Exercises

Introduction

Getting familiar with Julia REPL

Is is important in this course that you know how to navigate on the Julia command line. This exercise will help you to become more familiar with the REPL. Do the following steps:

• Start a Julia session. In the Julian mode, compute the sum the numbers 5 and 6

• Change to the shell mode and display the current directory

• Now, go to the package mode and list the currently installed packages

• Finally, display help information of the function println in help mode.

Solution for the centres

$ julia
julia> 5 + 6
julia>;
shell> pwd
julia>]
pkg> status
julia>?
help?> println

Load and run

Loading modules and running scripts

Load the Julia version 1.8.5 and run the following serial script (serial-sum.jl) which accepts two integer arguments as input:

x = parse( Int32, ARGS[1] )
y = parse( Int32, ARGS[2] )
summ = x + y
println("The sum of the two numbers is ", summ)

Solution for HPC2N

This batch script is for Kebnekaise.

$ ml purge  > /dev/null 2>&1       # recommended purge
$ ml Julia/1.8.5-linux-x86_64      # Julia module

$ julia serial-sum.jl Arg1 Arg2    # run the serial script

Solution for UPPMAX

This batch script is for UPPMAX. Adding the numbers 2 and 3.

$ ml julia/1.8.5      # Julia module

$ julia serial-sum.jl Arg1 Arg2    # run the serial script

Packages and isolated environments

Project environment

Create a project environment called new-env and activate it. Then, install the package CSV in this environment. For your knowledge, CSV is a package that offers tools for dealing with .csv files. After this, check that this package was installed. Finally, deactivate the environment.

Solution for both centres

shell> mkdir new-env
shell> cd new-env
(@v1.8) pkg> activate .
      Activating new project at `path-to-folder\new-env`
(new-env) pkg> add CSV
(new-env) pkg> status
      Status `path-to-folder\new-env\Project.toml`
      [336ed68f] CSV v0.10.9
(new-env) pkg> activate

Package environment

Create a package environment called new_pack and activate it. Then, install the package CSV in this environment. For your knowledge, CSV is a package that offers tools for dealing with .csv files. After this, check that this package was installed. Finally, deactivate the environment.

Solution for both centres

shell> pwd            #Check were you are currently located
(@v1.8) pkg> generate new_pack
     Generating  project new_pack:
     new_pack\Project.toml
     new_pack\src\new_pack.jl
shell> cd new_pack
     `path-to-folder\new_pack`
(@v1.8) pkg> activate .
       Activating project at `path-to-folder\new_pack`
(new_pack) pkg> add CSV
(new_pack) pkg> status
       Project new_pack v0.1.0
       Status `path-to-folder\new_pack\Project.toml`
       [336ed68f] CSV v0.10.9
(new_pack) pkg> activate

Batch mode

Serial code

Run a serial script

Run the serial script serial-sum.jl:

x = parse( Int32, ARGS[1] )
y = parse( Int32, ARGS[2] )
summ = x + y
println("The sum of the two numbers is ", summ)

This scripts accepts two integers as command line arguments.

Solution for HPC2N

This batch script is for Kebnekaise.

#!/bin/bash
#SBATCH -A hpc2n2023-110     # your project_ID
#SBATCH -J job-serial        # name of the job
#SBATCH -n 1                 # nr. tasks
#SBATCH --time=00:03:00      # requested time
#SBATCH --error=job.%J.err   # error file
#SBATCH --output=job.%J.out  # output file

ml purge  > /dev/null 2>&1   # recommended purge
ml Julia/1.8.5-linux-x86_64  # Julia module

julia serial-sum.jl Arg1 Arg2    # run the serial script

Solution for UPPMAX

This batch script is for UPPMAX. Adding the numbers 2 and 3.

#!/bin/bash -l
#SBATCH -A naiss2023-22-914  # Change to your own after the course
#SBATCH -J job-serial        # name of the job
#SBATCH -n 1                 # nr. tasks
#SBATCH --time=00:05:00 # Asking for 5 minutes
#SBATCH --error=job.%J.err   # error file
#SBATCH --output=job.%J.out  # output file
module load julia/1.8.5

julia serial-sum.jl Arg1 Arg2    # run the serial script

GPU code

Run the GPU script

Run the following script script-gpu.jl. Why are we running the simulations twice? Note that at UPPMAX you will need a project will access to Snowy (or Bianca)

using CUDA

CUDA.versioninfo()

N = 2^8
x = rand(N, N)
y = rand(N, N)

A = CuArray(x)
B = CuArray(y)

# Calculation on CPU
@time x*y
# Calculation on GPU
@time A*B

# Calculation on CPU
@time x*y
# Calculation on GPU
@time A*B

Solution for HPC2N

This batch script is for Kebnekaise. We run the simulation twice because in this way, the reported time is more reliable for the computing time as in the first simulation, data transfer and other settings could be added to the reported time.

#!/bin/bash
#SBATCH -A hpc2n2024-025     # your project_ID
#SBATCH -J job-serial        # name of the job
#SBATCH -n 1                 # nr. tasks
#SBATCH --time=00:03:00      # requested time
#SBATCH --error=job.%J.err   # error file
#SBATCH --output=job.%J.out  # output file
#SBATCH --gres=gpu:v100:1     # 1 GPU v100 card

ml purge  > /dev/null 2>&1
ml Julia/1.8.5-linux-x86_64
ml CUDA/11.4.1

julia script-gpu.jl

Output:

0.689096 seconds (2.72 M allocations: 132.617 MiB, 6.27% gc time, 99.62% compilation time)

1.194153 seconds (1.24 M allocations: 62.487 MiB, 3.41% gc time, 55.13% compilation time)

0.000933 seconds (2 allocations: 512.047 KiB)

0.000311 seconds (5 allocations: 192 bytes)

Solution for UPPMAX

This batch script is for UPPMAX. Adding the numbers 2 and 3.

#SBATCH -A <project with Snowy/Bianca access    # your project_ID
#SBATCH -M snowy
#SBATCH -p node
##SBATCH -C gpu   #NB: Only for Bianca
#SBATCH -N 1
#SBATCH --job-name=juliaGPU         # create a short name for your job
#SBATCH --gpus-per-node=1             # number of gpus per node (Bianca 2, Snowy 1)
#SBATCH --time=00:15:00          # total run time limit (HH:MM:SS)
#SBATCH --qos=short              # if test run t<15 min

ml Julia/1.8.5-linux-x86_64

julia script-gpu.jl

Output:

Downloading artifact: CUDNN Downloading artifact: CUTENSOR CUDA toolkit 11.7, artifact installation NVIDIA driver 525.85.12, for CUDA 12.0 CUDA driver 12.0

Libraries: - CUBLAS: 11.10.1 - CURAND: 10.2.10 - CUFFT: 10.7.2 - CUSOLVER: 11.3.5 - CUSPARSE: 11.7.3 - CUPTI: 17.0.0 - NVML: 12.0.0+525.85.12 - CUDNN: 8.30.2 (for CUDA 11.5.0) - CUTENSOR: 1.4.0 (for CUDA 11.5.0)

Toolchain: - Julia: 1.8.5 - LLVM: 13.0.1 - PTX ISA support: 3.2, 4.0, 4.1, 4.2, 4.3, 5.0, 6.0, 6.1, 6.3, 6.4, 6.5, 7.0, 7.1, 7.2 - Device capability support: sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_62, sm_70, sm_72, sm_75, sm_80, sm_86

1 device:

0: Tesla T4 (sm_75, 14.605 GiB / 15.000 GiB available) 0.988437 seconds (2.72 M allocations: 132.556 MiB, 4.72% gc time, 99.10% compilation time) 5.707402 seconds (1.30 M allocations: 65.564 MiB, 0.72% gc time, 19.70% compilation time) 0.000813 seconds (2 allocations: 512.047 KiB) 0.000176 seconds (16 allocations: 384 bytes)

Machine Learning job on GPUs (HPC2N)

Julia has already several packages for ML, one of them is Flux (https://fluxml.ai/). We will work with one of the test cases provided by Flux which deals with a data set of tiny images (CIFAR10). Follow this steps:

• Create an environment called ML, move to that environment directory and activate it

• Fetch the vgg_cifar10.jl test case from Flux repo (wget https://raw.githubusercontent.com/FluxML/model-zoo/master/vision/vgg_cifar10/vgg_cifar10.jl)

• Load CUDA toolkit 11.4.1

• Install (add) the following packages: CUDA, MLDatasets, MLUtils

• The first time you use the data set CIFAR10, it will ask you to download it and accept. Do this in Julian mode:

julia> using MLDatasets: CIFAR10
julia> x, y = CIFAR10(:train)[:]

• Change the number of epochs in the vgg_cifar10.jl script from 50 to something shorter like 5.

• Submit the job with the script:

#!/bin/bash
#SBATCH -A Project-ID        # your project_ID
#SBATCH -J job-serial        # name of the job
#SBATCH -n 1                 # nr. tasks
#SBATCH --time=00:20:00      # requested time
#SBATCH --error=job.%J.err   # error file
#SBATCH --output=job.%J.out  # output file
#SBATCH --gres=gpu:v100:1     # 1 GPU v100 card

ml purge  > /dev/null 2>&1
ml Julia/1.8.5-linux-x86_64
ml CUDA/11.4.1

julia <fix-activate-environment> <fix-name-script>.jl

Solution for HPC2N

ml Julia/1.8.5-linux-x86_64
ml CUDA/11.4.1
mkdir ML
cd ML
wget https://raw.githubusercontent.com/FluxML/model-zoo/master/vision/vgg_cifar10/vgg_cifar10.jl

julia
(v1.8) pkg> activate .
(ML) pkg> add CUDA
(ML) pkg> add Flux
(ML) pkg> add MLDatasets
(ML) pkg> add MLUtils
julia> using MLDatasets: CIFAR10
julia> x, y = CIFAR10(:train)[:]

The batch script looks like:

#!/bin/bash
#SBATCH -A hpc2n20xx-xyz     # your project_ID
#SBATCH -J job-serial        # name of the job
#SBATCH -n 1                 # nr. tasks
#SBATCH --time=00:20:00      # requested time
#SBATCH --error=job.%J.err   # error file
#SBATCH --output=job.%J.out  # output file
#SBATCH --gres=gpu:v100:1     # 1 GPU v100 card

ml purge  > /dev/null 2>&1
ml Julia/1.8.5-linux-x86_64
ml CUDA/11.4.1

julia --project=. vgg_cifar10.jl

At HPC2N you can use the tool job-usage on the command line:

job-usage job_ID   # job_ID number you get upon using sbatch

This will give you a URL that you can paste on your local browser. It would display statistics after a couple of minutes the job started.