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PARALLEL_EXAMPLES(7)		    parallel		    PARALLEL_EXAMPLES(7)

GNU PARALLEL EXAMPLES
   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' | 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'.

   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:

       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.

     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: Argument list too long

     because there are too many files. 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).

     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.

   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 in-
     stead --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} does not exist \
	 ::: {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} \
	 ::: 12/31/1969 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.

   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 cor-
     responding --match  so  each  field  can  be  accessed  as  {columnname.#}.
     s/(.)/$1.	/ is a perl expression that appends ". " if the name has an ini-
     tial. :%02d formats single digits as two digits. uc upper cases  the  argu-
     ment.

   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() {
	 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 com-
     mand; 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
	   # 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

   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  num-
     ber to the low number.  This will keep 10 old versions of the log:

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

       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 processing files removing the file extension using {.} is  often  use-
     ful.

     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:

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

     Put all converted 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  will  have	files  like bar-
     code1_R1.fq.gz,	barcode1_R2.fq.gz,    barcode2_R1.fq.gz,    and     bar-
     code2_R2.fq.gz.

     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  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 --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  {/reg-
     exp/replacement} and 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 the dates in YYYYMMDD with $1
     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 -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  collec-
     tions  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  ex-
     tract  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 |
	 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.

       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.

   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"

     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=$1
       # Stay inside the start dir
       baseurl=$(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.

       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 hand-
     ing 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:

       (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

     can be written like this:

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

     If  the  body of the loop is bigger, it improves readability to use a func-
     tion:

       (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

     can both 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

     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

   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	replace-
     ment string:

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

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

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

   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 --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

     {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

     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 --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

     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:

       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)

     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}"

   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/dif-
     fcount/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. Com-
     pare the speed of these:

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

     If your program cannot take multiple arguments, then you can use GNU paral-
     lel 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.  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 --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.

   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 <\$\$\$>"'!' 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 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 -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'" ::: '!'

     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 ::: '!'

   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:

       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}

   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  visi-
     ble:

       parallel --tag wget --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-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 es-
     cape 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}

     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. Some-
     times 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 an-
     other system. -k will make sure the order of output will be in the same or-
     der as input even if later jobs end before earlier jobs.

     Append a string to every line in a text file:

       cat textfile | parallel -k echo {} append_string

     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 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 -k traceroute ::: qubes-os.org debian.org freenetproject.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 paral-
     lel: 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 -r 10000000-19999999 https://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.

       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

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

     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 -f regexps.txt bigfile

     Or if the regexps are fixed strings:

       grep -F -f regexps.txt bigfile

     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 mem-
     ory (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.

     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 paral-
     lel. 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.

     Bigger problem

     If  the problem is too big to be solved by this, you are probably ready for
     Lucene.

   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 ac-
     cept 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.

     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  be-
     low.  Then  it  will transfer the files to server.example.com to the corre-
     sponding 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 com-
     puter 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  com-
     puter 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  trans-
     ferred 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
     Put the password into passwordfile then run:

       parallel --ssh 'cat passwordfile | ssh' --nonall \
	 -S user@server1,user@server2 sudo -S ls -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' -S host1 echo ::: DOES NOT WORK

     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 -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  sim-
     ply:

       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  tor-
     socks 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/Prox-
     ies_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 connec-
     tion 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 --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.

       cd src-dir
       find . -type f |
	 parallel -j10 -X rsync -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/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 in-
     puts (using -N2) and replaces {1} and {2} with the inputs.

     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 (use-
     ful 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.

     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 data-
     base 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 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

     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:

       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). 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 --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 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 sep-
     arator  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  reason-
     able --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:

       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

   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 in-
     stance 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.

   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.

   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 con-
     tinue 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  re-
     moving  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://code-
     berg.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  man-
     ual of xargs from GNU findutils 4.4.2.

LICENSE
     This program is free software; you can redistribute it and/or modify it un-
     der  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  FIT-
     NESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
     details.

     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 distrib-
	      ute the resulting work only under the same, similar or a	compati-
	      ble 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  un-
	      der  applicable  law, that status is in no way affected by the li-
	      cense.

     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  copy-
		right 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  LI-
     CENCES/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)

20260122			   2026-01-31		    PARALLEL_EXAMPLES(7)
GNU PARALLEL EXAMPLES | AUTHOR | LICENSE | SEE ALSO
Want to link to this manual page? Use this URL:
<https://man.freebsd.org/cgi/man.cgi?query=parallel_examples&sektion=7&manpath=FreeBSD+Ports+15.1.quarterly>
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