EXAMPLE: Working as xargs -n1. Argument appending

GNU parallel can work similar to xargs -n1.

To compress all html files using gzip run:

find.-name'*.html'|parallelgzip--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.-typef-print0|\
parallel-q0perl-i-pe's/FOO BAR/FUBAR/g'

Note -q is needed because of the space in 'FOO BAR'.

EXAMPLE: Reading arguments from command line

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

parallelgzip--best:::*.html

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

parallellame{}-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.

To run gzip foo and bzip2 bar in parallel run:

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

or:

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

EXAMPLE: Inserting multiple arguments

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

bash:/bin/mv:Argumentlisttoolong

because there are too many files. You can instead do:

ls|grep-E'\.log$'|parallelmv{}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-mmv{}destdir

In many shells you can also use printf:

printf'%s0円'*.log|parallel-0-mmv{}destdir

EXAMPLE: Context replace

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

seq-w09999|parallelrmpict{}.jpg

You could also do:

seq-w09999|perl-pe's/(.*)/pict1ドル.jpg/'|parallel-mrm

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).

You could also run:

seq-w09999|parallel-Xrmpict{}.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-geometry120foo.jpgthumb_foo.jpg

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

ls*.jpg|parallelconvert-geometry120{}thumb_{}

To do it recursively use find:

find.-name'*.jpg'|\
parallelconvert-geometry120{}{}_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'|\
parallelconvert-geometry120{}{.}_thumb.jpg

EXAMPLE: Substitution and redirection

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

parallelzcat{}">"{.}:::*.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.

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.

caturlfile|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/dirmirror_dir
findmirror_dir-typel|parallel-mrm{}'&&'touch{}

Find the files in a list that do not exist

catfile_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ドル/=}; mv {} {=s/(.).*/1ドル/=}':::*

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

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

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}doesnotexist\
:::{00..23}:::{00..55..5}

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}\
:::12/31/19691/19/20386/1/2002

Match url into domain and path:

parallel--match'https://(.*?)/(.*)'echoDomain:{1.1}Path:{1.2}\
:::https://example.com/dir/pagehttps://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.

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

Match host.domain:port from a log file:

catlog|
parallel--match'\b([a-z0-9.]+):(\d+)\b'echohost:{1.1}port:{1.2}

Reorder comma-separated values:

parallel--match'(.*),(.*)'echoSecond:{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\
echoFrom{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.

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ドル. /;=}'{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ドル. / is a perl expression that appends ". " if the name has an initial. :%02d formats single digits as two digits. uc upper cases the argument.

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.

doit(){
echoDoingitfor1ドル
sleep2
echoDonewith1ドル
}
export-fdoit
paralleldoit:::123
doubleit(){
echoDoingitfor1ドル2ドル
sleep2
echoDonewith1ドル2ドル
}
export-fdoubleit
paralleldoubleit:::123:::ab

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

parallel--envdoit-Sserverdoit:::123
parallel--envdoubleit-Sserverdoubleit:::123:::ab

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"$@")>&/dev/null;then
perl-e'printf "033円[30;102m[ OK ]033円[0m @ARGV\n"'"$@"
else
perl-e'printf "033円[30;101m[FAIL]033円[0m @ARGV\n"'"$@"
fi
}
export-ftester
paralleltestermy_program:::arg1arg2
paralleltesterexit:::1020

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.

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ドル
# This fails 1% (328 of 32768)
if[$RANDOM-lt328];then
echoFailed1ドル
fi
}
export-fjob-with-few-failures
seq1000|parallel--bar--tagjob-with-few-failures

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')

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:

seq9-11|parallel-j1mvlog.{}log.'{= $_++ =}'
mvloglog.1

EXAMPLE: Simple network scanner

prips can generate IP-addresses from CIDR notation. With GNU parallel you can build a simple network scanner to see which addresses respond to ping:

prips130.229.16.0/20|\
parallel--timeout2-j0\
'ping -c 1 {} >/dev/null && echo {}'2>/dev/null

EXAMPLE: Removing file extension when processing files

When processing files removing the file extension using {.} is often useful.

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

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

Recompress all .gz files in 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:

findsounddir-typef-name'*.wav'|parallellame{}-o{.}.mp3

Put all converted in the same directory:

findsounddir-typef-name'*.wav'|\
parallellame{}-omydir/{/.}.mp3

EXAMPLE: Replacing parts of file names

If you deal with paired end reads, you will have files like barcode1_R1.fq.gz, barcode1_R2.fq.gz, barcode2_R1.fq.gz, and barcode2_R2.fq.gz.

You want barcodeN_R1 to be processed with barcodeN_R2.

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

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

parallel--plusmyprocess{}{/_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 directory with tar.gz files and want these extracted in 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--plusecho{%_demo}:::mycode_demokeep_demo_here

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

parallel--plusecho{#demo_} ::: demo_mycode keep_demo_here

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

parallel--plusecho{/demo_/}:::demo_mycoderemove_demo_here

You can often also use --match:

parallel--match'(.*)demo_(.*)'echo{1.1}{1.2}:::demo_mycoderemove_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=$(date-d"today -1ドル days"+%Y%m%d)
num=2ドル
echowgethttps://www.example.com/path/to/${date}_${num}.jpg
}
export-fgetit
parallelgetit:::$(seq30):::$(seq-w24)

$(date -d "today -1ドル days" +%Y%m%d) will give the dates in YYYYMMDD with 1ドル days subtracted.

EXAMPLE: Download world map from NASA

NASA provides tiles to download on earthdata.nasa.gov. Download tiles for 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-qwget"$url"-O{1}_{2}.jpg:::{0..19}:::{0..39}
parallelevalconvert+append{}_{0..39}.jpgline{}.jpg:::{0..19}
convert-appendline{0..19}.jpgworld.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 collection 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.

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|
parallelwget-O-|
jq-r.[]|
greplarge|
parallelwget

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.

url='youtu.be/watch?v=0wOf2Fgi3DE&list=UU_cznB5YZZmvAmeq7Y3EriQ'
exporturl
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

parallelmv{}'{= $a=pQ($_); $b=$_;'\
'$_=qx{date -r "$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.

EXAMPLE: Save output in ISO8601 dirs

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

seq1000|parallel-N0-j1--delay1\
--results'{= $_=`date -Isec`; chomp=}/'psaux

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

EXAMPLE: Aggregating content of files

This:

parallel--header:echox{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:

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

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

catx1y*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|paralleldo_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ドル"
}
queue="1ドル"
shift
if[-d"1ドル"];then
echo"queueing 1ドル"
find"1ドル"-mindepth1-maxdepth1>"$queue"
if[!-s"$queue"];then
# Ignore empty dirs
rm"$queue"
fi
else
process_file"1ドル"
fi
}
export-fprocess
# Queue lists
queue=$(mktemp)
queuenew=$(mktemp-d)
# Start dir
echo.>"$queue"
while[-s"$queue"];do
# Run one round for every directory level
# (Breadth first)
cat"$queue"|
parallelprocess"$queuenew"/{#} {}
# Each job may create a list in "$queuenew"/job_no
cat"$queuenew"/*>"$queue"2>/dev/null
rm-f"$queuenew"/*
done
rmdir"$queuenew"
rm"$queue"

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.

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ドル
# Stay inside the start dir
baseurl=$(echo"$url"|perl-pe's:#.*::; s:(//.*/)[^/]*:1ドル:')
urllist=$(mktemp-turllist.XXXX)
newurllist=$(mktemp-turllist.XXXX)
seen=$(mktemp-tseen.XXXX)
# Add start url to the list
echo"$url">"$urllist"
cp"$urllist""$seen"
process_single_url(){
# Find all links in the url
lynx-listonly-image_links-dump"1ドル"
# Remove this line to get spider only
wget-qm-l1-Q1"1ドル"
echo
echoSpidered:"1ドル">&2
}
export-fprocess_single_url
unique(){
# Like `sort -u` but without the sorting
perl-ne's/#.*//; s/\s+\d+.\s(\S+)$/1ドル/ and do { $seen{1ドル}++ or print }'
}
while[-s"$urllist"];do
cat"$urllist"|
parallelprocess_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.

tarxvffoo.tgz|perl-ne'print $l;$l=$_;END{print $l}'|\
parallelecho

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:

(forxin`catlist`;do
do_something$x
done)|process_output

and while-read-loops like this:

catlist|(whilereadx;do
do_something$x
done)|process_output

can be written like this:

catlist|paralleldo_something|process_output

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

cathosts.txt|parallel-P100host|grep1.2.3.4

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

(forxin`catlist`;do
no_extension=${x%.*};
do_step1$xscale$no_extension.jpg
do_step2<$x$no_extension
done)|process_output

and while-loops like this:

catlist|(whilereadx;do
no_extension=${x%.*};
do_step1$xscale$no_extension.jpg
do_step2<$x$no_extension
done)|process_output

can be written like this:

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

If the body of the loop is bigger, it improves readability to use a function:

(forxin`catlist`;do
do_something$x
[...100linesthatdosomethingwith$x...]
done)|process_output
catlist|(whilereadx;do
do_something$x
[...100linesthatdosomethingwith$x...]
done)|process_output

can both be rewritten as:

doit(){
x=1ドル
do_something$x
[...100linesthatdosomethingwith$x...]
}
export-fdoit
catlist|paralleldoit

EXAMPLE: Rewriting nested for-loops

Nested for-loops like this:

(forxin`catxlist`;do
foryin`catylist`;do
do_something$x$y
done
done)|process_output

can be written like this:

paralleldo_something{1}{2}::::xlistylist|process_output

Nested for-loops like this:

(forcolourinredgreenblue;do
forsizeinSMLXLXXL;do
echo$colour$size
done
done)|sort

can be written like this:

parallelecho{1}{2}:::redgreenblue:::SMLXLXXL|sort

EXAMPLE: Finding the lowest difference between files

diff is good for finding differences in text files. diff | wc -l gives an indication of the size of the difference. To find the differences between all files in the current dir do:

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

This way it is possible to see if some files are closer to other files.

EXAMPLE: for-loops with column names

When doing multiple nested for-loops it can be easier to keep track of the loop variable if is is named instead of just having a number. Use --header : to let the first argument be an named alias for the positional replacement string:

parallel--header:echo{colour}{size}\
:::colourredgreenblue:::sizeSMLXLXXL

This also works if the input file is a file with columns:

cataddressbook.tsv|\
parallel--colsep'\t'--header:echo{Name}{E-mailaddress}

EXAMPLE: All combinations in a list

GNU parallel makes all combinations when given two lists.

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

parallel--plusecho{choose_k}:::ABCD:::ABCD
parallel--plusecho2{2choose_k}1{1choose_k}:::ABCD:::ABCD

{choose_k} works for any number of input sources:

parallel--plusecho{choose_k}:::ABCD:::ABCD:::ABCD

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

parallel--plusecho{uniq}:::ABC:::ABC:::ABC
parallel--plusecho{1uniq}+{2uniq}+{3uniq}\
:::ABC:::ABC:::ABC

The behaviour of {choose_k} is undefined, if the input values of each source are different.

EXAMPLE: From a to b and b to c

Assume you have input like:

aardvark
babble
cab
dab
each

and want to run combinations like:

aardvarkbabble
babblecab
cabdab
dabeach

If the input is in the file in.txt:

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

If the input is in the array $a here are two solutions:

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

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'.

EXAMPLE: Speeding up fast jobs

Starting a job on the local machine takes around 3-10 ms. This can be a big overhead if the job takes very few ms to run. Often you can group small jobs together using -X which will make the overhead less significant. Compare the speed of these:

seq-w09999|paralleltouchpict{}.jpg
seq-w09999|parallel-Xtouchpict{}.jpg

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

seq-w09999999|\
parallel-j10-q-I,,--pipeparallel-j0touchpict{}.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. To raise the 32000 jobs limit raise /proc/sys/kernel/pid_max to 4194303.

If you do not 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--block10Msh

E.g.

mygenerator(){
seq10000000|perl-pe'print "echo This is fast job number "';
}
mygenerator|parallel--pipe--block10Msh

The overhead is 100000 times smaller namely around 100 nanoseconds per job.

EXAMPLE: Using shell variables

When using shell variables you need to quote them correctly as they may otherwise be interpreted by the shell.

Notice the difference between:

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

and:

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

When using variables in the actual command that contains 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-qecho"$VAR":::'!'
exportVAR
parallelecho'"$VAR"':::'!'

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

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

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

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

EXAMPLE: Group output lines

When running jobs that output data, you often do not want the output of multiple jobs to run together. 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. If you want output to be printed as soon as possible you can use -u.

Compare the output of:

parallelwget--progress=dot--limit-rate=100k\
https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2\
:::{12..16}
parallel--line-bufferwget--progress=dot--limit-rate=100k\
https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2\
:::{12..16}
parallel--latest-linewget--progress=dot--limit-rate=100k\
https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2\
:::{12..16}
parallel-uwget--progress=dot--limit-rate=100k\
https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2\
:::{12..16}

EXAMPLE: Tag output lines

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

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

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

parallel--tag--line-bufferwget--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..--nonalluptime

EXAMPLE: Colorize output

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

seq10|\
parallel--tagstring'033円[30;3{=$_=++$::color%8=}m'seq{}
parallel--rpl'{color} $_="033円[30;3".(++$::color%8)."m"'\
--tagstring{color}seq{}:::{1..10}

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

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

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

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

Append a string to every line in a text file:

cattextfile|parallel-kecho{}append_string

If you remove -k some of the lines may come out in the wrong order.

Another example is traceroute:

paralleltraceroute:::qubes-os.orgdebian.orgfreenetproject.org

will give traceroute of qubes-os.org, debian.org and freenetproject.org, but it will be sorted according to which job completed first.

To keep the order the same as input run:

parallel-ktraceroute:::qubes-os.orgdebian.orgfreenetproject.org

This will make sure the traceroute to qubes-os.org will be 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 are downloading a big file you can download the file in chunks in parallel.

To download byte 10000000-19999999 you can use curl:

curl-r10000000-19999999https://example.com/the/big/file>file.part

To download a 1 GB file we need 100 10MB chunks downloaded and combined in the correct order.

seq099|parallel-kcurl-r\
{}0000000-{}9999999https://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
sleep0.1ドル
echo"$@"ERR>&2
echo"$@"out
}
export-fdoit
parallel-k-udoit{='seq() > 1 and $opt::ungroup = 0'=}:::9123

It will output job 1 with less overhead.

EXAMPLE: Parallel grep

grep -r greps recursively through directories. GNU parallel can often speed this up.

find.-typef|parallel-k-j150%-n1000-mgrep-H-nSTRING{}

This will run 1.5 job per CPU, and give 1000 arguments to grep.

There are situations where the above will be slower than grep -r:

• If 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 is caused by two factors:

• 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.

• NVMe drives often perform better by having multiple command running in parallel.

EXAMPLE: Grepping n lines for m regular expressions.

The simplest solution to grep a big file for a lot of regexps is:

grep-fregexps.txtbigfile

Or if the regexps are fixed strings:

grep-F-fregexps.txtbigfile

There are 3 limiting factors: CPU, RAM, and disk I/O.

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.

ID1foobarbazIdentifier1quux
fubarID2foobarbazIdentifier2

ID1.*Identifier1
ID2.*Identifier2

ID1foobarbazIdentifier1
ID2foobarbazIdentifier2

parallel--pipe-part-aregexps.txt--block1Mgrep-F-f--nbigfile|\
sort-un|perl-pe's/^\d+://'

free=$(awk'/^((Swap)?Cached|MemFree|Buffers):/ { sum += 2ドル }
 END { print sum }'/proc/meminfo)
percpu=$((free/200/$(parallel--number-of-threads)))k
parallel--pipe-part-aregexps.txt--block$percpu--compress\
grep-F-f--nbigfile|\
sort-un|perl-pe's/^\d+://'

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

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

parallel--pipe-part--block100M-abigfile-k--compress\
grep-fregexps.txt

parallel--pipe-part--block100M-abigfile--catcatregexps.txt\
\|parallel--pipe-L1000--round-robingrep-f-{}

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
(echoservera;echoserverb)>.parallel/my_cluster
# Make sure .ssh/config exist
touch.ssh/config
cp.ssh/config.ssh/config.backup
# Disable StrictHostKeyChecking temporarily
(echo'Host *';echoStrictHostKeyCheckingno)>>.ssh/config
parallel--slfmy_cluster--nonalltrue
# 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:

seq10|parallel--sshloginserver.example.comecho

To run commands on more than one remote computer run:

seq10|parallel--sshlogins1.example.com,s2.example.netecho

Or:

seq10|parallel--sshloginserver.example.com\
--sshloginserver2.example.netecho

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

seq10|parallel--sshloginserver.example.com\
--sshloginfoo@server2.example.netecho

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

seq10|parallel-Sfoo@server{1..88}.example.netecho

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:

seq10|parallel--sshloginfilemycomputersecho

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.

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.

seq10|parallel--sshlogin8/server.example.comecho

EXAMPLE: Transferring of files

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

findlogs/-name'*.gz'|\
parallel--sshloginserver.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:

findlogs/-name'*.gz'|\
parallel--sshloginserver.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:

findlogs/-name'*.gz'|\
parallel--sshloginserver.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:

findlogs/-name'*.gz'|\
parallel--sshloginserver.example.com,server2.example.com\
--sshloginserver3.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:

findlogs/-name'*.gz'|\
parallel--sshloginserver.example.com,server2.example.com\
--sshloginserver3.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:

findlogs/-name'*.gz'|\
parallel--sshloginserver.example.com,server2.example.com\
--sshloginserver3.example.com\
--sshlogin:\
--trc{.}.bz2"zcat {} | bzip2 -9 >{.}.bz2"

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

findlogs/-name'*.gz'|parallel--sshloginfilemycomputers\
--trc{.}.bz2"zcat {} | bzip2 -9 >{.}.bz2"

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

findlogs/-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-Sserver--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-Sserver2,:\
'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-Sserver1,server2uptime

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

parallel--tag--onall-Sserver1,server2echo:::123

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

Put the password into passwordfile then run:

parallel--ssh'cat passwordfile | ssh'--nonall\
-Suser@server1,user@server2sudo-Sls-l/root

EXAMPLE: Using remote computers behind NAT wall

If the workers are behind a NAT wall, you need some trickery to get to them.

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'-Shost1echo:::DOESNOTWORK

It does not work because the command is dequoted by ssh twice where as GNU parallel only expects it to be dequoted once.

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

jumpssh(){ssh-Ajumphostssh$(parallel--shellquote:::"$@");}
export-fjumpssh
parallel--sshjumpssh-Shost1echo:::thisworks

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

Hosthost1host2host3
ProxyCommandsshjumphost.domainnc-w1%h22

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

parallel-Shost1,host2,host3echo:::Thisdoeswork

Hosthost1.v
Port22001
Hosthost2.v
Port22002
Hosthost3.v
Port22003
Host*.v
Hostnamefirewall

parallel-Shost1.v,host2.v,host3.vecho:::abc

HiddenServiceDir/var/lib/tor/hidden_service/
HiddenServicePort22127.0.0.1:22

parallel--ssh'torsocks ssh'-Sizjafdceobowklhz.onion\
-Szfcdaeiojoklbwhz.onion,auclucjzobowklhi.onionecho:::abc

parallel-S'torsocks ssh izjafdceobowklhz.onion,host2,host3'\
echo:::abc

EXAMPLE: Use sshpass with ssh

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

seq10|parallel-Suser-with-password:MyPassword@serverecho

or:

exportSSHPASS='MyPa$$w0rd'
seq10|parallel-Suser-with-password:@serverecho

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.

You can use outrun in an sshlogin this way:

parallel-S'outrun user@server'command

or:

parallel--sshoutrun-Sservercommand

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
modulepurge
moduleloadgnu_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"echoThisisjob{}:::{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.

cdsrc-dir
find.-typef|
parallel-j10-Xrsync-zR-Ha./{}fooserver:/dest-dir/

Adjust -j10 until you find the optimal number.

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/filefooserver:/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-w099|parallelrsync-Havesshfooserver:src/*{}.pngdestdir/

EXAMPLE: Use multiple inputs in one command

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

ls*.es.*|perl-pe'print; s/\.es//'|parallel-N2cp{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.

Count in binary:

parallel-kecho:::01:::01:::01:::01:::01:::01

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

parallel--link-a<(seq6)-a<(seq6-11)echo
parallel--linkecho::::<(seq6)<(seq6-11)

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

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

Alternative version:

find.-typef|sort|parallelconvert{}{#}.png

EXAMPLE: Use a table as input

Content of table_file.tsv:

foo<TAB>bar
baz<TAB>quux

To run:

cmd-obar-ifoo
cmd-oquux-ibaz

you can run:

parallel-atable_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-atable_file.tsv--trimn--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=$dburl/mytable.csv
parallel--sqlandworker$dbtableurlseq:::{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.

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

dburl=sqlite3:///%2Ftmp%2Fmy.sqlite
dbtableurl=$dburl/mytable
parallel--sqlandworker$dbtableurlseq:::{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=$dburl/mytable
parallel--sqlandworker$dbtableurlseq:::{1..10}
sql$dburl\
"COPY (SELECT * FROM mytable) TO stdout DELIMITER ',' CSV HEADER;"

Or MySQL:

dburl=mysql://user:pass@host/mydb
dbtableurl=$dburl/mytable
parallel--sqlandworker$dbtableurlseq:::{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ドル}/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--resultsmy.csvseq:::102030
R
>mydf<-read.csv("my.csv");
>print(mydf[2,])
>write(as.character(mydf[2,c("Stdout")]),'')

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-uwget'{= 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:

seq10|parallel-n0my_commandmy_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-c110.0.0.1
wgethttp://example.com/status.cgi?ip=10.0.0.1
ping-c110.0.0.2
wgethttp://example.com/status.cgi?ip=10.0.0.2
...
ping-c110.0.0.255
wgethttp://example.com/status.cgi?ip=10.0.0.255

To run 100 processes simultaneously do:

parallel-j100<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:

>Sequencename1
sequence
sequencecontinued
>Sequencename2
sequence
sequencecontinued
moresequence

To call myprog with the sequence as argument run:

catfile.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:1:1101:14011:10011: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:2:2104:15343:1973931:Y:18:ATCACG
@EAS139:136:FC706VJ:2:2104:15343:1973931: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-abig.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:

catbigfile|parallel--pipe--recend''-kgzip-9>bigfile.gz

This will split bigfile into blocks of 1 MB and pass that to gzip -9 in parallel. One gzip will be run per CPU. The output of gzip -9 will be kept in order and saved to bigfile.gz

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

catbigfile|parallel--pipe--filessort|\
parallel-Xj1sort-m{}';'rm{}>bigfile.sort

Here bigfile is split into blocks of around 1MB, each block ending in '\n' (which is the default for --recend). Each block is passed to sort and the output from sort is saved into files. These files are passed to the second parallel that runs sort -m on the files before it removes the files. The output is saved to bigfile.sort.

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--block100m-abigfile--filessort|\
parallel-Xj1sort-m{}';'rm{}>bigfile.sort

EXAMPLE: Grouping input lines

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

TransactionCustomerItem
1a53
2b65
3b82
4c96
5c67
6c13
7d90
8d43
9d91
10d84
11e72
12e102
13e63
14e56
15e74

Let us assume 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.

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);'`
catmy.csv|\
perl-ape'$F[1] ne $l and print "'$sep'"; $l = $F[1]'|\
parallel--recend$sep--rrs--pipe-N1wc

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. That means that if you just spawn more GNU parallels then each of them can run 250 jobs. This will spawn up to 2500 jobs:

catmyinput|\
parallel--pipe-N50--round-robin-j50parallel-j50your_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):

catmyinput|\
parallel--pipe-N250--round-robin-j250parallel-j250your_prg

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:

foriin*.log;do
echo$i
sem-j10gzip$i";"echodone
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.

seq3|parallelsemsed-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:

seq3|parallelsem--idmymutexsed-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ドル --fg /bin/bash
echoThiswillrun
sleep5
echoexclusively

Here perl:

#!/usr/bin/sem --shebang-wrap -u --id 0ドル --fg /usr/bin/perl
print"This will run ";
sleep5;
print"exclusively\n";

Here python:

#!/usr/local/bin/sem --shebang-wrap -u --id 0ドル --fg /usr/bin/python
importtime
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:

catfilelist|parallel--tty-Xemacs
catfilelist|parallel--tty-Xvi

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:

parallelsudoecho:::Thisisabadidea

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

sudoparallelecho:::Thisisagoodidea

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

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--tagping'{= $SIG{INT}=sub {} =}'\
:::1.1.1.18.8.8.89.9.9.921.21.21.2180.80.80.8088.88.88.88

--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.

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-fjobqueue|parallel

To submit your jobs to the queue:

echomy_commandmy_arg>>jobqueue

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

true>jobqueue;tail-n+0-fjobqueue|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;
whiletrue;do
tail-n+0-fjobqueue|
(parallel-EStOpHeRe-S..;echoGNUParallelisnowdone;
perl-e'while(<>){/StOpHeRe/ and last};print <>'jobqueue>j2;
(seq1000>>jobqueue&);
echoDoneappendingdummydataforcingtailtoexit)
echotailexited;
mvj2jobqueue
done

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

# Day time
echo50%>jobfile
cpday_server_list~/.parallel/sshloginfile
# Night time
echo100%>jobfile
cpnight_server_list~/.parallel/sshloginfile
tail-n+0-fjobqueue|parallel--jobsjobfile-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-qmreMOVED_TO-eCLOSE_WRITE--format%w%fmy_dir|\
parallel-uecho

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-qmreMOVED_TO-eCLOSE_WRITE--format%w%fmy_dir|\
parallel-S..-uecho

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
[...]
checkingforsomething.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-j0apt-filesearch

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

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• to Share

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• to Remix

to adapt the work

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• Attribution

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With the understanding that:

• Waiver

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• Public Domain

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