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GNU Parallel 20180722 ('Crimson Hexagon') released [alpha]

Item posted by Ole Tange <tange> on Sun 22 Jul 2018 07:41:25 AM UTC.

GNU Parallel 20180722 ('Crimson Hexagon') [alpha] has been released. It is available for download at:

This release has significant changes and is considered alpha quality.

Quote of the month:

  I've been using GNU Parallel very much and effectively lately.
  Such an easy way to get huge speed-ups with my simple bash/Perl/Python
  programs -- parallelize them!
    -- Ken Youens-Clark @kycl4rk@twitter

New in this release:

  • The quoting engine has been changed. Instead of using \-quoting GNU Parallel now uses '-quoting in bash/ash/dash/ksh. This should improve compatibility with different locales. This is a big change causing this release to be alpha quality.
  • The CPU calculation has changed. By default GNU Parallel uses the number of CPU threads as the number of CPUs. This can be changed to the number of CPU cores or number of CPU sockets with --use-cores-instead-of-threads or --use-sockets-instead-of-threads.
  • The detected number of sockets, cores, and threads can be shown with --number-of-sockets, --number-of-cores, and --number-of-threads.
  • env_parallel now support mksh using env_parallel.mksh.
  • Bug fixes and man page updates.

GNU Parallel - For people who live life in the parallel lane.

About 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 and pipe it into commands 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 can even replace nested loops.

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.

You can find more about GNU Parallel at:

You can install GNU Parallel in just 10 seconds with: (wget -O - || curl | bash

Watch the intro video on

Walk through the tutorial (man parallel_tutorial). Your commandline will love you for it.

When using programs that use GNU Parallel to process data for publication please cite:

O. Tange (2018): GNU Parallel 2018, April 2018,

If you like GNU Parallel:

  • Give a demo at your local user group/team/colleagues
  • Post the intro videos on Reddit/Diaspora*/forums/blogs/ lists
  • Get the merchandise
  • Request or write a review for your favourite blog or magazine
  • Request or build a package for your favourite distribution (if it is not already there)
  • Invite me for your next conference

If you use programs that use GNU Parallel for research:

  • Please cite GNU Parallel in you publications (use --citation)

If GNU Parallel saves you money:


GNU sql aims to give a simple, unified interface for accessing databases through all the different databases' command line clients. So far the focus has been on giving a common way to specify login information (protocol, username, password, hostname, and port number), size (database and table size), and running queries.

The database is addressed using a DBURL. If commands are left out you will get that database's interactive shell.

When using GNU SQL for a publication please cite:

O. Tange (2011): GNU SQL - A Command Line Tool for Accessing Different Databases Using DBURLs, ;login: The USENIX Magazine, April 2011:29-32.

About GNU Niceload

GNU niceload slows down a program when the computer load average (or other system activity) is above a certain limit. When the limit is reached the program will be suspended for some time. If the limit is a soft limit the program will be allowed to run for short amounts of time before being suspended again. If the limit is a hard limit the program will only be allowed to run when the system is below the limit.

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