Alsvinn

Alsvinn is a toolset consisting of a finite volume simulator (FVM) and modules for uncertaintity quantifications (UQ). All the major operations can be computed on either a multi-core CPU or an NVIDIA GPU (through CUDA). It also supports cluster configurations consisting of either CPUs or GPUs. It exhibits excellent scaling.

Alsvinn is maintained by Kjetil Olsen Lye at ETH Zurich. We want Alsvinn to be easy to use, so if you have issues compiling or running it, please don't hesitate to leave an issue.

Alsvinn is also available as a Docker container. See below for how to run with Docker or Singularity without any installation needed.

Supported equations

The Compressible Euler Equations
The scalar Burgers' Equation
A scalar cubic conservation law
Buckley-Leverett

It is also possible to add new equations without issue (tutorial coming soon).

Initial data

Initial data can easily be specified through a python script. For instance, the Sod Shock tube can be specified as

if x < 0.0:
    rho = 1.
    ux = 0.
    p = 1.0
else:
    rho = .125
    ux = 0.
    p = 0.1

Notable implemented initial data

While it's easy to implement new configurations, we already have a wide variety of configuraitons implemented, including:

Kelvin-Helmholtz instability
Richtmeyer-Meshkov instability
Sod shock tube
Cloudshock interaction
Shockvortex
Fractional Brownian motion

Requirements

C++14 compiler (tested with clang and gcc)
gtest (optional)
boost (including boost-numpy)
python Tested with 3.7, should work with 2.7
hdf5,
netcdf
parallel-netcdf NOTE: This is not the same as building netcdf with parallel support
doxygen (optional)
NVIDIA CUDA (optional)

see Installing necessary software for more information.

Cloning

This project uses a git submodule, the easiest way to clone the repository is by

git clone --recursive https://github.com/alsvinn/alsvinn.git

Compiling

Should be as easy as running (for advanced cmake-users: the location of the build folder can be arbitrary)

mkdir build
cd build
cmake ..

note that you should probably run it with -DCMAKE_BUILD_TYPE=Release, ie

mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release

Compiling with CUDA

If you do not have a CUDA device on your computer, or if you do not have CUDA installed, you should run CMAKE with the -DALSVINN_USE_CUDA=OFF option.

Running tests

Before you try to run the simulations, it's probably a good idea to validate that the build was successful by running the unittests. From the build folder, run

./test/library_test/alstest
bash run_mpi_tests.sh

Running alsvinn

The basic input of alsvinn are .xml-files specifying the different options. You can view the different examples under examples. The initial data is usually specified in a .py-file (named in the xml file).

Deterministic runs

You make a deterministic run by running the alsvinncli utility. From the build folder, run

./alsvinncli/alsvinncli <path to xml file>

it has some options for mpi parallelization (for all options run alsvinncli/alsvinncli --help). To run with MPI support, run eg

 mpirun -np <number of processes> ./alsvinncli/alsvinncli --multi-x <number of procs in x direction> path-to-xml.xml

UQ run

You make a UQ run by running the alsuqcli utility. From the build folder, run

./alsuqcli/alsuqcli <path to xml file>

it has some options for mpi parallelization (for all options run alsuqcli/alsuqcli --help). To run with MPI support, run eg

 mpirun -np <number of processes> ./alsuqcli/alsuqcli --multi-sample <number of procs in sample direction> path-to-xml.xml

Output files

Most output is saved as a NetCDF file. These can easily be read in programming languages such as python.

Python scripts

There is a simple python API for running alsvinn under the python folder. Check out the readme file there for more information.

Running in Docker or Singularity

To run alsvinn using Docker and get the output to the current directory, all you have to do is

docker run --rm -v $(pwd):$(pwd) -w $(pwd) alsvinn/alsvinn_cuda /examples/kelvinhelmholtz/kelvinhelmholtz.xml

NOTE Replace alsvinn_cuda with alsvinn_cpu if you are running a CPU only setup.

NOTE You can also make your own configuration files and specify them instead of /examples/kelvinhelmholtz/kelvinhelmholtz.xml

Running with Singularity

On LSF systems with Singularity installed, Alsvinn can be run as

# Cluster WITHOUT GPUs
bsub -R singularity singularity run -B $(pwd):$(pwd) \
     docker://alsvinn/alsvinn_cpu 
     /examples/kelvinhelmholtz/kelvinhelmholtz.xml
 
# Clusters WITH GPUs:
bsub <other options to get GPU> \
     -R singularity singularity run --nv -B $(pwd):$(pwd) \
     docker://alsvinn/alsvinn_cuda \
     /examples/kelvinhelmholtz/kelvinhelmholtz.xml

Note that it is probably a good idea to pull the image first and then run the downloaded image, eg.

 bsub -R light -J downloading_image -R singularity \
     singularity pull docker://alsvinn/alsvinn_cuda
     
 bsub -R singularity -w "done(downloading_image)" \
     singularity run --nv -B $(pwd):$(pwd) \
     alsvinn_cuda.simg \
     /examples/kelvinhelmholtz/kelvinhelmholtz.xml

To run with MPI, we strongly recommend using MVAPICH2 (or any mpich ABI compatible implementation) on the cluster. On a lot of cluster, you would do something like:

# Or whatver the mvapich2 module is called
module load mvapich2
bsub -R light -J downloading_image -R singularity \
     singularity pull docker://alsvinn/alsvinn_cuda
     
 bsub -R singularity -w "done(downloading_image)" \
     mpirun -np <number of cores> singularity run --nv -B $(pwd):$(pwd) \
     alsvinn_cuda.simg  --multi-y <number of cores> \
     /examples/kelvinhelmholtz/kelvinhelmholtz.xml

Note about Boost and Python

Be sure the python version you link with is the same as the the python version that was used with boost. Usually, this is taken care of by using the libraries corresponding to the python executable (this is especially true for CSCS Daint. On Euler you probably have to build boost with numpy support yourself).

Note about GCC versions and CUDA

CUDA on Linux does at the moment not support GCC versions later than 6, therefore, to build with GPU support, you need to set the compiler to GCC-6.

After you have installed GCC-6 on your distribution, you can set the C/C++ compiler as

cmake .. -DCMAKE_CXX_COMPILER=`which g++-6` -DCMAKE_C_COMPILER=`which gcc-6`

Notes on Windows

You will need to download ghostcript in order for doxygen to work. Download from

 https://code.google.com/p/ghostscript/downloads/detail?name=gs910w32.exe&can=2&q=

Sometimes cmake finds libhdf5 as the dll and not the lib-file. Check that the HDF5_C_LIBRARY is set to hdf5.lib in cmake

gtest should be built with "-Dgtest_force_shared_crt=ON" via cmake. This will ensure that there is no library compability issues.

You will also want to compile a Debug AND Release version of Gtest, and set each library manually in Cmake.

If you have installed Anaconda AND HDF5, make sure the right HDF5 version is picked (it is irrelevant which one you pick, but the two can intertwine in one or several of: include directories, libraries and Path (for dll).)

Installing necessary software

Ubuntu 19.04

Simply run

sudo apt-get update
sudo apt-get install libnetcdf-mpi-dev libnetcdf-dev
	cmake python3 python3-matplotlib python3-numpy python3-scipy git \
libopenmpi-dev gcc g++ libgtest-dev libboost-all-dev doxygen \
    build-essential graphviz libhdf5-mpi-dev libpnetcdf-dev

if you want CUDA (GPU) support, you have to install the CUDA packages as well

sudo apt-get install nvidia-cuda-dev nvidia-cuda-toolkit

Compiling with CUDA

Compile with

cd alsvinn/
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DALSVINN_PYTHON_VERSION=3.7
make

(If you are using a different python version, replace 3.7 with whatever python version you are using.)

Compiling without CUDA

Compile with

cd alsvinn/
mkdir build
cd build
cmake .. -DALSVINN_USE_CUDA=OFF -DCMAKE_BUILD_TYPE=Release \
    -DALSVINN_PYTHON_VERSION=3.7
make

(If you are using a different python version, replace 3.7 with whatever python version you are using.)

Arch Linux

Pacman should have all needed packages, simply install

pacman -S git cmake cuda g++ netcdf parallel-netcdf-openmpi hdf5-openmpi doxygen gtest boost python

Manually installing parallel-netcdf

On some old distributions, you have to manually compile parallel-netcdf. You can download the latest version from the webpage and install using autotools. Notice that you should compile with the CFLAG -fPIC. A quick way to install parallel-netcdf would be

wget http://cucis.ece.northwestern.edu/projects/PnetCDF/Release/parallel-netcdf-1.9.0.tar.gz
tar xvf parallel-netcdf-1.9.0.tar.gz
cd parallel-netcdf-1.9.0
export CFLAGS='-fPIC'
./configure --prefix=<some location>
make install

remember to specify <some location> to -DCMAKE_PREFIX_PATH afterwards

Using alsvinn as a library

While it is not recommend, there are guides available on how to run alsvinn as a standalone library.

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alsvinn / alsvinn

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Alsvinn

Supported equations

Initial data

Notable implemented initial data

Requirements

Cloning

Compiling

Compiling with CUDA

Running tests

Running alsvinn

Deterministic runs

UQ run

Output files

Python scripts

Running in Docker or Singularity

Running with Singularity

Note about Boost and Python

Note about GCC versions and CUDA

Notes on Windows

Installing necessary software

Ubuntu 19.04

Compiling with CUDA

Compiling without CUDA

Arch Linux

Manually installing parallel-netcdf

Using alsvinn as a library