NAME

parallel - build and execute shell command lines from standard input in parallel

SYNOPSIS

parallel [options] [command [arguments]] < list_of_arguments

parallel [options] [command [arguments]] ( ::: arguments | :::+ arguments | :::: argfile(s) | ::::+ argfile(s) ) ...

parallel --semaphore [options] command

#!/usr/bin/parallel --shebang [options] [command [arguments]]

#!/usr/bin/parallel --shebang-wrap [options] [command [arguments]]

DESCRIPTION

STOP!

Read the Reader's guide below if you are new to GNU parallel.

GNU parallel is a shell tool for executing jobs in parallel using one or more computers. A job can be a single command or a small script that has to be run for each of the lines in the input. The typical input is a list of files, a list of hosts, a list of users, a list of URLs, or a list of tables. A job can also be a command that reads from a pipe. GNU parallel can then split the input into blocks and pipe a block into each command in parallel.

If you use xargs and tee today you will find GNU parallel very easy to use as GNU parallel is written to have the same options as xargs. If you write loops in shell, you will find GNU parallel may be able to replace most of the loops and make them run faster by running several jobs in parallel.

GNU parallel makes sure output from the commands is the same output as you would get had you run the commands sequentially. This makes it possible to use output from GNU parallel as input for other programs.

For each line of input GNU parallel will execute command with the line as arguments. If no command is given, the line of input is executed. Several lines will be run in parallel. GNU parallel can often be used as a substitute for xargs or cat | bash.

Reader's guide

GNU parallel includes the 4 types of documentation: Tutorial, how-to, reference and explanation.

Tutorial

If you prefer reading a book buy GNU Parallel 2018 at https://www.lulu.com/shop/ole-tange/gnu-parallel-2018/paperback/product-23558902.html or download it at: https://doi.org/10.5281/zenodo.1146014 Read at least chapter 1+2. It should take you less than 20 minutes.

Otherwise start by watching the intro videos for a quick introduction: https://youtube.com/playlist?list=PL284C9FF2488BC6D1

If you want to dive deeper: spend a couple of hours walking through the tutorial (man parallel_tutorial). Your command line will love you for it.

How-to

You can find a lot of EXAMPLEs of use after the list of OPTIONS in man parallel (Use LESS=+/EXAMPLE: man parallel). That will give you an idea of what GNU parallel is capable of, and you may find a solution you can simply adapt to your situation.

Reference

If you need a one page printable cheat sheet you can find it on: https://www.gnu.org/software/parallel/parallel_cheat.pdf

The man page is the reference for all options.

Design discussion

If you want to know the design decisions behind GNU parallel, try: man parallel_design. This is also a good intro if you intend to change GNU parallel.

OPTIONS

command

Command to execute.

If command or the following arguments contain replacement strings (such as {}) every instance will be substituted with the input.

If command is given, GNU parallel solve the same tasks as xargs. If command is not given GNU parallel will behave similar to cat | sh.

The command must be an executable, a script, a composed command, an alias, or a function.

Bash functions: export -f the function first or use env_parallel.

Bash, Csh, or Tcsh aliases: Use env_parallel.

Zsh, Fish, Ksh, and Pdksh functions and aliases: Use env_parallel.

{}

Input line.

This replacement string will be replaced by a full line read from the input source. The input source is normally stdin (standard input), but can also be given with --arg-file, :::, or ::::.

The replacement string {} can be changed with -I.

If the command line contains no replacement strings then {} will be appended to the command line.

Replacement strings are normally quoted, so special characters are not parsed by the shell. The exception is if the command starts with a replacement string; then the string is not quoted.

See also: --plus {.} {/} {//} {/.} {#} {%} {n} {=perl expression=}

{.}

Input line without extension.

This replacement string will be replaced by the input with the extension removed. If the input line contains . after the last /, the last . until the end of the string will be removed and {.} will be replaced with the remaining. E.g. foo.jpg becomes foo, subdir/foo.jpg becomes subdir/foo, sub.dir/foo.jpg becomes sub.dir/foo, sub.dir/bar remains sub.dir/bar. If the input line does not contain . it will remain unchanged.

The replacement string {.} can be changed with --extensionreplace

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: 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: 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: 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 filenames 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/=}' ::: *

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: 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 command; otherwise a green OK will be printed followed by the command.

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:

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

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:

  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: 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 {/regexp/replacement} and leave the replacement empty:

  parallel --plus echo {/demo_/} ::: 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 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 |
    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..12} ::: {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 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 http://gatt.org.yeslab.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. http://gatt.org.yeslab.org/
  URL=$1
  # Stay inside the start dir
  BASEURL=$(echo $URL | perl -pe 's:#.*::; s:(//.*/)[^/]*:$1:')
  URLLIST=$(mktemp urllist.XXXX)
  URLLIST2=$(mktemp urllist.XXXX)
  SEEN=$(mktemp seen.XXXX)

  # Spider to get the URLs
  echo $URL >$URLLIST
  cp $URLLIST $SEEN

  while [ -s $URLLIST ] ; do
    cat $URLLIST |
      parallel lynx -listonly -image_links -dump {} \; \
        wget -qm -l1 -Q1 {} \; echo Spidered: {} \>\&2 |
        perl -ne 's/#.*//; s/\s+\d+.\s(\S+)$/$1/ and
          do { $seen{$1}++ or print }' |
      grep -F $BASEURL |
      grep -v -x -F -f $SEEN | tee -a $SEEN > $URLLIST2
    mv $URLLIST2 $URLLIST
  done

  rm -f $URLLIST $URLLIST2 $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 handing it to GNU parallel.

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

for-loops like this:

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

and while-read-loops like this:

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

can be written like this:

  cat list | parallel do_something | process_output

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

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

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

  (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 function:

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

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

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 replacement 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

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/diffcount/1/A/2/B'.

EXAMPLE: Speeding up fast jobs

Starting a job on the local machine takes around 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 -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 parallel to spawn multiple GNU parallels:

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

If -j0 normally spawns 252 jobs, then the above will try to spawn 2520 jobs. On a normal GNU/Linux system you can spawn 32000 jobs using this technique with no problems. 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 --limit-rate=100k \
    https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
    ::: {12..16}
  parallel --line-buffer wget --limit-rate=100k \
    https://ftpmirror.gnu.org/parallel/parallel-20{}0822.tar.bz2 \
    ::: {12..16}
  parallel -u wget --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 --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 escape code "\033[30;3Xm" where 0 <= X <= 7:

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

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:

  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 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 -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: Parallel grep

grep -r greps recursively through directories. On multicore CPUs 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.

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 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.

Limiting factor: RAM

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

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

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

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

then your regexps.txt can be converted from:

  ID1.*Identifier1
  ID2.*Identifier2

into:

  ID1 foo bar baz Identifier1
  ID2 foo bar baz Identifier2

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

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

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

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

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

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

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

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

Limiting factor: CPU

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

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

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

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

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

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

  parallel --pipe-part --block 100M -a bigfile --cat cat regexps.txt \
    \| parallel --pipe -L1000 --round-robin grep -f - {}

If a line matches multiple regexps, the line may be duplicated.

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 accept the host key for every host. You want to accept them the first time and be warned if they are ever changed. To do that:

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

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

To run echo on server.example.com:

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

To run commands on more than one remote computer run:

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

Or:

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

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

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

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

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

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

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

Then run:

  seq 10 | parallel --sshloginfile mycomputers echo

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

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

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

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

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

EXAMPLE: Transferring of files

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EXAMPLE: Advanced file transfer

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

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

EXAMPLE: Distributing work to local and remote computers

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

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

EXAMPLE: Running the same command on remote computers

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

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

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

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

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

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

EXAMPLE: Running 'sudo' on remote computers

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 simply:

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

No jumphost, but port forwards

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

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

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

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

No jumphost, no port forwards

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

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

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

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

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

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

If not all hosts are accessible through TOR:

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

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

EXAMPLE: Use sshpass with ssh

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

  export SSHPASS=MyPa$$w0rd
  seq 10 |
    parallel -S '4/sshpass -e ssh user-with-password@server' echo
  seq 10 |
    parallel --ssh 'sshpass -e ssh' -S 4/user-with-password@server' echo

Currently the remote detection of CPU cores is incompatible with sshpass so use the '4/' syntax to tell the server has 4 cores.

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 --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 --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 inputs (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 (useful for making input PNG's for ffmpeg):

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

Alternative version:

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

EXAMPLE: Use a table as input

Content of table_file.tsv:

  foo<TAB>bar
  baz <TAB> quux

To run:

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

you can run:

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

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

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

EXAMPLE: Output to database

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

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

It is rather slow and takes up a lot of CPU time because GNU parallel parses the whole CSV file for each update.

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

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

This takes around a second per job.

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

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

Or MySQL:

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

EXAMPLE: Output to CSV-file for R

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

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

EXAMPLE: Use 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 core 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 separator before each customer (column 2 = $F[1]). Here we first make a 50 character random string, which we then use as the separator:

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

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

EXAMPLE: Running more than 250 jobs workaround

If you need to run a massive amount of jobs in parallel, then you will likely hit the filehandle limit which is often around 250 jobs. If you are super user you can raise the limit in /etc/security/limits.conf but you can also use this workaround. The filehandle limit is per process. 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 instance can be run at a time. With sem and --shebang-wrap the script can be made to wait for other instances to finish. Here in bash:

  #!/usr/bin/sem --shebang-wrap -u --id $0 --fg /bin/bash
  
  echo This will run
  sleep 5
  echo exclusively

Here perl:

  #!/usr/bin/sem --shebang-wrap -u --id $0 --fg /usr/bin/perl
  
  print "This will run ";
  sleep 5;
  print "exclusively\n";

Here python:

  #!/usr/local/bin/sem --shebang-wrap -u --id $0 --fg /usr/bin/python
  
  import time
  print "This will run ";
  time.sleep(5)
  print "exclusively";

EXAMPLE: Start editor with filenames 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. You can either do:

  sudo echo This
  parallel sudo echo ::: is a good idea

or:

  sudo parallel echo ::: This is a good idea

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

EXAMPLE: GNU Parallel as queue system/batch manager

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

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

To submit your jobs to the queue:

  echo my_command my_arg >> jobqueue

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

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

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

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

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

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

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

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

EXAMPLE: GNU Parallel as dir processor

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

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

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

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

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

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

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

EXAMPLE: Locate the missing package

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

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

Often it is not obvious which package you should install to get that file. Debian has `apt-file` to search for a file. `tracefile` from https://gitlab.com/ole.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

SPREADING BLOCKS OF DATA

--round-robin, --pipe-part, --shard, --bin and --group-by are all specialized versions of --pipe.

In the following n is the number of jobslots given by --jobs. A record starts with --recstart and ends with --recend. It is typically a full line. A chunk is a number of full records that is approximately the size of a block. A block can contain half records, a chunk cannot.

--pipe starts one job per chunk. It reads blocks from stdin (standard input). It finds a record end near a block border and passes a chunk to the program.

--pipe-part starts one job per chunk - just like normal --pipe. It first finds record endings near all block borders in the file and then starts the jobs. By using --block -1 it will set the block size to size-of-file/n. Used this way it will start n jobs in total.

--round-robin starts n jobs in total. It reads a block and passes a chunk to whichever job is ready to read. It does not parse the content except for identifying where a record ends to make sure it only passes full records.

--shard starts n jobs in total. It parses each line to read the value in the given column. Based on this value the line is passed to one of the n jobs. All lines having this value will be given to the same jobslot.

--bin works like --shard but the value of the column is the jobslot number it will be passed to. If the value is bigger than n, then n will be subtracted from the value until the values is smaller than or equal to n.

--group-by starts one job per chunk. Record borders are not given by --recend/--recstart. Instead a record is defined by a number of lines having the same value in a given column. So the value of a given column changes at a chunk border. With --pipe every line is parsed, with --pipe-part only a few lines are parsed to find the chunk border.

--group-by can be combined with --round-robin or --pipe-part.

TIME POSTFIXES

Arguments that give a duration are given in seconds, but can be expressed as floats postfixed with s, m, h, or d which would multiply the float by 1, 60, 60*60, or 60*60*24. Thus these are equivalent: 100000 and 1d3.5h16.6m4s.

UNIT PREFIX

Many numerical arguments in GNU parallel can be postfixed with K, M, G, T, P, k, m, g, t, or p which would multiply the number with 1024, 1048576, 1073741824, 1099511627776, 1125899906842624, 1000, 1000000, 1000000000, 1000000000000, or 1000000000000000, respectively.

You can even give it as a math expression. E.g. 1000000 can be written as 1M-12*2.024*2k.

QUOTING

GNU parallel is very liberal in quoting. You only need to quote characters that have special meaning in shell:

  ( ) $ ` ' " < > ; | \

and depending on context these needs to be quoted, too:

  ~ & # ! ? space * {

Therefore most people will never need more quoting than putting '\' in front of the special characters.

Often you can simply put \' around every ':

  perl -ne '/^\S+\s+\S+$/ and print $ARGV,"\n"' file

can be quoted:

  parallel perl -ne \''/^\S+\s+\S+$/ and print $ARGV,"\n"'\' ::: file

However, when you want to use a shell variable you need to quote the $-sign. Here is an example using $PARALLEL_SEQ. This variable is set by GNU parallel itself, so the evaluation of the $ must be done by the sub shell started by GNU parallel:

  seq 10 | parallel -N2 echo seq:\$PARALLEL_SEQ arg1:{1} arg2:{2}

If the variable is set before GNU parallel starts you can do this:

  VAR=this_is_set_before_starting
  echo test | parallel echo {} $VAR

Prints: test this_is_set_before_starting

It is a little more tricky if the variable contains more than one space in a row:

  VAR="two  spaces  between  each  word"
  echo test | parallel echo {} \'"$VAR"\'

Prints: test two spaces between each word

If the variable should not be evaluated by the shell starting GNU parallel but be evaluated by the sub shell started by GNU parallel, then you need to quote it:

  echo test | parallel VAR=this_is_set_after_starting \; echo {} \$VAR

Prints: test this_is_set_after_starting

It is a little more tricky if the variable contains space:

  echo test |\
    parallel VAR='"two  spaces  between  each  word"' echo {} \'"$VAR"\'

Prints: test two spaces between each word

$$ is the shell variable containing the process id of the shell. This will print the process id of the shell running GNU parallel:

  seq 10 | parallel echo $$

And this will print the process ids of the sub shells started by GNU parallel.

  seq 10 | parallel echo \$\$

If the special characters should not be evaluated by the sub shell then you need to protect it against evaluation from both the shell starting GNU parallel and the sub shell:

  echo test | parallel echo {} \\\$VAR

Prints: test $VAR

GNU parallel can protect against evaluation by the sub shell by using -q:

  echo test | parallel -q echo {} \$VAR

Prints: test $VAR

This is particularly useful if you have lots of quoting. If you want to run a perl script like this:

  perl -ne '/^\S+\s+\S+$/ and print $ARGV,"\n"' file

It needs to be quoted like one of these:

  ls | parallel perl -ne '/^\\S+\\s+\\S+\$/\ and\ print\ \$ARGV,\"\\n\"'
  ls | parallel perl -ne \''/^\S+\s+\S+$/ and print $ARGV,"\n"'\'

Notice how spaces, \'s, "'s, and $'s need to be quoted. GNU parallel can do the quoting by using option -q:

  ls | parallel -q  perl -ne '/^\S+\s+\S+$/ and print $ARGV,"\n"'

However, this means you cannot make the sub shell interpret special characters. For example because of -q this WILL NOT WORK:

  ls *.gz | parallel -q "zcat {} >{.}"
  ls *.gz | parallel -q "zcat {} | bzip2 >{.}.bz2"

because > and | need to be interpreted by the sub shell.

If you get errors like:

  sh: -c: line 0: syntax error near unexpected token
  sh: Syntax error: Unterminated quoted string
  sh: -c: line 0: unexpected EOF while looking for matching `''
  sh: -c: line 1: syntax error: unexpected end of file
  zsh:1: no matches found:

then you might try using -q.

If you are using bash process substitution like <(cat foo) then you may try -q and prepending command with bash -c:

  ls | parallel -q bash -c 'wc -c <(echo {})'

Or for substituting output:

  ls | parallel -q bash -c \
    'tar c {} | tee >(gzip >{}.tar.gz) | bzip2 >{}.tar.bz2'

Conclusion: If this is confusing consider avoiding having to deal with quoting by writing a small script or a function (remember to export -f the function) and have GNU parallel call that.

LIST RUNNING JOBS

If you want a list of the jobs currently running you can run:

  killall -USR1 parallel

GNU parallel will then print the currently running jobs on stderr (standard error).

COMPLETE RUNNING JOBS BUT DO NOT START NEW JOBS

If you regret starting a lot of jobs you can simply break GNU parallel, but if you want to make sure you do not have half-completed jobs you should send the signal SIGHUP to GNU parallel:

  killall -HUP parallel

This will tell GNU parallel to not start any new jobs, but wait until the currently running jobs are finished before exiting.

ENVIRONMENT VARIABLES

$PARALLEL_HOME

Dir where GNU parallel stores config files, semaphores, and caches information between invocations. Default: $HOME/.parallel.

$PARALLEL_ARGHOSTGROUPS

When using --hostgroups GNU parallel sets this to the hostgroups of the job.

Remember to quote the $, so it gets evaluated by the correct shell. Or use --plus and {agrp}.

$PARALLEL_HOSTGROUPS

When using --hostgroups GNU parallel sets this to the hostgroups of the sshlogin that the job is run on.

Remember to quote the $, so it gets evaluated by the correct shell. Or use --plus and {hgrp}.

$PARALLEL_JOBSLOT

Set by GNU parallel and can be used in jobs run by GNU parallel. Remember to quote the $, so it gets evaluated by the correct shell. Or use --plus and {slot}.

$PARALLEL_JOBSLOT is the jobslot of the job. It is equal to {%} unless the job is being retried. See {%} for details.

$PARALLEL_PID

Set by GNU parallel and can be used in jobs run by GNU parallel. Remember to quote the $, so it gets evaluated by the correct shell.

This makes it possible for the jobs to communicate directly to GNU parallel.

Example: If each of the jobs tests a solution and one of jobs finds the solution the job can tell GNU parallel not to start more jobs by: kill -HUP $PARALLEL_PID. This only works on the local computer.

$PARALLEL_RSYNC_OPTS

Options to pass on to rsync. Defaults to: -rlDzR.

$PARALLEL_SHELL

Use this shell for the commands run by GNU parallel:

$PARALLEL_SHELL. If undefined use:
The shell that started GNU parallel. If that cannot be determined:
$SHELL. If undefined use:
/bin/sh

$PARALLEL_SSH

GNU parallel defaults to using the ssh command for remote access. This can be overridden with $PARALLEL_SSH, which again can be overridden with --ssh. It can also be set on a per server basis (see --sshlogin).

$PARALLEL_SSHHOST

Set by GNU parallel and can be used in jobs run by GNU parallel. Remember to quote the $, so it gets evaluated by the correct shell. Or use --plus and {host}.

$PARALLEL_SSHHOST is the host part of an sshlogin line. E.g.

  4//usr/bin/specialssh user@host

becomes:

  host

$PARALLEL_SSHLOGIN

Set by GNU parallel and can be used in jobs run by GNU parallel. Remember to quote the $, so it gets evaluated by the correct shell. Or use --plus and {sshlogin}.

The value is the sshlogin line with number of cores removed. E.g.

  4//usr/bin/specialssh user@host

becomes:

  /usr/bin/specialssh user@host

$PARALLEL_SEQ

Set by GNU parallel and can be used in jobs run by GNU parallel. Remember to quote the $, so it gets evaluated by the correct shell.

$PARALLEL_SEQ is the sequence number of the job running.

Example:

  seq 10 | parallel -N2 \
    echo seq:'$'PARALLEL_SEQ arg1:{1} arg2:{2}

{#} is a shorthand for $PARALLEL_SEQ.

$PARALLEL_TMUX

Path to tmux. If unset the tmux in $PATH is used.

$TMPDIR

Directory for temporary files.

DEFAULT PROFILE (CONFIG FILE)

The global configuration file /etc/parallel/config, followed by user configuration file ~/.parallel/config (formerly known as .parallelrc) will be read in turn if they exist. Lines starting with '#' will be ignored. The format can follow that of the environment variable $PARALLEL, but it is often easier to simply put each option on its own line.

Options on the command line take precedence, followed by the environment variable $PARALLEL, user configuration file ~/.parallel/config, and finally the global configuration file /etc/parallel/config.

Note that no file that is read for options, nor the environment variable $PARALLEL, may contain retired options such as --tollef.

PROFILE FILES

If --profile set, GNU parallel will read the profile from that file rather than the global or user configuration files. You can have multiple --profiles.

Profiles are searched for in ~/.parallel. If the name starts with / it is seen as an absolute path. If the name starts with ./ it is seen as a relative path from current dir.

Example: Profile for running a command on every sshlogin in ~/.ssh/sshlogins and prepend the output with the sshlogin:

  echo --tag -S .. --nonall > ~/.parallel/nonall_profile
  parallel -J nonall_profile uptime

Example: Profile for running every command with -j-1 and nice

  echo -j-1 nice > ~/.parallel/nice_profile
  parallel -J nice_profile bzip2 -9 ::: *

Example: Profile for running a perl script before every command:

  echo "perl -e '\$a=\$\$; print \$a,\" \",'\$PARALLEL_SEQ',\" \";';" \
    > ~/.parallel/pre_perl
  parallel -J pre_perl echo ::: *

Note how the $ and " need to be quoted using \.

Example: Profile for running distributed jobs with nice on the remote computers:

  echo -S .. nice > ~/.parallel/dist
  parallel -J dist --trc {.}.bz2 bzip2 -9 ::: *

EXIT STATUS

Exit status depends on --halt-on-error if one of these is used: success=X, success=Y%, fail=Y%.

0: All jobs ran without error. If success=X is used: X jobs ran without error. If success=Y% is used: Y% of the jobs ran without error.
1-100: Some of the jobs failed. The exit status gives the number of failed jobs. If Y% is used the exit status is the percentage of jobs that failed.
101: More than 100 jobs failed.
255: Other error.
-1 (In joblog and SQL table): Killed by Ctrl-C, timeout, not enough memory or similar.
-2 (In joblog and SQL table): skip() was called in {= =}.
-1000 (In SQL table): Job is ready to run (set by --sqlmaster).
-1220 (In SQL table): Job is taken by worker (set by --sqlworker).

If fail=1 is used, the exit status will be the exit status of the failing job.

DIFFERENCES BETWEEN GNU Parallel AND ALTERNATIVES

See: man parallel_alternatives

BUGS

Quoting of newline

Because of the way newline is quoted this will not work:

  echo 1,2,3 | parallel -vkd, "echo 'a{}b'"

However, these will all work:

  echo 1,2,3 | parallel -vkd, echo a{}b
  echo 1,2,3 | parallel -vkd, "echo 'a'{}'b'"
  echo 1,2,3 | parallel -vkd, "echo 'a'"{}"'b'"

Speed

Startup

GNU parallel is slow at starting up - around 250 ms the first time and 150 ms after that.

Job startup

SSH

When using multiple computers GNU parallel opens ssh connections to them to figure out how many connections can be used reliably simultaneously (Namely SSHD's MaxStartups). This test is done for each host in serial, so if your --sshloginfile contains many hosts it may be slow.

If your jobs are short you may see that there are fewer jobs running on the remote systems than expected. This is due to time spent logging in and out. -M may help here.

Disk access

A single disk can normally read data faster if it reads one file at a time instead of reading a lot of files in parallel, as this will avoid disk seeks. However, newer disk systems with multiple drives can read faster if reading from multiple files in parallel.

If the jobs are of the form read-all-compute-all-write-all, so everything is read before anything is written, it may be faster to force only one disk access at the time:

  sem --id diskio cat file | compute | sem --id diskio cat > file

If the jobs are of the form read-compute-write, so writing starts before all reading is done, it may be faster to force only one reader and writer at the time:

  sem --id read cat file | compute | sem --id write cat > file

If the jobs are of the form read-compute-read-compute, it may be faster to run more jobs in parallel than the system has CPUs, as some of the jobs will be stuck waiting for disk access.

--nice limits command length

The current implementation of --nice is too pessimistic in the max allowed command length. It only uses a little more than half of what it could. This affects -X and -m. If this becomes a real problem for you, file a bug-report.

Aliases and functions do not work

If you get:

  Can't exec "command": No such file or directory

or:

  open3: exec of by command failed

or:

  /bin/bash: command: command not found

it may be because command is not known, but it could also be because command is an alias or a function. If it is a function you need to export -f the function first or use env_parallel. An alias will only work if you use env_parallel.

Database with MySQL fails randomly

The --sql* options may fail randomly with MySQL. This problem does not exist with PostgreSQL.

REPORTING BUGS

Report bugs to <parallel@gnu.org> or https://savannah.gnu.org/bugs/?func=additem&group=parallel

When you write your report, please keep in mind, that you must give the reader enough information to be able to run exactly what you run. So you need to include all data and programs that you use to show the problem.

See a perfect bug report on https://lists.gnu.org/archive/html/bug-parallel/2015-01/msg00000.html

Your bug report should always include:

The error message you get (if any). If the error message is not from GNU parallel you need to show why you think GNU parallel caused this.
The complete output of parallel --version. If you are not running the latest released version (see https://ftp.gnu.org/gnu/parallel/) you should specify why you believe the problem is not fixed in that version.
A minimal, complete, and verifiable example (See description on https://stackoverflow.com/help/mcve).
It should be a complete example that others can run which shows the problem including all files needed to run the example. This should preferably be small and simple, so try to remove as many options as possible.

A combination of yes, seq, cat, echo, wc, and sleep can reproduce most errors.

If your example requires large files, see if you can make them with something like seq 100000000 > bigfile or yes | head -n 1000000000 > file. If you need multiple columns: paste <(seq 1000) <(seq 1000 1999)

If your example requires remote execution, see if you can use localhost - maybe using another login.

If you have access to a different system (maybe a VirtualBox on your own machine), test if your MCVE shows the problem on that system. If it does not, read below.
The output of your example. If your problem is not easily reproduced by others, the output might help them figure out the problem.
Whether you have watched the intro videos (https://www.youtube.com/playlist?list=PL284C9FF2488BC6D1), walked through the tutorial (man parallel_tutorial), and read the EXAMPLE section in the man page (man parallel - search for EXAMPLE:).

Bug dependent on environment

If you suspect the error is dependent on your environment or distribution, please see if you can reproduce the error on one of these VirtualBox images: https://sourceforge.net/projects/virtualboximage/files/ https://www.osboxes.org/virtualbox-images/

Specifying the name of your distribution is not enough as you may have installed software that is not in the VirtualBox images.

If you cannot reproduce the error on any of the VirtualBox images above, see if you can build a VirtualBox image on which you can reproduce the error. If not you should assume the debugging will be done through you. That will put a lot more burden on you and it is extra important you give any information that help. In general the problem will be fixed faster and with much less work for you if you can reproduce the error on a VirtualBox - even if you have to build a VirtualBox image.

In summary

Your report must include:

parallel --version
output + error message
full example including all files
VirtualBox image, if you cannot reproduce it on other systems

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.

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

LICENSE

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

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

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

Documentation license I

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

Documentation license II

You are free:

to Share: to copy, distribute and transmit the work
to Remix: to adapt the work

Under the following conditions:

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

With the understanding that:

Waiver: Any of the above conditions can be waived if you get permission from the copyright holder.
Public Domain: Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
Other Rights: In no way are any of the following rights affected by the license:

Your fair dealing or fair use rights, or other applicable copyright exceptions and limitations;
The author's moral rights;
Rights other persons may have either in the work itself or in how the work is used, such as publicity or privacy rights.

Notice: For any reuse or distribution, you must make clear to others the license terms of this work.

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

DEPENDENCIES

GNU parallel uses Perl, and the Perl modules Getopt::Long, IPC::Open3, Symbol, IO::File, POSIX, and File::Temp.

For --csv it uses the Perl module Text::CSV.

For remote usage it uses rsync with ssh.