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mwiesenberger / feltor

Numerical methods for edge and SOL blob and turbulence simulations
http://feltor-dev.github.io
MIT License
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= Welcome to the FELTOR project! :source-highlighter: pygments :toc: macro

(Visit our project https://feltor-dev.github.io[Homepage] for documentations, user-guides, examples and more)

image::3dpic.jpg[3dsimulation]

FELTOR (Full-F ELectromagnetic code in TORoidal geometry) is both a numerical library and a scientific software package built on top of it.

Its main physical target are plasma edge and scrape-off layer (gyro-)fluid simulations. The numerical methods centre around discontinuous Galerkin methods on structured grids. Our core level functions are parallelized for a variety of hardware from multi-core cpu to hybrid MPI{plus}GPU, which makes the library incredibly fast. Note that the library ships with a multitude of test and benchmark programs.

https://zenodo.org/badge/latestdoi/14143578[image:https://zenodo.org/badge/14143578.svg[DOI]] link:LICENSE[image:https://img.shields.io/badge/License-MIT-yellow.svg[License: MIT]] https://github.com/feltor-dev/feltor/releases/latest[image:https://img.shields.io/github/v/release/feltor-dev/feltor[GitHub release (latest by date)]]

toc::[]

== 1. Quick start guide [[sec_quickstart]] This guide discusses how to setup, test and benchmark the FELTOR library installation. Please read it before you proceed to the https://feltor-dev.github.io/user-guide[user guide] to learn how to use the library in your own programs. We also present how to use the FELTOR software package, which requires additional external libraries to be installed on the system.

If you run into trouble during the setup, there is a chance that we already know of the problem and have an entry in <<sec_troubleshooting,troubleshooting>> page. If you cannot find a solution there, please inform us of the issue.

The first part assumes a Linux operating system. If you want to work on Windows, jump to <<sec_windows,Using Feltor on Windows>>.

=== System setup

Open a terminal and clone the repository into any folder you like

[source,sh]

git clone https://www.github.com/feltor-dev/feltor

You also need to clone https://github.com/nvidia/thrust[thrust] and https://github.com/cusplibrary/cusplibrary[cusp] distributed under the Apache-2.0 license. Also, we need Agner Fog's https://github.com/vectorclass/version1[vcl] library (Apache 2.0). So again in a folder of your choice

[source,sh]

git clone https://www.github.com/nvidia/thrust git clone https://www.github.com/cusplibrary/cusplibrary git clone https://www.github.com/vectorclass/version1 vcl

We need to checkout an older version of thrust compatible with cusp

cd thrust git checkout 1.9.3

Our code only depends on external libraries that are themselves openly available. We use version1 of the vectorclass library, as version2 requires C{plus}{plus}-17 and does not work with the intel compiler.

.System requirements [[tab_requirements]]

[cols='3,10,14',options="header"] |======================================================================= | | Minimum system requirements | Recommended system requirements | CPU | Any |support for AVX and FMA instruction set | Compiler| gcc-5.1 or msvc-15 or icc-17.0 (C{plus}{plus}-14 standard)| gcc-9.3, OpenMP-4 support, avx, fma instruction set flags | GPU | - | NVidia GPU with compute-capability > 6 and nvcc-11.0 | MPI | - | mpi installation compatible with compiler (ideally cuda-aware in case hybrid MPI+GPU is the target system) |=======================================================================

Our GPU backend uses the https://developer.nvidia.com/cuda-zone[Nvidia-CUDA] programming environment and in order to compile and run a program for a GPU a user needs at least the nvcc-8.0 compiler (available free of charge) and a NVidia GPU. However, we explicitly note here that due to the modular design of our software a user does not have to possess a GPU nor the nvcc compiler. The CPU version of the backend is equally valid and provides the same functionality. Analogously, an MPI installation is only required if the user targets a distributed memory system.

=== Running a FELTOR test or benchmark program

In order to compile one of the many test and benchmark codes inside the FELTOR library you need to tell the FELTOR configuration where the external libraries are located on your computer. The default way to do this is to go into your HOME directory, make an include directory and link the paths in this directory

[source,sh]

cd ~ mkdir include cd include ln -s path/to/thrust/thrust # Yes, thrust is there twice! ln -s path/to/cusplibrary/cusp ln -s path/to/vcl

If you do not like this, you can also set the include paths in your own config file as described link:config/README.md[here].

Now let us compile the first benchmark program.

[source,sh]

cd path/to/feltor/inc/dg

make blas_b device=cpu #(for a single thread CPU version)

or

make blas_b device=omp #(for an OpenMP version)

or

make blas_b device=gpu #(if you have a GPU and nvcc )

Run the code with

[source,sh]

./blas_b

and when prompted for input vector sizes type for example 3 100 100 10 which makes a grid with 3 polynomial coefficients, 100 cells in x, 100 cells in y and 10 in z. If you compiled for OpenMP, you can set the number of threads with e.g. export OMP_NUM_THREADS=4.

This is a benchmark program to benchmark various elemental functions the library is built on. Go ahead and vary the input parameters and see how your hardware performs. You can compile and run any other program that ends in _t.cu (test programs) or _b.cu (benchmark programs) in feltor/inc/dg in this way.

Now, let us test the mpi setup

You can of course skip this if you don't have mpi installed on your computer. If you intend to use the MPI backend, an implementation library of the mpi standard is required. Per default mpic++ is used for compilation.

[source,sh]

cd path/to/feltor/inc/dg

make blas_mpib device=cpu # (for MPI+CPU)

or

make blas_mpib device=omp # (for MPI+OpenMP)

or

make blas_mpib device=gpu # (for MPI+GPU, requires CUDA-aware MPI installation)

Run the code with $ mpirun -n '# of procs' ./blas_mpib then tell how many process you want to use in the x-, y- and z- direction, for example: 2 2 1 (i.e. 2 procs in x, 2 procs in y and 1 in z; total number of procs is 4) when prompted for input vector sizes type for example 3 100 100 10 (number of cells divided by number of procs must be an integer number). If you compiled for MPI{plus}OpenMP, you can set the number of OpenMP threads with e.g. export OMP_NUM_THREADS=2.

=== Running a FELTOR simulation

Now, we want to compile and run a simulation program. To this end, we have to download and install some additional libraries for I/O-operations.

First, we need to install jsoncpp (distributed under the MIT License), which on linux is available as libjsoncpp-dev through the package managment system. For a manual build check the instructions on https://www.github.com/open-source-parsers/jsoncpp[JsonCpp]. [source,sh]

You may have to manually link the include path

cd ~/include ln -s /usr/include/jsoncpp/json

For data output we use the http://www.unidata.ucar.edu/software/netcdf/[NetCDF-C] library under an MIT - like license (we use the netcdf-4 file format). The underlying https://www.hdfgroup.org/HDF5/[HDF5] library also uses a very permissive license. Both can be installed easily on Linux through the libnetcdf-dev and libhdf5-dev packages. For a manual build follow the build instructions in the https://www.unidata.ucar.edu/software/netcdf/docs/getting_and_building_netcdf.html[netcdf-documentation]. Note that by default we use the serial netcdf and hdf5 libraries alson in the mpi versions of applications.

Some desktop applications in FELTOR use the https://github.com/mwiesenberger/draw[draw library] (developed by us also under MIT), which depends on http://www.glfw.org[glfw3], an OpenGL development library under a BSD-like license. There is a libglfw3-dev package for convenient installation. Again, link path/to/draw in the include folder.

If you are on a HPC cluster, you may need to set INCLUDE and LIB variables manually. For details on how FELTOR's Makefiles are configured please see the link:config/README.md[config] file. There are also examples of some existing Makefiles in the same folder.

We are now ready to compile and run a simulation program

[source,sh]

cd path/to/feltor/src/toefl # or any other project in the src folder

make toefl device=gpu # (compile for gpu, cpu or omp) cp input/default.json inputfile.json # create an inputfile ./toefl inputfile.json # (behold a live simulation with glfw output on screen)

or

make toefl_hpc device=gpu # (compile for gpu, cpu or omp) cp input/default_hpc.json inputfile_hpc.json # create an inputfile ./toefl_hpc inputfile_hpc.json outputfile.nc # (a single node simulation with output stored in a file)

or

make toefl_mpi device=omp # (compile for gpu, cpu or omp) export OMP_NUM_THREADS=2 # (set OpenMP thread number to 1 for pure MPI) echo 2 2 | mpirun -n 4 ./toefl_mpi inputfile_hpc.json outputfile.nc

(a multi node simulation with now in total 8 threads with output stored in a file)

The mpi program will wait for you to type the number of processes in x and y direction before

running. That is why the echo is there.

Default input files are located in path/to/feltor/src/toefl/input. All three programs solve the same equations. The technical documentation on what equations are discretized, input/output parameters, etc. can be generated as a pdf with make doc in the path/to/feltor/src/toefl directory.

=== Using FELTOR as a library

FELTOR's library is the dg-library (from discontinuous Galerkin). Note that the library is header-only, which means that you just have to include the relevant header(s) and you're good to go. For example in the following program we compute the square L2 norm of a function:

.test.cpp [[test_cpp]] [source,c++]

include

//include the basic dg-library

include "dg/algorithm.h"

double function(double x, double y){return exp(x)*exp(y);} int main() { //create a 2d discretization of [0,2]x[0,2] with 3 polynomial coefficients dg::CartesianGrid2d g2d( 0, 2, 0, 2, 3, 20, 20); //discretize a function on this grid const dg::DVec x = dg::evaluate( function, g2d); //create the volume element const dg::DVec vol2d = dg::create::volume( g2d); //compute the square L2 norm on the device double norm = dg::blas2::dot( x, vol2d, x); // norm is now: (exp(4)-exp(0))^2/4 std::cout << norm <<std::endl; return 0; }

To compile and run this code for a GPU use (assuming the external libraries are linked in the include folder as described above)

[source,sh]

nvcc -x cu -std=c++14 --extended-lambda -Ipath/to/feltor/inc -Ipath/to/include test.cpp -o test ./test

Or if you want to use OpenMP and gcc instead of CUDA for the device functions you can also use

[source,sh]

g++ -std=c++14 -fopenmp -mavx -mfma -DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_OMP -Ipath/to/feltor/inc -Ipath/to/include test.cpp -o test export OMP_NUM_THREADS=4 ./test

If you do not want any parallelization, you can use a single thread version [source,sh]

g++ -std=c++14 -mavx -mfma -DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_CPP -Ipath/to/feltor/inc -Ipath/to/include test.cpp -o test ./test

If you want to use mpi, just include the MPI header before any other FELTOR header and use our convenient typedefs like so:

.test_mpi.cpp [source,c++]

include

//activate MPI in FELTOR

include "mpi.h"

include "dg/algorithm.h"

double function(double x, double y){return exp(x)exp(y);} int main(int argc, char argv[]) { //init MPI and create a 2d Cartesian Communicator assuming 4 MPI threads MPI_Init( &argc, &argv); int periods[2] = {true, true}, np[2] = {2,2}; MPI_Comm comm; MPI_Cart_create( MPI_COMM_WORLD, 2, np, periods, true, &comm); //create a 2d discretization of [0,2]x[0,2] with 3 polynomial coefficients dg::CartesianMPIGrid2d g2d( 0, 2, 0, 2, 3, 20, 20, comm); //discretize a function on this grid const dg::MDVec x = dg::evaluate( function, g2d); //create the volume element const dg::MDVec vol2d = dg::create::volume( g2d); //compute the square L2 norm double norm = dg::blas2::dot( x, vol2d, x); //on every thread norm is now: (exp(4)-exp(0))^2/4 //be a good MPI citizen and clean up MPI_Finalize(); return 0; }

Compile e.g. for a hybrid MPI {plus} OpenMP hardware platform with

[source,sh]

mpic++ -std=c++14 -mavx -mfma -fopenmp -DTHRUST_DEVICE_SYSTEM=THRUST_DEVICE_SYSTEM_OMP -Ipath/to/feltor/inc -Ipath/to/include test_mpi.cpp -o test_mpi export OMP_NUM_THREADS=2 mpirun -n 4 ./test_mpi

Note the striking similarity to the previous program. Especially the line calling the dot function did not change at all. The compiler chooses the correct implementation for you! This is a first example of a container free numerical algorithm.

=== Using FELTOR's Makefile system In order to simplify compilation in your own project we suggest to use Makefile and import the feltor configuration like so: [source,sh]

device=omp # default device FELTOR_PATH=path/to/feltor

use Feltor's configuration

include $(FELTOR_PATH)/config/default.mk include $(FELTOR_PATH)/config/*.mk include $(FELTOR_PATH)/config/devices/devices.mk

INCLUDE+=-I$(FELTOR_PATH)/inc/

See feltor/config/README for a list of all defined variables

For example here the feltor/src/toefl project is set up

The toefl.cpp program can be compiled in three different ways

and each can be compiled for various device values e.g.

make toefl device=gpu

all: toefl toefl_hpc toefl_mpi

shared memory version using glfw, jsoncpp and netcdf

toefl: toefl.cpp toefl.h parameters.h diag.h $(CC) $(OPT) $(CFLAGS) $< -o $@ $(INCLUDE) $(GLFLAGS) $(LIBS) $(JSONLIB) -DWITH_GLFW -g

shared memory version using jsoncpp and netcdf without glfw

toefl_hpc: toefl.cpp toefl.h parameters.h diag.h $(CC) $(OPT) $(CFLAGS) $< -o $@ $(INCLUDE) $(LIBS) $(JSONLIB) -g

mpi version using jsoncpp and netcdf without glfw

toefl_mpi: toefl.cpp toefl.h parameters.h diag.h $(MPICC) $(OPT) $(MPICFLAGS) $< -o $@ $(INCLUDE) $(LIBS) $(JSONLIB)

.PHONY: clean

clean: rm -f toefl toefl_hpc toefl_mpi

=== Using FELTOR on Windows [[sec_windows]] FELTOR has been developed mostly on Linux machines. Recently, it has become possible to develop also on Windows using https://visualstudio.microsoft.com/[Microsoft Visual Studio]. We here describe how to work with FELTOR's OpenMP shared memory backend on Windows.

Unfortunately, the msvc compiler only supports an outdated OpenMP version so consider a performance penalty of approximately a factor 2, when running the OpenMP backend on Windows.

===== Basic Setup

We suggest to install the Github desktop https://desktop.github.com. Please clone all four of the following URLs using File -> Clone repository... [source,sh]

https://www.github.com/feltor-dev/feltor https://www.github.com/nvidia/thrust

Checkout thrust to 1.9.3

https://www.github.com/cusplibrary/cusplibrary https://www.github.com/vectorclass/version1 # local path "vcl"

Please also have a look at the relevant <<tab_requirements, system requirements>> Table.

===== Creating a basic FELTOR Property Sheet In Visual Studio we suggest to create a Property Sheet for FELTOR. The Property Sheet can then be conveniently added to any project that includes the FELTOR library headers dg/algorithm.h and/or dg/geometries/geometries.h

That's it. You can add your Feltor Property Sheet to any new project by switching to the Property Manager again: click Add Existing Property Sheet and select FeltorPropertySheet.

We suggest that you generate a new project for each executable program.

===== Basic test In order to test the Feltor Property Sheet let us add a source file to the project and compile

If you want to prevent the console from closing on program exit, set Properties -> Linker -> System -> SubSystem -> Console (/SUBSYSTEM:CONSOLE) in your Property Sheet.

===== Advanced simulation projects

Our simulation codes typically depend on jsoncpp for parameter input, glfw3 for plotting or netcdf-4 for file output and come with a LaTeX file containing documentation. You will need to download these additional libraries and adapt the project properties accordingly.

jsoncpp::

Glfw3::

NetCDF::

LaTeX:: Install https://miktex.org/[MikTex] and https://texstudio.org[TeXstudio] (in that order) in order to be able to compile the tex file(s) of the documentation.

=== Troubleshooting [[sec_troubleshooting]] ===== I get a compile error: identifier "__thrust_compiler_fence" is undefined

This is an error of the unmaintained cusp that does not like the newly updated thrust version on github. Currently, you can either go back to version 1.9.3 in thrust:

cd path/to/thrust
git checkout 1.9.3

or alternatively there is a fix in cusp that can be accessed via

cd path/to/cusplibrary
git checkout cuda10

== 2. Documentation

The https://mwiesenberger.github.io/feltor/dg/html/modules.html[documentation] of the dg library was generated with http://www.doxygen.org[Doxygen] and LateX. You can generate a local version directly from source code. This depends on the doxygen, libjs-mathjax and graphviz packages and LateX (for equations). Type make doc in the folder path/to/feltor/doc and open index.html (a symbolic link to dg/html/modules.html) with your favorite browser. Finally, also note the documentations of https://thrust.github.io/doc/modules.html[thrust] and https://cusplibrary.github.io/[cusp].

We maintain tex files in every src folder for technical documentation, which can be compiled using pdflatex with make doc in the respective src folder.

== 3. Authors, Acknowledgements, Contributions

FELTOR has been developed by Matthias Wiesenberger and Markus Held. Please see the https://feltor-dev.github.io/about/[Acknowledgements] section on our homepage for a full list of contributors and funding. Contribution guidelines can be found in the link:CONTRIBUTING.md[CONTRIBUTING] file.

== License

This project is licensed under the MIT license - see link:LICENSE[LICENSE] for details.