> Common -> Slice. Change Normal to (0, 0, 1). Unselect Show Plane. Click Apply.

4. In the toolbar, change Solid Color to data. Select Magnitude for the velocity magnitude, or X, Y or Z for respective components.

5. It may be necessary to adjust the color scale. Click Rescale to Data Range icon (possibly 2nd row, 4th icon).

6. Press Play.