This is the companion repository for the talk
Julia for adaptive high-order multi-physics simulations
Michael Schlottke-Lakemper
Numerical Analysis Seminar, Lund University
27th January 2021, 3:15pm CET
(see abstract below). Here you can find the presentation slides talk-julia-adaptive-multi-physics-simulations-20200127.pdf as well as the the Jupyter notebook getting_started_with_julia_and_trixi.ipynb, which was used during the talk for a live demonstration of Julia and Trixi.jl. Note that to play the video linked in the presentation, you also need to download the media/ directory and place it in the same folder as the PDF. There are also some additional Trixi elixirs (simulation setups) in the examples directory.
The easiest way to get started is to click on the Launch Binder badge above (or here). This launches the notebook for interactive use in your browser without the need to download or install anything locally.
In this case, you can skip the rest of this Getting started section. A Jupyter instance will be started automagically in the cloud via mybinder.org, and the notebook will loaded directly from this repository.
Note: Depending on current usage and available resources, it typically takes 1-2 minutes to launch a notebook with mybinder.org (sometimes a little longer), so try to remain patient. Similarly, the first two cells of the notebook take much longer to execute than usual (around 1.5 minutes for the first Trixi simulation and about 1 minute for the first plot), since Julia compiles all methods "just-ahead-of-time" at first use. Subsequent runs will be much faster.
Alternatively, you can also clone this repository and open the notebook on your local machine. This is recommended if you already have a Julia + Jupyter setup or if you plan to try out Julia anyways.
To obtain Julia, go to https://julialang.org/downloads/ and download the latest stable release (v1.5.3 as of 2021-01-14; neither use the LTS release nor Julia Pro). Then, follow the platform-specific instructions to install Julia on your machine. Note that there is no need to compile anything if you are using Linux, MacOS, or Windows.
After the installation, open a terminal and start the Julia REPL (i.e., the interactive prompt) with
juliaTo use the notebook, you also need to get the IJulia package, which provides a Julia backend for Jupyter. In the REPL, execute
using Pkg
Pkg.add("IJulia")to install IJulia.
To make the notebook fully reproducible, we have used Julia's package manager to pin all packages to a fixed release. This ensures that you always have a Julia environment in which all examples in this notebook work. Later you can always install the latest versions of Trixi and its dependencies by following the instructions in the Trixi documentation.
If you have not done it yet, clone the repository where this notebook is stored:
git clone git@github.com:trixi-framework/talk-2021-julia-adaptive-multi-physics-simulations.gitThen, navigate to your repository folder and install the required packages:
cd talk-2021-julia-adaptive-multi-physics-simulations
julia --project=. -e 'using Pkg; Pkg.instantiate()'This will download and build all required packages, including the ODE package
OrdinaryDiffEq, the visualization
package Plots, and of course
Trixi.
The --project=. argument tells Julia to use the Project.toml
and Manifest.toml files from this repository to figure out which packages to install.
To run the notebook, open a Julia REPL and execute
using IJulia
notebook()to start a Jupyter server in your browser. You can then navigate to the cloned
repository and launch the getting_started_with_julia_and_trixi.ipynb notebook.
At first invocation, it will ask if it should install a Julia-internal Jupyter
instance (which you should definitely do if you do not have an existing Jupyter
installation).
For more details, especially on how to use an existing Jupyter installation,
please refer to the IJulia documentation.
As an alternative to running the examples in the notebook directly, you may also just view the notebook statically by opening it within Jupyter NBViewer. Then, you can copy the individual code snippets and execute them in the REPL by navigating to the repository folder and starting Julia with
julia --project=.General note: Make sure that you execute the examples (either in the notebook or in the REPL) in order, at least for the first time. Both the notebook and the Julia REPL maintain an internal state and and some snippets depend on earlier statements having been executed.
Julia has been touted as a programming language especially well-suited for numerical analysis and scientific computing. However, while its prevalence is steadily increasing, it has not yet seen widespread adoption in the computational science or high-performance computing communities. One of the hurdles is a (perceived) lack of real-world examples that show how Julia can be used to conduct numerical simulations and what its advantages and drawbacks are for scientific applications.
To remediate this, in this talk we discuss the development of a purely hyperbolic method for self-gravitating gas dynamics within our Julia-based open source simulation framework Trixi.jl. In this approach, we reformulate the elliptic gravity problem into a hyperbolic diffusion problem, which is solved in pseudotime using the same explicit high-order discontinuous Galerkin method we use for the flow solution. A key benefit is that in the resulting multi-physics simulation problem, we can reuse existing hyperbolic solvers while retaining advanced features such as non-conforming and solution-adaptive meshes.
Next to presenting numerical results, we will critically examine our experience with building a multi-physics simulation framework with Julia. We will discuss its strengths and weaknesses as a programming language for research software engineering, including an assessment of how Julia's claimed benefits hold up against scientific reality, and give a live demonstration of Julia and Trixi.jl in action.
To make the shown examples reproducible by the audience, the Jupyter notebook used for the live demonstration is available in this repository (see above). It can be either run from a local Julia/Jupyter installation or in the cloud via Binder (without having to install Julia locally).
This repository was initiated by Michael Schlottke-Lakemper.
The contents of this repository are licensed under the MIT license (see LICENSE.md).