Julia: Difference between revisions

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IA migration §5: new Julia page (P2.11) (via create-page on MediaWiki MCP Server)
 
Add table with Julia versions for every bucket
 
Line 7: Line 7:
<syntaxhighlight lang="bash">
<syntaxhighlight lang="bash">
module load 2024
module load 2024
module load Julia
module load Julia/1.11.6
</syntaxhighlight>
</syntaxhighlight>


To see which versions are available, use <code>module spider Julia</code> (see [[Environment Modules]]).
To see which versions are available, use <code>module key Julia</code> (see [[Environment Modules]]).
 
There is at least one version of Julia installed in each bucket:
{| class="wikitable"
|+
!Bucket
!Julia Version
|-
|2023
|1.11.3
|-
|2024
|1.11.6
|-
|2025
|1.12.3
|}


<!-- TODO: confirm the exact Julia module name(s) and version(s) available in the current buckets. -->


== Packages ==
== Packages ==

Latest revision as of 12:51, 11 August 2026

Julia is a high-level, high-performance language for technical computing. This page covers using Julia on Anunna.

Loading Julia

Julia is provided through the module system. Load a bucket first, then Julia:

module load 2024
module load Julia/1.11.6

To see which versions are available, use module key Julia (see Environment Modules).

There is at least one version of Julia installed in each bucket:

Bucket Julia Version
2023 1.11.3
2024 1.11.6
2025 1.12.3


Packages

Julia manages its own packages with the built-in package manager (Pkg). By default packages install into your home directory under ~/.julia. Because home is small and not meant for heavy I/O, point Julia's depot at a larger Lustre location by setting JULIA_DEPOT_PATH before starting Julia:

export JULIA_DEPOT_PATH=/lustre/nobackup/<group>/<user>/julia_depot

Then add packages from the Julia REPL:

import Pkg
Pkg.add("DataFrames")

Running Julia in a job

Run Julia scripts as batch jobs through the scheduler — see Batch Jobs. For interactive work, start an interactive job first.

To use multiple cores, start Julia with the -t (threads) option, or use Julia's distributed/parallel facilities — see Performance Optimization/Multiple CPUs:

julia -t $SLURM_CPUS_PER_TASK myscript.jl

See also