Julia
Julia
Julia [1] is a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages. Julia provides ease and expressiveness for high-level numerical computing, in the same way as languages such as R, MATLAB, and Python, but also supports general programming. To achieve this, Julia builds upon the lineage of mathematical programming languages, but also borrows much from popular dynamic languages, including Lisp, Perl, Python, Lua, and Ruby.
Running Julia in HPC
Interactive Job
Request a node (rider). For multithreading, use the flag -n (e.g. -n 4 for 4 threads)
srun --pty bash
Note: In Rider, Julia can run only in particular set of nodes (srun -C dodeca96gb --pty bash). Also, syntax may have been changed e.g. need to use include instead of require.
Load the module
module load julia
Run Julia (serial)
julia
julia> string(10*15)
"150"
julia> import Pkg
julia> Pkg.installed() # check the installed packages
Dict{String,VersionNumber} with 1 entry:
"DistributedArrays" => v"0.6.5"
Pkg.status()
Status `~/.julia/environments/v1.7/Project.toml`
[2e9cd046] Gurobi v0.11.3
julia> exit()
Run Julia (multithreading)
julia -t 4
julia> Threads.nthreads()
4
Note: julia gets transitioned to shell mode by typing colon (;) after the prompt. For details, visit [2]. Press "Ctrol + d" to exit
Batch Job
Example: WordCount
Copy the directory "examples" from /usr/local/doc/JULIA and cd to that directory
cp -r /usr/local/doc/JULIA/examples .
cd examples
Multithreaded Job:
Submit the Parallel job
sbatch job-multi.slurm
You will see the output at slurm-<jobID>.
Distributed Job:
Install DistributedArrays package [2]
module laod juila/1.6.2
julia
julia> import Pkg
julia> Pkg.add("DistributedArrays")
Find the slurm script "job-distributed.slurm" and julia script "distributed.jl" at the examples directory that you copied and run the job.
sbatch job-distributed.slurm
MPI Job:
Installation instruction at MPI [3]
CUDA Job:
Build or pull Julia singularity container from NVIDIA NGC (https://ngc.nvidia.com/catalog/containers/hpc:julia) and follow the instructions to run the examples.
Package Installation:
Check HPC Pkg installation Guide for details. CUDA package can also be installed [4].
References:
[1] Julia Home
[4] CUDA Package