Setup

Please follow the instructions on this page to install both Julia and VS Code with the Julia plugin on your machine.

Installing Julia

There are two ways to install Julia:

Downloading an installer for your operating system for the latest stable Julia version.
Using Juliaup, the Julia version manager.

Option 2 is (as of October 2022) the recommended installation method on Windows, and while juliaup is marked as a pre-release on MacOS and Linux it already works smoothly there too.

The benefit of juliaup is that it allows users to install specific Julia versions, it alerts users when new Julia versions are released and it provides a convenient Julia release channel abstraction.

Both installation methods are documented here. If you are on Windows we recommend using juliaup. If you are on MacOS or Linux, choose the installation method you feel most comfortable with.

1. Using the Julia installer

First download the latest stable release of Julia for your operating system from the julialang.org website.

Follow the instructions to complete the installation.

Platform-specific instructions can be found at https://julialang.org/downloads/platform/. It is convenient to be able to run Julia from the command line, so follow the instructions for “adding Julia to PATH”. For Windows users who do not already have a terminal installed, we recommend to install the Windows Terminal from the Microsoft Store.

2. Using Juliaup

Full instructions can be found at https://github.com/JuliaLang/juliaup.

In short:

On Windows you can install Julia and Juliaup either through the Windows store or on a command line by executing winget install julia -s msstore.
On MacOS or Linux, type curl -fsSL https://install.julialang.org | sh on a command line and follow the instructions.

Checking your installation

Regardless of how you installed Julia, please ensure that you can open the Julia REPL by typing julia on the command line in a terminal, or by clicking the Julia icon on your Desktop or Applications folder. You should see something like in the image below (nevermind the version number).

To exit the REPL again, hit CTRL-d or type exit().

Installing Visual Studio Code

First install VSCode according to the official documentation: https://code.visualstudio.com/Download

Installing the VSCode Julia extension

After starting VSCode, click the Extensions button on the left-side menu, type Julia and click Install to install the Julia extension.

You now need to configure the Julia extension and set the path to the Julia executable. Click the cogwheel button next to the Julia extension:

Then find the “Julia: Executable Path” field:

In this field enter the path to the Julia executable that you have installed.

If you are curious, scroll through the other possible configuration settings!

Installing packages

A number of Julia packages is used in this lesson. These can be installed on-the-fly during a workshop, but you can also follow the instructions below to install all packages in your global Julia environment.

Copy-paste the following text into a file called Project.toml, which you can for example place under a new directory julia in your home directory:

name = "Julia-for-HPC"

[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
Dagger = "d58978e5-989f-55fb-8d15-ea34adc7bf54"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
MPIPreferences = "3da0fdf6-3ccc-4f1b-acd9-58baa6c99267"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
Profile = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
SharedArrays = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"

Then open a Julia REPL and specify the location of the Project.toml file:

$ julia --project=/path/to/Project.toml

Then copy-paste the following code into your Julia session:

using Pkg
Pkg.instantiate()

It could take a couple of minutes to download and install the packages. After it completes you should be able to list all installed packages:

Pkg.status()

# Status `~/julia/Project.toml`
#   [6e4b80f9] BenchmarkTools v1.3.1
#   [da04e1cc] MPI v0.20.3
#   [3da0fdf6] MPIPreferences v0.1.6
#   [91a5bcdd] Plots v1.35.6
#   [90137ffa] StaticArrays v1.5.9
#   [8ba89e20] Distributed
#   [37e2e46d] LinearAlgebra
#   [9abbd945] Profile
#   [1a1011a3] SharedArrays

Configuring MPI

Using MPI in Julia requires an MPI library. On an HPC system it is best to use a preinstalled MPI library, but on personal computers it’s best to let the MPI.jl package install its own library and wrapper for the mpiexec command. Do this by:

using MPI
MPI.install_mpiexecjl()

This installs a mpiexecjl under ~/.julia/bin, which should be added to your PATH variable.

Using EuroHPC systems

To reserve an interactive node (replace project (-A), QOS (-q) and reservation (–res) as needed):

$ salloc -A p200051 -t 0:10:0 -p cpu  -n 1  -q dev --res cpudev

To run a batch job to run an MPI job, create a job script similar to the following:

#!/bin/bash -l
#SBATCH -A p200051
#SBATCH -t 00:10:00
#SBATCH -q test
#SBATCH -p cpu
#SBATCH -N 1
#SBATCH --ntasks-per-node=8

module load OpenMPI
module load Julia

n=$SLURM_NTASKS
srun -n $n julia mpi_program.jl

To load Julia, type:

$ module use /appl/local/csc/modulefiles

$ module load julia           # for non-GPU version
$ module load julia-amdgpu    # for GPU version

To start a new bash session on a reserved interactive CPU node for multithreaded or distributed jobs:

$ srun --account=project_465000693 --partition=small --nodes=1 --ntasks-per-node=1 --cpus-per-task=8 --mem-per-cpu=1000 --time=00:15:00 --pty bash

To run MPI jobs on a CPU node:

$ srun --account=project_465000693 --partition=small --nodes=1 --ntasks-per-node=8 --cpus-per-task=1 --mem-per-cpu=1000 --time=00:15:00 --pty bash

To run instead on a GPU node:

$ srun --account=project_465000693 --partition=small-g --nodes=1 --ntasks-per-node=1 --cpus-per-task=16 --gpus-per-node=1 --mem-per-cpu=1750 --time=00:15:00 --pty  bash

To run a batch job to run an MPI job, create a job script similar to the following:

#!/bin/bash -l
#SBATCH -A project_465000693
#SBATCH -t 00:15:00
#SBATCH -p small
#SBATCH --nodes 1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=8

module use /appl/local/csc/modulefiles
module load julia

# Instantiate the project environment
julia --project -e 'using Pkg; Pkg.instantiate()'

# Run the julia script
julia --project script.jl

#!/bin/bash -l
#SBATCH -A project_465000693
#SBATCH -t 00:15:00
#SBATCH -p small
#SBATCH --nodes 1
#SBATCH --ntasks-per-node=8

module use /appl/local/csc/modulefiles
module load julia

# Instantiate the project environment
julia --project -e 'using Pkg; Pkg.instantiate()'

# Run the julia script
julia --project script.jl

#!/bin/bash -l
#SBATCH -A project_465000693
#SBATCH -t 00:15:00
#SBATCH -p small-g
#SBATCH --nodes 1
#SBATCH --ntasks-per-node=8
#SBATCH --cpus-per-task=16
#SBATCH --gpus-per-node=1

module use /appl/local/csc/modulefiles
module load julia-amdgpu

# Instantiate the project environment
julia --project -e 'using Pkg; Pkg.instantiate()'

# Run the julia script
srun julia --project script.jl

(Optional) Installing JupyterLab and a Julia kernel

JupyterLab can most easily be installed through the full Anaconda distribution of Python packages or the minimal Miniconda distribution.

To install Anaconda, visit https://www.anaconda.com/products/individual , download an installer for your operating system and follow the instructions. JupyterLab and an IPython kernel are included in the distribution.

To install Miniconda, visit https://docs.conda.io/en/latest/miniconda.html , download an installer for your operating system and follow the instructions. After activating a conda environment in your terminal, you can install JupyterLab with the command conda install jupyterlab.

Add Julia to JupyterLab

To be able to use a Julia kernel in a Jupyter notebook you need to install the IJulia Julia package. Open the Julia REPL and type:

using Pkg
Pkg.add("IJulia")

Create a Julia notebook

Now you should be able to open up a JupyterLab session by typing jupyter-lab in a terminal, and create a Julia notebook by clicking on Julia in the JupyterLab Launcher or by selecting File > New > Notebook and selecting a Julia kernel in the drop-down menu that appears.