Job Arrays

Job arrays are best used for tasks that are completely independent, such as parameter sweeps, permutation analysis or simulation, that could be executed in any order and don't have to run at the same time. This kind of work is often described as embarrassingly parallel.
An embarrassingly parallel problem is one that requires no communication or dependency between the tasks (unlike distributed computing problems that need communication between tasks).

A job array will submit the same script repeatedly over a designated index using the SBATCH command #SBATCH --array

For example, the following code:

#!/bin/bash -e
#SBATCH --job-name=ArrayJob             # job name (shows up in the queue)
#SBATCH --time=00:01:00                 # Walltime (HH:MM:SS)
#SBATCH --mem=512MB                     # Memory
#SBATCH --array=1-2                     # Array jobs

pwd
echo "This is result ${SLURM_ARRAY_TASK_ID}"

will submit, ArrayJob_1 and ArrayJob_2, which will return the results This is result 1 and This is result 2 respectively.

Using SLURM_ARRAY_TASK_ID

Use of the environment variable ${SLURM_ARRAY_TASK_ID} is the recommended method of variation between the jobs.

• As a direct input to a function.

  matlab -nodisplay -r "myFunction(${SLURM_ARRAY_TASK_ID})"
  

• As an index to an array.

  inArray=(1 2 4 8 16 32 64 128)
  input=${inArray[$SLURM_ARRAY_TASK_ID]}
  

• For selecting input files.

  input=inputs/mesh_${SLURM_ARRAY_TASK_ID}.stl
  

• As a seed for a pseudo-random number.

  • In R

    task_id = as.numeric(Sys.getenv("SLURM_ARRAY_TASK_ID"))
    set.seed(task_id)
    

  • In MATLAB

    task_id = str2num(getenv('SLURM_ARRAY_TASK_ID'))
    rng(task_id)
    

  Using a seed is important, otherwise multiple jobs may receive the same pseudo-random numbers.

• As an index to an array of filenames. 

  files=( inputs/*.dat )
  input=${files[SLURM_ARRAY_TASK_ID]}
  # If there are 5 '.dat' files in 'inputs/' you will want to use '#SBATCH --array=0-4' 
  

  This example will submit a job array with each job using a .dat file in 'inputs' as the variable input (in alphabetcial order).

Environment variables will not work in the Slurm header. In place of ${SLURM_ARRAY_TASK_ID}, you can use the token %a. This can be useful for sorting your output files e.g.

#SBATCH --output=outputs/run_%a/slurm_output.out
#SBATCH --output=outputs/run_%a/slurm_error.err

As an index to an array

inArray=(1 2 4 8 16 32 64 128)
input=${inArray[$SLURM_ARRAY_TASK_ID]}

For selecting input files

input=inputs/mesh_${SLURM_ARRAY_TASK_ID}.stl

As a seed for a pseudo-random number

In R

task_id = as.numeric(Sys.getenv("SLURM_ARRAY_TASK_ID"))
set.seed(task_id)

In MATLAB

task_id = str2num(getenv('SLURM_ARRAY_TASK_ID'))
rng(task_id)

Using a seed is important, otherwise multiple jobs may receive the same pseudo-random numbers.

As an index to an array of filenames

files=( inputs/*.dat )
input=${files[SLURM_ARRAY_TASK_ID]}

Note: If there are 5 .dat files in inputs/ you will want to use #SBATCH --array=0-4.

This example will submit a job array with each job using a .dat file in 'inputs' as the variable input (in alphabetical order).

Multidimensional array example

#!/bin/bash -e

#SBATCH --open-mode append
#SBATCH --output week_times.out
#SBATCH --array 0-167 #This needs to be equal to combinations (in this case 7*24), and zero based.

# Define your dimensions in bash arrays.
arr_time=({00..23})
arr_day=("Mon" "Tue" "Wed" "Thur" "Fri" "Sat" "Sun") 

# Index the bash arrays based on the SLURM_ARRAY_TASK)
n_time=${arr_time[$(($SLURM_ARRAY_TASK_ID%${#arr_time[@]}))]} # '%' for finding remainder.
n_day=${arr_day[$(($SLURM_ARRAY_TASK_ID/${#arr_time[@]}))]}

echo "$n_day $n_time:00"

Avoiding Conflicts

As all the array jobs could theoretically run at the same time, it is important that all file references are unique and independent.

If your program makes use of a working directory make sure you set it e.g.

mkdir .tmp/run_${SLURM_ARRAY_TASK_ID}          # Create new directory
export TMPDIR=.tmp/run_${SLURM_ARRAY_TASK_ID}  # Set TMPDIR to point there

If you have no control over the name/path of an output used by a program, this can be resolved in a similar manner.

mkdir run_${SLURM_ARRAY_TASK_ID}                             # Create new directory
cd run_${SLURM_ARRAY_TASK_ID}                                # CD to new directory

bash job.sh

mv output.log ../outputs/output_${SLURM_ARRAY_TASK_ID}.log   # Move and rename output
rm -r ../run_${SLURM_ARRAY_TASK_ID}                          # Clear directory

The Slurm documentation on job arrays can be found here.