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GNU Parallel a simple but powerful way to run independent tasks in parallel. Although it is possible to run on multiple nodes, it is simplest to run on multiple cpus of a single node, and that is what we will consider here. Note that what is presented here just scratches the surface of what parallel can do.

Basic Examples


Let's parallelize the following bash loop that prints the letters a through f using bash's brace expansion:

for letter in {a..f};
    echo $letter

... which produces the following output:


To achieve the same result, parallel starts some number of workers and then runs tasks on them. The number of workers and tasks need not be the same. You specify the number of workers with -j. The tasks can be generated with a list of arguments specified after the separator :::. For parallel to perform well, you should allocate at least the same number of CPUs as workers with the slurm option --cpus-per-task or more simply -c.

srun --pty -p interactive -c 4 bash
module load parallel
parallel -j 4 "echo {}" ::: {a..f}

This runs four workers that each run echo, filling in the argument {} with the next item in the list. This produces the output:

Nested Loop

Let's parallelize the following nested bash loop.

for letter in {a..c}
    for number in {1..7..2}
        echo $letter $number

... which produces the following output:

a 1
a 2
a 3
b 1
b 2
b 3
c 1
c 2
c 3

You can use the ::: separator with parallel to specify multiple lists of parameters you would like to iterate over. Then you can refer to them by one-based index, e.g. list one is {1}. Using these, you can ask parallel to execute combinations of parameters. Here is a way to recreate the result of the serial bash loop above:

parallel -j 4 "echo {1} {2}" ::: {a..c} ::: {1..3}

Advanced Examples


You have a number of files scattered throughout a directory tree. Their names end with fastq.gz, e.g. d1/d3/sample3.fastq.gz. You'd like to run md5sum on each, and put the output in a file in the same directory, with a filename ending with .md5sum, e.g. d1/d3/sample3.md5sum. Here is a script that will do that in parallel, using 16 cpus on one node of the cluster:

#SBATCH -c 16

module load parallel
parallel -j ${SLURM_CPUS_PER_TASK} --plus "echo {}; md5sum {} > {/fastq.gz/}" ::: $(find . -name "*.fastq.gz" -print)

The $(find . -name "*.fastq.gz" -print) portion of the command returns all of the files of interest. They will be plugged into the {} in the md5sum command. {/fastq.gz/} does a string replacement on the filename, producing the desired output filename. String replacement requires the --plus flag to parallel, which enables a number of powerful string manipulation features. Finally, we pass -j ${SLURM_CPUS_PER_TASK} so that parallel will use all of the allocated cpus, however many there are.

Parameter Sweep

You want to run a simulation program that takes a number of input parameters, and you want to sample a variety of values for each parameter.

#SBATCH -c 16
module load parallel
parallel -j ${SLURM_CPUS_PER_TASK} simulate {1} {2} {3} ::: {1..5} ::: 2 16 ::: {5..50..5}

This will run 100 jobs, each with parameters that vary as :

simulate 1 2 5
simulate 1 2 10
simulate 1 2 15
simulate 5 16 45
simulate 5 16 50

If simulate doesn't create unique output based on parameters, you can use redirection so you can review results from each task. You'll need to use quotes so that the > is seen as part of the command:

parallel -j ${SLURM_CPUS_PER_TASK} "simulate {1} {2} {3} > results_{1}_{2}_{3}.out" ::: $(seq 1 5) ::: 2 16 ::: $(seq 5 5 50)

Last update: July 9, 2020