Julia Language

Julia language is a high-performance, dynamic programming language. Julia is excellent for scientific computing because it can compile efficient native code using LLVM and includes mathematical functions, parallel computing capabilities, and a package manager in the standard library. Furthermore, Julia's syntax is intuitive and easy to learn, the multiple-dispatch paradigm allows writing composable code, increasing the ability to reuse existing code, and environments enable executing code in a reproducible way.

• Julia Language
  • License
  • Citation
  • Available
  • Usage
    • Using the Julia module
    • Using the package manager
    • Placing the Julia depot directory
    • Multi-threading
    • Multi-processing and distributed computing
      • Distributed and SlurmClusterManager.jl
      • MPI.jl
    • GPU programming
      • CUDA.jl
      • AMDGPU.jl
    • Further reading
  • Running Julia batch jobs on CSC clusters
    • Serial program
    • Multi-threading on single node
    • Multi-processing on single node
    • Multi-processing on multiple nodes
    • MPI program
    • Single GPU
    • GPU-aware MPI
  • Notes
    • Multi-threading in linear algebra

License

Julia language is licensed under free and open source MIT license.

Citation

If you use Julia in your research, please cite the following paper:

Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B. Shah (2017). Julia: A Fresh Approach to Numerical Computing. SIAM Review, 59(1), 65–98. DOI: 10.1137/141000671

To find the correct citation for a specific Julia package, you can use the pkg> status command or consult the package's documentation and repository.

Available

Julia language is available on Roihu-CPU, Roihu-GPU, Mahti, and LUMI from the command line using the module system. It is also available on the web interface via Jupyter and VSCode.

If you find issues in using Julia on the cluster, you should contact the servicedesk.

Usage

Using the Julia module

Julia language is available from the julia module.

Roihu-CPU, Roihu-GPU and MahtiLUMI

On Roihu-CPU, Roihu-GPU and Mahti, we can load the module as follows:

module load julia

On LUMI, we must add the module files under CSC's local directory to the module path before loading the module.

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

After loading the Julia module, we can use Julia with the julia command. Without arguments, it starts an interactive Julia REPL.

julia

For available command line options, we can run julia --help or read the manual man julia. For questions about the features of Julia language, we recommend the official documentation and the discourse channel.

Using the package manager

The standard method for installing Julia packages is to use the package manager, Pkg, from the standard library. In Julia, we can import it as follows:

import Pkg

The common functions we use are Pkg.add to add packages, Pkg.activate to activate environments, and Pkg.instantiate to install all packages defined in the active environment. The Pkg documentation provides more information on how to use Julia's package manager.

Placing the Julia depot directory

The first directory on the Julia depot path controls where Julia stores installed packages, compiled files, log files, and other depots. It is $HOME/.julia by default. The home directory has a relatively small quota on Roihu, Mahti, and LUMI. If you install large packages, we recommend placing the depot directory under Projappl to avoid running out of quota. We can change the depot directory by prepending a new directory to JULIA_DEPOT_PATH environment variable.

For example, we can use the following by replacing the <project> with a CSC project.

export JULIA_DEPOT_PATH="/projappl/<project>/$USER/.julia:$JULIA_DEPOT_PATH"

Afterward, you can safely remove the default depot directory using rm -r $HOME/.julia. For more information, you can read more about the depot path documentation.

Multi-threading

Julia provides the Threads library for multi-threading. It is included in the base library and imported by default in a Julia session. We can start Julia with multiple threads by setting the JULIA_NUM_THREADS environment variable or starting Julia with the --threads option which overrides the value in the environment variable. If Julia module is loaded within a Slurm job and the environment variable is not set, it is set to the amount of requested CPU cores (--cpus-per-task). The default thread count is one. We recommend reading the multi-threading section in Julia's manual for more details.

Multi-processing and distributed computing

Distributed and SlurmClusterManager.jl

For multiprocessing and distributed computing, Julia provides the Distributed standard library. We use it for multi-processing on the local node. We can extend Distributed by installing the SlurmClusterManager.jl package, which allows us to add workers' processes to multiple nodes via Slurm using SlurmManager. We recommend reading the multi-processing and distributed computing section in Julia manual for more details.

MPI.jl

We can use MPI for distributed computing, especially over multiple nodes, in Julia on Roihu-CPU, Roihu-GPU, Mahti, and LUMI using the MPI.jl package. We can install it using the package manager as follows:

import Pkg
Pkg.add("MPI")

We can load the julia-mpi module which sets global preferences to the environment such that MPI.jl uses the system MPI installation and the correct command to start MPI processes.

module load julia-mpi

For more information, we recommend reading the MPI.jl documentation.

GPU programming

CUDA.jl

The GPU nodes on Roihu-GPU and Mahti contain NVidia GPUs which can be programmed using CUDA. We can install the CUDA.jl package for CUDA programming in Julia using the package manager as follows:

import Pkg
Pkg.add("CUDA")

We can load the julia-cuda module which sets global preferences to the environment such that CUDA.jl uses the system CUDA installation.

module load julia-cuda

For more information, we recommend reading the CUDA.jl documentation.

AMDGPU.jl

The GPU nodes on LUMI contain AMD GPUs. We can install the AMDGPU.jl package for programming AMD GPUs in Julia using the package manager as follows:

import Pkg
Pkg.add("AMDGPU")

We can load the julia-amdgpu module which sets global preferences to the environment such that AMDGPU.jl uses the system ROCm installation.

module load julia-amdgpu

For more information, we recommend reading the AMDGPU.jl documentation.

Further reading

For further reading about parallel and high-performance computing with Julia, we recommend the Julia for high-performance scientific computing from ENCCS and the A brief tour of Julia for high-performance computing by Kjartan Thor Wikfeldt. HLRS's training material for the Julia for High-Performance Computing course offers a deep dive into programming high-performance code with Julia. Finally, the Julia on HPC Clusters lists general notes about using and installing Julia on an HPC cluster.

Running Julia batch jobs on CSC clusters

This section contains examples for running various Julia batch jobs on Roihu-CPU, Roihu-GPU, Mahti and LUMI clusters. They demonstrate the usage of the Julia environment described above for various batch jobs. They are adapted from the general instructions of running jobs on Roihu and Mahti and on LUMI. Note that we do not use srun to start processes in the batch script. Instead we use Julia for process management or call srun inside the Julia code.

Before running the examples, we need to instantiate the Julia project on the login node. That is, run the following command in the directory with your Julia environment where Project.toml file is located.

Roihu-CPU and Roihu-GPUMahtiLUMI

module purge
module load julia
julia --project=. --threads=1 -e 'using Pkg; Pkg.instantiate()'

module load julia
julia --project=. --threads=1 -e 'using Pkg; Pkg.instantiate()'

module use /appl/local/csc/modulefiles
module load julia
julia --project=. --threads=1 -e 'using Pkg; Pkg.instantiate()'

You can use multiple threads --threads=10 which will speed up the precompilation.

Serial program

We use the following directory structure and assume it is our working directory.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
└── script.jl     # Julia script

script.jl

println("Hello world!")

Roihu-CPUMahtiLUMI

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1000

module purge
module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=interactive
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1875

module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1000

module use /appl/local/csc/modulefiles
module load julia
julia --project=. script.jl

Multi-threading on single node

We use the following directory structure and assume it is our working directory.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
└── script.jl     # Julia script

script.jl

# Number of threads
n = Threads.nthreads()
println(n)

# Lets fill the id of each thread to the ids array.
ids = zeros(Int, n)
Threads.@threads for i in eachindex(ids)
    ids[i] = Threads.threadid()
end
println(ids)

# Alternatively, we can use the @spawn macro to run task on threads.
ids = zeros(Int, n)
@sync for i in eachindex(ids)
    Threads.@spawn ids[i] = Threads.threadid()
end
println(ids)

Roihu-CPUMahtiLUMI

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=3
#SBATCH --mem-per-cpu=1000

module purge
module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=medium
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=128
#SBATCH --mem-per-cpu=0

module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=3
#SBATCH --mem-per-cpu=1000

module use /appl/local/csc/modulefiles
module load julia
julia --project=. script.jl

Multi-processing on single node

We use the following directory structure and assume it is our working directory.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
└── script.jl     # Julia script

Project.toml

[deps]
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"

script.jl

using Distributed

# We set one worker process per core.
proc_num = Sys.CPU_THREADS

# Environment variables that we pass to the worker processes.
# We set the thread count to one since each process uses one core.
proc_env = [
    "JULIA_NUM_THREADS"=>"1",
    "JULIA_CPU_THREADS"=>"1",
    "OPENBLAS_NUM_THREADS"=>"1",
]

# We add worker processes to the local node using LocalManager.
addprocs(proc_num; env=proc_env, exeflags="--project=.")

# We use the `@everywhere` macro to include the task function in the worker processes.
# We must call `@everywhere` after adding worker processes; otherwise the code won't be included in the new processes.
@everywhere function task()
    return (worker=myid(), hostname=gethostname(), pid=getpid())
end

# We run the task function in each worker process.
futures = [@spawnat worker task() for worker in workers()]

# Then, we fetch the output from the processes.
outputs = fetch.(futures)

# Remove processes after we are done.
rmprocs.(workers())

# Print the outputs of master and worker processes.
println(task())
println.(outputs)

Roihu-CPUMahtiLUMI

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=3
#SBATCH --mem-per-cpu=1000

module purge
module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=medium
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=128
#SBATCH --mem-per-cpu=0

module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=3
#SBATCH --mem-per-cpu=1000

module use /appl/local/csc/modulefiles
module load julia
julia --project=. script.jl

Multi-processing on multiple nodes

We use the following directory structure and assume it is our working directory.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
└── script.jl     # Julia script

Project.toml

[deps]
SlurmClusterManager = "c82cd089-7bf7-41d7-976b-6b5d413cbe0a"
Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"

script.jl

using Distributed
using SlurmClusterManager

# Environment variables that we pass to the worker processes.
# We set the thread count to one since each process uses one core.
n = get(ENV, "SLURM_CPUS_PER_TASK", "1")
proc_env = [
    "JULIA_NUM_THREADS"=>"$n",
    "JULIA_CPU_THREADS"=>"$n",
    "OPENBLAS_NUM_THREADS"=>"$n",
]

# We add worker processes across the allocated nodes using SlurmManager
manager = SlurmManager(; launch_timeout=300)
addprocs(manager; env=proc_env, exeflags="--project=.")

# We use the `@everywhere` macro to include the task function in the worker processes.
# We must call `@everywhere` after adding worker processes; otherwise the code won't be included in the new processes.
@everywhere function task()
    return (worker=myid(), hostname=gethostname(), pid=getpid())
end

# We run the task function in each worker process.
futures = [@spawnat worker task() for worker in workers()]

# Then, we fetch the output from the processes.
outputs = fetch.(futures)

# Remove processes after we are done.
rmprocs.(workers())

# Print the outputs of master and worker processes.
println(task())
println.(outputs)

Roihu-CPUMahtiLUMI

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=medium
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=384
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1000

module purge
module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=medium
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=128
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=0

module load julia
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=standard
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=128
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=0

module use /appl/local/csc/modulefiles
module load julia
julia --project=. script.jl

MPI program

We launch the MPI program using Julia's mpiexec wrapper function. The wrapper function substitutes the correct command from local preferences to the mpirun variable to run the MPI program. The command is srun in Roihu, Mahti, and LUMI. The wrapper allows us to write more flexible code, such as mixing MPI and non-MPI code, and more portable code because the command to run MPI programs can vary across platforms. We note that for large-scale Julia MPI jobs with thousands of ranks, we have to distribute the depot directory to local node storage or memory and modify the depot paths accordingly. Otherwise, package loading will become extremely slow.

We use the following directory structure and assume it is our working directory.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
├── prog.jl       # Julia MPI program
└── script.jl     # Julia script

Project.toml

[deps]
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"

script.jl

using MPI
mpiexec(mpirun -> run(`$mpirun julia --project=. prog.jl`))

prog.jl

using MPI

MPI.Init()
comm = MPI.COMM_WORLD
rank = MPI.Comm_rank(comm)
size = MPI.Comm_size(comm)
println("Hello from rank $(rank) out of $(size) from host $(gethostname()) and process $(getpid()).")
MPI.Barrier(comm)

Roihu-CPUMahtiLUMI

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=medium
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=384
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=1000

module purge
module load julia
module load julia-mpi
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=medium
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=128
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=0

module load julia
module load julia-mpi
julia --project=. script.jl

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=standard
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=128
#SBATCH --cpus-per-task=1
#SBATCH --mem-per-cpu=0

module use /appl/local/csc/modulefiles
module load julia
module load julia-mpi
julia --project=. script.jl

Single GPU

We use the following directory structure and assume it is our working directory.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
└── script.jl     # Julia script

Roihu-GPUMahtiLUMI

Project.toml

[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

script.jl

using CUDA

A = rand(2^9, 2^9)
A_d = CuArray(A)
B_d = A_d * A_d

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpumedium
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=72
#SBATCH --gres=gpu:gh200:1

module purge
module load julia
module load julia-cuda
julia --project=. script.jl

Project.toml

[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

[compat]
CUDA = "< 5.9"

script.jl

using CUDA

A = rand(2^9, 2^9)
A_d = CuArray(A)
B_d = A_d * A_d

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpusmall
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=32
#SBATCH --gres=gpu:a100:1

module load julia
module load julia-cuda
julia --project=. script.jl

Project.toml

[deps]
AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"

script.jl

using AMDGPU

A = rand(2^9, 2^9)
A_d = ROCArray(A)
B_d = A_d * A_d

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small-g
#SBATCH --time=00:15:00
#SBATCH --nodes=1
#SBATCH --gpus-per-node=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=8
#SBATCH --mem-per-cpu=1750

module use /appl/local/csc/modulefiles
module load julia
module load julia-amdgpu
julia --project=. script.jl

GPU-aware MPI

We use the following directory structure and assume it is our working directory. The prog.jl code is adapted from this source.

.
├── Project.toml  # Julia environment
├── batch.sh      # Slurm batch script
├── prog.jl       # Julia GPU-aware MPI program
└── script.jl     # Julia script

script.jl

using MPI
mpiexec(mpirun -> run(`$mpirun julia --project=. prog.jl`))

Roihu-GPULUMI

Project.toml

[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"

prog.jl

using MPI
using CUDA

const gpu_devices = CUDA.devices()
const num_devices = length(gpu_devices)

MPI.Init()
comm = MPI.COMM_WORLD
rank = MPI.Comm_rank(comm)
# select device
comm_l = MPI.Comm_split_type(comm, MPI.COMM_TYPE_SHARED, rank)
rank_l = MPI.Comm_rank(comm_l)
device = CUDA.device!(mod(rank_l, num_devices))
gpu_id = CUDA.deviceid(CUDA.device())
# select device
size = MPI.Comm_size(comm)
dst  = mod(rank+1, size)
src  = mod(rank-1, size)
println("rank=$rank rank_loc=$rank_l (gpu_id=$gpu_id - $device), size=$size, dst=$dst, src=$src")
N = 2^16  # Minimum array size for gdrcopy to work.
send_mesg = CuArray{Float64}(undef, N)
recv_mesg = CuArray{Float64}(undef, N)
fill!(send_mesg, Float64(rank))
CUDA.synchronize()
rank==0 && println("start sending...")
MPI.Sendrecv!(send_mesg, dst, 0, recv_mesg, src, 0, comm)
println("sum(recv_mesg) on proc $rank: $(sum(recv_mesg))")
rank==0 && println("done.")
MPI.Finalize()

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=gpumedium
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=72
#SBATCH --gres=gpu:gh200:4

module purge
module load julia
module load julia-mpi
module load julia-cuda
julia --project=. script.jl

Project.toml

[deps]
AMDGPU = "21141c5a-9bdb-4563-92ae-f87d6854732e"
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"

prog.jl

using MPI
using AMDGPU
MPI.Init()
comm = MPI.COMM_WORLD
rank = MPI.Comm_rank(comm)
# select device
comm_l = MPI.Comm_split_type(comm, MPI.COMM_TYPE_SHARED, rank)
rank_l = MPI.Comm_rank(comm_l)
device = AMDGPU.device_id!(rank_l+1)
gpu_id = AMDGPU.device_id(AMDGPU.device())
# select device
size = MPI.Comm_size(comm)
dst  = mod(rank+1, size)
src  = mod(rank-1, size)
println("rank=$rank rank_loc=$rank_l (gpu_id=$gpu_id - $device), size=$size, dst=$dst, src=$src")
N = 4
send_mesg = ROCArray{Float64}(undef, N)
recv_mesg = ROCArray{Float64}(undef, N)
AMDGPU.synchronize()
rank==0 && println("start sending...")
MPI.Sendrecv!(send_mesg, dst, 0, recv_mesg, src, 0, comm)
println("recv_mesg on proc $rank: $recv_mesg")
rank==0 && println("done.")

batch.sh

#!/bin/bash
#SBATCH --account=<project>
#SBATCH --partition=small-g
#SBATCH --time=00:15:00
#SBATCH --nodes=2
#SBATCH --gpus-per-node=8
#SBATCH --ntasks-per-node=8
#SBATCH --cpus-per-task=8
#SBATCH --mem-per-cpu=0

module use /appl/local/csc/modulefiles
module load julia
module load julia-mpi
module load julia-amdgpu
julia --project=. script.jl

Notes

Multi-threading in linear algebra

Julia uses OpenBLAS as the default LinearAlgebra backend. External linear algebra backends such as OpenBLAS use internal threading. We can set their thread counts using environment variables. The julia module sets them to the number of CPU threads.

export OPENBLAS_NUM_THREADS=$JULIA_CPU_THREADS

We must be careful not to oversubscribe cores when using BLAS operations within Julia threads or processes. We can change the amount of BLAS threads at runtime using the BLAS.set_num_threads function.

using LinearAlgebra

# Number of threads
n = Threads.nthreads()

# Define a matrix
X = rand(1000, 1000)

# Set the number of threads to one before performing BLAS operations of multiple Julia threads.
BLAS.set_num_threads(1)
Y = zeros(n)
Threads.@threads for i in 1:n  # uses n Julia threads
    Y[i] = sum(X * X)          # uses one BLAS thread
end

# Set the number of threads back to the default when performing BLAS operation on a single Julia Thread.
BLAS.set_num_threads(n)
Z = zeros(n)
for i in 1:n                   # uses one Julia thread
    Z[i] = sum(X * X)          # uses n BLAS threads
end

There are caveats for using different numbers than one or all cores of BLAS threads on OpenBLAS.

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