M Carmesin at 10:38, 11 November 2026

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	== Tips & Tricks ==		== Tips & Tricks ==
			* On the Justus3 nodes with AMD processors, you may improve the performance of linear algebra operations by loading the <code>AOCL</code> package, that uses the AMD Optimizing CPU Libraries as a drop-in replacement for OpenBLAS.

			* On Intel nodes, you should try the package <code>MKL</code> for best performance with dense linear algebra operations, and <code>MKLSparse</code> for sparse linar algebra.

	* [[Justus3/Software/Julia/Parallel_Programming\|Parallel Programming]]		* [[Justus3/Software/Julia/Parallel_Programming\|Parallel Programming]]

M Carmesin: Created page with "{{Softwarepage|math/julia}} {| width=600px class="wikitable" |- ! Description !! Content |- | module load | math/julia |- | Availability | bwUniCluster | JUSTUS2 |- | License | MIT License |- |Citing | [https://github.com/JuliaLang/julia/blob/master/CITATION.bib] |- | Links | [https://julialang.org/ Project homepage] | [https://docs.julialang.org/en/v1/ Documentation] |- | Graphical Interface | No |} == Introduction == Julia is a high-level, high-per..."

2026-11-11T10:26:43Z

Created page with "{{Softwarepage|math/julia}} {| width=600px class="wikitable" |- ! Description !! Content |- | module load | math/julia |- | Availability | bwUniCluster | JUSTUS2 |- | License | MIT License |- |Citing | [https://github.com/JuliaLang/julia/blob/master/CITATION.bib] |- | Links | [https://julialang.org/ Project homepage] | [https://docs.julialang.org/en/v1/ Documentation] |- | Graphical Interface | No |} == Introduction == Julia is a high-level, high-per..."

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| [https://github.com/JuliaLang/julia/blob/master/CITATION.bib]
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| [https://julialang.org/ Project homepage] | [https://docs.julialang.org/en/v1/ Documentation]
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== Introduction ==

Julia is a high-level, high-performance, dynamic programming language, being designed with scientific computing in mind. Parallel programming features, such as multi-threading are included in the core language, while there also exist packages leveraging the power of MPI and CUDA.

There are no packages preinstalled besides the Julia language core, please use the Julia package manager to install any required Julia package.

The Julia module on Justus loads suitable versions of CUDA and OpenMPI and the corresponding Julia packages CUDA.jl and MPI.jl will be automatically configured to use these libraries after being installed by the user. Any changes, either by loading modules with different MPI and/ or CUDA versions as well as using the ones that come as Julia artifacts are likely to lead to errors.

== Environments and Package Installation ==

It is highly recommended to use an separate Julia environment for every project. If Julia is started with the option <code>--project=.</code> the current folder will be used as environment and the <code>Project.toml</code> file containing the information on the installed packages will be created, if not yet present.

In an interactive Julia session, the [https://pkgdocs.julialang.org/v1/getting-started/#Basic-Usage package manager] is activated by entering <code>]</code>. The most importent commands are
* <code>add PACKAGENAME</code> install package PACKAGENAME in the current environment
* <code>instantiate</code>: install all packages with dependencies as stated in Project.toml and Manifest.toml, e.g. after copying the existing code to the cluster
* <code>activate PATH_TO_ENV</code>: use the environment located at the path <code>PATH_TO_ENV</code> and initialize it, if necessary.

== Interactive Example ==

Load Julia module and start interactive REPL session with 8 threads, using the environment in the current directory:

<pre>
$ module load math/julia
$ julia -t 8 --project=.
</pre>

Enter ']' to go into package manager and install package [https://github.com/JuliaPlots/UnicodePlots.jl?tab=readme-ov-file <code>UnicodePlots</code>].
<pre>
add UnicodePlots
</pre>

Leave the package manager with the backspace key.

Create a vector with 64 elements set to 0 and fill it using all 8 threads with the corresponding tread id number.

<pre>
vec = zeros(64)
Threads.@threads for i in eachindex(vec)
vec[i]= Threads.threadid()
end
</pre>

Load the <code>UnicodePlots</code> package and draw a scatter plot of the contents of <code>vec</code>

<pre>
using UnicodePlots
scatterplot(vec)
</pre>

== Further documentation ==

* [https://modernjuliaworkflows.org Modern Julia Workflows]: A collection of best practices

* [https://github.com/carstenbauer/JuliaHLRS24 Julia Workshop at HLRS]: The material of this workshop is in large parts also valid for the Justus cluster (on Justus you only need the module math/julia).

== Tips & Tricks ==

* [[Justus3/Software/Julia/Parallel_Programming|Parallel Programming]]

Justus3/Software/Julia - Revision history

M Carmesin at 10:38, 11 November 2026