PARALLEL_EXAMPLES(7)

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💖 GNU PARALLEL EXAMPLES 💖

okay bestie, buckle up 🎀 this is the PARALLEL_EXAMPLES(7) page from the parallel manual, and it's literally just a glow-up tour of everything parallel can do. no cap. we're going example by example, and every single flag and command stays exactly as it is because the facts are sacred ✨

🌸 EXAMPLE: Working as xargs -n1. Argument appending 🌸

GNU parallel can work similar to xargs -n1. it's giving "same energy, better fit" 💅

To compress all html files using gzip run:

find . -name '*.html' | parallel gzip --best

If the file names may contain a newline use -0 🫶 Substitute FOO BAR with FUBAR in all files in this dir and subdirs:

find . -type f -print0 | \
  parallel -q0 perl -i -pe 's/FOO BAR/FUBAR/g'
Note -q is needed because of the space in 'FOO BAR'. the space! it's the villain 😭

✨ EXAMPLE: Reading arguments from command line ✨

GNU parallel can take the arguments from command line instead of stdin (standard input). iconic behavior honestly. To compress all html files in the current dir using gzip run:

parallel gzip --best ::: *.html

To convert *.wav to *.mp3 using LAME running one process per CPU run:

parallel lame {} -o {.}.mp3 ::: *.wav

🦋 EXAMPLE: Running full commands in parallel 🦋

If there is no command given to GNU parallel, then the arguments are treated as a command line. periodt.

To run gzip foo and bzip2 bar in parallel run:

parallel ::: "gzip foo" "bzip2 bar"

or:

(echo "gzip foo"; echo "bzip2 bar") | parallel

two paths, same slay 💜

👑 EXAMPLE: Inserting multiple arguments 👑

When moving a lot of files like this: mv *.log destdir you will sometimes get the error:

bash: /bin/mv: Argument list too long

because there are too many files 😭 the shell simply cannot. You can instead do:

ls | grep -E '\.log$' | parallel mv {} destdir

This will run mv for each file. It can be done faster if mv gets as many arguments that will fit on the line:

ls | grep -E '\.log$' | parallel -m mv {} destdir

In many shells you can also use printf:

printf '%s\0' *.log | parallel -0 -m mv {} destdir

🎀 EXAMPLE: Context replace 🎀

To remove the files pict0000.jpg .. pict9999.jpg you could do:

seq -w 0 9999 | parallel rm pict{}.jpg

You could also do:

seq -w 0 9999 | perl -pe 's/(.*)/pict$1.jpg/' | parallel -m rm

The first will run rm 10000 times, while the last will only run rm as many times needed to keep the command line length short enough to avoid Argument list too long (it typically runs 1-2 times). efficiency queen 🧚

You could also run:

seq -w 0 9999 | parallel -X rm pict{}.jpg

This will also only run rm as many times needed to keep the command line length short enough.

💅 EXAMPLE: Compute intensive jobs and substitution 💅

If ImageMagick is installed this will generate a thumbnail of a jpg file:

convert -geometry 120 foo.jpg thumb_foo.jpg

This will run with number-of-cpus jobs in parallel for all jpg files in a directory:

ls *.jpg | parallel convert -geometry 120 {} thumb_{}

To do it recursively use find:

find . -name '*.jpg' | \
  parallel convert -geometry 120 {} {}_thumb.jpg

Notice how the argument has to start with {} as {} will include path (e.g. running convert -geometry 120 ./foo/bar.jpg thumb_./foo/bar.jpg would clearly be wrong 👀). The command will generate files like ./foo/bar.jpg_thumb.jpg.

Use {.} to avoid the extra .jpg in the file name. This command will make files like ./foo/bar_thumb.jpg:

find . -name '*.jpg' | \
  parallel convert -geometry 120 {} {.}_thumb.jpg

🌈 EXAMPLE: Substitution and redirection 🌈

This will generate an uncompressed version of .gz-files next to the .gz-file:

parallel zcat {} ">"{.} ::: *.gz

Quoting of > is necessary to postpone the redirection. Another solution is to quote the whole command:

parallel "zcat {} >{.}" ::: *.gz

Other special shell characters (such as * ; $ > < | >> <<) also need to be put in quotes, as they may otherwise be interpreted by the shell and not given to GNU parallel. protect them bestie 🫶

🍓 EXAMPLE: Composed commands 🍓

A job can consist of several commands. This will print the number of files in each directory:

ls | parallel 'echo -n {}" "; ls {}|wc -l'

To put the output in a file called <name>.dir:

ls | parallel '(echo -n {}" "; ls {}|wc -l) >{}.dir'

Even small shell scripts can be run by GNU parallel:

find . | parallel 'a={}; name=${a##*/};' \
  'upper=$(echo "$name" | tr "[:lower:]" "[:upper:]");'\
  'echo "$name - $upper"'

ls | parallel 'mv {} "$(echo {} | tr "[:upper:]" "[:lower:]")"'

Given a list of URLs, list all URLs that fail to download. Print the line number and the URL.

cat urlfile | parallel "wget {} 2>/dev/null || grep -n {} urlfile"

Create a mirror directory with the same file names except all files and symlinks are empty files.

cp -rs /the/source/dir mirror_dir
find mirror_dir -type l | parallel -m rm {} '&&' touch {}

Find the files in a list that do not exist

cat file_list | parallel 'if [ ! -e {} ] ; then echo {}; fi'

💜 EXAMPLE: Composed command with perl replacement string 💜

You have a bunch of file. You want them sorted into dirs. The dir of each file should be named the first letter of the file name.

parallel 'mkdir -p {=s/(.).*/$1/=}; mv {} {=s/(.).*/$1/=}' ::: *

In practice you would probably not use a perl replacement string but instead --match:

parallel --match '(.)' 'mkdir -p {1.1} && mv {} {1.1}' ::: *

ngl --match is the moment ✨

🔥 EXAMPLE: Composed command with multiple input sources 🔥

You have a dir with files named as 24 hours in 5 minute intervals: 00:00, 00:05, 00:10 .. 23:55. You want to find the files missing:

parallel [ -f {1}:{2} ] "||" echo {1}:{2} does not exist \
  ::: {00..23} ::: {00..55..5}

two input sources, one chaotic little ||. it's giving detective work 🔍

⭐ EXAMPLE: Match parts of input source ⭐

Match first initial and last name:

parallel --match '(.).* (.*)' echo {1.1}. {1.2} \
  ::: "Arthur Dent" "Ford Prefect" "Tricia McMillan" "Zaphod Beeblebrox"

Re-arrange (stupid) US date format into (nice) ISO-8601:

parallel --match '(.*)/(.*)/(.*)' echo {1.3}-{1.1:%02d}-{1.2:%02d} \
  ::: 12/31/1969 1/19/2038 6/1/2002

Match url into domain and path:

parallel --match 'https://(.*?)/(.*)' echo Domain: {1.1} Path: {1.2} \
  ::: https://example.com/dir/page https://gnu.org/s/parallel

Get URLs into dirs named by 2nd level domain name, e.g. https://www.gnu.org/s/parallel will be put into the dir gnu.org.

cat urls | parallel --match '//[^/]*?([^/.]+\.[^/.]+)/' \
  'mkdir -p {1.1} && cd {1.1} && wget {}'

Match host.domain:port from a log file:

cat log |
  parallel --match '\b([a-z0-9.]+):(\d+)\b' echo host:{1.1} port:{1.2}

Reorder comma-separated values:

parallel --match '(.*),(.*)' echo Second: {1.2}, First: {1.1} \
  ::: "Arthur,Babel fish" "Adams,Betelgeuse" "Arcturan,Bistro"

Capitalize word:

parallel --match '([a-z])([a-z]*) ([a-z])([a-z]*)' \
  echo '{=1.1 $_=uc($_) =}{1.2} {=1.3 $_=uc($_) =}{1.4}' \
  ::: "pan galactic" "gargle blaster"

Make an international dialing prefix table:

dial=(
  "DK(Denmark) 00,45"
  "US(United States) 011,1"
  "JP(Japan) 010,81"
  "AU(Australia) 0011,61"
  "CA(Canada) 011,1"
  "RU(Russia) 810,7"
  "TH(Thailand) 001,66"
  "TW(Taiwan) 002,886"
)
parallel --match '(.*)\((.*)\) (.*),(.*)' --match +1 \
  echo From {1.1}/{1.2} to {2.1}/{2.2} dial {1.3}-{2.4} \
  ::: "${dial[@]}" ::: "${dial[@]}"
Note how input source 2 reuses the --match from input source 1. recycling! we love to see it ♻️

🐚 EXAMPLE: Replacement fields from CSV file with headers 🐚

This is an advanced example. You have:

Date;Name;Location
3/8/1978;"Beeblebrox; Zaphod";"Betelgeuse V"
10/12/1979;"Dent; Arthur";Earth
1/5/1981;Slartibartfast;Magrathea

You want:

Z. Beeblebrox: 1978-03-08/BET
A. Dent: 1979-10-12/EAR
Slartibartfast: 1981-01-05/MAG

Run:

parallel --csv --colsep ';' --header : --match "(\d+)/(\d+)/(\d+)" \
  --match "^([^;]+)(; (.))?" --match "(...)"   \
  echo '{=Name.3 s/(.)/$1. /;=}'{Name.1}: \
    {Date.3}-{Date.1:%02d}-{Date.2:%02d}/'{=Location.1 $_=uc =}' \
  :::: people.csv

--csv parses the input as CSV with --colsep ; as the separator - dealing correctly with quoted strings. The input is split into 3 columns. --header : makes the columns available as {columnname}. Each column has their corresponding --match so each field can be accessed as {columnname.#}. s/(.)/$1. / is a perl expression that appends ". " if the name has an initial. :%02d formats single digits as two digits. uc upper cases the argument. absolute wizardry, no notes 🧙‍♀️

🌟 EXAMPLE: Calling Bash functions 🌟

If the composed command is longer than a line, it becomes hard to read. In Bash you can use functions. Just remember to export -f the function. (do not forget. i am watching 👀)

doit() {
  echo Doing it for $1
  sleep 2
  echo Done with $1
}
export -f doit
parallel doit ::: 1 2 3

doubleit() {
  echo Doing it for $1 $2
  sleep 2
  echo Done with $1 $2
}
export -f doubleit
parallel doubleit ::: 1 2 3 ::: a b

To do this on remote servers you need to transfer the function using --env:

parallel --env doit -S server doit ::: 1 2 3
parallel --env doubleit -S server doubleit ::: 1 2 3 ::: a b

If your environment (aliases, variables, and functions) is small you can copy the full environment without having to export -f anything. See env_parallel.

🎯 EXAMPLE: Function tester 🎯

To test a program with different parameters:

tester() {
  if (eval "$@") >&/dev/null; then
    perl -e 'printf "\033[30;102m[ OK ]\033[0m @ARGV\n"' "$@"
  else
    perl -e 'printf "\033[30;101m[FAIL]\033[0m @ARGV\n"' "$@"
  fi
}
export -f tester
parallel tester my_program ::: arg1 arg2
parallel tester exit ::: 1 0 2 0

If my_program fails a red FAIL will be printed followed by the failing command; otherwise a green OK will be printed followed by the command. traffic light but make it useful 🚦

💀 EXAMPLE: Identify few failing jobs 💀

--bar works best if jobs have no output. If the failing jobs have output you can identify the jobs like this:

job-with-few-failures() {
    # Force reproducibility
    RANDOM=$1
    # This fails 1% (328 of 32768)
    if [ $RANDOM -lt 328 ] ; then
      echo Failed $1
    fi
}
export -f job-with-few-failures
seq 1000 | parallel --bar --tag job-with-few-failures

a little chaos generator, as a treat 🙈

🌸 EXAMPLE: Continously show the latest line of output 🌸

It can be useful to monitor the output of running jobs.

This shows the most recent output line until a job finishes. After which the output of the job is printed in full:

parallel '{} | tee >(cat >&3)' ::: 'command 1' 'command 2' \
  3> >(perl -ne '$|=1;chomp;printf"%.'$COLUMNS's\r",$_." "x100')

live feed energy, we are seated 🍿

🌈 EXAMPLE: Log rotate 🌈

Log rotation renames a logfile to an extension with a higher number: log.1 becomes log.2, log.2 becomes log.3, and so on. The oldest log is removed. To avoid overwriting files the process starts backwards from the high number to the low number. This will keep 10 old versions of the log:

…and the tail end of that counter situation looks like this 💫

seq 9 -1 1 | parallel -j1 mv log.{} log.'{= $_++ =}'
mv log log.1

✨ EXAMPLE: Simple network scanner ✨

prips can generate IP-addresses straight from CIDR notation, which is so handy. And with GNU parallel you can build yourself a lil network scanner to see which addresses actually answer a ping — iconic lil recon moment 🔥

prips 130.229.16.0/20 | \
  parallel --timeout 2 -j0 \
    'ping -c 1 {} >/dev/null && echo {}' 2>/dev/null

🎀 EXAMPLE: Removing file extension when processing files 🎀

When you're processing files, dropping the file extension using {.} is lowkey useful like all the time, bestie.

Make a directory for each zip-file and unzip it in that dir:

parallel 'mkdir {.}; cd {.}; unzip ../{}' ::: *.zip

Recompress every .gz file in the current directory using bzip2, running 1 job per CPU in parallel:

parallel "zcat {} | bzip2 >{.}.bz2 && rm {}" ::: *.gz

Convert all WAV files to MP3 using LAME:

find sounddir -type f -name '*.wav' | parallel lame {} -o {.}.mp3

Put all the converted ones in the same directory:

find sounddir -type f -name '*.wav' | \
  parallel lame {} -o mydir/{/.}.mp3

🦋 EXAMPLE: Replacing parts of file names 🦋

If you deal with paired end reads, you're gonna have files like barcode1_R1.fq.gz, barcode1_R2.fq.gz, barcode2_R1.fq.gz, and barcode2_R2.fq.gz. the way paired end reads always come in matching lil outfits 💅

You want barcodeN_R1 to be processed with barcodeN_R2.

parallel --plus myprocess {} {/_R1.fq.gz/_R2.fq.gz} ::: *_R1.fq.gz

If the barcode does not contain _R1, you can do:

parallel --plus myprocess {} {/_R1/_R2} ::: *_R1.fq.gz

Or you can use --match:

parallel --match '(.*)_R1(.*)' myprocess {} {1.1}_R2{1.2} ::: *_R1.fq.gz

🌸 EXAMPLE: Removing strings from the argument 🌸

If you have a directory with tar.gz files and you want them extracted into the corresponding dir (e.g foo.tar.gz will be extracted in the dir foo) you can do:

parallel --plus 'mkdir {..}; tar -C {..} -xf {}' ::: *.tar.gz

If you want to remove a different ending, you can use {%string}:

parallel --plus echo {%_demo} ::: mycode_demo keep_demo_here

You can also remove a starting string with {#string}

parallel --plus echo {#demo_} ::: demo_mycode keep_demo_here

To remove a string anywhere, you can use regular expressions with {/regexp/replacement} and just leave the replacement empty:

parallel --plus echo {/demo_/} ::: demo_mycode remove_demo_here

You can often also use --match:

parallel --match '(.*)demo_(.*)' echo {1.1}{1.2} ::: demo_mycode remove_demo_here

🫶 EXAMPLE: Download 24 images for each of the past 30 days 🫶

Let us assume a website stores images like:

https://www.example.com/path/to/YYYYMMDD_##.jpg

where YYYYMMDD is the date and ## is the number 01-24. This will download images for the past 30 days:

getit() {
  date=$(date -d "today -$1 days" +%Y%m%d)
  num=$2
  echo wget https://www.example.com/path/to/${date}_${num}.jpg
}
export -f getit

parallel getit ::: $(seq 30) ::: $(seq -w 24)

$(date -d "today -$1 days" +%Y%m%d) will give you the dates in YYYYMMDD with $1 days subtracted. math but make it dates ⭐

🔥 EXAMPLE: Download world map from NASA 🔥

NASA provides tiles to download on earthdata.nasa.gov. Download tiles for the Blue Marble world map and create a 10240x20480 map.

base=https://map1a.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi
service="SERVICE=WMTS&REQUEST=GetTile&VERSION=1.0.0"
layer="LAYER=BlueMarble_ShadedRelief_Bathymetry"
set="STYLE=&TILEMATRIXSET=EPSG4326_500m&TILEMATRIX=5"
tile="TILEROW={1}&TILECOL={2}"
format="FORMAT=image%2Fjpeg"
url="$base?$service&$layer&$set&$tile&$format"

parallel -j0 -q wget "$url" -O {1}_{2}.jpg ::: {0..19} ::: {0..39}
parallel eval convert +append {}_{0..39}.jpg line{}.jpg ::: {0..19}
convert -append line{0..19}.jpg world.jpg

💜 EXAMPLE: Download Apollo-11 images from NASA using jq 💜

Search NASA using their API to get JSON for images related to 'apollo 11' and has 'moon landing' in the description.

The search query returns JSON containing URLs to JSON containing collections of pictures. One of the pictures in each of these collections is large.

wget is used to get the JSON for the search query. jq is then used to extract the URLs of the collections. parallel then calls wget to get each collection, which is passed to jq to extract the URLs of all images. grep filters out the large images, and parallel finally uses wget to fetch the images. a lil pipeline relay race and everybody ate 🏃‍♀️✨

base="https://images-api.nasa.gov/search"
q="q=apollo 11"
description="description=moon landing"
media_type="media_type=image"
wget -O - "$base?$q&$description&$media_type" |
  jq -r .collection.items[].href |
  parallel wget -O - |
  jq -r .[] |
  grep large |
  parallel wget

🌈 EXAMPLE: Download video playlist in parallel 🌈

youtube-dl is an excellent tool to download videos. It can, however, not download videos in parallel. This takes a playlist and downloads 10 videos in parallel. not me fixing youtube-dl's one flaw 💅

url='youtu.be/watch?v=0wOf2Fgi3DE&list=UU_cznB5YZZmvAmeq7Y3EriQ'
export url
youtube-dl --flat-playlist "https://$url" |
  parallel --tagstring {#} --lb -j10 \
    youtube-dl --playlist-start {#} --playlist-end {#} '"https://$url"'

🍓 EXAMPLE: Prepend last modified date (ISO8601) to file name 🍓

parallel mv {} '{= $a=pQ($_); $b=$_;' \
  '$_=qx{date -r "$a" +%FT%T}; chomp; $_="$_ $b" =}' ::: *

{= and =} mark a perl expression. pQ perl-quotes the string. date +%FT%T is the date in ISO8601 with time. perl girlies rise up 🧚

⭐ EXAMPLE: Save output in ISO8601 dirs ⭐

Save output from ps aux every second into dirs named yyyy-mm-ddThh:mm:ss+zz:zz.

seq 1000 | parallel -N0 -j1 --delay 1 \
  --results '{= $_=`date -Isec`; chomp=}/' ps aux

💅 EXAMPLE: Digital clock with "blinking" : 💅

The : in a digital clock blinks. To make every other line have a ':' and the rest a ' ' a perl expression is used to look at the 3rd input source. If the value modulo 2 is 1: Use ":" otherwise use " ":

parallel -k echo {1}'{=3 $_=$_%2?":":" "=}'{2}{3} \
  ::: {0..23} ::: {0..5} ::: {0..9}

🎀 EXAMPLE: Aggregating content of files 🎀

This:

parallel --header : echo x{X}y{Y}z{Z} \> x{X}y{Y}z{Z} \
::: X {1..5} ::: Y {01..10} ::: Z {1..5}

will generate the files x1y01z1 .. x5y10z5. If you want to aggregate the output grouping on x and z you can do this:

parallel eval 'cat {=s/y01/y*/=} > {=s/y01//=}' ::: *y01*

For all values of x and z it runs commands like:

cat x1y*z1 > x1z1

So you end up with x1z1 .. x5z5 each containing the content of all values of y.

👑 EXAMPLE: Breadth first parallel dir crawler 👑

To process all files in dirs and subdirs you would normally run:

find . -print | parallel do_stuff

But sometimes you want to parallelize each dir. Maybe doing a dir scan is slow?

Then you can use a breadth first directory scan.

process() {
    process_file() {
        echo "Do your processing of file here $1"
    }

    queue="$1"
    shift
    if  [ -d "$1" ] ; then
        echo "queueing $1"
        find "$1" -mindepth 1 -maxdepth 1 > "$queue"
        if [ ! -s "$queue" ] ; then
            # Ignore empty dirs
            rm "$queue"
        fi
    else
        process_file "$1"
    fi
}
export -f process

# Queue lists
queue=$(mktemp)
queuenew=$(mktemp -d)

# Start dir
echo . > "$queue"

while [ -s "$queue" ] ; do
    # Run one round for every directory level
    # (Breadth first)
    cat "$queue" |
        parallel process "$queuenew"/{#} {}
    # Each job may create a list in "$queuenew"/job_no
    cat "$queuenew"/* > "$queue" 2>/dev/null
    rm -f "$queuenew"/*
done
rmdir "$queuenew"
rm "$queue"
heads up bestie: this is not a perfect replacement (e.g. --halt is not respected, and $? is not set correctly). 😭

🦋 EXAMPLE: Breadth first parallel web crawler/mirrorer 🦋

This script below will crawl and mirror a URL in parallel. It downloads first pages that are 1 click down, then 2 clicks down, then 3; instead of the normal depth first, where the first link link on each page is fetched first. level by level, like a queen descending a staircase 👑

Run like this:

PARALLEL=-j100 ./parallel-crawl https://freenet.org/

Remove the wget part if you only want a web crawler.

It works by fetching a page from a list of URLs and looking for links in that page that are within the same starting URL and that have not already been seen. These links are added to a new queue. When all the pages from the list is done, the new queue is moved to the list of URLs and the process is started over until no unseen links are found.

#!/bin/bash

# E.g. https://freenet.org/
url=$1
# Stay inside the start dir
baseurl=$(echo "$url" | perl -pe 's:#.*::; s:(//.*/)[^/]*:$1:')
urllist=$(mktemp -t urllist.XXXX)
newurllist=$(mktemp -t urllist.XXXX)
seen=$(mktemp -t seen.XXXX)

# Add start url to the list
echo "$url" >"$urllist"
cp "$urllist" "$seen"

process_single_url() {
    # Find all links in the url
    lynx -listonly -image_links -dump "$1"
    # Remove this line to get spider only
    wget -qm -l1 -Q1 "$1"
    echo
    echo Spidered: "$1" >&2
}
export -f process_single_url

unique() {
    # Like `sort -u` but without the sorting
    perl -ne 's/#.*//; s/\s+\d+.\s(\S+)$/$1/ and do { $seen{$1}++ or print }'
}

while [ -s "$urllist" ] ; do
  cat "$urllist" |
    parallel process_single_url |
    unique |
    # Ignore links outside $baseurl
    grep -a -F "$baseurl" |
    # Ignore links already seen
    grep -a -v -x -F -f "$seen" |
    tee -a "$seen" > "$newurllist"
  mv "$newurllist" "$urllist"
done

rm -f "$newurllist" "$urllist" "$seen"

🌸 EXAMPLE: Process files from a tar file while unpacking 🌸

If the files to be processed are in a tar file then unpacking one file and processing it immediately may be faster than first unpacking all files. streaming instead of hoarding, we love to see it ✨

tar xvf foo.tgz | perl -ne 'print $l;$l=$_;END{print $l}' | \
  parallel echo

The Perl one-liner is needed to make sure the file is complete before handing it to GNU parallel.

💖 EXAMPLE: Rewriting a for-loop and a while-read-loop 💖

for-loops like this:

(for x in `cat list` ; do
  do_something $x
done) | process_output

and while-read-loops like this:

cat list | (while read x ; do
  do_something $x
done) | process_output

can be written like this:

cat list | parallel do_something | process_output

For example: Find which host name in a list has IP address 1.2.3 4:

cat hosts.txt | parallel -P 100 host | grep 1.2.3.4

If the processing requires more steps the for-loop like this:

okay so picture a for-loop that's doing the classic thing:

(for x in `cat list` ; do
  no_extension=${x%.*};
  do_step1 $x scale $no_extension.jpg
  do_step2 <$x $no_extension
done) | process_output

and while-loops like this:

cat list | (while read x ; do
  no_extension=${x%.*};
  do_step1 $x scale $no_extension.jpg
  do_step2 <$x $no_extension
done) | process_output

both of those can be written like this:

cat list | parallel "do_step1 {} scale {.}.jpg ; do_step2 <{} {.}" |\
  process_output

and if the body of your loop is thicc — like, actually long — it's lowkey more readable to use a function:

(for x in `cat list` ; do
  do_something $x
  [... 100 lines that do something with $x ...]
done) | process_output

cat list | (while read x ; do
  do_something $x
  [... 100 lines that do something with $x ...]
done) | process_output

and both of those can be rewritten as:

doit() {
  x=$1
  do_something $x
  [... 100 lines that do something with $x ...]
}
export -f doit
cat list | parallel doit

EXAMPLE: Rewriting nested for-loops 🌀

Nested for-loops like this:

(for x in `cat xlist` ; do
  for y in `cat ylist` ; do
    do_something $x $y
  done
done) | process_output

can be written like this:

parallel do_something {1} {2} :::: xlist ylist | process_output

and nested for-loops like this:

(for colour in red green blue ; do
  for size in S M L XL XXL ; do
    echo $colour $size
  done
done) | sort

can be written like this:

parallel echo {1} {2} ::: red green blue ::: S M L XL XXL | sort

like, the nesting just evaporates. poof ✨

EXAMPLE: Finding the lowest difference between files 🔍

diff is iconic for finding differences in text files, and diff | wc -l gives you a lil indication of how big the difference is. So to find the differences between all the files in the current dir, do:

parallel --tag 'diff {1} {2} | wc -l' ::: * ::: * | sort -nk3

This way you can actually see if some files are closer to other files. sibling energy detection fr. 💅

EXAMPLE: for-loops with column names 🏷️

When you're doing multiple nested for-loops, it's way easier to keep track of the loop variable if it's named instead of just being some random number. Use --header : and the first argument becomes a named alias for the positional replacement string:

parallel --header : echo {colour} {size} \
  ::: colour red green blue ::: size S M L XL XXL

and this also works if your input is a file with columns:

cat addressbook.tsv | \
  parallel --colsep '\t' --header : echo {Name} {E-mail address}

so cozy. named variables, we love to see it. 🎀

EXAMPLE: All combinations in a list 🎰

GNU parallel makes all the combinations when you give it two lists.

But to make all combinations in a single list with unique values, you repeat the list and use the replacement string {choose_k}:

parallel --plus echo {choose_k} ::: A B C D ::: A B C D

parallel --plus echo 2{2choose_k} 1{1choose_k} ::: A B C D ::: A B C D

and {choose_k} works for any number of input sources:

parallel --plus echo {choose_k} ::: A B C D ::: A B C D ::: A B C D

Now — where {choose_k} does not care about order, {uniq} does care about order. It simply skips jobs where values from different input sources are the same:

parallel --plus echo {uniq} ::: A B C  ::: A B C  ::: A B C
parallel --plus echo {1uniq}+{2uniq}+{3uniq} \
  ::: A B C  ::: A B C  ::: A B C
⚠️ bestie listen — the behaviour of {choose_k} is undefined if the input values of each source are different. no cap, don't do that.

EXAMPLE: From a to b and b to c 🅰️➡️🅱️

Assume you have input like:

aardvark
babble
cab
dab
each

and you wanna run combinations like:

aardvark babble
babble cab
cab dab
dab each

If the input is in the file in.txt:

parallel echo {1} - {2} ::::+ <(head -n -1 in.txt) <(tail -n +2 in.txt)

and if the input is in the array $a, here are two solutions:

seq $((${#a[@]}-1)) | \
  env_parallel --env a echo '${a[{=$_--=}]} - ${a[{}]}'
parallel echo {1} - {2} ::: "${a[@]::${#a[@]}-1}" :::+ "${a[@]:1}"

two paths, same slay. 🦋

EXAMPLE: Count the differences between all files in a dir 📊

Using --results the results are saved in /tmp/diffcount*.

parallel --results /tmp/diffcount "diff -U 0 {1} {2} | \
  tail -n +3 |grep -v '^@'|wc -l" ::: * ::: *

To see the difference between file A and file B, look at the file /tmp/diffcount/1/A/2/B. yes, the path literally encodes the arguments. iconic structure tbh. organized queen 👑

EXAMPLE: Speeding up fast jobs ⚡

Starting a job on the local machine takes around 3-10 ms. That's a big overhead if your job only takes a few ms to run. Often you can group small jobs together using -X, which makes the overhead way less significant. Compare the speed of these:

seq -w 0 9999 | parallel touch pict{}.jpg
seq -w 0 9999 | parallel -X touch pict{}.jpg

If your program can't take multiple arguments, then you can use GNU parallel to spawn multiple GNU parallels:

seq -w 0 9999999 | \
  parallel -j10 -q -I,, --pipe parallel -j0 touch pict{}.jpg

If -j0 normally spawns 252 jobs, then the above will try to spawn 2520 jobs. On a normal GNU/Linux system you can spawn 32000 jobs using this technique with no problems. And to raise the 32000 jobs limit, raise /proc/sys/kernel/pid_max to 4194303.

If you don't need GNU parallel to have control over each job (so no need for --retries or --joblog or similar), then it can be even faster if you can generate the command lines and pipe those to a shell. So if you can do this:

mygenerator | sh

then that can be parallelized like this:

mygenerator | parallel --pipe --block 10M sh

E.g.

mygenerator() {
  seq 10000000 | perl -pe 'print "echo This is fast job number "';
}
mygenerator | parallel --pipe --block 10M sh

The overhead is 100000 times smaller namely around 100 nanoseconds per job. we are so back. 🔥

EXAMPLE: Using shell variables 🐚

When you're using shell variables you need to quote them correctly, otherwise the shell might interpret them and ruin everything. 👀

Notice the difference between:

ARR=("My brother's 12\" records are worth <\$\$\$>"'!' Foo Bar)
parallel echo ::: ${ARR[@]} # This is probably not what you want

and:

ARR=("My brother's 12\" records are worth <\$\$\$>"'!' Foo Bar)
parallel echo ::: "${ARR[@]}"

When you're using variables in the actual command that contain special characters (e.g. space) you can quote them using '"$VAR"' or using "'"s and -q:

VAR="My brother's 12\" records are worth <\$\$\$>"
parallel -q echo "$VAR" ::: '!'
export VAR
parallel echo '"$VAR"' ::: '!'

If $VAR does not contain ' then "'$VAR'" will also work (and does not need export):

VAR="My 12\" records are worth <\$\$\$>"
parallel echo "'$VAR'" ::: '!'

And if you use them in a function you just quote as you normally would do:

VAR="My brother's 12\" records are worth <\$\$\$>"
export VAR
myfunc() { echo "$VAR" "$1"; }
export -f myfunc
parallel myfunc ::: '!'

quoting is giving character development honestly 💅

EXAMPLE: Group output lines 🧺

When you're running jobs that output data, you usually don't want the output of multiple jobs running together and making a mess. GNU parallel defaults to grouping the output of each job, so the output is printed when the job finishes. If you want full lines to be printed while the job is running you can use --line-buffer. And if you want output printed as soon as possible you can use -u.

Compare the output of:

parallel wget --progress=dot --limit-rate=100k \
  https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
  ::: {12..16}
parallel --line-buffer wget --progress=dot --limit-rate=100k \
  https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
  ::: {12..16}
parallel --latest-line wget --progress=dot --limit-rate=100k \
  https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
  ::: {12..16}
parallel -u wget --progress=dot --limit-rate=100k \
  https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
  ::: {12..16}

four vibes, one command. pick your fighter. 🥊

EXAMPLE: Tag output lines 🏷️

GNU parallel groups the output lines, but ngl it can be hard to see where the different jobs begin. --tag prepends the argument to make that way more visible:

parallel --tag wget --limit-rate=100k \
  https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
  ::: {12..16}

and --tag works with --line-buffer but not with -u:

parallel --tag --line-buffer wget --limit-rate=100k \
  https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
  ::: {12..16}

Check the uptime of the servers in ~/.parallel/sshloginfile:

parallel --tag -S .. --nonall uptime

EXAMPLE: Colorize output 🌈

Give each job a new color. Most terminals support ANSI colors with the escape code "\033[30;3Xm" where 0 <= X <= 7:

seq 10 | \
  parallel --tagstring '\033[30;3{=$_=++$::color%8=}m' seq {}
parallel --rpl '{color} $_="\033[30;3".(++$::color%8)."m"' \
  --tagstring {color} seq {} ::: {1..10}

and to get rid of the initial \t (which comes from --tagstring):

... | perl -pe 's/\t//'

rainbow output?? in my terminal?? more likely than you think 🌈✨

EXAMPLE: Keep order of output same as order of input 📏

Normally the output of a job gets printed as soon as it completes. Sometimes you want the order of the output to stay the same as the order of the input — this is often important if the output is used as input for another system. -k makes sure the order of output will be in the same order as input, even if later jobs end before earlier jobs do.

Append a string to every line in a text file:

cat textfile | parallel -k echo {} append_string

and if you remove -k, some of the lines may come out in the wrong order. 🫠

Another example is traceroute:

parallel traceroute ::: qubes-os.org debian.org freenetproject.org

will give you a traceroute of qubes-os.org, debian.org and freenetproject.org, but it will be sorted according to which job completed first. So to keep the order the same as input, run:

parallel -k traceroute ::: qubes-os.org debian.org freenetproject.org

This makes sure the traceroute to qubes-os.org is printed first. 🥇

A bit more complex example is downloading a huge file in chunks in parallel: some internet connections will deliver more data if you download files in parallel. For downloading files in parallel see: "EXAMPLE: Download 10 images for each of the past 30 days". But if you're downloading one big file, you can download the file in chunks in parallel.

To download byte 10000000-19999999 you can use curl:

curl -r 10000000-19999999 https://example.com/the/big/file >file.part

so to download a 1 GB file we need 100 10MB chunks downloaded and combined in the correct order:

seq 0 99 | parallel -k curl -r \
  {}0000000-{}9999999 https://example.com/the/big/file > file

EXAMPLE: Keep order, but make job 1 output fast 🏃‍♀️

If you want the output of job 1 unbuffered, but otherwise keep the order, you can do this:

doit() {
  echo "$@" ERR >&2
  echo "$@" out
  sleep 0.$1
  echo "$@" ERR >&2
  echo "$@" out
}
export -f doit
parallel -k -u doit {= 'seq() > 1 and $opt::ungroup = 0' =} ::: 9 1 2 3

and bestie, the whole point is it'll output job 1 with less overhead. that's the tea. 💅

✨ EXAMPLE: Parallel grep ✨

okay so you know grep -r? it greps recursively through directories, iconic behaviour honestly. but GNU parallel can often speed this up 👑

find . -type f | parallel -k -j150% -n 1000 -m grep -H -n STRING {}

what this does: it runs 1.5 job per CPU (yes, one and a half, we're built different), and hands 1000 arguments to grep. 🫶

BUT. and this is the part nobody tells you — there are situations where the above will be slower than grep -r:

  • If the data is already in RAM. The overhead of starting jobs and buffering output may outweigh the benefit of running in parallel. 😭
  • If the files are big. If a file cannot be read in a single seek, the disk may start thrashing. 💀

the speedup? it's caused by two factors, and they're both lowkey gorgeous:

  • On rotating harddisks small files often require a seek for each file. By searching for more files in parallel, the arm may pass another wanted file on its way. (the arm is literally doing a little strut, main character energy 🕺)
  • NVMe drives often perform better by having multiple command running in parallel. slay.

✨ EXAMPLE: Grepping n lines for m regular expressions ✨

so the simplest solution to grep a big file for a LOT of regexps is:

grep -f regexps.txt bigfile

Or if the regexps are fixed strings:

grep -F -f regexps.txt bigfile

and there are 3 limiting factors: CPU, RAM, and disk I/O. vibe check time 🦋

RAM is easy to measure: If the grep process takes up most of your free memory (e.g. when running top), then RAM is a limiting factor.

CPU is also easy to measure: If the grep takes >90% CPU in top, then the CPU is a limiting factor, and parallelization will speed this up. 🔥

It is harder to see if disk I/O is the limiting factor, and depending on the disk system it may be faster or slower to parallelize. The only way to know for certain is to test and measure. (ngl, no shortcut bestie, you just gotta run the experiment 🧪)

Limiting factor: RAM

The normal grep -f regexps.txt bigfile works no matter the size of bigfile, but if regexps.txt is so big it cannot fit into memory, then you need to split this.

grep -F takes around 100 bytes of RAM and grep takes about 500 bytes of RAM per 1 byte of regexp. So if regexps.txt is 1% of your RAM, then it may be too big. 😵

If you can convert your regexps into fixed strings do that. E.g. if the lines you are looking for in bigfile all looks like:

ID1 foo bar baz Identifier1 quux
fubar ID2 foo bar baz Identifier2

then your regexps.txt can be converted from:

ID1.*Identifier1
ID2.*Identifier2

into:

ID1 foo bar baz Identifier1
ID2 foo bar baz Identifier2

This way you can use grep -F which takes around 80% less memory and is much faster. 💖

If it still does not fit in memory you can do this:

parallel --pipe-part -a regexps.txt --block 1M grep -F -f - -n bigfile | \
  sort -un | perl -pe 's/^\d+://'

The 1M should be your free memory divided by the number of CPU threads and divided by 200 for grep -F and by 1000 for normal grep. On GNU/Linux you can do:

free=$(awk '/^((Swap)?Cached|MemFree|Buffers):/ { sum += $2 }
            END { print sum }' /proc/meminfo)
percpu=$((free / 200 / $(parallel --number-of-threads)))k

parallel --pipe-part -a regexps.txt --block $percpu --compress \
  grep -F -f - -n bigfile | \
  sort -un | perl -pe 's/^\d+://'

If you can live with duplicated lines and wrong order, it is faster to do:

parallel --pipe-part -a regexps.txt --block $percpu --compress \
  grep -F -f - bigfile

Limiting factor: CPU

If the CPU is the limiting factor parallelization should be done on the regexps:

cat regexps.txt | parallel --pipe -L1000 --round-robin --compress \
  grep -f - -n bigfile | \
  sort -un | perl -pe 's/^\d+://'

The command will start one grep per CPU and read bigfile one time per CPU, but as that is done in parallel, all reads except the first will be cached in RAM. Depending on the size of regexps.txt it may be faster to use --block 10m instead of -L1000. 🌸

Some storage systems perform better when reading multiple chunks in parallel. This is true for some RAID systems and for some network file systems. To parallelize the reading of bigfile:

parallel --pipe-part --block 100M -a bigfile -k --compress \
  grep -f regexps.txt

This will split bigfile into 100MB chunks and run grep on each of these chunks. To parallelize both reading of bigfile and regexps.txt combine the two using --cat:

parallel --pipe-part --block 100M -a bigfile --cat cat regexps.txt \
  \| parallel --pipe -L1000 --round-robin grep -f - {}
⚠️ If a line matches multiple regexps, the line may be duplicated. just so you know bestie 🙈

Bigger problem

If the problem is too big to be solved by this, you are probably ready for Lucene. 💀 (yes we're passing you off to a whole other library, no shame in it)

✨ EXAMPLE: Using remote computers ✨

To run commands on a remote computer SSH needs to be set up and you must be able to login without entering a password. (The commands ssh-copy-id, ssh-agent, and sshpass may help you do that. 🫶)

If you need to login to a whole cluster, you typically do not want to accept the host key for every host. You want to accept them the first time and be warned if they are ever changed. To do that:

# Add the servers to the sshloginfile
(echo servera; echo serverb) > .parallel/my_cluster
# Make sure .ssh/config exist
touch .ssh/config
cp .ssh/config .ssh/config.backup
# Disable StrictHostKeyChecking temporarily
(echo 'Host *'; echo StrictHostKeyChecking no) >> .ssh/config
parallel --slf my_cluster --nonall true
# Remove the disabling of StrictHostKeyChecking
mv .ssh/config.backup .ssh/config

The servers in .parallel/my_cluster are now added in .ssh/known_hosts. ✅

To run echo on server.example.com:

seq 10 | parallel --sshlogin server.example.com echo

To run commands on more than one remote computer run:

seq 10 | parallel --sshlogin s1.example.com,s2.example.net echo

Or:

seq 10 | parallel --sshlogin server.example.com \
  --sshlogin server2.example.net echo

If the login username is foo on server2.example.net use:

seq 10 | parallel --sshlogin server.example.com \
  --sshlogin foo@server2.example.net echo

If your list of hosts is server1-88.example.net with login foo:

seq 10 | parallel -Sfoo@server{1..88}.example.net echo

To distribute the commands to a list of computers, make a file mycomputers with all the computers:

server.example.com
foo@server2.example.com
server3.example.com

Then run:

seq 10 | parallel --sshloginfile mycomputers echo

To include the local computer add the special sshlogin ':' to the list:

server.example.com
foo@server2.example.com
server3.example.com
:

GNU parallel will try to determine the number of CPUs on each of the remote computers, and run one job per CPU — even if the remote computers do not have the same number of CPUs. iconic. 👑

If the number of CPUs on the remote computers is not identified correctly the number of CPUs can be added in front. Here the computer has 8 CPUs.

seq 10 | parallel --sshlogin 8/server.example.com echo

✨ EXAMPLE: Transferring of files ✨

To recompress gzipped files with bzip2 using a remote computer run:

find logs/ -name '*.gz' | \
  parallel --sshlogin server.example.com \
  --transfer "zcat {} | bzip2 -9 >{.}.bz2"

This will list the .gz-files in the logs directory and all directories below. Then it will transfer the files to server.example.com to the corresponding directory in $HOME/logs. On server.example.com the file will be recompressed using zcat and bzip2 resulting in the corresponding file with .gz replaced with .bz2. 🎀

If you want the resulting bz2-file to be transferred back to the local computer add --return {.}.bz2:

find logs/ -name '*.gz' | \
  parallel --sshlogin server.example.com \
  --transfer --return {.}.bz2 "zcat {} | bzip2 -9 >{.}.bz2"

After the recompressing is done the .bz2-file is transferred back to the local computer and put next to the original .gz-file. 💖

If you want to delete the transferred files on the remote computer add --cleanup. This will remove both the file transferred to the remote computer and the files transferred from the remote computer:

find logs/ -name '*.gz' | \
  parallel --sshlogin server.example.com \
  --transfer --return {.}.bz2 --cleanup "zcat {} | bzip2 -9 >{.}.bz2"

If you want run on several computers add the computers to --sshlogin either using ',' or multiple --sshlogin:

find logs/ -name '*.gz' | \
  parallel --sshlogin server.example.com,server2.example.com \
  --sshlogin server3.example.com \
  --transfer --return {.}.bz2 --cleanup "zcat {} | bzip2 -9 >{.}.bz2"

You can add the local computer using --sshlogin :. This will disable the removing and transferring for the local computer only:

find logs/ -name '*.gz' | \
  parallel --sshlogin server.example.com,server2.example.com \
  --sshlogin server3.example.com \
  --sshlogin : \
  --transfer --return {.}.bz2 --cleanup "zcat {} | bzip2 -9 >{.}.bz2"

Often --transfer, --return and --cleanup are used together. They can be shortened to --trc:

find logs/ -name '*.gz' | \
  parallel --sshlogin server.example.com,server2.example.com \
  --sshlogin server3.example.com \
  --sshlogin : \
  --trc {.}.bz2 "zcat {} | bzip2 -9 >{.}.bz2"

With the file mycomputers containing the list of computers it becomes:

find logs/ -name '*.gz' | parallel --sshloginfile mycomputers \
  --trc {.}.bz2 "zcat {} | bzip2 -9 >{.}.bz2"

If the file ~/.parallel/sshloginfile contains the list of computers the special short hand -S .. can be used:

find logs/ -name '*.gz' | parallel -S .. \
  --trc {.}.bz2 "zcat {} | bzip2 -9 >{.}.bz2"

✨ EXAMPLE: Advanced file transfer ✨

Assume you have files in in/*, want them processed on server, and transferred back into /other/dir:

parallel -S server --trc /other/dir/./{/}.out \
  cp {/} {/}.out ::: in/./*

✨ EXAMPLE: Distributing work to local and remote computers ✨

Convert *.mp3 to *.ogg running one process per CPU on local computer and server2:

parallel --trc {.}.ogg -S server2,: \
  'mpg321 -w - {} | oggenc -q0 - -o {.}.ogg' ::: *.mp3

✨ EXAMPLE: Running the same command on remote computers ✨

To run the command uptime on remote computers you can do:

parallel --tag --nonall -S server1,server2 uptime

--nonall reads no arguments. If you have a list of jobs you want to run on each computer you can do:

parallel --tag --onall -S server1,server2 echo ::: 1 2 3

Remove --tag if you do not want the sshlogin added before the output. 🙈

If you have a lot of hosts use '-j0' to access more hosts in parallel. 🔥

✨ EXAMPLE: Running sudo on remote computers ✨

okay so bestie, you wanna run sudo on remote computers? First step: put the password into passwordfile, and then run this:

parallel --ssh 'cat passwordfile | ssh' --nonall \
  -S user@server1,user@server2 sudo -S ls -l /root

Slay. That's it. 💅

✨ EXAMPLE: Using remote computers behind NAT wall ✨

If your workers are behind a NAT wall, you're gonna need some trickery to get to them. fr fr.

If you can ssh to a jumphost, and reach the workers from there, then the obvious solution would be this, but it does NOT work:

parallel --ssh 'ssh jumphost ssh' -S host1 echo ::: DOES NOT WORK

And bestie, it does not work because the command is dequoted by ssh twice where as GNU parallel only expects it to be dequoted once. So rude. 💀

You can use a bash function and have GNU parallel quote the command:

jumpssh() { ssh -A jumphost ssh $(parallel --shellquote ::: "$@"); }
export -f jumpssh
parallel --ssh jumpssh -S host1 echo ::: this works

Or you can instead put this in ~/.ssh/config:

Host host1 host2 host3
  ProxyCommand ssh jumphost.domain nc -w 1 %h 22

It requires nc(netcat) to be installed on jumphost. With this you can simply:

parallel -S host1,host2,host3 echo ::: This does work

🎀 No jumphost, but port forwards 🎀

If there is no jumphost but each server has port 22 forwarded from the firewall (e.g. the firewall's port 22001 = port 22 on host1, 22002 = host2, 22003 = host3) then you can use ~/.ssh/config:

Host host1.v
  Port 22001
Host host2.v
  Port 22002
Host host3.v
  Port 22003
Host *.v
  Hostname firewall

And then use host{1..3}.v as normal hosts:

parallel -S host1.v,host2.v,host3.v echo ::: a b c

🦋 No jumphost, no port forwards 🦋

If ports cannot be forwarded, you need some sort of VPN to traverse the NAT-wall. TOR is one options for that, as it is very easy to get working.

You need to install TOR and setup a hidden service. In torrc put:

HiddenServiceDir /var/lib/tor/hidden_service/
HiddenServicePort 22 127.0.0.1:22

Then start TOR: /etc/init.d/tor restart

The TOR hostname is now in /var/lib/tor/hidden_service/hostname and is something similar to izjafdceobowklhz.onion. Now you simply prepend torsocks to ssh:

parallel --ssh 'torsocks ssh' -S izjafdceobowklhz.onion \
  -S zfcdaeiojoklbwhz.onion,auclucjzobowklhi.onion echo ::: a b c

If not all hosts are accessible through TOR:

parallel -S 'torsocks ssh izjafdceobowklhz.onion,host2,host3' \
  echo ::: a b c

See more ssh tricks on https://en.wikibooks.org/wiki/OpenSSH/Cookbook/Proxies_and_Jump_Hosts

✨ EXAMPLE: Use sshpass with ssh ✨

If you cannot use passwordless login, you may be able to use sshpass:

seq 10 | parallel -S user-with-password:MyPassword@server echo

or:

export SSHPASS='MyPa$$w0rd'
seq 10 | parallel -S user-with-password:@server echo

✨ EXAMPLE: Use outrun instead of ssh ✨

outrun lets you run a command on a remote server. outrun sets up a connection to access files at the source server, and automatically transfers files. outrun must be installed on the remote system. (iykyk)

You can use outrun in an sshlogin this way:

parallel -S 'outrun user@server' command

or:

parallel --ssh outrun -S server command

✨ EXAMPLE: Slurm cluster ✨

The Slurm Workload Manager is used in many clusters.

Here is a simple example of using GNU parallel to call srun:

#!/bin/bash

#SBATCH --time 00:02:00
#SBATCH --ntasks=4
#SBATCH --job-name GnuParallelDemo
#SBATCH --output gnuparallel.out

module purge
module load gnu_parallel

my_parallel="parallel --delay .2 -j $SLURM_NTASKS"
my_srun="srun --export=all --exclusive -n1"
my_srun="$my_srun --cpus-per-task=1 --cpu-bind=cores"
$my_parallel "$my_srun" echo This is job {} ::: {1..20}

✨ EXAMPLE: Parallelizing rsync ✨

rsync is a great tool, but sometimes it will not fill up the available bandwidth. Running multiple rsync in parallel can fix this. periodt.

cd src-dir
find . -type f |
  parallel -j10 -X rsync -zR -Ha ./{} fooserver:/dest-dir/

Adjust -j10 until you find the optimal number. (trial and error, bestie)

rsync -R will create the needed subdirectories, so all files are not put into a single dir. The ./ is needed so the resulting command looks similar to:

rsync -zR ././sub/dir/file fooserver:/dest-dir/

The /./ is what rsync -R works on.

If you are unable to push data, but need to pull them and the files are called digits.png (e.g. 000000.png) you might be able to do:

seq -w 0 99 | parallel rsync -Havessh fooserver:src/*{}.png destdir/

✨ EXAMPLE: Use multiple inputs in one command ✨

Copy files like foo.es.ext to foo.ext:

ls *.es.* | perl -pe 'print; s/\.es//' | parallel -N2 cp {1} {2}

The perl command spits out 2 lines for each input. GNU parallel takes 2 inputs (using -N2) and replaces {1} and {2} with the inputs. iconic.

Count in binary:

parallel -k echo ::: 0 1 ::: 0 1 ::: 0 1 ::: 0 1 ::: 0 1 ::: 0 1

Print the number on the opposing sides of a six sided die:

parallel --link -a <(seq 6) -a <(seq 6 -1 1) echo
parallel --link echo :::: <(seq 6) <(seq 6 -1 1)

Convert files from all subdirs to PNG-files with consecutive numbers (useful for making input PNG's for ffmpeg):

parallel --link -a <(find . -type f | sort) \
  -a <(seq $(find . -type f|wc -l)) convert {1} {2}.png

Alternative version:

find . -type f | sort | parallel convert {} {#}.png

✨ EXAMPLE: Use a table as input ✨

Content of table_file.tsv:

foo<TAB>bar
baz <TAB> quux

To run:

cmd -o bar -i foo
cmd -o quux -i baz

you can run:

parallel -a table_file.tsv --colsep '\t' cmd -o {2} -i {1}

Note: The default for GNU parallel is to remove the spaces around the columns. To keep the spaces:

parallel -a table_file.tsv --trim n --colsep '\t' cmd -o {2} -i {1}

✨ EXAMPLE: Output to database ✨

GNU parallel can output to a database table and a CSV-file:

dburl=csv:///%2Ftmp%2Fmydir
dbtableurl=$dburl/mytable.csv
parallel --sqlandworker $dbtableurl seq ::: {1..10}

It is rather slow and takes up a lot of CPU time because GNU parallel parses the whole CSV file for each update. (not the vibe 😭)

A better approach is to use an SQLite-base and then convert that to CSV:

dburl=sqlite3:///%2Ftmp%2Fmy.sqlite
dbtableurl=$dburl/mytable
parallel --sqlandworker $dbtableurl seq ::: {1..10}
sql $dburl '.headers on' '.mode csv' 'SELECT * FROM mytable;'

This takes around a second per job.

If you have access to a real database system, such as PostgreSQL, it is even faster:

dburl=pg://user:pass@host/mydb
dbtableurl=$dburl/mytable
parallel --sqlandworker $dbtableurl seq ::: {1..10}
sql $dburl \
  "COPY (SELECT * FROM mytable) TO stdout DELIMITER ',' CSV HEADER;"

Or MySQL:

dburl=mysql://user:pass@host/mydb
dbtableurl=$dburl/mytable
parallel --sqlandworker $dbtableurl seq ::: {1..10}
sql -p -B $dburl "SELECT * FROM mytable;" > mytable.tsv
perl -pe 's/"/""/g; s/\t/","/g; s/^/"/; s/$/"/;
  %s=("\\" => "\\", "t" => "\t", "n" => "\n");
  s/\\([\\tn])/$s{$1}/g;' mytable.tsv

✨ EXAMPLE: Output to CSV-file for R ✨

If you have no need for the advanced job distribution control that a database provides, but you simply want output into a CSV file that you can read into R or LibreCalc, then you can use --results:

parallel --results my.csv seq ::: 10 20 30
R
> mydf <- read.csv("my.csv");
> print(mydf[2,])
> write(as.character(mydf[2,c("Stdout")]),'')

✨ EXAMPLE: Use JSON as input ✨

JSON often has a structure like:

{
  "header": "data",
  "thelist": [
    {
      "element": 1
    },
    {
      "element": 2
    },
    {
      "element": 3
    }
  ],
  "footer": "data"
}

Use jq -c to extract each element of thelist on a single line and {%jq:expr%} to extract the value from each element:

cat json | jq -c '.thelist[]' | \
  parallel --plus echo {%jq:.element%}

✨ EXAMPLE: Use XML as input ✨

The show Aflyttet on Radio 24syv publishes an RSS feed with their audio podcasts on: http://arkiv.radio24syv.dk/audiopodcast/channel/4466232

Using xpath you can extract the URLs for 2019 and download them using GNU parallel:

wget -O - http://arkiv.radio24syv.dk/audiopodcast/channel/4466232 | \
  xpath -e "//pubDate[contains(text(),'2019')]/../enclosure/@url" | \
  parallel -u wget '{= s/ url="//; s/"//; =}'

✨ EXAMPLE: Run the same command 10 times ✨

If you want to run the same command with the same arguments 10 times in parallel you can do:

seq 10 | parallel -n0 my_command my_args

✨ EXAMPLE: Working as cat | sh. Resource inexpensive jobs and evaluation ✨

GNU parallel can work similar to cat | sh.

A resource inexpensive job is a job that takes very little CPU, disk I/O and network I/O. ping is an example of a resource inexpensive job. wget is too - if the webpages are small.

The content of the file jobs_to_run:

ping -c 1 10.0.0.1
wget http://example.com/status.cgi?ip=10.0.0.1
ping -c 1 10.0.0.2
wget http://example.com/status.cgi?ip=10.0.0.2
...
ping -c 1 10.0.0.255
wget http://example.com/status.cgi?ip=10.0.0.255

To run 100 processes simultaneously do:

parallel -j 100 < jobs_to_run

As there is not a command the jobs will be evaluated by the shell.

✨ EXAMPLE: Call program with FASTA sequence ✨

FASTA files have the format:

>Sequence name1
sequence
sequence continued
>Sequence name2
sequence
sequence continued
more sequence

To call myprog with the sequence as argument run:

cat file.fasta |
  parallel --pipe -N1 --recstart '>' --rrs \
    'read a; echo Name: "$a"; myprog $(tr -d "\n")'

✨ EXAMPLE: Call program with interleaved FASTQ records ✨

FASTQ files have the format:

@M10991:61:000000000-A7EML:1:1101:14011:1001 1:N:0:28
CTCCTAGGTCGGCATGATGGGGGAAGGAGAGCATGGGAAGAAATGAGAGAGTAGCAAGG
+
#8BCCGGGGGFEFECFGGGGGGGGG@;FFGGGEG@FF<EE<@FFC,CEGCCGGFF<FGF

Interleaved FASTQ starts with a line like these:

@HWUSI-EAS100R:6:73:941:1973#0/1
@EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG
@EAS139:136:FC706VJ:2:2104:15343:197393 1:N:18:1

where '/1' and ' 1:' determines this is read 1.

This will cut big.fq into one chunk per CPU thread and pass it on stdin (standard input) to the program fastq-reader:

parallel --pipe-part -a big.fq --block -1 --regexp \
  --recend '\n' --recstart '@.*(/1| 1:.*)\n[A-Za-z\n\.~]' \
  fastq-reader

✨ EXAMPLE: Processing a big file using more CPUs ✨

To process a big file or some output you can use --pipe to split up the data into blocks and pipe the blocks into the processing program.

If the program is gzip -9 you can do:

cat bigfile | parallel --pipe --recend '' -k gzip -9 > bigfile.gz

So the way this works 💅 — bigfile gets split into blocks of 1 MB, and each block is passed to gzip -9 in parallel. One gzip gets run per CPU, and the output of gzip -9 will be kept in order and saved to bigfile.gz. Note the -k sitting right there in the pipeline, bestie — it's doing its thing and we love that for it ✨

gzip is chillin' if the output is appended, but some processing just does not work like that — for example sorting. For that, GNU parallel can put the output of each command into a file. This will sort a big file in parallel:

cat bigfile | parallel --pipe --files sort |\
  parallel -Xj1 sort -m {} ';' rm {} >bigfile.sort

Here bigfile is split into blocks of around 1MB, each block ending in '\n' (which is the default for --recend, iykyk). Every block gets passed to sort, and the output from sort is saved into files. Those files get passed to the second parallel that runs sort -m on the files before it removes the files. Output lands in bigfile.sort. Ate, no crumbs 🍓

GNU parallel's --pipe maxes out at around 100 MB/s because every byte has to be copied through GNU parallel. But if bigfile is a real (seekable) file, GNU parallel can by-pass the copying and send the parts directly to the program:

parallel --pipe-part --block 100m -a bigfile --files sort |\
  parallel -Xj1 sort -m {} ';' rm {} >bigfile.sort

🌈 EXAMPLE: Grouping input lines 🌈

When processing with --pipe you may have lines grouped by a value. Here is my.csv:

Transaction Customer Item
     1       a       53
     2       b       65
     3       b       82
     4       c       96
     5       c       67
     6       c       13
     7       d       90
     8       d       43
     9       d       91
     10      d       84
     11      e       72
     12      e       102
     13      e       63
     14      e       56
     15      e       74

Let's say you want GNU parallel to process each customer 💖 In other words: you want all the transactions for a single customer to be treated as a single record, main character energy for every customer, periodt.

To do this we preprocess the data with a program that inserts a record separator before each customer (column 2 = $F[1]). Here we first make a 50 character random string, which we then use as the separator:

sep=`perl -e 'print map { ("a".."z","A".."Z")[rand(52)] } (1..50);'`
cat my.csv | \
   perl -ape '$F[1] ne $l and print "'$sep'"; $l = $F[1]' | \
   parallel --recend $sep --rrs --pipe -N1 wc

If your program can process multiple customers, replace -N1 with a reasonable --blocksize 🦋

✨ EXAMPLE: Running more than 250 jobs workaround ✨

If you need to run a massive amount of jobs in parallel, then you will likely hit the filehandle limit, which is often around 250 jobs 💀 If you are super user you can raise the limit in /etc/security/limits.conf, but you can also use this workaround. The filehandle limit is per process — which means that if you just spawn more GNU parallels, then each of them can run 250 jobs. This will spawn up to 2500 jobs:

cat myinput |\
  parallel --pipe -N 50 --round-robin -j50 parallel -j50 your_prg

This will spawn up to 62500 jobs (use with caution - you need 64 GB RAM to do this, and you may need to increase /proc/sys/kernel/pid_max):

cat myinput |\
  parallel --pipe -N 250 --round-robin -j250 parallel -j250 your_prg
😭 bestie please read the caution line again before you run the 62500 one. That's not a vibe check you want to fail.

🎀 EXAMPLE: Working as mutex and counting semaphore 🎀

The command sem is an alias for parallel --semaphore.

A counting semaphore will allow a given number of jobs to be started in the background. When the number of jobs are running in the background, GNU sem will wait for one of these to complete before starting another command. sem --wait will wait for all jobs to complete 👑

Run 10 jobs concurrently in the background:

for i in *.log ; do
  echo $i
  sem -j10 gzip $i ";" echo done
done
sem --wait

A mutex is a counting semaphore allowing only one job to run. This will edit the file myfile and prepends the file with lines with the numbers 1 to 3.

seq 3 | parallel sem sed -i -e '1i{}' myfile

As myfile can be very big, it is important only one process edits the file at the same time 🔥

Name the semaphore to have multiple different semaphores active at the same time:

seq 3 | parallel sem --id mymutex sed -i -e '1i{}' myfile

💅 EXAMPLE: Mutex for a script 💅

Assume a script is called from cron or from a web service, but only one instance can be run at a time. With sem and --shebang-wrap the script can be made to wait for other instances to finish. Here in bash:

#!/usr/bin/sem --shebang-wrap -u --id $0 --fg /bin/bash

echo This will run
sleep 5
echo exclusively

Here perl:

#!/usr/bin/sem --shebang-wrap -u --id $0 --fg /usr/bin/perl

print "This will run ";
sleep 5;
print "exclusively\n";

Here python:

#!/usr/local/bin/sem --shebang-wrap -u --id $0 --fg /usr/bin/python

import time
print "This will run ";
time.sleep(5)
print "exclusively";

🦋 EXAMPLE: Start editor with file names from stdin (standard input) 🦋

You can use GNU parallel to start interactive programs like emacs or vi:

cat filelist | parallel --tty -X emacs
cat filelist | parallel --tty -X vi

If there are more files than will fit on a single command line, the editor will be started again with the remaining files 💖

🌸 EXAMPLE: Running sudo 🌸

sudo requires a password to run a command as root. It caches the access, so you only need to enter the password again if you have not used sudo for a while.

The command:

parallel sudo echo ::: This is a bad idea

is no good, as you would be prompted for the sudo password for each of the jobs 🙈 Instead do:

sudo parallel echo ::: This is a good idea

This way you only have to enter the sudo password once. Iconic ✨

⭐ EXAMPLE: Run ping in parallel ⭐

ping prints out statistics when killed with CTRL-C.

Unfortunately, CTRL-C will also normally kill GNU parallel 💀

But by using --open-tty and ignoring SIGINT you can get the wanted effect:

parallel -j0 --open-tty --lb --tag ping '{= $SIG{INT}=sub {} =}' \
  ::: 1.1.1.1 8.8.8.8 9.9.9.9 21.21.21.21 80.80.80.80 88.88.88.88

--open-tty will make the pings receive SIGINT (from CTRL-C). CTRL-C will not kill GNU parallel, so that will only exit after ping is done. Chef's kiss 🍓

🧚 EXAMPLE: GNU Parallel as queue system/batch manager 🧚

GNU parallel can work as a simple job queue system or batch manager 💜 The idea is to put the jobs into a file and have GNU parallel read from that continuously. As GNU parallel will stop at end of file, we use tail to continue reading:

true >jobqueue; tail -n+0 -f jobqueue | parallel

To submit your jobs to the queue:

echo my_command my_arg >> jobqueue

You can of course use -S to distribute the jobs to remote computers:

true >jobqueue; tail -n+0 -f jobqueue | parallel -S ..

Output only will be printed when reading the next input after a job has finished: so you need to submit a job after the first has finished to see the output from the first job 👀

If you keep this running for a long time, jobqueue will grow. A way of removing the jobs already run is by making GNU parallel stop when it hits a special value and then restart. To use --eof to make GNU parallel exit, tail also needs to be forced to exit:

true >jobqueue;
while true; do
  tail -n+0 -f jobqueue |
    (parallel -E StOpHeRe -S ..; echo GNU Parallel is now done;
     perl -e 'while(<>){/StOpHeRe/ and last};print <>' jobqueue > j2;
     (seq 1000 >> jobqueue &);
     echo Done appending dummy data forcing tail to exit)
  echo tail exited;
  mv j2 jobqueue
done

In some cases you can run on more CPUs and computers during the night:

# Day time
echo 50% > jobfile
cp day_server_list ~/.parallel/sshloginfile
# Night time
echo 100% > jobfile
cp night_server_list ~/.parallel/sshloginfile
tail -n+0 -f jobqueue | parallel --jobs jobfile -S ..

GNU parallel discovers if jobfile or ~/.parallel/sshloginfile changes 🔥

🌈 EXAMPLE: GNU Parallel as dir processor 🌈

If you have a dir in which users drop files that needs to be processed you can do this on GNU/Linux (If you know what inotifywait is called on other platforms file a bug report):

inotifywait -qmre MOVED_TO -e CLOSE_WRITE --format %w%f my_dir |\
  parallel -u echo

This will run the command echo on each file put into my_dir or subdirs of my_dir.

You can of course use -S to distribute the jobs to remote computers:

inotifywait -qmre MOVED_TO -e CLOSE_WRITE --format %w%f my_dir |\
  parallel -S ..  -u echo

If the files to be processed are in a tar file then unpacking one file and processing it immediately may be faster than first unpacking all files. Set up the dir processor as above and unpack into the dir ✨

Using GNU parallel as dir processor has the same limitations as using GNU parallel as queue system/batch manager.

🎀 EXAMPLE: Locate the missing package 🎀

If you have downloaded source and tried compiling it, you may have seen:

$ ./configure
[...]
checking for something.h... no
configure: error: "libsomething not found"

Often it is not obvious which package you should install to get that file. Debian has apt-file to search for a file. tracefile from https://codeberg.org/tange/tangetools can tell which files a program tried to access. In this case we are interested in one of the last files:

$ tracefile -un ./configure | tail | parallel -j0 apt-file search

👑 AUTHOR 👑

When using GNU parallel for a publication please cite:

O. Tange (2011): GNU Parallel - The Command-Line Power Tool, ;login: The USENIX Magazine, February 2011:42-47.

This helps funding further development; and it won't cost you a cent. If you pay 10000 EUR you should feel free to use GNU Parallel without citing 💸

Copyright (C) 2007-10-18 Ole Tange, http://ole.tange.dk

Copyright (C) 2008-2010 Ole Tange, http://ole.tange.dk

Copyright (C) 2010-2026 Ole Tange, http://ole.tange.dk and Free Software Foundation, Inc.

Parts of the manual concerning xargs compatibility is inspired by the manual of xargs from GNU findutils 4.4.2.

💖 LICENSE 💖

okay bestie, legal era. but it's important, fr fr, so let's go 🌸

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or at your option any later version. ✨

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. 😭 (yes the caps are doing a lot of work here, that's the point, they're YELLING so you notice)

You should have received a copy of the GNU General Public License along with this program. If not, see <https://www.gnu.org/licenses/>.

🎀 Documentation license I 🎀

Permission is granted to copy, distribute and/or modify this documentation under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, with no Front-Cover Texts, and with no Back-Cover Texts. A copy of the license is included in the file LICENSES/GFDL-1.3-or-later.txt.

🦋 Documentation license II 🦋

You are free:

  • to Share — to copy, distribute and transmit the work
  • to Remix — to adapt the work

Under the following conditions: 👇

  • Attribution — You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work).
  • Share Alike — If you alter, transform, or build upon this work, you may distribute the resulting work only under the same, similar or a compatible license.

With the understanding that:

  • Waiver — Any of the above conditions can be waived if you get permission from the copyright holder.
  • Public Domain — Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
  • Other Rights — In no way are any of the following rights affected by the license:
    • Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations;
    • The author's moral rights;
    • Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights.
  • Notice — For any reuse or distribution, you must make clear to others the license terms of this work.

A copy of the full license is included in the file as LICENCES/CC-BY-SA-4.0.txt 💅

🌈 SEE ALSO 🌈

parallel(1), parallel_tutorial(7), env_parallel(1), parset(1), parsort(1), parallel_alternatives(7), parallel_design(7), niceload(1), sql(1), ssh(1), ssh-agent(1), sshpass(1), ssh-copy-id(1), rsync(1)

like literally go read them all, they're the whole friend group 🫶

aaaand that's a wrap on the whole page, bestie, we did it 👑 This edition is version 20260822, dated 2026-09-21, and it's page PARALLEL_EXAMPLES(7) — keep it in your bookmarks, it's giving reference material main character energy 💜✨

📦 where this page comes from